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Report to Congressional Requesters: 

May 2005: 

Securities Markets: 

Decimal Pricing Has Contributed to Lower Trading Costs and a More 
Challenging Trading Environment: 

GAO-05-535: 

GAO Highlights: 

Highlights of GAO-05-535, a report to congressional requesters: 

Why GAO Did This Study: 

In early 2001, U.S. stock and option markets began quoting prices in 
decimal increments rather than fractions of a dollar. At the same time, 
the minimum price increment, or tick size, was reduced to a penny on 
the stock markets and to 10 cents and 5 cents on the option markets. 
Although many believe that decimal pricing has benefited small 
individual (retail) investors, concerns have been raised that the 
smaller tick sizes have made trading more challenging and costly for 
large institutional investors, including mutual funds and pension 
plans. In addition, there is concern that the financial livelihood of 
market intermediaries, such as the broker-dealers that trade on floor-
based and electronic markets, has been negatively affected by the lower 
ticks, potentially altering the roles these firms play in the U.S. 
capital market. GAO assessed the effect of decimal pricing on retail 
and institutional investors and on market intermediaries. 

What GAO Found: 

Trading costs, a key measure of market quality, have declined 
significantly for retail and institutional investors since the 
implementation of decimal pricing in 2001. Retail investors now pay 
less when they buy and receive more when they sell stock because of the 
substantially reduced spreads—the difference between the best quoted 
prices to buy or sell. GAO’s analysis of data from firms that analyze 
institutional investor trades indicated that trading costs for large 
investors have also declined, falling between 30 to 53 percent. 
Further, 87 percent of the 23 institutional investor firms we contacted 
reported that their trading costs had either declined or remained the 
same since decimal pricing began. Although trading is less costly, the 
move to the 1-cent tick has reduced market transparency. Fewer shares 
are now generally displayed as available for purchase or sale in U.S. 
markets. However, large investors have adapted by breaking up large 
orders into smaller lots and increasing their use of electronic trading 
technologies and alternative trading venues. 
 
Although conditions in the securities industry overall have improved 
recently, market intermediaries, particularly exchange specialists and 
NASDAQ market makers, have faced more challenging operating conditions 
since 2001. From 2000 to 2004, the revenues of the broker-dealers 
acting as New York Stock Exchange specialists declined over 50 percent, 
revenues for firms making markets on NASDAQ fell over 70 percent, and 
the number of firms conducting such activities shrank from almost 500 
to about 260. However, factors other than decimal pricing have also 
contributed to these conditions, including the sharp decline in overall 
stock prices since 2000, increased electronic trading, and heightened 
competition from trading venues. 

Average Quoted Spreads Before and After Decimal Pricing Implemented in 
Cents per Share, February 2000 through November 2004: 

[See PDF for image]

[End of figure]

What GAO Recommends: 

GAO observes that the goals for implementing decimal pricing have been 
met and that investors have adapted to the new environment and continue 
to trade large numbers of shares at lower cost. 

www.gao.gov/cgi-bin/getrpt?GAO-05-535.

To view the full product, including the scope and methodology, click on 
the link above. For more information, contact Richard J. Hillman at 
(202) 512-8678 or hillmanr@gao.gov.

[End of section]

Contents: 

Letter: 

Background: 

Results in Brief: 

Investors' Trading Costs Have Declined Since Decimalization, but 
Reduced Market Transparency Has Caused Firms to Adopt New Trading 
Strategies: 

Some Stock Intermediaries Have Experienced Lower Profits since 
Decimalization, but Other Factors Have Contributed to the Declines: 

Decimal Pricing Has Had a Limited Impact on the Options Markets, but 
Other Factors Have Helped Improve Market Quality: 

Observations: 

Agency Comments: 

Appendixes: 

Appendix I: Scope and Methodology: 

Methodology for Assessing Impact on Institutional Investors: 

Methodology for Assessing Impact on Market Intermediaries: 

Methodology for Assessing Impact on Options Markets: 

Appendix II: Methodology for GAO Analysis of Trade and Quotes Data: 

Appendix III: Measurement of Institutional Investors' Trading Costs in 
Basis Points Shows Decline since Decimal Pricing Implemented: 

Appendix IV: Additional Analysis Using Trade and Quotes Data: 

Appendix V: GAO Contacts and Staff Acknowledgments: 

Tables: 

Table 1: Average Quoted Spreads Before and After Decimalization, 2000- 
2004 (cents per share): 

Table 2: Average Effective Spreads Before and After Decimalization, 
2000-2004 (cents per share): 

Table 3: Volume-weighted Average Effective Spreads Before and After 
Decimalization for Selected NYSE and NASDAQ Stocks, by Market 
Capitalization (cents per share): 

Table 4: Institutional Investor Positions on Changes to Trading Costs 
After Decimalization: 

Table 5: Price Change Volatility for NYSE and NASDAQ Stocks Before and 
After Decimalization: 

Table 6: Average Number of Shares Displayed at the Best Quoted Prices 
Reported by NYSE and NASDAQ in Studies of Their Markets Before and 
After Decimalization: 

Table 7: Average Trade Size for NYSE and NASDAQ, 1999-2004 (in shares): 

Table 8: NYSE Specialist Firm Revenues and Profits, 1999-2004 (in 
millions of dollars): 

Table 9: NYSE Reported Trades, Average Daily Volume, and Average Trade 
Size, 1999-2004: 

Table 10: NYSE Member Broker-Dealer Revenues from NASDAQ Market Making 
Activities, 1999-2004 (in millions of dollars): 

Table 11: NASDAQ Average Trade Size, and Average Daily Volume, 1999-
2004: 

Table 12: Number of Specialist Firms Operating on Selected Stock 
Markets, 1999-2004: 

Table 13: Consolidation among NASDAQ Market Makers, 1999-2004: 

Table 14: Pre-and Postdecimalization Sample Weeks: 

Table 15: Price Characteristics of NYSE-Listed and NASDAQ Stocks: 

Table 16: Volume Characteristics of NYSE-Listed and NASDAQ Stocks: 

Table 17: Average Quoted Spreads Before and After Decimalization, 2000- 
2004 (basis points): 

Table 18: Average Effective Spreads Before and After Decimalization, 
2000-2004 (basis points): 

Figures: 

Figure 1: Average Quoted Spreads Before and After Decimalization (cents 
per share): 

Figure 2: Total Trading Costs from a Trade Analytics Firm for NYSE and 
NASDAQ Stocks, 1999-2003 (cents per share): 

Figure 3: Trading Cost Components from a Trade Analytics Firm for NYSE 
and NASDAQ Stocks, 2001 and 2003 (cents per share): 

Figure 4: Total Trading Costs from Two Trade Analytics Firms for NYSE, 
1998-2004 (cents per share): 

Figure 5: Total Trading Costs from Two Trade Analytics Firms for NASDAQ 
Stocks, 1998-2004 (cents per share): 

Figure 6: Trading Cost Components from Two Trade Analytics Firms for 
NYSE Stocks, 2001 and 2004 (cents per share): 

Figure 7: Trading Cost Components from Two Trade Analytics Firms for 
NASDAQ Stocks, 2001 and 2004 (cents per share): 

Figure 8: Volume-weighted Average Number of Shares Displayed at the 
Best Quoted Prices on the NYSE and NASDAQ Before and After 
Decimalization, Sample Weeks from February 2000-November 2004: 

Figure 9: Proportion of Total Share Trading Volume NASDAQ and NYSE 
Stocks by ECNs, 1996-2003: 

Figure 10: Securities Industry Total Revenues and Net Income, 1994-
2004: 

Figure 11: NYSE Specialist Participation Rates, 1999-2004, in Percent 
of Trades: 

Figure 12: Securities Industry Revenues and Net Income as Compared to 
the Performance of the S&P 500 Stock Index, 1994-2004: 

Figure 13: Number of IPOs and Dollars Raised, 1994-2004: 

Figure 14: Total Contract Trading Volumes for Stock Options, 2000-2004 
(Volume in millions): 

Figure 15: Distribution of Average Daily Closing Prices for Full Sample 
of Matching Stocks and 300 Matched-Pairs Sample: 

Figure 16: Distribution of Average Daily Trading Volume for Full Sample 
of Matching Stocks and 300 Matched-Pairs Sample: 

Figure 17: Total Trading Costs from a Trade Analytics Firm for NYSE and 
NASDAQ Stocks, 1999-2004 (basis points): 

Figure 18: Trading Cost Components from One Trade Analytics Firm for 
NYSE and NASDAQ, 2001-2003 (basis points): 

Figure 19: Total Trading Costs from Two Trade Analytics Firms for NYSE 
Stocks, 2001-2004 (basis points): 

Figure 20: Total Trading Costs from Two Trade Analytics Firms for 
NASDAQ Stocks, 2001-2004 (basis points): 

Figure 21: Trading Cost Components from Two Trade Analytics Firms for 
NYSE Stocks, 2001 and 2004 (basis points): 

Figure 22: Trading Cost Components from Two Trade Analytics Firms for 
NASDAQ Stocks, 2001 and 2004 (basis points): 

Figure 23: Quote Clustering After Decimalization, 2001-2004: 

Figure 24: Quote Clustering After Decimalization, by Sample Week, 2001- 
2004: 

Abbreviations: 

Amex: American Stock Exchange: 

ATS: alternative trading system: 

BOX: Boston Options Exchange: 

bps: basis points: 

CBOE: Chicago Board Options Exchange: 

CQ: Consolidated Quotes: 

CMS: composite match score: 

ECN: electronic communication network: 

FOCUS: Financial and Operational Combined Uniform Single: 

GAO: Government Accountability Office: 

IBM: International Business Machines: 

IPO: initial public offering: 

ISE: International Securities Exchange: 

MMID: market maker identification: 

mps: messages per second: 

NASD: National Association of Securities Dealers: 

NASDAQ: National Association of Securities Dealers Automated 
Quotations: 

NBB: national best bid: 

NBO: national best offer: 

NBBO: national best bid and offer: 

NMS: National Market System: 

NYSE: New York Stock Exchange: 

OPRA: Options Price Reporting Authority: 

OTC: over-the-counter: 

OTCBB: over-the-counter bulletin board: 

PCX: Pacific Exchange: 

Phlx: Philadelphia Stock Exchange: 

SEC: Securities and Exchange Commission: 

SIA: Securities Industry Association: 

TAQ: Trade and Quote: 

VWAP: volume weighted average price: 

Letter May 31, 2005: 

The Honorable Michael Enzi: 
United States Senate: 

The Honorable Rick Santorum: 
United States Senate: 

With encouragement from Congress, in 2000 the Securities and Exchange 
Commission (SEC) ordered U.S. stock and option markets to begin quoting 
prices in decimal increments rather than fractions of a 
dollar.[Footnote 1] As U.S. markets implemented decimal pricing in 
early 2001, they also reduced the minimum price increment, or tick 
size, at which prices could be quoted. The minimum tick on the stock 
markets generally fell from 1/16 of a dollar to a penny and on the 
option markets from 1/8 and 1/16 of a dollar to 10 cents and 5 cents, 
respectively.[Footnote 2] The United States had been one of the last 
countries to use fractions on its markets, and decimal pricing was 
expected to simplify securities pricing for investors, help lower 
investors' trading costs and align U.S. pricing standards with those of 
other markets. 

Many market participants and others who have observed the markets 
believe that decimal pricing has benefited small retail investors 
seeking to buy or sell a few hundred shares of stock.[Footnote 3] But 
concerns have been raised that the smaller tick size has made trading 
more challenging and costly for large institutional investors, 
including mutual funds and pension plans, that trade large blocks of 
shares.[Footnote 4] In addition, concerns exist over whether trading in 
1-cent ticks has negatively affected the financial livelihood of market 
intermediaries, such as the broker-dealers that trade on floor-based 
and electronic markets, potentially altering the roles these firms play 
in the U.S. capital market. 

This report responds to your February 12, 2004, request that we study 
the impact of decimal pricing on the trading of U.S. stocks and 
options. As agreed with your staffs, our objectives were to study the 
impact of decimal pricing on (1) retail and institutional investors, 
(2) market intermediaries, and (3) options market investors and 
intermediaries. 

To determine the effect of decimalization on retail and institutional 
investors in securities, we analyzed a comprehensive database of all 
trades conducted on U.S. stock markets from February 2000 to November 
2004 to identify changes to key characteristics of stock markets, such 
as spreads, liquidity, trading volumes, and price volatility.[Footnote 
5] We also analyzed data on institutional investors' trading costs that 
were provided by three trade analytics firms in order to identify 
trends in these costs before and after decimalization. In addition, we 
reviewed relevant academic, industry, and regulatory studies that 
address the effects of decimal pricing on the stock markets. Finally, 
we interviewed almost 70 market participants, including securities 
traders, broker-dealers, and institutional investors such as pension 
and mutual fund investment managers, as well as representatives of 
regulatory agencies, stock markets, electronic trading systems, and 
industry associations. To determine decimalization's effect on 
intermediaries in U.S. stock markets, we reviewed studies and data on 
market participants' revenue and profitability and interviewed a 
variety of intermediaries, including broker-dealers, market makers, 
regional and national exchange specialists, and traders. We sought the 
perspectives of other market participants, including representatives 
from regulatory agencies, stock markets, industry associations, and 
institutional investors. To determine the effect of decimal pricing and 
the tick size reductions on investors and intermediaries in the options 
market, we reviewed studies by options exchanges; interviewed 
representatives of all six U.S. options markets, as well as broker- 
dealers and hedge funds that trade options; and reviewed comment 
letters that SEC received on potential changes in options market 
regulations. Appendixes I and II contain a full description of our 
scope and methodology. We conducted our work in Baltimore, Boston, 
Chicago, Los Angeles, New York, Philadelphia, San Francisco, and 
Washington, D.C., between May 2004 and May 2005 in accordance with 
generally accepted government auditing standards. 

Background: 

In implementing decimal pricing, regulators hoped to improve the 
quality of U.S. stock and option markets. The quality of a market can 
be assessed using various characteristics, but the trading costs that 
investors incur when they execute orders are a key aspect of market 
quality. Trading costs are generally measured differently for retail 
and institutional investors. In addition to the commission charges to 
paid broker-dealers that execute trades, the other primary trading cost 
for retail investors, who typically trade no more than a few hundred 
shares at a time, is measured by the spread, which is the difference 
between the best quoted "bid" and "ask" prices that prevail at the time 
the order is executed. The bid price is the best price at which market 
participants are willing to buy shares, and the ask price is the best 
price at which market participants are willing to sell shares.[Footnote 
6] The spread represents the cost of trading for small orders because 
if an investor buys shares at the ask price and then immediately sells 
them at the bid price, the resulting loss or cost is represented by the 
size of the spread. 

Because institutional orders are generally much larger than retail 
orders and completing one order can require multiple trades executed at 
varying prices, spreads are not generally used to measure institutional 
investors' trading costs. Instead, the components of trading costs for 
large institutional investors, who often seek to buy or sell large 
blocks of shares such as 50,000 or 1 million shares, include the 
order's market impact, broker commissions paid, and exchange fees 
incurred, among other things. An order's market impact is the extent to 
which the security changes in price after the investor begins trading. 
For example, if the price of a stock begins to rise in reaction to the 
increased demand after an investor begins executing trades to complete 
a large order, the average price at which the investor's total order is 
executed will be higher than the stock's price would have been without 
the order. 

In addition to trading costs, decimal pricing may have affected several 
other aspects of market quality, including liquidity, transparency, and 
price volatility. 

Liquidity. Liquid markets have many buyers and sellers willing to trade 
and have sufficient shares to execute trades quickly without markedly 
affecting share prices. Generally, the more liquid the overall market 
or markets for particular stocks are, the lower the market impact of 
any individual orders. Small orders for very liquid stocks will have 
minimal market impact and lower trading costs. However, larger orders, 
particularly for less liquid stocks, can affect prices more and thus 
have greater market impact and higher trading costs. 

Transparency. When markets are transparent, the number and prices of 
available shares are readily disclosed to all market participants, and 
prices and volumes of executed trades are promptly disseminated. A key 
factor that can affect market participants' perceptions of market 
transparency is the volume of shares publicly displayed as available at 
the best quoted bid and ask prices, as well as at points around these 
prices--known as market depth. Markets with small numbers of shares 
displayed in comparison to the size of investors' typical orders seem 
less transparent to investors because they have less information that 
can help them specify the price and size of their own orders so as to 
execute trades with minimal trading costs. 

Price volatility. Price volatility is a measure of the frequency of 
price changes as well as a measure of the amount by which prices change 
over a period of time. Highly volatile markets typically disadvantage 
investors that execute trades with less certainty of the prices they 
will receive. Conversely, market intermediaries, such as broker- 
dealers, can benefit from highly volatile markets because they may be 
able to earn more revenue from trading more frequently as prices rise 
and fall. 

The trading that occurs on U.S. securities markets is facilitated by 
broker-dealers that act as market intermediaries. These intermediaries 
perform different functions depending on the type of trading that 
occurs in each market. On markets that use centrally located trading 
floors to conduct trading, such as the New York Stock Exchange (NYSE), 
trading occurs primarily through certain broker-dealer firms that have 
been designated as specialists for particular stocks. These specialists 
are obligated to maintain fair and orderly markets by buying shares 
from or selling shares to the other broker-dealers who present orders 
from customers on the trading floor or through the electronic order 
routing systems used by the exchange. Interacting with the specialists 
on the trading floor are employees from large broker-dealer firms that 
receive orders routed from these firms' offices around the country. In 
addition, specialists receive orders from staff from small, independent 
broker-dealer firms who work only on the floor. 

In contrast, trading of the stocks listed on the NASDAQ Stock Market 
(NASDAQ), which does not have a central physical trading location, is 
conducted through electronic systems operated by broker-dealers acting 
as market makers or by alternative trading venues. For particular 
stocks, market makers enter quotes indicating the prices at which these 
firms are simultaneously willing to buy from or sell shares to other 
broker-dealers into NASDAQ's electronic system. The NASDAQ system 
displays these quotes to all other broker-dealers that are registered 
to trade on that market. Much of the trading in NASDAQ stocks now also 
takes place in alternative trading venues, including electronic 
communication networks (ECN), which are registered as broker-dealers 
and electronically match the orders they receive from their customers, 
much like an exchange. 

At the same time that decimal pricing was being implemented, other 
changes were also occurring in the marketplace. For example, in 1997, 
SEC enacted new rules regarding how market makers and specialists must 
handle the orders they received from their customers, including 
requiring firms to display these orders to the market when their prices 
are better than those currently offered by that broker.[Footnote 7] 
These rules facilitated the growth of additional trading venues such as 
the ECNs, which compete with the established markets, such as NYSE and 
NASDAQ, for trading volumes. The increased use of computerized trading 
has also provided alternative mechanisms for trading and reduced the 
role of specialists, market makers, and other intermediaries in the 
trading process. In addition, after rising significantly during the 
late 1990s, U.S. stock prices experienced several years of declines, 
affecting trading costs and market intermediary profits. Facing lower 
investment returns, institutional investors and professional traders 
have focused more on reducing trading costs to improve those returns. 
Regulators also began placing greater emphasis on institutional 
investors' duty to obtain the best execution for their trades, further 
increasing the pressure on these firms to better manage their trading 
costs.[Footnote 8]

Results in Brief: 

Trading costs, a key measure of market quality, have declined 
significantly for retail and institutional investors since the 
implementation of decimal pricing in 2001. Retail investors are now 
able to trade small orders that execute in one trade more cheaply as a 
result of the substantially reduced spreads that prevail in the stock 
markets. Data from firms that analyze institutional investors' trading 
costs and academic studies also showed that trading costs for large 
investors have also declined. Further, 20 of the 23 institutional 
investor firms we contacted (representing about 31 percent of assets 
managed by the top 300 U.S. money management firms) reported that their 
trading costs had either declined or remained the same since decimal 
pricing began. The extent to which decimal pricing is responsible for 
these improvements is not clear because other factors, including the 
multiyear downturn in stock prices that began in 2000, may have also 
contributed to the reduced trading costs. Although trading is less 
costly, the move to the 1-cent tick appears to have reduced market 
transparency as the number of shares that are generally displayed as 
available for purchase or sale in U.S. stock markets shrank. In part, 
institutional investors became less willing to display large orders to 
the markets because the 1-cent tick lowered the financial risks for 
other traders seeking to "step ahead" of these larger orders by 
entering orders priced just a penny better. Institutional investors 
told us that they had adapted to these new conditions by breaking up 
large orders into smaller lots and using electronic trading 
technologies to execute these smaller orders in the markets. In 
addition, they reported increasing their use of alternative trading 
venues, such as ECNs and crossing networks that anonymously match large 
institutional investor orders. Through these adaptations, institutional 
investors have been able to continue executing large orders at reduced 
costs. 

Although investors appear to have benefited since decimal pricing 
began, some market intermediaries have faced more challenging operating 
conditions. Despite overall improving conditions in the securities 
industry since 2001, broker-dealers acting as exchange specialists and 
NASDAQ market makers have seen their profits fall, forcing some to 
merge with other firms or to leave the industry. Between 2000 and 2004, 
the exchange specialist broker-dealers that match investor orders and 
buy and sell shares on the trading floors of various exchanges 
experienced reduced revenues and profits. For example, in 2004 NYSE 
exchange specialists reported aggregate revenues of $902 million, down 
by more than 50 percent from the $2.1 billion such firms earned in 
2000. Broker-dealers that make markets in NASDAQ and other non-exchange 
listed stocks appear to have been affected even more by the lower 
spreads and reductions in displayed liquidity that have accompanied 
decimal pricing. According to data from the Securities Industry 
Association, aggregate revenues for these firms declined more than 70 
percent between 2000 and 2004, falling from $9 billion to $2.5 billion. 
Since 2001, market intermediaries conducting certain activities have 
consolidated. For example, the number of NYSE specialist firms fell 
from 25 in 1999 to 7 in 2004, and the number of NASDAQ market makers 
declined from almost 500 in 2000 to about 260 in 2004. However, factors 
other than decimal pricing have also contributed to these conditions, 
including the sharp decline in overall stock prices since 2000, reduced 
revenues from customers' increasing use of electronic trading 
strategies, and heightened competition from ECNs and other electronic 
trading venues. Market participants noted that these trends had been in 
place before decimalization. We found that market intermediaries had 
attempted to adapt to the new conditions by changing their business 
practices. For example, NASDAQ market makers had begun charging 
commissions on trades, broker-dealers had invested heavily in 
technological trading devices and data management systems, and other 
firms had reduced the sizes of their trading staffs. These conditions 
and the perceived decline in displayed liquidity in U.S. stock markets 
has caused a proposal to be made to conduct a pilot study of the use of 
higher minimum ticks for stock trading. Such a pilot was favored by 
most of the market intermediaries we contacted but by only about half 
of the institutional investors interviewed, and some of those that were 
open to testing larger tick sizes for trading saw them as being useful 
primarily for less liquid stocks rather than for all stocks. 

The effect of decimal pricing for options trading has been less 
significant. In part, options markets were less affected because the 
tick sizes that accompanied decimal pricing did not represent large 
changes from those previously in use. Nevertheless, the quality of U.S. 
options markets, as measured by their trading costs, liquidity, and 
increased trading volumes, has improved since 2001. However, options 
markets participants attributed these improvements primarily to other 
changes, including the increased competition arising from multilisting 
(the trading of options on the same securities on multiple exchanges), 
which began in 1999, and the establishment of new electronic exchanges 
and trading systems. Decimal pricing's effect on options market 
intermediaries such as market makers and specialists has been mixed, 
with market participants indicating that floor-based firms have 
experienced declining revenues and profitability and electronic-based 
firms are seeing increased trading revenues and profitability. A 2004 
SEC release sought industry comments on a range of issues pertaining to 
options markets, including whether these markets should use 1-cent 
ticks. However, officials of options exchanges and firms we contacted 
and virtually all of those providing comments to SEC were strongly 
opposed to lowering minimum price increments to one penny for options. 
Many were concerned that penny ticks would generate large numbers of 
price quote messages that would overwhelm the transmission and 
processing capacity of the existing market and data vendor systems. 
They also feared that lower intermediary revenues and more price points 
would reduce liquidity in the options markets. 

In their comments on a draft of this report, staff from SEC's Division 
of Market Regulation and Office of Economic Analysis said that, 
overall, the report accurately depicted conditions in the markets after 
the implementation of decimal pricing. 

Investors' Trading Costs Have Declined Since Decimalization, but 
Reduced Market Transparency Has Caused Firms to Adopt New Trading 
Strategies: 

Trading costs for both retail and institutional investors fell after 
the implementation of decimal pricing and the corresponding reduction 
in tick size. While decimalization appears to have helped to lower 
these costs, other factors--such as the multiyear downturn in stock 
prices--also likely contributed to these cost reductions. Although 
trading costs and other market quality measures improved after decimal 
pricing's implementation, another measure--the transparency of U.S. 
stock markets--declined following the reduction in tick size in 2001 
because fewer shares were displayed as available for trading. However, 
most market participants we interviewed reported they have been able to 
continue to execute large orders by using electronic trading tools to 
submit a larger volume of smaller orders and making greater use of 
alternative trading venues. 

Decimal Pricing Reduced Trading Costs for Retail Investors: 

In ordering U.S. markets to convert to decimal pricing, SEC had several 
goals.[Footnote 9] These included making securities pricing easier for 
investors to understand and aligning U.S. markets' pricing conventions 
with those of foreign securities markets. Decimalization appears to 
have succeeded in meeting these goals. In addition, SEC hoped that 
decimal pricing would result in lower investor trading costs, as lower 
tick sizes would spur competition that would lead to reduced spreads. 
Narrower spreads benefit retail investors because retail size orders 
generally execute in one trade at one price. Prior to being ordered to 
implement decimal pricing, U.S. stock markets had voluntarily reduced 
their minimum ticks from 1/8 to 1/16 of a dollar, and studies of these 
actions found that spreads declined as a result. 

Following decimalization and the implementation of the 1-cent tick in 
2001, retail investor trading costs declined further as spreads were 
narrowed even more substantially.[Footnote 10] To analyze the effects 
of decimal pricing, we selected a sample of 300 pairs of NYSE-listed 
and NASDAQ stocks with similar characteristics (like share price and 
trading activity).[Footnote 11] We examined several weeks before and 
after the implementation of decimal pricing and found that spreads 
declined after decimal prices were implemented and remained low through 
2004. Our study considered 12 weeklong sample periods from February 
2000 to January 2001 (our predecimalization period) and 12 weeklong 
sample periods from April 2001 through November 2004 (our 
postdecimalization period). As shown in figure 1, quoted spreads 
continued a steady decline on both NYSE and NASDAQ following the 
implementation of decimal pricing, falling to levels well below those 
that existed before the conversion to decimal pricing. 

Figure 1: Average Quoted Spreads Before and After Decimalization (cents 
per share): 

[See PDF for image] 

Note: The figure presents the average spread for the stocks in our 
sample from a 5-day period (a trading week) in each of the above listed 
months. Our sample weeks exclude any from February and March 2001 
because not all stocks were trading using decimal prices during the 
transition period. The change in spread for each stock in this analysis 
was weighted by its trading volume relative to the total trading 
volume. See appendix II for a detailed explanation of the methodology 
for this analysis. 

[End of figure] 

Our analysis of the TAQ data also found that quoted spreads declined 
for stocks with varying levels of trading volume. As shown in table 1, 
quoted spreads declined significantly after decimal pricing began for 
the most actively traded stocks, those with medium levels of trading 
volume, and also for those with the lowest amount of daily trading 
activity, with the average quoted spread falling 73 percent for NYSE 
stocks and 68 percent for NASDAQ stocks. 

Table 1: Average Quoted Spreads Before and After Decimalization, 2000- 
2004 (cents per share): 

Stocks by average daily volume of shares traded: High; 
NYSE quoted spread: Average spread in cents before decimals: 14.93; 
NYSE quoted spread: Average spread in cents after decimals: 2.77; 
NYSE quoted spread: Percent change: -81%; 
NASDAQ quoted spread: Average spread in cents before decimals: 12.95; 
NASDAQ quoted spread: Average spread in cents after decimals: 2.74; 
NASDAQ quoted spread: Percent change: -79%. 

Stocks by average daily volume of shares traded: Medium; 
NYSE quoted spread: Average spread in cents before decimals: 14.94; 
NYSE quoted spread: Average spread in cents after decimals: 3.78; 
NYSE quoted spread: Percent change: -75%; 
NASDAQ quoted spread: Average spread in cents before decimals: 15.58; 
NASDAQ quoted spread: Average spread in cents after decimals: 4.47; 
NASDAQ quoted spread: Percent change: -71%. 

Stocks by average daily volume of shares traded: Low; 
NYSE quoted spread: Average spread in cents before decimals: 16.25; 
NYSE quoted spread: Average spread in cents after decimals: 5.26; 
NYSE quoted spread: Percent change: -68%; 
NASDAQ quoted spread: Average spread in cents before decimals: 18.97; 
NASDAQ quoted spread: Average spread in cents after decimals: 7.69; 
NASDAQ quoted spread: Percent change: -59%. 

Stocks by average daily volume of shares traded: All stocks; 
NYSE quoted spread: Average spread in cents before decimals: 15.39; 
NYSE quoted spread: Average spread in cents after decimals: 4.18; 
NYSE quoted spread: Percent change: -73%; 
NASDAQ quoted spread: Average spread in cents before decimals: 16.96; 
NASDAQ quoted spread: Average spread in cents after decimals: 5.39; 
NASDAQ quoted spread: Percent change: -68%. 

Source: GAO analysis of TAQ data. 

Note: Quoted spreads in the table represent the volume-weighted average 
quoted spread (i.e., stocks and weeks with more total trading volume 
have greater weight) over 12 sample weeks during the predecimals period 
(February 2000-January 2001) and 12 sample weeks during the 
postdecimals period (April 2001-November 2004) for our sample of 
stocks. Stocks were segregated by volume according to the following 
categories: 

* High volume stocks were those in our sample of stocks with average 
daily trading volumes exceeding 500,000 shares. 

* Medium volume stocks were those in our sample of stocks with average 
daily trading volumes between 100,000 and 499,999 shares. 

* Low volume stocks were those in our sample of stocks with average 
daily trading volumes of less than 100,000 shares. 

Quoted spreads are time-weighted across quotes (quotes in effect longer 
have greater weights) and volume-weighted across stocks (stocks with 
more shares traded have greater weight). 

[End of table]

While the quoted spread measure is useful for illustrative purposes, a 
better measure of the cost associated with the bid-ask spread is the 
effective spread, which is twice the difference between the price at 
which an investor's trade is executed and the midpoint between the 
quoted bid and ask prices that prevailed at the time the order was 
executed.[Footnote 12] Thus, the effective spread measures the actual 
costs of trades occurring rather than just the difference between the 
best quoted prices at the time of the trade. As shown in table 2, 
effective spreads declined by 62 percent for our NYSE sample stocks and 
59 percent for our NASDAQ sample stocks between the periods after 
decimal pricing was implemented. 

Table 2: Average Effective Spreads Before and After Decimalization, 
2000-2004 (cents per share): 

Stocks by average daily volume of shares traded: High; 
NYSE effective spreads: Average spread in cents before decimals: 14.85; 
NYSE effective spreads: Average spread in cents after decimals: 4.71; 
NYSE effective spreads: Percent change: -68%; 
NASDAQ effective spreads: Average spread in cents before decimals: 
15.85; 
NASDAQ effective spreads: Average spread in cents after decimals: 4.86; 
NASDAQ effective spreads: Percent change: -69%. 

Stocks by average daily volume of shares traded: Medium; 
NYSE effective spreads: Average spread in cents before decimals: 13.17; 
NYSE effective spreads: Average spread in cents after decimals: 4.95; 
NYSE effective spreads: Percent change: -62%; 
NASDAQ effective spreads: Average spread in cents before decimals: 
15.14; 
NASDAQ effective spreads: Average spread in cents after decimals: 6.15; 
NASDAQ effective spreads: Percent change: -59%. 

Stocks by average daily volume of shares traded: Low; 
NYSE effective spreads: Average spread in cents before decimals: 12.86; 
NYSE effective spreads: Average spread in cents after decimals: 6.37; 
NYSE effective spreads: Percent change: -50%; 
NASDAQ effective spreads: Average spread in cents before decimals: 
16.00; 
NASDAQ effective spreads: Average spread in cents after decimals: 8.27; 
NASDAQ effective spreads: Percent change: -48%. 

Stocks by average daily volume of shares traded: All stocks; 
NYSE effective spreads: Average spread in cents before decimals: 13.36; 
NYSE effective spreads: Average spread in cents after decimals: 5.05; 
NYSE effective spreads: Percent change: -62%; 
NASDAQ effective spreads: Average spread in cents before decimals: 
15.66; 
NASDAQ effective spreads: Average spread in cents after decimals: 6.48; 
NASDAQ effective spreads: Percent change: -59%. 

Source: GAO analysis of TAQ data. 

Note: Effective quoted spreads (the difference between the price at 
which a trade is executed and the midpoint between the prevailing 
quoted bid and ask prices) in the table represent the volume-weighted 
average effective spread (i.e., stocks and weeks with more total 
trading volume have greater weight) over 12 sample weeks during the 
predecimals period (February 2000-January 2001) and 12 sample weeks 
during the postdecimals period (April 2001-November 2004) for our 
sample of stocks. Stocks were segregated by volume according to the 
following categories: 

* High volume stocks were those in our sample of stocks with average 
daily trading volumes exceeding 500,000 shares. 

* Medium volume stocks were those in our sample of stocks with average 
daily trading volumes between 100,000 and 499,999 shares. 

* Low volume stocks were those in our sample of stocks with average 
daily trading volumes of less than 100,000 shares. 

[End of table]

In addition, several academic and industry studies found similar 
results. For example, one academic study examined differences in trade 
execution cost and market quality measures in 300 NYSE stocks and 300 
NASDAQ stocks (matched on market capitalization) for several weeks 
before decimal pricing was fully implemented on NYSE stocks and after 
both markets converted to decimal pricing. As shown in table 3, the 
study found that average effective spreads declined by 41 percent for 
the NYSE stocks and by 54 percent for the NASDAQ stocks from the 
predecimalization sample period (January 8-26, 2001) to the 
postdecimalization sample period (April 9-August 31, 2001).[Footnote 
13] As the table also shows, the study found that spreads declined the 
most for NYSE stocks with the largest market capitalizations and for 
NASDAQ stocks with the smallest market capitalizations.[Footnote 14]

Table 3: Volume-weighted Average Effective Spreads Before and After 
Decimalization for Selected NYSE and NASDAQ Stocks, by Market 
Capitalization (cents per share): 

Stocks by market capitalization: Large; 
NYSE effective spreads: Before decimals: (cents): 12.51; 
NYSE effective spreads: After decimals: (cents): 6.93; 
NYSE effective spreads: Percent change: -45%; 
NASDAQ effective spreads: Before decimals: (cents): 12.55; 
NASDAQ effective spreads: After decimals: (cents): 5.61; 
NASDAQ effective spreads: Percent change: -55%. 

Stocks by market capitalization: Medium; 
NYSE effective spreads: Before decimals: (cents): 11.78; 
NYSE effective spreads: After decimals: (cents): 9.76; 
NYSE effective spreads: Percent change: -17%; 
NASDAQ effective spreads: Before decimals: (cents): 14.76; 
NASDAQ effective spreads: After decimals: (cents): 8.97; 
NASDAQ effective spreads: Percent change: -39%. 

Stocks by market capitalization: Small; 
NYSE effective spreads: Before decimals: (cents): 17.05; 
NYSE effective spreads: After decimals: (cents): 12.50; 
NYSE effective spreads: Percent change: -27%; 
NASDAQ effective spreads: Before decimals: (cents): 18.89; 
NASDAQ effective spreads: After decimals: (cents): 7.56; 
NASDAQ effective spreads: Percent change: -60%. 

Stocks by market capitalization: All stocks; 
NYSE effective spreads: Before decimals: (cents): 12.67; 
NYSE effective spreads: After decimals: (cents): 7.45; 
NYSE effective spreads: Percent change: -41%; 
NASDAQ effective spreads: Before decimals: (cents): 12.66; 
NASDAQ effective spreads: After decimals: (cents): 5.78; 
NASDAQ effective spreads: Percent change: -54%. 

Source: Hendrik Bessembinder. 

[End of table]

Similar declines in spreads were also reported in studies that SEC 
required the various markets to conduct as part of its order directing 
them to implement decimal pricing. For example, in its impact study, 
NYSE reported that share-weighted average effective spreads declined 43 
percent for all 2,466 NYSE-listed securities trading in the pre-and 
postdecimalization sample periods the exchange selected.[Footnote 15] 
NASDAQ's study found that effective spreads declined between its sample 
periods by an average of 46 percent for the 4,766 NASDAQ securities 
that converted to penny increments on April 9, 2001.[Footnote 16] In 
addition, an official at a major U.S. stock market told us that all the 
research studies that he reviewed on the impact of decimal pricing 
concluded that spreads narrowed overall in response to the reduction in 
tick size. 

Many market participants we interviewed also indicated that retail 
investors benefited from the narrower spreads that followed 
decimalization and the adoption of 1-cent ticks. For example, a 
representative of a firm that analyzes trading activities of large 
investors told us that investors trading 100 shares are better off 
following decimalization because small trades can be executed at the 
now lower best quoted prices. Representatives from two broker-dealers 
stated that the narrower spreads that prevailed following 
decimalization meant that more money stayed with the buyers and sellers 
of stock rather than going to market intermediaries such as brokers- 
dealers and market makers. Furthermore, the chief financial officer of 
a small broker-dealer told us that retail investors had benefited from 
the adoption of the 1-cent tick because their orders can generally be 
executed with one transaction at a single price unlike those of 
institutional investors, which are typically larger than the number of 
shares displayed as available at the best prices. 

Institutional Investors' Trading Costs Have Also Declined Since 
Decimalization: 

Analysis of the multiple sources of data that we collected generally 
indicated that institutional investors' trading costs had declined 
since decimal prices were implemented. We obtained data from three 
leading firms that collect and analyze information about institutional 
investors' trading costs. These trade analytics firms (Abel/Noser, 
Elkins/McSherry, and Plexus Group) obtain trade data directly from 
institutional investors and brokerage firms and then calculate trading 
costs, including market impact costs, typically for the purpose of 
helping investors and traders limit costs of trading.[Footnote 17] 
These firms also aggregate client data in order to approximate total 
average trading costs for all their institutional investor clients. 
Generally, the client base represented in these firms' aggregate trade 
cost data is broad enough to be sufficiently representative of all 
institutional investors. For example, officials at one firm told us 
that its data captured 80 to 90 percent of all institutional investors 
and covers trading for every stock listed on the major U.S. stock 
markets.[Footnote 18] An official of a major U.S. stock market told us 
that these firms are well regarded and that their information is 
particularly informative because these firms measure costs from the 
point the customer makes the decision to trade by using the price at 
which stocks are trading at that time, which is data that exchanges and 
markets generally do not have. 

Although these firms use different methodologies, their data uniformly 
showed that costs had declined since decimal pricing was implemented. 
Our analysis of data from the Plexus Group showed that costs declined 
on both NYSE and NASDAQ in the 2 years after these markets converted to 
decimal pricing. Plexus Group analyzes various components of 
institutional investor trading costs, including the market impact of 
investors' trading.[Footnote 19] Total trading costs declined by about 
53 percent for NYSE stocks, falling from about 33 cents per share in 
early 2001 to about 15.5 cents (fig. 2). For NASDAQ stocks, the decline 
was about 44 percent, from about 25.7 cents to about 14.4 cents. The 
decline in trading costs, shown in figure 2, began before both markets 
implemented decimal pricing, indicating that causes other than decimal 
pricing were also affecting institutional investors' trading during 
this period. An official from a trade analytics firm told us that the 
spike in costs that preceded the decimalization of NASDAQ stocks 
correlated to the pricing bubble that technology sector stocks 
experienced in the late 1990s and early 2000s. An official from another 
trade analytics firm explained that trading costs increased during this 
time because when some stocks' prices would begin to rise, other 
investors--called momentum investors--would also begin making purchases 
and drive prices for these stocks up even faster. As a result, other 
investors faced greater than usual market impact costs when also 
trading these stocks. In general, trading during periods when stock 
prices are either rapidly rising or falling can make trading very 
costly. 

Figure 2: Total Trading Costs from a Trade Analytics Firm for NYSE and 
NASDAQ Stocks, 1999-2003 (cents per share): 

[See PDF for image] 

Note: Data are reported quarterly. After a phase-in period, all NYSE 
stocks were trading with decimal prices by January 29, 2001, and all 
NASDAQ stocks were converted by April 9, 2001. 

[End of figure] 

SIDEBAR: 

Cents Per Share Versus Basis Points: 

Institutional investors’ trading costs are commonly measured in two 
units: cents per share and basis points. Cents per share is an absolute 
measure of cost based on executing a single share. Basis 
points—measured in hundredths of a percentage point—show the absolute 
costs relative to the stock’s average share price. Costs reported in 
terms of basis points can show changes resulting solely from changes in 
the level of stock prices. 

In this section, we present our analysis of trade analytics firms’ data 
on institutional investor trading costs in cents per share because the 
period surrounding the U.S. markets’ implementation of decimal pricing 
coincided with a large decline in the overall prices of stocks. 
Therefore, we chose to present data on trading costs in cents per share 
units as a way to better isolate decimalization’s impact. However, we 
also calculated these same costs in basis points and present this 
analysis in appendix III. 

Source: GAO.

[End of sidebar] 

According to our analysis of the Plexus Group data, market impact and 
delays in submitting orders accounted for the majority of the decline 
in trading costs for NYSE stocks and NASDAQ stocks.[Footnote 20] 
Together, the reduction in these two cost components accounted for 
nearly 17 cents per share (or about 96 percent) out of a total decline 
of about 17.6 cents per share on NYSE. Delay costs declined about 11.2 
cents per share in the 2 years following the implementation of decimal 
pricing and 1-cent ticks on NYSE and market impact costs declining by 
about 5.8 cents (fig. 3). An SEC economist noted that declines in delay 
costs may reflect increased efficiency on the part of institutional 
investors in trading rather than changes in the markets themselves. 

Figure 3: Trading Cost Components from a Trade Analytics Firm for NYSE 
and NASDAQ Stocks, 2001 and 2003 (cents per share): 

[See PDF for image] 

Note: Data are from first quarter 2001 to second quarter 2003 for NYSE 
and second quarter 2001 to second quarter 2003 for NASDAQ. 

[End of figure] 

Figure 3 also shows that market impact and delay costs accounted for 
all declines to total NASDAQ trading costs. For example, market impact 
and delay costs declined about 14.1 cents per share between the second 
quarter of 2001 and the second quarter of 2003. However, at the same 
time that these cost components were improving, commission charges for 
NASDAQ stocks were rising. As shown in figure 3, commissions that 
market intermediaries charged for trading NASDAQ stocks increased about 
2.8 cents per share from second quarter of 2001 to second quarter of 
2003. Industry representatives told us these increases were the result 
of the broker-dealers that made markets in NASDAQ stocks transitioning 
from trading as a principal, in which a portion of the trade's final 
price included some compensation for the market maker, to trading as an 
agent for the customer and charging an explicit commission.[Footnote 
21] 

Analysis of data from the other two trade analytics firms from whom we 
obtained data, Elkins/McSherry and Abel/Noser, also indicated that 
institutional investor trading costs declined following the 
decimalization of U.S. stock markets in 2001. Because these two firms' 
methodologies do not include measures of delay, which the Plexus Group 
data shows can be significant, analysis of data from these two firms 
results in trading cost declines of a lower magnitude than those 
indicated by the Plexus Groupdata analysis. Nevertheless, the data we 
analyzed from Elkins/McSherry showed total costs for NYSE stocks 
declined about 40 percent between the first quarter of 2001 and year- 
end 2004 from about 11.5 cents per share to about 6.9 cents per share. 
Analysis of Abel/Noser data indicated that total trading costs for NYSE 
stocks declined about 30 percent, from 6.9 cents per share to 4.8 cents 
per share between year-end 2000 and 2004 (fig. 4). 

Figure 4: Total Trading Costs from Two Trade Analytics Firms for NYSE, 
1998-2004 (cents per share): 

[See PDF for image] 

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 
and the fourth quarter of 2004; Abel/Noser data are year-end totals for 
1998-2004. 

[End of figure] 

Our analysis of these firms' data also indicated that total trading 
costs declined for NASDAQ stocks, which appeared to have declined even 
more significantly than they did for NYSE stocks. For example, our 
analysis of the Elkins/McSherry data showed that total trading costs 
for NASDAQ stocks dropped by nearly 50 percent, from about 14.6 cents 
per share to about 7.4 cents per share, between the second quarter of 
2001 when that market decimalized and the end of 2004. Analysis of the 
Abel/Noser data indicated that total trading costs declined about 46 
percent for NASDAQ stocks between the end of 2000 and 2004, falling 
from 8.7 cents per share to 4.7 cents per share (fig. 5). 

Figure 5: Total Trading Costs from Two Trade Analytics Firms for NASDAQ 
Stocks, 1998-2004 (cents per share): 

[See PDF for image] 

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 
and the fourth quarter of 2004; Abel/Noser data are year-end totals for 
1998-2004. 

[End of figure] 

As our analysis of the Plexus Group data showed, the Elkins/McSherry 
and Abel/Noser data also indicated that reductions to market impact 
costs accounted for a vast proportion of overall reductions for NYSE 
stocks (fig. 6).[Footnote 22] Analysis of the Elkins/McSherry data 
indicated that these costs declined by 3.7 cents per share, accounting 
for about 80 percent of the total fall in trading costs during this 
period. The 1.1 cent per share reduction in market impact costs 
identified in the Abel/Noser data represented over half of the total 
trading cost reductions of 2.1 cents per share for NYSE stocks. 

Figure 6: Trading Cost Components from Two Trade Analytics Firms for 
NYSE Stocks, 2001 and 2004 (cents per share): 

[See PDF for image] 

Note: Abel/Noser does not account for exchange fees as a component of 
trading cost. For Elkins/McSherry, we obtained first quarter 2001 data 
and fourth quarter 2004. For Abel/Noser, we obtained data from the end 
of 2000 and 2004. 

[End of figure] 

Reductions to market impact costs explained the entire decline to total 
trading costs captured by the Elkins/McSherry and Abel/Noser data for 
NASDAQ stocks, and the total declines would have been even larger had 
commissions for these stocks not increased after 2001. Market impact 
costs declined about 10.6 cents per share (about 78 percent) according 
to our analysis of the Elkins/McSherry data, and 6.7 cents per share 
(about 87 percent) according to our analysis of the Abel/Noser data 
(fig. 7). However, during this period, commissions charged on NASDAQ 
stock trades included in these firms' data increased by more than 3 
cents per share, representing a more than threefold increase in 
commissions as measured by Elkins/McSherry and a more than sixfold rise 
according to Abel/Noser. 

Figure 7: Trading Cost Components from Two Trade Analytics Firms for 
NASDAQ Stocks, 2001 and 2004 (cents per share): 

[See PDF for image] 

Note: Abel/Noser does not account for exchange fees as a component of 
trading cost. For Elkins/McSherry, we obtained first quarter 2001 data 
and fourth quarter 2004. For Abel/Noser, we obtained data from the end 
of 2000 and 2004. 

[End of figure] 

Data from a fourth firm, ITG, which recently began measuring 
institutional trading costs, also indicates that such costs have 
declined. This firm began collecting data from its institutional 
clients in January 2003. Like the other trade analytics firms, its data 
is similarly broad based, representing about 100 large institutional 
investors and about $2 trillion worth of U.S. stock trades. ITG's 
measure of institutional investor trading cost is solely composed of 
market impact costs and does not include explicit costs, such as 
commissions and fees, in its calculations. Although changes in ITG's 
client base for its trade cost analysis service prevented direct period 
to period comparisons, an ITG official told us that its institutional 
investor clients' trading costs have been trending lower since 
2003.[Footnote 23]

Academic Studies Generally Showed Declining Costs for Institutional 
Investors: 

In attempting to identify all relevant research relating to the impact 
of decimal pricing on institutional investors, we found 15 academic 
studies that discussed the impact of decimalization but only 3 that 
specifically examined institutional investors' trading costs. As of May 
2005, none of these three studies had been published in an academic 
journal. Two of these studies used direct measures of trading costs, 
and the other used an indirect measure.[Footnote 24] Those that relied 
on more direct measures of these costs found that these costs had 
declined since the implementation of decimal pricing and 1-cent ticks. 
The first of these studies analyzed more than 80,000 orders in over 
1,600 NYSE-listed stocks that were traded by 32 institutional 
investors.[Footnote 25] To measure the change in trading costs after 
decimal pricing was implemented, this study used data from one of the 
leading trade analytics firms and computed trading costs over the 
period from November 28, 2000, to January 26, 2001 (before the change 
to decimal pricing), and the period from January 30 to March 31, 2001 
(after decimal pricing). The study found that institutional trading 
costs appeared to have declined by about 5 cents per share (or about 11 
percent), falling from 44 cents per share to 39 cents per share after 
NYSE switched to 1-cent ticks. 

The other study that used direct measures of institutional trading 
costs examined the trading of over 1,400 NASDAQ stocks.[Footnote 26] 
The author of this study obtained data on over 120,000 orders for 
NASDAQ stocks submitted by institutional investors, which allowed her 
to calculate the costs of trading orders of more than 9,999 shares 
before and after NASDAQ's adoption of 1-cent ticks. Given the 
potentially large volume of order data, the author studied three sample 
periods, each consisting of 5 trading days: February 1 through 8, 2001 
(before decimalization), and June 18 through 22 and November 26 through 
30, 2001 (after decimalization). Trading costs in this study are 
measured as the difference between an order's volume-weighted average 
execution price and a pre-execution benchmark price, the opening 
midquote (the midpoint between the quoted bid and ask prices). Using 
the opening midquote benchmark, the author found that average trading 
costs for orders of 10,000 shares and above fell about 19 cents per 
share (or about 49 percent), from about 39 cents per share to about 20 
cents per share during the 9 months or so after NASDAQ's adoption of 1- 
cent ticks. 

Unlike the other two studies we identified, the third study reported 
that costs for institutional investors had increased. However, this 
study relied on an indirect measure of these costs for its 
analysis.[Footnote 27] To assess the change in trading costs, the 
authors of this study examined a sample of 265 mutual funds chosen from 
a database of mutual funds compiled by Morningstar, an independent 
investment research firm. These firms were selected using two criteria-
-investing predominantly in U.S. stocks and having at least 90 percent 
of assets invested in stocks. However, the study did not obtain these 
mutual funds' actual trading data but instead attempted to identify 
costs by comparing the funds' daily returns (gain or loss from the 
prior day's closing price) to the daily returns of a synthetic 
benchmark for the periods before and after decimalization, from April 
17 through August 25, 2000, and from April 16 through August 24, 
2001.[Footnote 28] After finding that the returns of actively managed 
mutual funds were generally lower than the returns of the benchmark in 
the period after decimals were introduced, the authors attributed the 
lower returns to increases in the trading costs for these funds. 

Although this is a plausible explanation for these funds' lower 
returns, some of the market participants that we spoke with indicated 
that other factors could also account for the results. For example, 
officials from a large mutual fund company that had reviewed the study 
told us that the lower returns may have resulted from the 3-year 
decline in stock prices in the market. As the value of their assets 
decline, funds can report higher expenses because their fixed operating 
costs correspondingly represent a larger portion of a mutual fund's 
total costs, which would reduce reported returns. In addition, an 
academic regarded as an expert in applying technology to the financial 
markets noted that the lower returns could be the result of many of the 
funds in the study's sample having similar holdings that all performed 
more poorly than those in the benchmark portfolio in the months 
following decimalization. 

Institutional Investors Reported Reduced or Level Trading Costs after 
Decimalization: 

In addition to analyzing data from trade analytics firms and academic 
studies, we interviewed 23 institutional investors that represented 
nearly one-third of assets managed by a ranking of the 300 largest 
money managers.[Footnote 29] Representatives for 20 of these firms said 
that their trading costs had fallen or stayed about the same since 
decimals were implemented (table 4). 

Table 4: Institutional Investor Positions on Changes to Trading Costs 
After Decimalization: 

Trading cost change: Declined; 
Institutional investors: Number: 15; 
Institutional investors: Percent: 65%. 

Trading cost change: Increased; 
Institutional investors: Number: 3; 
Institutional investors: Percent: 13%. 

Trading cost change: Stayed about the same; 
Institutional investors: Number: 5; 
Institutional investors: Percent: 22%. 

Trading cost change: Total; 
Institutional investors: Number: 23; 
Institutional investors: Percent: 100%. 

Source: GAO. 

Note: Percentages rounded to the nearest full number. 

[End of table]

As shown in table 4, fifteen of these firms said that their trading 
costs had declined since decimals were introduced. These firms included 
large mutual fund companies, pension fund administrators, a hedge fund, 
and smaller asset management firms, indicating that cost declines in 
our sample were not limited solely to just larger firms with greater 
trading resources. For example, a representative of a small money 
management firm not ranked as one of the 300 largest noted that trading 
costs had decreased since decimalization. In addition, the president of 
a hedge fund that was ranked in the lower half of the rankings told us 
that his firm's trading costs had declined significantly since 2001. As 
shown in the table above, 5 of the 23 firms we interviewed said that 
their costs had remained about the same since decimal pricing was 
implemented. For example, representatives of one large mutual fund firm 
that measures its trading costs internally as well as through a trade 
analytics firm told us that their firm's transaction costs had not 
increased since decimal pricing was introduced, but had trended down to 
flat. Three institutional investors reported higher trading costs. One 
of these firms, a large mutual fund manager, attributed the increases 
to heightened levels of volatility following the reduction in tick 
size. For example, in his view, stock prices tended to trade in a wider 
daily range since decimals were implemented than they had before. The 
other two firms included a mutual fund firm and a mid-size asset 
management firm, with officials from the mutual fund noting that 
trading had become more involved and that completing trades of 
similarly sized orders takes longer since the conversion to decimal 
pricing. 

In discussing institutional investors' views on their trading costs 
since decimal pricing began, we found that the precision with which 
these firms measured their trading costs varied. Many firms told us 
that they used outside trade analytics firms, such as Abel/Noser, 
Elkins/McSherry, ITG, and Plexus Group, to measure their transaction 
costs. Representatives of some firms and a state pension plan 
administrator noted that their firms used trade cost analysis tools 
from more than one trade analytics firm. The head of trading for one 
firm said that his firm had been using a trade analytics firm to 
measure their trading costs for 10 years. Some firms said that they had 
developed in-house capabilities to measure their own transaction costs. 
These systems appeared to vary in their levels of sophistication. For 
example, representatives of a large money management firm told us that 
they had developed a sophisticated cost measurement system that shows 
them what a trade should cost before it is executed. The system takes 
into account factors such as the executing broker and the market venue 
where the trade executes. A managing partner of another firm noted that 
it measures costs of completed trades in-house, including the bid-ask 
spreads and the execution prices, and compares them to the volume- 
weighted average price for trades it executes. Some money managers told 
us that their firms did not measure their costs for trading. For 
example, officials from one firm said that while not formally measuring 
costs on their own, they sometimes were provided with data on the costs 
of their trades from their own clients who use trade analytics firms to 
evaluate the costs of using various money managers. Also, another state 
pension plan administrator told us that while his organization does not 
currently measure its trading costs, it plans to do so within the next 
2 years. 

Volatility Has Also Improved since Decimal Pricing Began: 

In addition to lower spreads and reduced market impact costs, some 
market participants noted that another measure of market quality--price 
volatility--had also improved since decimal pricing was implemented. 
According to some market participants, the smaller 1-cent ticks 
generally slowed price movement in the markets and narrowed the range 
of prices at which stocks trade over the course of time, such as a day. 
For example, a noted expert on market microstructure told us that price 
volatility has declined since the reduction in tick size because price 
changes occur in smaller increments.[Footnote 30] Our own study of NYSE 
and NASDAQ stocks using TAQ data showed that price volatility has 
declined since decimal pricing was implemented. To assess the change in 
volatility for the stocks in our sample, we calculated the percentage 
change in price for each one hour increment (between 10 a.m. and 4 
p.m.) each trading day. We also calculated the percentage change in 
price for each stock that occurred between 10 a.m. and 4 p.m. For each 
stock, we also calculated the standard deviation of these percentage 
changes, which measures how widely the individual price changes are 
dispersed around the average change, and reported the median (that is 
the middle) standard deviation. As shown in table 5, the volatility of 
the price changes in the stocks in our sample decreased for both the 
hourly percentage change between 10 a.m. and 4 p.m. each trading day 
and the percentage change from 10 a.m. to 4 p.m. each trading day after 
decimal prices were implemented. These findings were in agreement with 
a recently published academic study.[Footnote 31]

Table 5: Price Change Volatility for NYSE and NASDAQ Stocks Before and 
After Decimalization: 

Price change period: Hourly; 
Median standard deviation of price changes: NYSE stocks: Before 
decimals: 1.00%; 
Median standard deviation of price changes: NYSE stocks: After 
decimals: 0.79%; 
Median standard deviation of price changes: NASDAQ stocks: Before 
decimals: 0.97%; 
Median standard deviation of price changes: NASDAQ stocks: After 
decimals: 0.75%. 

Price change period: From 10 a.m. to 4 p.m; 
Median standard deviation of price changes: NYSE stocks: Before 
decimals: 2.48%; 
Median standard deviation of price changes: NYSE stocks: After 
decimals: 1.99%; 
Median standard deviation of price changes: NASDAQ stocks: Before 
decimals: 2.37%; 
Median standard deviation of price changes: NASDAQ stocks: After 
decimals: 1.87%. 

Source: GAO analysis of TAQ data. 

Note: The median standard deviation in this table is based on the 
continuously compounded percentage change in the quote midpoint for 
each stock. 

[End of table]

However, not all participants attributed the reduced price volatility 
to decimal pricing. For example, a representative of a trade analytics 
firm noted that with the Internet boom, investors increased their 
positions in technology-sector stocks in a hurry and when the prices of 
these stocks fell--which was coincident with the change to decimal 
pricing--investors quickly reversed their positions. By selling 
quickly, these investors incurred greater market impact costs. With the 
subsiding of this type of trading activity in ensuing years, markets 
have become calmer, which has made trading less costly. 

Despite Reduced Market Transparency for Large Orders, Institutional 
Investors Have Been Able to Complete Trades: 

Although some major elements of market quality--trading costs and 
volatility--have improved since decimal pricing began, another market 
quality element--transparency--appears to have been negatively 
affected. The transparency of a market can depend on whether large 
numbers of shares are publicly quoted as available to buy or sell. The 
various sources of data we collected and analyzed indicated that after 
decimal pricing and the 1-cent tick were implemented in 2001, the 
volume of shares shown as available for sale--or displayed depth--on 
U.S. stock markets declined significantly. For example, studies 
required by SEC on the impact of decimal pricing on trading, among 
other things, on U.S. markets showed that the average number of shares 
displayed for trading on NYSE and NASDAQ at the best quoted prices 
declined by about two-thirds between a sample period before the markets 
converted to decimal pricing and a period soon after the conversion 
took place (table 6).[Footnote 32]

Table 6: Average Number of Shares Displayed at the Best Quoted Prices 
Reported by NYSE and NASDAQ in Studies of Their Markets Before and 
After Decimalization: 

Market: NYSE[A]; 
Average shares displayed before: 7,930; 
Average shares displayed after: 2,657; 
Percent change: -67%. 

Market: NASDAQ[B]; 
Average shares displayed before: 13,974; 
Average shares displayed after: 4,539; 
Percent change: -68%. 

Source: GAO analysis of NYSE and NASDAQ data. 

[A] Averages on NYSE are trade weighted. Averages are for all 2,466 
NYSE-listed securities trading in both sample periods. NYSE's presample 
period is August 1-25, 2000; its postsample period is June 2001. 

[B] Averages are for 4,766 NASDAQ-listed securities that converted to 
decimal pricing on April 9, 2001. Another 211 securities converted to 
decimal pricing earlier. NASDAQ's pre sample period is the 2 weeks 
prior and 2 weeks after the conversion date. 

[End of table]

In addition, our own study of 300 matched pairs of NYSE and NASDAQ 
stocks found that the liquidity at the best quoted prices declined 
significantly. According to our analysis, the average number of shares 
displayed at the best quoted prices fell by 60 percent on NYSE and 34 
percent on NASDAQ over the nearly 5-year period between February 2000 
and November 2004 (fig. 8). The greatest declines occurred around the 
time that the markets converted to decimal pricing and 1-cent ticks. In 
its impact study, NASDAQ attributed declines in the volume of shares 
displayed at the best prices to the conversion to decimal pricing. 

Figure 8: Volume-weighted Average Number of Shares Displayed at the 
Best Quoted Prices on the NYSE and NASDAQ Before and After 
Decimalization, Sample Weeks from February 2000-November 2004: 

[See PDF for image] 

[End of figure] 

The amount of shares displayed as available for trading also declined 
at prices away from the best quoted prices. For example, the SEC- 
mandated NYSE impact study shows that the amount of shares displayed 
for trading within about a dollar of the midpoint between the best 
quoted prices generally declined to well under half of what it was when 
the tick size was 1/16 of a dollar. NASDAQ's own impact study reported 
that the cumulative amount of shares displayed for trading declined by 
about 37 percent within a fixed distance equal to twice the size of the 
average quoted spread from the midpoint between the best quoted 
prices.[Footnote 33] This decline in the volume of shares displayed 
across all prices--called market depth--is particularly significant for 
institutional investors because they are often executing large orders 
over multiple price points that are sometimes inferior to the best 
quoted prices. 

Various reasons can explain the reduced number of shares displayed at 
the best prices. First, the amount of shares displayed for trading at 
the best price likely declined because the decrease in the minimum tick 
size created more prices at which orders could be displayed. The 
reduction in tick size increased the number of price points per dollar 
at which shares could be quoted from 16, under the previous minimum 
tick size of 1/16 of a dollar, to 100. With more price points available 
to enter orders, some traders that may have previously priced their 
orders in multiples of 1/16 to match the best quoted price may now 
instead be sending orders priced 1, 2, or 3 cents away from the best 
price, depending on their own trading strategy. As a result, the volume 
of shares displayed as available at the best price is lower as more 
shares are now distributed over nearby prices. 

In addition to fewer shares displayed at the best price, displayed 
market depth may also have declined because the reduction in tick size 
reduced incentives to large-order investors to display their trading 
interest. Since the implementation of penny ticks, market participants 
said that displaying large orders is less advantageous than before 
because other traders could now submit orders priced one penny better 
and execute these orders ahead of the larger orders. This trading 
strategy, called "penny jumping" or "stepping ahead," harms 
institutional investors that display large orders and can increase 
their trading costs.[Footnote 34] For example, an investor wants to 
purchase a large quantity of shares of a stock (e.g., 15,000 shares) 
and submits an order to buy at a price of $10.00 (a limit 
order).[Footnote 35] Another trader, seeing this large trading 
interest, submits a smaller limit order (e.g., 100 shares) to buy the 
same stock at $10.01. This smaller order will be executed against the 
first market order (which are orders executed at the best price 
currently prevailing at the time they are presented for execution) that 
arrives. As a result, the investor's larger order will go unexecuted 
until that investor cancels its existing order at $10.00 and resubmits 
it at a higher price. In this case, the investor's trading costs 
increase due to price movements that occur in the process of completing 
a large order (i.e., market impact). 

The potential for stepping ahead has increased because in a 1-cent tick 
environment the financial risk to traders stepping ahead of larger 
displayed orders has been greatly reduced. For example, assume a trader 
who steps ahead of a larger order offering to buy shares at $10.00 by 
entering a limit order to buy 100 shares at a price of $10.01 is 
executed against an incoming market order. However, if the price of the 
stock appears to be ready to decline, such as when additional orders to 
sell are entered with prices lower than $10.00, the trader who 
previously stepped ahead can quickly enter an order to sell the 100 
shares back to the large investor whose order is displayed at $10.00. 
In such situations, the trader's loss is only one penny per share, 
whereas in the past, traders stepping ahead would have risked at least 
1/16 of a dollar per share. Many market participants we spoke to 
acknowledged that institutional investors are reluctant to display 
large orders in the markets following the switch to 1-cent ticks for 
fear that competing traders would improve the best quoted prices by one 
penny and drive up prices to execute large orders. 

The potential that the reduced tick size would increase the prevalence 
of stepping ahead was acknowledged prior to decimal pricing's 
implementation. For example, in 1997 a prominent academic researcher 
predicted that problems with stepping ahead would increase following 
decimalization because smaller price increments would make it easier 
(i.e., cheaper) for professional traders to step in front of displayed 
orders and that this would result in fewer shares being quoted and less 
transparency in the markets.[Footnote 36] However, some market 
participants we interviewed acknowledged that stepping ahead had been a 
problem before decimal pricing was implemented. For example, 
representatives of a hedge fund told us they were worried about getting 
stepped ahead of if they revealed their interest to trade large amounts 
of a stock by entering limit orders with large numbers of shares even 
when ticks were 1/8 and 1/16. An SEC staff person told us that 
instances of orders being stepped ahead of has increased since the 
penny tick was implemented, but he did not think that it negated the 
benefits of decimal pricing overall. 

Institutional Investors Have Adjusted Their Trading Methods to Continue 
Executing Large Orders: 

Although markets became less transparent following decimalization, 
institutional investors and traders appear to be able to execute large 
orders at a lower cost by adapting their trading strategies and 
technologies. For example, the academic study that studied around 
120,000 large orders submitted for NASDAQ stocks found that the average 
proportion of total order size that was executed (filled) increased 
slightly from 78 percent before the change to decimal pricing to about 
81 percent about 6 months following the change. Similarly, the study 
found the length of time required to fill orders--measured from the 
time the order arrived at a NASDAQ dealer to the time of the last 
completed trade--decreased from about 81 minutes before decimal pricing 
to about 78 minutes 6 months after.[Footnote 37] Eight of the 
institutional investment firms we contacted for this report also 
provided information about their experiences in completing trades. Of 
these, officials from seven of the eight told us that their fill rates 
had either stayed about the same or had increased. An official at one 
firm noted that the proportion of orders that were completely executed 
had risen by as much as 10 percent in the period following decimal 
pricing's introduction. 

One of the ways that institutional investors have adapted their trading 
strategies to continue trading large orders is to break up these orders 
into a number of smaller lots. These smaller orders can more easily be 
executed against the smaller number of shares displayed at the best 
prices. In addition, not displaying their larger orders all at once 
prevents other traders from stepping ahead. Evidence of this change in 
investors' trading strategy is illustrated by the decline in the 
average executed trade size on NYSE and NASDAQ. As table 7 shows, the 
average size of trades executed on these markets has declined about 67 
percent since 1999 on NYSE and by about 41 percent on NASDAQ. 

Table 7: Average Trade Size for NYSE and NASDAQ, 1999-2004 (in shares): 

Market: NYSE; 
1999: 1,205; 
2000: 1,187; 
2001: 907; 
2002: 666; 
2003: 488; 
2004: 393; 
Percent change 1999-2004: -67%. 

Market: NASDAQ; 
1999: 808; 
2000: 693; 
2001: 782; 
2002: 735; 
2003: 580; 
2004: 477; 
Percent change 1999-2004: -41%. 

Source: GAO analysis of NYSE and NASDAQ data. 

[End of table]

With average trade size down, some market participants noted that at 
least 4 to 5 times as many trades are required to fill some large 
orders since decimalization. For example, a representative of a large 
mutual fund company said that his traders have always broken their 
funds' large orders up into smaller lots so that they could trade 
without revealing their activity to others in the marketplace. Before 
decimalization, completing an order may have required 10 trades, but 
following the change to decimal pricing a similar order might require 
as many as 200 smaller trades. Referring to the increased difficulty of 
locating large blocks of shares available for trading, one 
representative of a money management firm stated that "decimalization 
changed the trading game from hunting elephants to catching mice." In 
fact, the number of trades that NYSE reported being executed on its 
market increased more than fourfold between 1999 and 2004, rising from 
about 169 million trades to about 933 million trades.[Footnote 38]

Institutional Investors Increasingly Use Electronic Trading and 
Alternative Trading Venues: 

To facilitate the trading of large orders while minimizing market 
impact costs, many market participants said that they had increased 
their use of electronic trading techniques. Many of these techniques 
involve algorithmic execution strategies, which are computer-driven 
models that segment larger orders into smaller ones and transmit these 
over specified periods of time and trading venues. The simplest 
algorithms may just break a large order into smaller pieces and route 
these to whichever exchange or alternative trading system offers the 
best price. Institutional investors often obtain these algorithms as 
part of systems offered by broker-dealers and third-party vendors. They 
may also develop them using their own staff and integrate them into the 
desktop order management systems they use to help conduct their 
trading. 

One of the primary purposes of using these algorithmic trading systems 
is to conduct trading in a way that prevents other traders from 
learning that a large buyer or seller is active in the market. 
Institutional investors want tools that allow them to trade more 
anonymously to reduce the extent to which others can profit at their 
expense, such as when other traders, realizing that a large buyer is 
active, also buy shares, which quickly causes prices to rise, in hopes 
of selling these now more expensive shares to this large buyer. Several 
market participants told us that the anonymity that algorithms provide 
reduces the potential for other traders to learn that a large buyer or 
seller is active in the market (known as information leakage), thus 
reducing the likely market impact of executing the entire order. 

The use of these tools is growing. A 2004 survey conducted by The Tabb 
Group, a financial markets' consulting firm, of more than 50 head and 
senior traders at institutional investor firms reported that over 60 
percent of these firms were using algorithmic trading 
vehicles.[Footnote 39] The report noted that this widespread adoption 
rate was higher than anticipated. Many of the market participants we 
contacted also told us they were actively using algorithms in their 
trading activities and those that were not currently using algorithms 
generally indicated that they planned to begin using them in their 
trading strategies in the near future. In its report, The Tabb Group 
predicted that algorithmic trading will grow by almost 150 percent over 
the next 2 years. 

To locate the additional shares available for trading that are 
otherwise not displayed, institutional investors are also increasingly 
using alternative trading venues outside the primary markets, such as 
NYSE and NASDAQ, to execute their large orders at lower cost. For 
example, institutional investors are conducting increasing portions of 
their trading on ECNs. Originally, ECNs were broker-dealers that 
operated as real-time electronic trading markets by allowing their 
customers to enter orders for stocks and obtain executions 
automatically when the prices of the orders entered matched those of 
orders entered by other customers. Recently, ECNs have entered into 
formal associations with existing stock exchanges.[Footnote 40]

Use of ECNS has been a growing trend. According to The Tabb Group, 88 
percent of the institutional investor firms it surveyed responded that 
they traded using ECNs. Furthermore, a 2004 survey by Institutional 
Investor magazine asked the trading staff of institutional investor 
firms to identify their preferred venues for executing stock trades. 
The survey reported that three of the top five trading venues for 
institutional stock trade execution were ECNs.[Footnote 41] According 
to data we obtained from a financial markets consulting firm, the share 
of ECN trading in NASDAQ and NYSE stocks has increased between 1996 and 
2003. For example, ECN trading volume increased from about 9 percent of 
all NASDAQ trading in 1996 to about 40 percent of total NASDAQ trading 
volume in 2003 (fig. 9). 

Figure 9: Proportion of Total Share Trading Volume NASDAQ and NYSE 
Stocks by ECNs, 1996-2003: 

[See PDF for image] 

[End of figure] 

The percent of trading volume for NYSE stocks conducted through ECNs 
has also increased, though to a much lesser degree than has these 
organizations' trading in NASDAQ stocks. According to some market 
participants, ECNs have been less successful in gaining greater market 
share in NYSE stocks because of rules that result in most orders being 
sent to that exchange. For example, one regulation--the trade through 
rule--requires that broker-dealers send orders to the venue offering 
the best price, and in most cases NYSE has the best quoted price for 
its listed stocks. However, in a report issued by a financial market 
consulting firm, ECN officials called the trade through rule 
anticompetitive because the rule fails to acknowledge that some 
investors value the certainty and speed of execution more than they do 
price. They noted that under current rules, the NYSE specialists have 
as long as 30 seconds to decide whether to execute an order sent to 
them or take other actions. During this time, market participants told 
us that the price of the stock can change and their order may not be 
executed or will be executed at an undesirable price. On April 6, 2005, 
SEC approved Regulation NMS (National Market System) which, among other 
things, limits the applicability of trade through requirements to 
quotes that are immediately accessible.[Footnote 42]

Institutional investors we spoke with highlighted anonymity, speed, and 
the quality of the prices they receive as reasons for their increased 
use of ECNs. The respondents to The Tabb Group survey indicated that 
their firms used ECNs to reduce market impact costs and to take 
advantage of lower fee structures. Many market participants we 
interviewed and studies we reviewed also indicated that trading using 
ECNs lowered institutional trading costs. According to market 
participants we interviewed, decimalization accelerated technology 
innovation, which they believe has been significant in reducing trading 
costs primarily by providing a means for investors to directly access 
the markets and reducing the need for intermediation. However, many 
acknowledged that increasing use of ECNs has been a growing trend since 
1997, when SEC implemented rule changes that allowed ECNs to better 
compete against NASDAQ market makers.[Footnote 43]

Other alternative trading venues that institutional investors are 
increasingly using to execute their large orders are block trading 
platforms operated by broker-dealers called crossing networks. These 
networks are operated by brokers such as ITG, Liquidnet, and Pipeline 
Trading Systems. Crossing networks generally provide an anonymous venue 
for institutional investors to trade large blocks of stock (including 
orders involving tens or hundreds of thousands of shares) directly with 
other institutional investors. For example, one crossing network 
integrates its software with the investor's desktop order management 
system so that all of the investor's orders are automatically submitted 
to this crossing network in an effort to identify a match with another 
institutional investor. Once a match is identified, the potential buyer 
and seller are notified, at which time they negotiate the number of 
shares and price at which a trade would occur. The heads of stock 
trading for two large money management firms told us an advantage of 
using crossing networks is that they minimize market impact costs by 
allowing investors to trade in large blocks without disclosing their 
trading interests to others in the markets. Also, the chief executive 
officer of a crossing network noted that the absence of market 
intermediaries in the negotiation of trades on crossing networks 
provides the customers' traders with the ability to control the price 
and quantity of their executions. However, we were told that crossing 
networks may not be the preferred strategy for all kinds of 
institutional orders because orders remain unexecuted if a natural 
match cannot be found. 

Crossing networks are gaining in prominence among institutional 
investors as a destination of choice for trading large quantities of 
stock. According to The Tabb Group's survey of head and senior traders, 
70 percent of all firms reported using crossing networks.[Footnote 44] 
In Institutional Investor's 2004 survey, Liquidnet, a crossing network 
established in 2002, ranked second on the list of institutional 
investors' favorite venues for trade executions.[Footnote 45]

Despite advances in electronic trading technologies that give 
institutional investors increased access to markets, some institutional 
investors continue to use full-service brokers to locate natural 
sources of liquidity as they did before decimal pricing began. 
According to institutional investor officials we interviewed, with 
fewer shares displayed as available for trading and reductions in 
average trade size, they are more patient about the time required to 
completely execute (fill) large orders using brokers in this way. In 
addition, some noted they increasingly use NYSE floor brokers to 
facilitate the trading of large orders in less-liquid stocks, 
explaining that floor brokers have information advantages in the 
current market structure that help to minimize adverse price changes. 

Market Conditions May Also Have Helped Lower Institutional Investors' 
Trading Costs: 

In addition to increased use of electronic trading, overall market 
conditions also likely helped lower trading costs for institutional 
investors. For example, prices on U.S. stock markets began a multiyear 
downturn around 2000. As stock prices declined, asset managers faced 
increased pressure to manage costs and boost investment returns. 
Representatives of all four leading firms we interviewed that analyze 
institutional investors' trading activity noted that the declining 
market that persisted after the implementation of decimal pricing also 
had led to reduced costs. Representatives of two of these trade 
analytics firms noted specifically that institutional buyers and 
sellers appeared more cost sensitive as a result of the 3-year 
declining stock market, which caused investment returns to decline 
substantially. This increased the incentive for institutional investors 
to take actions to lower their trading costs as a way to offset some of 
the reduced market returns. 

Some Stock Intermediaries Have Experienced Lower Profits since 
Decimalization, but Other Factors Have Contributed to the Declines: 

Although overall securities industry profits have returned to levels 
similar to those in the past, some market intermediaries, particularly 
those broker-dealers acting as exchange specialists and NASDAQ market 
makers, have been significantly affected by the implementation of 
decimal pricing. Between 2000 and 2004, exchange specialists and NASDAQ 
market makers generally saw their revenues and profits from stock 
trading fall, forcing some smaller market intermediaries out of the 
market. Decimal pricing was not the only force behind these declines, 
however. Sharp declines in the overall level of prices in the stock 
market, the growing use of trading strategies that bypass active 
intermediary involvement, and heightened competition from ECNs and 
other electronic trading venues have affected revenues and profits. We 
found that intermediaries were adapting to the new conditions by 
changing their business practices--for example, by investing in 
electronic trading devices and data management systems, reducing the 
size of their trading staffs, or changing how they priced their 
services. In response to the negative conditions that some believe 
exist in U.S. stock markets, a proposal has been made to conduct a 
pilot test of the use of a higher minimum tick for trading. Many of the 
market intermediaries but fewer than half of the institutional 
investors we contacted favored this move. 

Conditions in the Overall Securities Industry Appear to Be Improving: 

The business environment for the securities industry as a whole, which 
saw reduced revenues after 2000, appears to be improving. The 
Securities Industry Association (SIA), which represents the broker- 
dealers holding the majority of assets in the securities industry, has 
compiled data on all of its member broker-dealers that have conducted 
business with public customers in the United States over the last 25 
years.[Footnote 46] As shown in figure 10, the data SIA compiles are 
derived from filings broker-dealers are required to make with the SEC 
and detail, among other things, revenues and expenses for market 
activities such as trading in stocks, debt securities, and options and 
managing assets.[Footnote 47] SIA's 2004 data show that industry 
revenues of $237 billion, while down from the height of the bull market 
in 2000, are now similar to revenues earned before the unprecedented 
gains of 2000.[Footnote 48] In addition, the industry's total pretax 
net income of $24.0 billion in 2003 and $20.7 billion in 2004 represent 
some of the highest levels of pretax industry profits of the past 25 
years. 

Figure 10: Securities Industry Total Revenues and Net Income, 1994- 
2004: 

[See PDF for image] 

[End of figure] 

Further, our review indicated these improved industry conditions are 
not only the result of improved performance among the largest firms. By 
examining the trend in this data after excluding the results for the 25 
largest broker-dealers, the revenue and net-income trend for the 
remaining firms revealed the same pattern of improvement. 

Decimalization Has Negatively Affected Exchange Specialists: 

Despite these improvements, some market intermediaries, such as stock 
exchange specialists, have been negatively affected by the shift to 
decimal pricing. Stock exchange specialists buy or sell shares from 
their own accounts when insufficient demand exists to match orders from 
public customers directly. The lower spreads that have prevailed since 
decimal pricing have reduced the income that exchange specialists can 
earn from this activity. In addition, the number of shares displayed as 
being available for purchase or sale has declined, leaving specialist 
firms with less information about market trends and thus less ability 
to trade profitably.[Footnote 49] According to NYSE data, between 2000 
and 2004 aggregate NYSE specialist revenues declined by more than 50 
percent, falling from $2.1 billion to $902 million (table 8). 

Table 8: NYSE Specialist Firm Revenues and Profits, 1999-2004 (in 
millions of dollars): 

Category: Revenues; 
1999: $1,566; 
2000: $2,136; 
2001: $1,776; 
2002: $1,645; 
2003: $987; 
2004: $902. 

Category: After-tax profits; 
1999: $476; 
2000: $708; 
2001: $414; 
2002: $397; 
2003: $3[A]; 
2004: ($38)[B]. 

Source: GAO analysis of NYSE data. 

[A] Result reflects the booking of approximately $147 million in fines 
that NYSE specialist firms paid to settle charges with SEC and NYSE for 
trading violations. 

[B] Result reflects the booking of approximately $109 million in fines 
that NYSE specialist firms paid to settle charges with SEC and NYSE for 
trading violations. 

[End of table]

Further, since decimal pricing began, the extent to which specialist 
firms participate in trades on their own exchanges has been low, 
falling below predecimalization levels. The participation rate shows 
the percentage of the total shares traded represented by trades 
conducted by specialists as part of their obligation to purchase shares 
when insufficient demand exists or sell shares when insufficient 
numbers of shares are being offered. After climbing during the first 
year decimal pricing was implemented, the percentage of trades on NYSE 
in which NYSE specialists participated declined from 15.1 percent in 
2001 to 10.2 percent in 2004 (fig. 11). 

Figure 11: NYSE Specialist Participation Rates, 1999-2004, in Percent 
of Trades: 

[See PDF for image] 

Note: These percentages are calculated by dividing the total number of 
shares that specialists trade by twice the total volume of the trading 
on the exchange to reflect that specialists are usually only either 
buying shares or selling shares as part of a trade with other 
customers. 

[End of figure] 

The trend toward smaller order sizes and more trade executions that 
have accelerated since the introduction of decimal pricing (as 
discussed earlier in this report) has also impacted the operating 
expenses of exchange specialists. The average trade execution size on 
the NYSE dropped from 1,205 shares per execution in 1999 to 393 shares 
per execution in 2004, so that specialists now generally process more 
trades to execute orders than they did before decimal pricing began. 
This trend toward greater numbers of executions, which many market 
participants indicated was exacerbated by decimal pricing, has required 
exchange specialists to absorb additional processing costs and make 
related investments in more robust data management and financial 
reporting tools. For example, each trade that is submitted for 
clearance and settlement carries a fee, paid to the National Securities 
Clearing Corporation, of between $0.0075 to $0.15 per trade.[Footnote 
50] Several smaller regional exchange specialist firms we spoke with 
highlighted these kinds of increased operating costs as significant to 
their ability to continue profitable operations. Additionally, a floor 
brokerage firm we spoke with said that other charges had contributed to 
its declining operating performance. These charges included those from 
clearing firms, which typically charge in the range of $0.20 cents per 
100 shares to process trades, and execution fees from exchange 
specialists related to the processing of more trades and typically paid 
by floor brokers. 

As shown in table 9 below, average trade size has declined over the 
past 6 years as the number of executions on NYSE has risen. As the 
table shows, volumes have remained relatively consistent since 2002, 
even though exchange specialists and floor brokers have seen their 
revenue and profits decline during this period. 

Table 9: NYSE Reported Trades, Average Daily Volume, and Average Trade 
Size, 1999-2004: 

Category: Total reported trades (in millions of shares); 
1999: 169; 
2000: 221; 
2001: 339; 
2002: 546; 
2003: 723; 
2004: 933. 

Category: Average daily share volume; (in millions of shares); 
1999: 809; 
2000: 1,042; 
2001: 1,240; 
2002: 1,441; 
2003: 1,398; 
2004: 1,457. 

Category: Average trade size; (number of shares); 
1999: 1,205; 
2000: 1,187; 
2001: 907; 
2002: 666; 
2003: 488; 
2004: 393. 

Source: NYSE Fact Book. 

[End of table]

Broker-dealers Revenues from NASDAQ Activities Have Also Fallen since 
Decimal Pricing Began: 

Decimal pricing has also generally negatively affected the 
profitability of firms that make markets in NASDAQ stocks. 
Traditionally, these firms earned revenue by profitably managing their 
inventories of shares and earning the spread between the prices at 
which they bought and sold shares. With the reduced bid-ask spreads and 
declines in displayed liquidity that have accompanied decimal pricing, 
the ability of broker-dealers to profitably make markets in NASDAQ 
stocks has been significantly adversely affected. For example, an 
official from one firm said that penny spreads had severely curtailed 
the amount of revenues that market makers could earn from their 
traditional principal trading. Table 10 presents SIA data on all NYSE 
members, which SIA indicates is often used as a proxy for the entire 
industry. As the table shows, these firms' revenues from NASDAQ market 
making activities, after rising between 1999 and 2000, declined about 
73 percent between 2000 and 2004, falling from nearly $9 billion to 
about $2.5 billion. 

Table 10: NYSE Member Broker-Dealer Revenues from NASDAQ Market Making 
Activities, 1999-2004 (in millions of dollars): 

Category: Revenues; 
1999: $6,786; 
2000: $8,994; 
2001: $4,648; 
2002: $2,742; 
2003: $2,385; 
2004: $2,462. 

Source: SIA Databank. 

[End of table]

Firms acting as NASDAQ market makers have also seen their operating 
expenses rise since decimal pricing began. Officials at one broker- 
dealer said that because the average trade size is smaller, market 
makers now generally process more trades to execute the same volume. 
This increase in the number of executions has required NASDAQ market 
makers to absorb additional processing and clearing costs. 
Additionally, the increased number of executions associated with 
decimal pricing has required some NASDAQ market makers to increase 
their investments in information technology systems. Table 11 shows the 
reduced average order size on the NASDAQ market over the past 6 years. 

Table 11: NASDAQ Average Trade Size, and Average Daily Volume, 1999- 
2004: 

Category: Average trade size (number of shares); 
1999: 808; 
2000: 693; 
2001: 782; 
2002: 735; 
2003: 580; 
2004: 477. 

Category: Average daily volume; (in millions of shares); 
1999: 1,071; 
2000: 1,752; 
2001: 1,923; 
2002: 1,754; 
2003: 1,702; 
2004: 1,808. 

Source: NASDAQ. 

[End of table]

Declining Intermediary Profits Have Accelerated Industry Consolidation: 

Declining revenues and increased operating expenses since the 
implementation of decimal pricing have encouraged some firms to merge 
with other entities and forced other smaller market intermediaries out 
of the market, accelerating a trend toward consolidation among stock 
exchange specialists and NASDAQ market makers. Generally, to date, two 
developments have contributed to the decline in the number of 
specialists: acquisitions of smaller firms by larger entities and, on 
the regional exchanges, smaller specialist firms and proprietorships 
leaving the business. As shown in table 12, the number of specialist 
firms operating on various floor-based stock exchanges has declined 
significantly in recent years. 

Table 12: Number of Specialist Firms Operating on Selected Stock 
Markets, 1999-2004: 

Market: Boston Stock Exchange; 
Number of specialist firms: 1999: 16; 
Number of specialist firms: 2004: 7. 

Market: NYSE; 
Number of specialist firms: 1999: 25; 
Number of specialist firms: 2004: 7. 

Market: Philadelphia Stock Exchange; 
Number of specialist firms: 1999: 20; 
Number of specialist firms: 2004: 3. 

Sources: Boston Stock Exchange, NYSE, and Philadelphia Stock Exchange. 

[End of table]

The number of firms that make markets on NASDAQ has similarly declined. 
Between 2000, when 491 firms were acting as NASDAQ market makers, and 
2004, the number of firms making markets in NASDAQ stocks declined to 
258--a drop of more than 47 percent. According to an industry 
association official, NASDAQ market-making activity is increasingly not 
a stand-alone profitable business activity with firms but instead is 
conducted to support other lines of business. For example, an official 
of a broker-dealer that makes markets in NASDAQ stocks told us that his 
firm has made no profits on its market-making operations in the last 3 
years but continues the activity in order to present itself as a full- 
service firm to customers. 

Although fewer firms are now acting as market makers, the overall 
NASDAQ market has not necessarily been affected. Since 2000, the number 
of stocks traded on NASDAQ has declined from 4,831 to 3,295, 
potentially reducing the need for market makers. In addition, some 
firms that continue to make markets have expanded the number of stocks 
in which they are active. For example, one large broker-dealer expanded 
its market-making activities from 500 stocks to more than 1,500. A 
NASDAQ official told us that with reduced numbers of stocks being 
traded, the average number of market makers per stock has increased 
since decimal pricing began. As shown in table 13, our analysis of data 
from NASDAQ indicated that although the number of NASDAQ market makers 
has declined, the number of firms making markets in the top 100 most 
active NASDAQ stocks actually grew between 1999 and 2004. 

Table 13: Consolidation among NASDAQ Market Makers, 1999-2004: 

Number of NASDAQ market makers; 
1999: 528; 
2000: 491; 
2001: 459; 
2002: 384; 
2003: 316; 
2004: 258. 

Market makers per top 100 NASDAQ stocks; 
1999: 23; 
2000: 25; 
2001: 28; 
2002: 32; 
2003: 29; 
2004: 63. 

Source: NASDAQ. 

[End of table]

Improved technology has likely helped market makers increase their 
ability to make markets in more stocks. An official at one market maker 
we spoke with explained that his firm had invested in systems that 
automatically update the firm's price quotes across multiple stocks 
when overall market prices change, allowing the firm to manage the 
trading of more stocks with the same or fewer staff. The use of such 
technology helps explain why the number of market makers per stock has 
not fallen as the overall number of market-making firms has declined. 

Other Factors Have Contributed to Declining Intermediary Revenues and 
Profits: 

Although decimal pricing affected market intermediaries' operations, 
the changes in these firms' revenues, profits, and viability are not 
exclusively related to the reduction in the minimum tick size. One 
major impact on firms' revenues since 2000 has been the sharp multiyear 
decline in overall stock market prices. Securities industry revenues 
have historically been correlated with the performance of U.S. stock 
markets (fig. 12). After 5 consecutive years of returns exceeding 10 
percent, prices on U.S. stock markets began declining in March 2000, 
and these losses continued until January 2003. The performance record 
for U.S. stocks during this period represents some of the poorest 
investment returns for U.S. stocks over the last 75 years. Because 
intermediary revenues tend to be correlated with broader stock market 
returns, as measured by the Standard & Poor's 500 (S&P 500) Stock 
Index, many market observers we spoke with told us that the 3-year down 
market, which coincided with the transition to decimal pricing, 
contributed to reduced intermediary revenues and profits. 

Figure 12: Securities Industry Revenues and Net Income as Compared to 
the Performance of the S&P 500 Stock Index, 1994-2004: 

[See PDF for image] 

[End of figure] 

The widespread emergence of technology-driven trading techniques, such 
as algorithmic trading models, has also reportedly affected market 
intermediaries negatively. These new techniques allow institutional 
investors, which account for the bulk of stock trading volume, to 
execute trades with less active intermediary involvement. Although only 
broker-dealers can legally submit trades for execution on U.S. stock 
markets, broker-dealers are reportedly only charging around 1 cent per 
share to transmit orders sent electronically as part of algorithmic 
trading models, an amount that represents much less revenue than the 
standard commission of around 5 cents per share for orders broker- 
dealers execute using their own trading systems and staff. Market 
intermediaries' revenues are also reduced by institutional investors 
increasing use of alternative execution venues such as crossing 
networks to execute trades. The commissions these venues charge are 
less than those of traditional broker-dealers, specialists, and market 
makers. Several market observers said that because crossing networks 
and algorithmic trading solutions divert order flow from and create 
price competition for traditional broker-dealers, their increased use 
is a probable factor in the reduced profitability of exchange 
specialists, floor brokers, and NASDAQ market makers. 

The increasing use of ECNs also has also likely reduced the revenues 
earned by market intermediaries. Several market participants we spoke 
with told us that the increased number of executions on ECNs, such as 
Bloomberg Tradebook, Brut, and INET, has reduced the profits of 
exchange specialists, floor brokers, and NASDAQ market makers. ECN 
executions are done on an agency/commission basis, typically in the 
range of 1 to 3 cents per share, compared with traditional broker- 
dealer execution fees of approximately 5 cents per share. As a result, 
the activities that lower investors trading costs can result in lower 
revenues for market intermediaries. However, market participants noted 
that institutional investors' use of electronic trading technologies 
and ECNs had been increasing even before decimal pricing was 
implemented. 

Brokerage Firms Have Made Adjustments to Business Activities and 
Personnel Levels: 

We found that in response to the changes brought about by decimal 
pricing and particularly to changes in institutional investors' trading 
behavior, many stock market intermediaries had adapted their business 
operations by making investments in technology to improve trading tools 
and data management systems, reducing the size of their trading staffs, 
and changing the pricing and mix of services they offer. Most exchange 
specialists, floor brokers, NASDAQ market makers, and the broker-dealer 
staff that trade stocks listed on the exchanges we spoke with had made 
investments in new technology since the implementation of decimal 
pricing. For example, some NASDAQ market makers and listed traders were 
increasingly using aggregation software to locate pools of liquidity 
instead of relying on telephone contacts with other broker-dealers as 
they had in the past. Several intermediaries were also using 
algorithmic trading solutions more frequently to execute routine 
customer orders, allowing more time for their staff to work on more 
complex transactions or the trading of less liquid stocks. 

Other intermediary firms have responded to the more challenging 
business environment since 2000 by reducing the size of their trading 
staffs. Most stock broker-dealer firms we spoke with employed fewer 
human traders in 2004 than they had before 2001. Senior traders at the 
firms we spoke with cited reduced profits and the increased number of 
electronic and automated executions as the primary reasons for the 
reductions in the number of traders they employed. Consequently, 
although trades executed by broker-dealers using computer-generated 
algorithms typically generated lower revenues from commissions than 
traditional executions, the reduced salary and overhead costs 
associated with employing fewer traders, we were told, had made it 
easier for some broker-dealers to maintain viable stock trading 
operations. 

We also found that market intermediaries were adapting to the new 
business environment by modifying the pricing and mix of the services 
they offered. For example, instead of trading as principals, using 
their own capital to purchase or sell shares for customers, many NASDAQ 
market makers have begun acting as agents that match such orders to 
other orders in the market. Like ECNs, these market makers charge 
commissions to match buy and sell orders. The agency/commission model 
provides the benefit of reduced risk for NASDAQ market makers because 
they were using less of their own capital to conduct trading activity. 
However, market participants told us that this activity may not 
generally be as profitable for market makers as traditional 
principal/dealer trading operations. Other firms had attempted to 
diversify or broaden their service offerings. For example, a NYSE floor 
brokerage firm we spoke with was attempting to make up for lost 
revenues by developing a NASDAQ market-making function. 

Some firms were also expanding into other product lines. For example, 
one large NASDAQ market maker we spoke with was attempting to make up 
for declining stock trading revenue by becoming a more active market 
maker in other over-the-counter stocks outside those traded on NASDAQ's 
National Market System, including those sold on the Over-the-Counter 
Bulletin Board (OTCBB) market, which trades stocks of companies whose 
market valuations, earnings, or revenues are not large enough to 
qualify them for listing on a national securities market like NYSE or 
NASDAQ.[Footnote 51] These stocks often trade with higher spreads on a 
percentage basis than do the stocks listed on the national exchanges. 
Finally, other firms had moved staff and other resources formerly used 
to trade stocks to support the trading of other instruments, such as 
corporate bonds, credit derivatives, or energy futures. 

Decimal Pricing Did Not Appear to Affect Businesses' Ability to Raise 
Capital: 

The willingness and ability of broker-dealers to assist companies with 
raising capital in U.S. markets also does not appear to have diminished 
as a result of decimal pricing. Broker-dealers, acting as investment 
banks, help American businesses raise funds for operations through 
sales of stock and bonds and other securities to investors. After the 
initial public offering (IPO), such securities can be traded among 
investors in the secondary markets on the stock exchanges and other 
trading venues.[Footnote 52] Several market observers had voiced 
concerns that the reduced displayed liquidity and declining ability of 
market makers to profit from trading could reduce the liquidity for 
newly issued and less active stocks. In turn, this loss of liquidity 
could make it more difficult for firms to raise capital. We found that 
in 2002 and 2003, U.S. stock underwriting activity was down 
significantly from recent years (fig. 13). However, as figure 13 shows, 
although stock IPOs are down from record levels of the bull market of 
the late 1990s, 247 companies offered stock to the public for the first 
time in 2004--up from the 2002 and 2003 levels of 86 and 85 companies, 
respectively. Additionally, stock underwriting activity measured in 
dollars rose to $47.9 billion in 2004, a level consistent with activity 
in the late 1990s. 

Figure 13: Number of IPOs and Dollars Raised, 1994-2004: 

[See PDF for image]

[End of figure]

Of the market participants that we spoke with, most did not believe 
that decimal pricing had affected companies' ability to raise capital 
in U.S markets, noting that underwriting activity is primarily related 
to investors' overall demand for stocks. More IPOs generally occur 
during periods with strong economic growth and good stock market 
performance. Institutional investors we spoke with noted that the poor 
growth of the U.S. economy after 2000 and the associated uncertainty 
about future business conditions had contributed more than decimal 
pricing to the reduced level of new stock issues in 2002 and 2003. 
Others cited the new Sarbanes-Oxley Act corporate governance and 
disclosure requirements, which can increase the costs of being a public 
company, as a factor that may be discouraging some firms that otherwise 
would have to sought to raise capital from filing an IPO. However, one 
broker-dealer official said that his firm was less willing to help 
small companies raise capital because of its reduced ability since 
decimal pricing began to profitably make a market in the new firm's 
stock after its IPO. 

Proposed Pilot for Higher Minimum Tick Size Receives Mixed Responses: 

In response to the drop in displayed liquidity and other negative 
conditions that some believe to exist in the U.S. stock markets, a 
proposal has been made to conduct a pilot that would test the use of a 
higher minimum tick for trading, but opinions among the various market 
participants we spoke with were mixed. The proposal, which was put 
forth by a senior official at one NYSE specialist firm, calls on SEC to 
oversee a pilot program that would test a 5-cent tick on 200 to 300 
NYSE stocks across all markets. The purpose of the pilot program would 
be to provide SEC with information it could use to decide whether 
larger-sized ticks improve market quality in U.S. stock markets. 

Proponents believe that larger ticks would address some of the 
perceived negative conditions such as the reduction in displayed 
liquidity brought about with the change to penny ticks. For example, 
some proponents anticipate that investors would be more willing to 
display large orders because larger tick sizes would increase the 
financial risk of stepping ahead for other traders. Some also expected 
that market intermediaries would be more willing to trade in less 
liquid stocks because of the increased potential to profit from larger 
spreads. Some proponents of a pilot program believed 5-cent ticks would 
also increase the cost efficiency, speed, and simplicity of execution 
for large-order investors, especially in less liquid stocks. Most of 
the market intermediaries we spoke with supported the proposed 5-cent 
pilot for stocks. Opinions from the representatives of the markets we 
spoke with were more mixed, with officials from floor-based exchanges 
supporting the pilot, while officials from two of the electronic 
markets we spoke with did not support a change and officials from two 
others supporting the pilot under the belief that larger ticks would 
benefit less liquid stocks. 

Of the 23 institutional investors we talked with, 10 indicated support 
for a proposed 5-cent pilot, 9 did not see a need for such a pilot, and 
4 were indifferent or had no opinion. Of those institutional investors 
who did not see the need to conduct a pilot, most indicated that 5-cent 
ticks would not increase liquidity in the markets because the negative 
conditions that are attributed to decimal pricing are more the result 
of the inefficiencies they believed existed in markets that rely on 
executing trades manually rather than using technology to execute them 
automatically. In addition, officials at several firms noted that such 
a pilot is unnecessary because institutional investors have already 
adjusted to penny ticks. For example, an official of a very large 
institutional investment firm noted that the challenges of locating 
sufficient numbers of shares for trading large orders had already been 
solved with advances in electronic trading and crossing networks. 

Some of these investors were also concerned that conducting such a 
pilot could have negative consequences. For example, one firm noted 
that having different ticks for different stocks could potentially 
confuse investors. Also, a trade association official noted that 
mandating that some stocks trade only in 5-cent ticks could be viewed 
as a form of price fixing, particularly for highly liquid stocks that 
were already trading efficiently using a 1-cent tick. An official from 
a financial markets consulting and research firm noted that if a pilot 
program were to occur, NASDAQ stocks should be included; this would 
better isolate the effects of a larger tick size on market quality 
factors since NYSE appears to be undergoing changes towards a more 
electronic marketplace, potentially making it more difficult to 
interpret the study's results. 

In addition, some of the 10 institutional investors that supported a 
pilot of nickel-sized ticks indicated that they saw such ticks as being 
useful primarily for less-liquid stocks that generally have fewer 
shares displayed for trading, including smaller capitalized stocks. 
These proponents told us that 5-cent ticks might increase displayed 
liquidity for such stocks. In addition, they stated that 5-cent ticks 
could provide financial incentive for intermediaries to increase their 
participation in the trading of such stocks, including providing 
greater compensation for market makers and specialists to commit more 
capital to facilitate large-order trades. Many also anticipated a 
reduction in stepping ahead since it would become more costly to do so. 
SEC staff that we asked about the pilot told us that conducting such a 
test did not appear to be warranted because, to date, the benefits of 
penny pricing--most notably the reduction in trading costs through 
narrower spreads--seem clearly to justify the costs. They also noted 
that penny pricing does not, and is not designed to, establish the 
optimal spread in a particular security, which will be driven by market 
forces. 

Decimal Pricing Has Had a Limited Impact on the Options Markets, but 
Other Factors Have Helped Improve Market Quality: 

Decimal pricing in U.S. options markets has generally had a more 
limited impact on the options market than it has on the stock market. 
Although various measures of market quality, including trading costs 
and liquidity, have improved in U.S. options markets, factors other 
than decimal pricing are believed to be the primary contributors. 
First, the tick size reductions adopted for options trading were less 
dramatic than those adopted in the stock markets. Second, other 
factors, including increased competition among exchanges to list the 
same options, the growing use of electronic trading, and a new system 
that electronically links the various markets, were seen as being more 
responsible for improvement in U.S. options markets. Options market 
intermediaries such as market makers and specialists have had mixed 
experiences since decimal pricing began, with floor-based firms facing 
declining revenues and profitability and electronic-based firms seeing 
increased trading revenues and profitability. As part of a concept 
release on a range of issues pertaining to the options markets, SEC has 
sought views on reducing tick sizes further in the options markets by 
lowering them from the current 5 and 10 cents to one penny. Options 
market participants were generally strongly opposed to such a move for 
a variety of reasons, including the possibility that the number of 
quotes could increase dramatically, overwhelming information systems, 
and the potential for reduced displayed liquidity. 

The Shift to Decimal Pricing Did Not Reduce Tick Sizes for Options as 
Much as for Stocks: 

One reason that decimal pricing's impact on options markets was not 
seen as significant was that the tick size reductions for options 
market were not as large as those adopted for the stock markets. 
Options markets had previously used a minimum tick size of 1/8 of a 
dollar (12.5 cents) for options contracts priced at $3 and more and a 
tick size of 1/16 of a dollar (6.25 cents) for options priced at less 
than $3. After decimal pricing came into effect, these tick sizes fell 
to 10 cents and 5 cents, respectively--a decrease of 20 percent. This 
decline was far less than the 84 percent reduction in tick size in the 
stock market, where the bid-ask spread dropped from 1/16 of a dollar to 
1 cent. 

Studies done by four options exchanges in 2001 to assess the impact of 
decimal prices on, among other factors, options contract bid-ask 
spreads did not find that decimal pricing had any significant effect on 
the spreads for options.[Footnote 53] Most market participants shared 
this view. For example, an official of a large market-making firm 
stated that decimalization in the options market was "a small ripple in 
a huge pond."

Although Decimal Pricing Not Significant, Key Measures of Options 
Markets' Quality Have Improved: 

Although decimal pricing's impact was not seen as significant, various 
measures used to assess market quality have shown improvements in U.S. 
options markets in recent years. Unlike for stocks, data on trading 
costs in options markets was not generally available. For example, we 
could not identify any trade analytics or other firms that collected 
and analyzed data for options trading. However, some market 
participants we interviewed indicated that bid-ask spreads, which 
represent a measure of cost of trading in options markets, have 
narrowed since the 1990s. In addition, the studies done by SEC and 
others also indicated that spreads have declined for options markets. 

In addition to lower trading costs, liquidity, which is another measure 
that could be used to assess the quality of the options market, has 
improved since decimal pricing was implemented. According to industry 
participants we interviewed, liquidity in the options market has 
increased since 2001. They noted that trading volumes (which can be an 
indicator of liquidity) had reached historic levels and that many new 
liquidity providers, such as hedge funds and major securities firms, 
had entered the market. As shown in figure 14, options trading volumes 
have grown significantly (61 percent) since 2000, rising from about 673 
million contracts to an all-time high of 1.08 billion contracts in 
2004. 

Figure 14: Total Contract Trading Volumes for Stock Options, 2000-2004 
(Volume in millions): 

[See PDF for image] 

[End of figure] 

However, some market participants noted that the implementation of 
decimal pricing in the stock markets had negatively affected options 
traders. According to these participants, the reduced number of shares 
displayed in the underlying stock markets and quote flickering in stock 
prices had made buying and selling shares in the stock markets and 
determining an accurate price for the underlying stocks more 
difficult.[Footnote 54] As a result, options traders' and market 
makers' attempts to hedge the risks of their options positions by 
trading in the stock markets had become more challenging and costly. 

Factors Other Than Decimal Pricing Have Been Credited with Improving 
the Quality of Options Markets: 

Market participants attributed the improvements in market quality for 
U.S. options markets not to decimal pricing but to other developments, 
including the practice of listing options contracts on more than one 
exchange (multilisting), the growing use of electronic exchanges, and 
the development of electronic linkages among markets. These 
developments have increased competition in these markets. Multilisting, 
one of the most significant changes, created intense competition among 
U.S. options markets.[Footnote 55] Although SEC had permitted 
multilistings since the early 1990s, the options exchanges had 
generally tended not to list options already being actively traded on 
another exchange, but began doing so more frequently in August 
1999.[Footnote 56] According to an SEC study, in August 1999, 32 
percent of stock options were traded on more than one exchange, and 
that percentage rose steadily to 45 percent in September 2000. The 
study also showed that the percentage of total options volume traded on 
only one exchange fell from 61 percent to 15 percent during the same 
period. Almost all actively traded stock options are now listed on more 
than one U.S. options exchange. 

Multilisting has been credited with increasing price competition among 
exchanges and market participants. The SEC study examined, among other 
things, how multiple listings impacted pricing and spreads in the 
options market and found that the heightened competition had produced 
significant economic benefits to investors in the form of lower quoted 
and effective spreads.[Footnote 57] The study looked at 1-week periods, 
beginning with August 9 through 13, 1999 (a benchmark period prior to 
widespread multilisting of actively traded options), and ending with 
October 23 through 27, 2000 (a benchmark period during which the 
actively traded options in the study were listed on more than one 
exchange). During this period, the average quoted spreads for the most 
actively traded stock options declined 8 percent. Quoted spreads across 
all options exchanges over this same period showed a much more dramatic 
change, declining approximately 38 percent. The actual transaction 
costs that investors paid for their options executions, as measured by 
effective spreads, also declined, falling 19 percent for options priced 
below $20 and 35 percent for retail orders of 50 contracts or less. 
Several academic studies also showed results consistent with SEC's 
findings that bid-ask spreads had declined since the widespread 
multiple listing of the most active options.[Footnote 58]

The introduction of the first all-electronic options exchange in 2000 
also increased competition in the options markets. Traditionally, 
trading on U.S. options markets had occurred on the floors of the 
various exchanges. On the new International Securities Exchange (ISE), 
which began operations in May 2000, multiple (i.e., competing) market 
makers and specialists can submit separate quotes on a single options 
contract electronically. The quotes are then displayed on the screens 
of other market makers and at the facilities of broker-dealers with 
customers interested in trading options, enhancing competition for 
customer orders. ISE also introduced the practice of including with its 
quotes the number of contracts available at the quoted price. According 
to market participants, the additional information benefited retail and 
institutional investors by providing them with better information on 
the depth of the market and the price at which an order was likely to 
be executed. Finally, ISE allowed customers to execute trades in 
complete anonymity and attracted additional sources of liquidity by 
allowing market makers to access its market remotely. 

In response, the four floor-based options exchanges--the American Stock 
Exchange, Chicago Board Options Exchange (CBOE), the Pacific Exchange 
(PCX), and the Philadelphia Stock Exchange--also began including the 
number of available contracts with their quotations and offering 
electronic trading systems in addition to their existing floor-based 
trading model.[Footnote 59] Another new entrant, the Boston Options 
Exchange (BOX) (an affiliate of the Boston Stock Exchange) also began 
all-electronic operations in 2004. The result has been increased quote 
competition among markets and their participants that has helped to 
further narrow spreads and has opened markets to a wide range of new 
liquidity providers, including broker-dealers, institutional firms, and 
hedge funds. 

Electronic linkages were first introduced to U.S. options markets in 
2003, offering the previously unavailable opportunity to route orders 
among all the registered options exchanges. In January 2003, SEC 
announced that the options markets had implemented the intermarket 
linkage plan, so that U.S. options exchanges could electronically route 
orders and messages to one another. The new linkages further increased 
competition in the options industry and made the markets more 
efficient, largely by giving brokers, dealers, and investors' better 
access to displayed market information. According to SEC and others, as 
a result of this development investors can now receive the best 
available prices across all options exchanges, regardless of the 
exchange to which an order was initially sent. Intermarket linkages are 
as essential to the effective functioning of the options markets as 
they are to the functioning of the stock markets and will further 
assist in establishing a national options market system. 

The Impact on Options Market Intermediaries Varied since Decimal 
Pricing Began: 

Decimal pricing and other changes in options markets appear to have 
affected the various types of market intermediaries differently. 
Representatives of firms that trade primarily on floor-based exchanges 
told us that their revenues and profits from market making had fallen 
while their expenses had increased. For example, one options specialist 
said that his firm's profitability had declined on a per-option basis 
and was now back to pre-1995 levels. However, he noted that the cost of 
technology to operate in today's market had increased substantially and 
that adverse market conditions and increased competition were more 
responsible for his firm's financial conditions than were decimal 
prices. 

The increasingly competitive and challenging environment has also led 
to continued consolidation among firms that trade on the various 
options exchange floors. According to data from one floor-based options 
exchange, the number of market intermediaries active on its market 
declined approximately 22 percent between 2000 and 2004. Market 
intermediaries and exchange officials we spoke with noted in particular 
that the smaller broker-dealer firms that trade options and sometimes 
have just one or two employees had been the most affected, with many 
either merging with other firms or going out of business because of 
their inability to compete in the new trading environment. 

In contrast, the introduction of electronic exchanges and expanded 
opportunities for electronic trading at other exchanges has been 
beneficial for some market intermediaries. Officials of some broker- 
dealers that trade options electronically told us that their firms' 
operations had benefited from the increased trading volume and the 
efficiency of electronic trading. The officials added that other firms, 
such as large financial institutions, had increased their participation 
in the options marketplace. They also noted that the availability of 
electronic trading systems and the inherent economies of scale 
associated with operating such systems had attracted new marketplace 
entrants, including some hedge funds and major securities firms. For 
example, representatives of ISE and several broker-dealers told us that 
the ability to trade electronically had encouraged several large broker-
dealers that were not previously active in options markets to begin 
acting as market makers on that exchange. These firms, they explained, 
were able to enter into the options markets because making markets 
electronically is less expensive than investing in the infrastructure 
and staff needed to support such operations on a trading floor. 
According to market participants we spoke with, these new entrants 
appeared to have provided increased competition and positively affected 
spreads, product innovation, and liquidity in the options industry. 

Options Market Participants Oppose Lower Minimum Ticks for the Options 
Industry: 

In 2004, SEC issued a concept release that sought public comments on 
options-related issues that have emerged since the multiple listing of 
options began in 1999, including whether the markets should reduce the 
minimum tick sizes for options from 5 and 10 cents to 1-cent 
increments.[Footnote 60] According to the release, SEC staff believed 
that penny pricing in the options market would improve the efficiency 
and competitiveness of options trading, as it has in the markets for 
stocks, primarily by tightening spreads. If lower ticks did lead to 
narrower spreads for options prices, investors trading costs would 
likely similarly decline. As of May 2004, SEC has received and reviewed 
comments on the concept release but has taken no further action. 

All of the options exchanges and virtually all of the options firms we 
spoke with, as well as 15 of the 16 organizations and individuals that 
submitted public comments on SEC's 1-cent tick size proposal, were 
opposed to quoting options prices in increments lower than those 
currently in use (10 and 5 cents, depending on the price of an options 
contract). One of the primary reasons for this opposition was that 
trading options contracts in 1-cent increments would significantly 
increase quotation message traffic, potentially overwhelming the 
capacity of the existing systems that process options quotes and 
disrupting the dissemination of market data. For any given stock, 
hundreds of different individual options contracts can be 
simultaneously trading, with each having a different strike price (the 
specified price at which the holder can buy or sell underlying stock) 
and different expiration date.[Footnote 61] Because options are 
contracts that provide their holders with the right to either buy or 
sell a particular stock at the specified strike price, an option's 
value and therefore its price also changes as the underlying stock's 
price changes. If options were priced in pennies, market participants 
said that thousands of new option price quotes could be generated 
because prices would need to adjust more rapidly to remain accurate 
than they do using nickel or dime increments. 

Markets and market participants also expressed concerns that penny 
pricing would exacerbate an already existing problem for the industry-
-ensuring that the information systems used to process and transmit 
price quotations to market participants have adequate capacity. The 
quotes generated by market makers on the various markets are 
transmitted by the systems overseen by the Options Price Reporting 
Authority (OPRA). The OPRA system has been experiencing message 
capacity issues for several years. In terms of the number of messages 
per second (mps) that can be processed, the OPRA system had a maximum 
mps of 3,000 in January 2000. Since then, the processing and 
transmission capacity of the system has had to be expanded 
significantly to accommodate the growth in options' quoting volumes, 
and as of April 2005, the OPRA system was capable of processing 
approximately 160,000 mps. Prior to the implementation of decimal 
pricing in 2001, similar concerns about the impact on message traffic 
volumes were also raised for stocks, but the magnitude of the 
anticipated increases were much larger for options. 

To address the capacity constraints in the options market systems thus 
far, the administrators of the OPRA system have tried to reduce 
quotation traffic by having the options exchanges engage in quote 
mitigation. Quote mitigation requires the exchanges to agree to 
prioritize their own quotes and trade report message volumes so that 
the amount of traffic submitted does not exceed a specified percentage 
of the system's total capacity. As of April 2005, the OPRA 
administrators were limiting the volume of messages that exchanges were 
able to transmit to just 88,000 mps based on requests from the six 
options exchanges. 

Two market participants that commented on SEC's proposal noted that 
with options market data continuing to grow at a phenomenal rate each 
year, OPRA would have to continue increasing its current message 
capacity to meet ongoing demand. If penny quoting were to create even 
faster growth in the total number of price quotes generated, market 
participants indicated that options exchanges, market data vendors, and 
broker-dealers would need to spend substantial sums of money on 
operational and technological improvements to their capacity and 
communication systems in order to handle the increased amounts of 
market data. These costs, they said, would likely be passed on to 
investors. 

Another reason that market participants objected to lowering tick sizes 
for options trading was that doing so would likely reduce market 
intermediaries' participation in the markets. Because these 
intermediaries make their money from the spreads between the bid and 
offer prices, narrower spreads that would likely accompany penny ticks 
would also reduce these intermediaries' revenues and profits. This, in 
turn, would reduce these firms' ability and willingness to provide 
liquidity, especially for options that are traded less frequently. 
According to the commenters on the proposal and the participants we 
contacted, intermediaries would likely become reluctant to provide 
continuous two-sided markets (e.g., offering both to buy and sell 
options simultaneously) to facilitate trading, since profit potential 
would be limited by the 80 percent or more reduction in tick size. And 
because the 1-cent tick could increase the chance of other traders 
stepping ahead of an order, such intermediaries could become reluctant 
to display large orders. With the options markets having hundreds of 
options for one underlying stock, market intermediaries would likely 
quote fewer numbers of contracts, which would further reduce displayed 
liquidity, and market transparency. 

Market participants also raised other concerns about trading in penny 
ticks for options. For example, they worried that option prices quoted 
in 1-cent increments would change in price too rapidly, resulting in 
more quote "flickering." They also noted that the options market could 
experience some of the other negative effects that have occurred in the 
stock markets, including increasing instances of stepping ahead by 
other traders. 

SEC staff responsible for options markets oversight told us that they 
would like to see tick sizes reduced in the options markets as a means 
of lowering costs to investors. They acknowledged that the benefits of 
such tick size reductions would have to be balanced with the likely 
accompanying negative impacts. SEC staff responsible for options 
markets oversight told us that they would like to see tick sizes 
reduced in the options markets as a means of lowering costs to 
investors. They acknowledged that the benefits of such tick size 
reductions would have to be balanced with the likely accompanying 
negative impacts. They noted that recent innovations permit a small 
amount of trading in pennies and that continued innovation and 
technological advances may lead to approaches more favorable to 
investors without substantial negative effects. 

Observations: 

In advocating decimal pricing, Congress and SEC expected to make stock 
and options pricing easier for the average investor to understand and 
reduce trading costs, particularly for retail investors, from narrower 
bid-ask spreads. These goals appear to have been met. Securities priced 
in dollars and cents are clearly more understandable, and the narrower 
spreads that have accompanied this change have made trading less costly 
for retail investors. Although the resulting trading environment has 
become more challenging for institutional investors, they too appear to 
have benefited from generally lower trading costs since decimal pricing 
was implemented. In response to the reduced displayed market depth, 
institutional investors are splitting larger orders into smaller lots 
to reduce the market impact of their trading and accelerating their 
adoption of electronic trading technologies and alternative trading 
venues. As a result of these adaptations, institutional investors have 
been able to continue to trade large numbers of shares and at even less 
total cost than before. 

However, since decimal pricing was introduced, the activities performed 
by some market intermediaries have become less profitable. Decimal 
prices have adversely affected broker-dealers' ability to earn revenues 
and profits from their stock trading activities. But one of the goals 
of decimal pricing was to lower the artificially established tick size, 
and thus the loss of revenue for market intermediaries that had 
benefited from this price constraint was a natural outcome. Various 
other factors, including institutional investors' adoption of 
electronic technologies that reduce the need for direct intermediation, 
can also explain some of market intermediaries' reduced revenues. 
Nevertheless, the depressed financial condition of some intermediaries 
would be of more concern if conditions were also similarly negative for 
investors, which we found was not the case. 

In response to the changes since decimal pricing began, a proposal has 
been made to conduct a pilot program to test higher tick sizes. This 
program would provide regulators with data on the impacts, both 
positive and negative, of such trading. However, given that many 
investors and market intermediaries have made considerable efforts to 
adapt their trading strategies and invest in technologies that allow 
them to be successful in the penny tick trading environment, the need 
for increased tick sizes appears questionable. 

Although decimal pricing has been a less significant development in 
U.S. options markets, other factors, such as new entrants and the 
increased use of electronic trading and linkages, have served to 
improve the quality of these markets. SEC's proposal to further reduce 
tick sizes in the options markets has been met with widespread 
opposition from industry participants, and many of the concerns market 
participants raised, including the potential for significant increases 
in quote traffic and less displayed liquidity, appear to have merit. 
The magnitude of these potential impacts appears larger than those that 
accompanied the implementation of penny ticks for stocks. As a result, 
it is not clear that additional benefits of the narrower spreads that 
could accompany mandated tick size reductions would be greater than the 
potentially negative impacts and increased costs arising from greatly 
increased quote processing traffic. 

Agency Comments: 

We provided a draft of this report to SEC for comments and we received 
oral comments from staff in SEC's Division of Market Regulation and 
Office of Economic Analysis. Overall, these staff said that our report 
accurately depicted conditions in the markets after the implementation 
of decimal pricing. They also provided various technical comments that 
we incorporated where appropriate. 

As agreed with your offices, unless you publicly announce its contents 
earlier, we plan no further distribution of this report until 30 days 
after the date of this report. At that time, we will send copies of 
this report to the Chairman and Ranking Minority Member, Subcommittee 
on Securities and Investments, Senate Committee on Banking, Housing, 
and Urban Affairs. We will also send copies of this report to the 
Chairman, SEC. We will make copies available to others upon request. 
This report will also be available at no charge on GAO's Web site at 
[Hyperlink, http://www.gao.gov]. 

Please contact me at (202) 512-8678 if you or your staff have any 
questions concerning this report. Contact points for our Offices of 
Congressional Relations and Public Affairs may be found on the last 
page of this report. Key contributors to this report are listed in 
appendix V. 

Signed by: 

Richard J. Hillman:
Director, Financial Markets and Community Investment: 

[End of section]

Appendixes: 

Appendix I: Scope and Methodology: 

To determine the impact of decimal pricing on retail investors, we 
analyzed data from a database of trades and quotes from U.S. stock 
markets between February 2000 and November 2004. Appendix II contains a 
detailed methodology of this analysis. Using this data, we selected a 
sample of stocks traded on the New York Stock Exchange (NYSE) and the 
NASDAQ Stock Market (NASDAQ) and calculated how the trading in these 
stocks had changed between a 1-year period before and an almost 4-year 
period after decimal pricing began. As part of this analysis, we 
examined the changes in spreads on these stocks (the relevant measure 
of trading costs for retail investors). We also undertook steps to 
assess the reliability of the data in the TAQ database by performing a 
variety of error checks on the data and using widely accepted methods 
for removing potential errors from data to ensure its reliability. 
Based on these discussions, we determined that these data were 
sufficiently reliable for our purposes. We also reviewed market and 
academic studies of decimal pricing's impact on spreads. In addition, 
we interviewed officials from over 30 broker-dealers, the Securities 
and Exchange Commission (SEC), NASD, two academics, and five 
alternative trading venues, eight stock markets, four trade analytics 
firms, a financial markets consulting and research firm, and four 
industry trade groups. 

Methodology for Assessing Impact on Institutional Investors: 

To analyze the impact of decimal pricing on institutional investors, we 
obtained and analyzed institutional trading cost data from three 
leading trade analytics firms--Plexus Group, Elkins/McSherry, and 
Abel/Noser--spanning from the first quarter of 1999 through second 
quarter of 2003 from the Plexus Group and from the fourth quarter of 
1998 to the end of 2004 from Elkins/McSherry and Abel/Noser--to 
determine how trading costs for institutional investors responded to 
decimalization. These firms' data do not include costs for trades that 
do not fully execute. To address this issue, we interviewed 
institutional investors on their experiences in filling large orders. 
We also undertook steps to assess the reliability of the trade 
analytics firms' data by interviewing their staffs about the steps the 
firms follow to ensure the accuracy of their data. Based on these 
discussions, we determined that these data were sufficiently reliable 
for our purposes. 

To identify all relevant research that had been conducted on the impact 
of decimal pricing on institutional investors' trading costs, we 
searched public and private academic and general Internet databases and 
spoke with academics, regulators, and market participants. We 
identified 15 academic studies that met our criteria for scope and 
methodological considerations. Of these, 3 addressed trading costs for 
institutional investors and 12 addressed trading costs for retail 
investors. 

To determine the impact of pricing on investors' ability to trade, we 
interviewed roughly 70 judgmentally selected agencies and firms, 
including representatives of 23 institutional investors with assets 
under management ranging from $2 billion to more than $1 trillion. The 
assets being managed by these 23 firms represented 31 percent of the 
assets under management by the largest 300 money managers in 2003. In 
addition, we also discussed the impact on intuitional investors during 
our interviews with broker-dealers, securities regulators, academics, 
and alternative trading venues, stock exchanges, trade analytics firms, 
a financial market consulting and research firm, and industry trade 
groups. 

Methodology for Assessing Impact on Market Intermediaries: 

To assess the impact of decimal pricing on stock market intermediaries, 
we obtained data on the revenues of the overall securities industry 
from the Securities Industry Association (SIA). SIA's revenue data come 
from the reports that each broker-dealer conducting business with 
public customers is required to file with SEC--the Financial and 
Operational Combined Uniform Single (FOCUS) reports. We used these data 
to analyze the trend in revenues for the industry as a whole as well as 
to identify the revenues associated with making markets in NASDAQ 
stocks. In addition, we obtained data on the specialist broker-dealer 
revenues and participation rates and on executed trade sizes from NYSE. 
For the number of specialist firms participating on U.S. markets, we 
sought data from NYSE and the other exchanges, including the American 
Stock Exchange (Amex), the Boston Stock Exchange, the Chicago Stock 
Exchange, the Pacific Exchange (PCX), and the Philadelphia Stock 
Exchange (Phlx). We obtained data on the number of market makers and 
the trend in executed trade size from NASDAQ. We discussed how these 
organizations ensure the reliability of their data with officials from 
the organizations where relevant and determined that their data were 
sufficiently reliable for our purposes. We also discussed the impact of 
decimals on market intermediaries during our interviews with officials 
from broker-dealers, securities regulators, alternative trading venues, 
stock exchanges, trade analytics firms, a financial market consulting 
and research firm, and industry trade groups, as well as experts from 
academia. 

Methodology for Assessing Impact on Options Markets: 

To determine the impact of decimal pricing on the options markets, both 
investors and intermediaries, we reviewed studies that four U.S. 
options exchanges, including Amex, Chicago Board Options Exchange 
(CBOE), PCX, and Phlx, submitted to SEC in 2001 on the impact of 
decimalization on their markets. We also performed literature searches 
on the Internet for academic and other studies that examined the impact 
of decimal pricing on options markets. In addition, we also attempted 
to identify any sources or organizations that collected and analyzed 
options trading costs. 

To determine the impact on intermediaries, we interviewed officials of 
all six U.S. options exchanges, including Amex, Boston Options 
Exchange, CBOE, International Securities Exchange, PCX, and Phlx, and 
various market participants (an independent market maker, designated 
primary market makers, specialists, a floor broker, hedge funds and a 
retail investor firm) to ascertain their perspectives on the impact of 
the conversion to decimalization on them, investors, and the markets. 

To determine the potential impact of reducing the minimum price tick in 
the options markets to a penny, we interviewed officials from the 
option exchanges and market participants. We also reviewed all comment 
letters that SEC had received on its concept release discussing 
potential changes in options market regulation, including lowering the 
minimum tick size in the options markets to a penny. We reviewed those 
letters posted on SEC's Web site as of May 4, 2005. Sixteen of these 
letters specifically commented on the penny-pricing proposal. 

[End of section]

Appendix II: Methodology for GAO Analysis of Trade and Quotes Data: 

To assess the impact of decimal pricing, one of the activities we 
performed was to analyze data from the New York Stock Exchange (NYSE) 
Trade and Quote (TAQ) database spanning the 5-year period between 
February 2000 (before the conversion to decimal pricing) and November 
2004 (after the adoption of decimal pricing) to determine how trading 
costs for retail investors changed and how various market statistics 
changed, such as the average number of shares displayed at the best 
prices before and after decimalization. Although maintained by NYSE, 
this database includes all trades and quotes that occurred on the 
various exchanges and the NASDAQ Stock Market (NASDAQ). Using this 
database, we performed an event-type study analyzing the behavior of 
trading cost and market quality variables for NYSE and NASDAQ stocks in 
pre-and postdecimalization environments.[Footnote 62] For each of our 
sample stocks, we used information on each recorded trade and quote 
(that is, intraday trade and quote data) for each trading day in our 
sample period. We generally followed the methods found in two recently 
published academic studies that examined the impact of decimalization 
on market quality and trade execution costs.[Footnote 63] In 
particular, we analyzed the pre-and postdecimalization behavior of 
several trading cost and market quality variables, including various 
bid-ask spread measures and price volatility, and we also analyzed 
quote and trade execution price clustering across NYSE and NASDAQ 
environments. We generally presented our results on an average basis 
for sample stocks in a given market in the pre-and postdecimalization 
periods; in some cases we separated sample stocks into groups based on 
their average daily trading volume and reported our results so that any 
differences across stock characteristics could be observed.[Footnote 64]

Our analysis was based on intraday trade and quote data from the TAQ 
database, which includes all trade and quote data (but not order 
information) for all NYSE-listed and NASDAQ stocks, among others. TAQ 
data allowed us to study variables that are based on trades and quotes 
but did not allow us to study any specific effects on or make any 
inferences regarding orders or institutional trading costs.[Footnote 
65] 

Our data consisted of trade and quote activity for all stocks listed on 
NYSE, NASDAQ, and the American Stock Exchange (Amex) from February 1, 
2000, through November 30, 2004, excluding the month of September 2001. 
We focused on NYSE-listed and NASDAQ issues, as is typical in the 
literature, since the potential sample size from eligible Amex stocks 
tends to be much smaller. Our analysis compared 300 matched NYSE and 
NASDAQ stock pairs over the 12 months prior to decimalization and 12 
months selected from the period spanning April 2001 through November 
2004.[Footnote 66] In constructing our sample period, we omitted the 
months of February and March 2001 from consideration, because not all 
stocks were trading using decimal prices during the transition period. 

Because there were a host of concurrent factors impacting the equities 
markets around the time of and since the transition to decimal pricing, 
it is unlikely that any of our results can be attributed solely to 
decimalization. Any determination of statistically significant 
differences in pre-and postdecimalization trading cost and market 
quality variables was likely due to the confluence of decimalization 
and these other factors. 

Determining the Sample Period for Our Analysis: 

Determining the best sample period presented a challenge because 
decimalization was implemented at different times on NYSE and NASDAQ. 
The transition to decimal pricing was completed on NYSE on January 29, 
2001, while on NASDAQ it was completed on April 9, 2001. In addition, 
there were selected decimalization pilots on NYSE and NASDAQ prior to 
full decimalization on each. Researchers who have analyzed the 
transition to decimal pricing have generally divided up the pre-and 
postdecimalization sample periods differently depending on the 
particular focus of their research.[Footnote 67] Relatively short 
sample periods too close to the transition might suffer from unnatural 
transitory effects related to the learning process in a new trading 
environment, while sample periods farther from the implementation date 
or longer in scope might suffer from the influence of confounding 
factors. Analyses comparing different months before and after 
decimalization (e.g., December 2000 versus May 2001) might suffer from 
seasonal influences. We extended the current body of research, which 
includes studies by academic and industry researchers, exchanges and 
markets, and regulators, by including more recent time periods in our 
analysis, providing an expanded view of the trend in trade execution 
cost and market quality variables since 2000. However, to the extent 
that the influence of other factors introduced by expanding the sample 
window outweighed any influence of decimalization on trade cost and 
market quality measures, our results should be interpreted with 
caution. 

Our sample period spanned February 2000 through November 2004 (table 
14). The predecimalization period included February 1, 2000, through 
January 19, 2001, and the postdecimalization period included April 23, 
2001, through November 5, 2004, excluding September 2001 (due to the 
effects of the September 11 terrorist attacks). We selected one week 
from each month, allowing for monthly five-trading day comparisons that 
avoided holidays and options expiration days as well controlling for 
seasonality issues.[Footnote 68] Our predecimalization period consisted 
of a 1-week sample from each of the 12 months and our 
postdecimalization period consisted of twelve 1-week sample periods 
excerpted from April 2001 through November 2004, excluding the month of 
September 2001. 

Table 14: Pre-and Postdecimalization Sample Weeks: 

Period: Predecimals: 

Year: 2000; 
Month: February; 
Day of the week: Monday: 7; 
Day of the week: Tuesday: 8; 
Day of the week: Wednesday: 9; 
Day of the week: Thursday: 10; 
Day of the week: Friday: 11. 

Year: 2000; 
Month: March; 
Day of the week: Monday: 20; 
Day of the week: Tuesday: 21; 
Day of the week: Wednesday: 22; 
Day of the week: Thursday: 23; 
Day of the week: Friday: 24. 

Year: 2000; 
Month: April; 
Day of the week: Monday: 10; 
Day of the week: Tuesday: 11; 
Day of the week: Wednesday: 12; 
Day of the week: Thursday: 13; 
Day of the week: Friday: 14. 

Year: 2000; 
Month: May; 
Day of the week: Monday: 8; 
Day of the week: Tuesday: 9; 
Day of the week: Wednesday: 10; 
Day of the week: Thursday: 11; 
Day of the week: Friday: 12. 

Year: 2000; 
Month: June; 
Day of the week: Monday: 19; 
Day of the week: Tuesday: 20; 
Day of the week: Wednesday: 21; 
Day of the week: Thursday: 22; 
Day of the week: Friday: 23. 

Year: 2000; 
Month: July; 
Day of the week: Monday: 10; 
Day of the week: Tuesday: 11; 
Day of the week: Wednesday: 12; 
Day of the week: Thursday: 13; 
Day of the week: Friday: 14. 

Year: 2000; 
Month: August; 
Day of the week: Monday: 21; 
Day of the week: Tuesday: 22; 
Day of the week: Wednesday: 23; 
Day of the week: Thursday: 24; 
Day of the week: Friday: 25. 

Year: 2000; 
Month: September; 
Day of the week: Monday: 18; 
Day of the week: Tuesday: 19; 
Day of the week: Wednesday: 20; 
Day of the week: Thursday: 21; 
Day of the week: Friday: 22. 

Year: 2000; 
Month: October; 
Day of the week: Monday: 23; 
Day of the week: Tuesday: 24; 
Day of the week: Wednesday: 25; 
Day of the week: Thursday: 26; 
Day of the week: Friday: 27. 

Year: 2000; 
Month: November; 
Day of the week: Monday: 6; 
Day of the week: Tuesday: 7; 
Day of the week: Wednesday: 8; 
Day of the week: Thursday: 9; 
Day of the week: Friday: 10. 

Year: 2000; 
Month: December; 
Day of the week: Monday: 18; 
Day of the week: Tuesday: 19; 
Day of the week: Wednesday: 20; 
Day of the week: Thursday: 21; 
Day of the week: Friday: 22. 

Year: 2001; 
Month: January; 
Day of the week: Monday: 22; 
Day of the week: Tuesday: 23; 
Day of the week: Wednesday: 24; 
Day of the week: Thursday: 25; 
Day of the week: Friday: 26. 

Period: Postdecimals: 

Year: 2001; 
Month: April; 
Day of the week: Monday: 23; 
Day of the week: Tuesday: 24; 
Day of the week: Wednesday: 25; 
Day of the week: Thursday: 26; 
Day of the week: Friday: 27. 

Year: 2001; 
Month: August; 
Day of the week: Monday: 20; 
Day of the week: Tuesday: 21; 
Day of the week: Wednesday: 22; 
Day of the week: Thursday: 23; 
Day of the week: Friday: 24. 

Year: 2001; 
Month: December; 
Day of the week: Monday: 10; 
Day of the week: Tuesday: 11; 
Day of the week: Wednesday: 12; 
Day of the week: Thursday: 13; 
Day of the week: Friday: 14. 

Year: 2002; 
Month: January; 
Day of the week: Monday: 7; 
Day of the week: Tuesday: 8; 
Day of the week: Wednesday: 9; 
Day of the week: Thursday: 10; 
Day of the week: Friday: 11. 

Year: 2002; 
Month: May; 
Day of the week: Monday: 6; 
Day of the week: Tuesday: 7; 
Day of the week: Wednesday: 8; 
Day of the week: Thursday: 9; 
Day of the week: Friday: 10. 

Year: 2002; 
Month: September; 
Day of the week: Monday: 23; 
Day of the week: Tuesday: 24; 
Day of the week: Wednesday: 25; 
Day of the week: Thursday: 26; 
Day of the week: Friday: 27. 

Year: 2003; 
Month: February; 
Day of the week: Monday: 24; 
Day of the week: Tuesday: 25; 
Day of the week: Wednesday: 26; 
Day of the week: Thursday: 27; 
Day of the week: Friday: 28. 

Year: 2003; 
Month: June; 
Day of the week: Monday: 2; 
Day of the week: Tuesday: 3; 
Day of the week: Wednesday: 4; 
Day of the week: Thursday: 5; 
Day of the week: Friday: 6. 

Year: 2003; 
Month: October; 
Day of the week: Monday: 20; 
Day of the week: Tuesday: 21; 
Day of the week: Wednesday: 22; 
Day of the week: Thursday: 23; 
Day of the week: Friday: 24. 

Year: 2004; 
Month: March; 
Day of the week: Monday: 8; 
Day of the week: Tuesday: 9; 
Day of the week: Wednesday: 10; 
Day of the week: Thursday: 11; 
Day of the week: Friday: 12. 

Year: 2004; 
Month: July; 
Day of the week: Monday: 19; 
Day of the week: Tuesday: 20; 
Day of the week: Wednesday: 21; 
Day of the week: Thursday: 22; 
Day of the week: Friday: 23. 

Year: 2004; 
Month: November; 
Day of the week: Monday: 1; 
Day of the week: Tuesday: 2; 
Day of the week: Wednesday: 3; 
Day of the week: Thursday: 4; 
Day of the week: Friday: 5. 

Source: GAO. 

Note: The sample weeks selected avoided holidays and partial trading 
days either before or after holidays, as well as other noted trading 
stoppages, options expiration days (the third Friday of each month), 
and end of quarter days, all of which may lead to unusual trading 
activity. 

[End of table]

Generating the Sample of Stocks for Our Analysis: 

Generally following the methods used by other researchers, we generated 
our list by including only common shares of domestic companies that 
were active over our period of interest and that were not part of 
decimalization pilot programs in effect before January 29, 2001. 
Specifically, we excluded preferred stocks, warrants, lower class 
common shares (for example, Class B and Class C shares), as well as 
NASDAQ stocks with five-letter symbols not representing Class A 
shares.[Footnote 69] We then eliminated from consideration stocks with 
average share prices that were below $5 or above $150 over the February 
2000 through December 2000 period. We also eliminated stocks for which 
there were no recorded trades on 10 percent or more of the trading 
days, to ensure sufficient data, leaving us with 981 NYSE-listed and 
1,361 NASDAQ stocks in the potential sample universe. Our stock samples 
for the analysis ultimately consisted of 300 matched pairs of NYSE- 
listed and NASDAQ stocks. 

Generating the Matched Pairs: 

The NYSE-listed and NASDAQ stocks were matched on variables that are 
generally thought to help explain interstock differences in spreads. To 
the extent that our matching samples of NYSE-listed and NASDAQ stocks 
had similar attributes, any differences in spreads between the groups 
should have been due to reasons other than these attributes. The 
attributes we considered were (1) share price, (2) share price 
volatility, (3) number of trades, and (4) trade size.[Footnote 70] For 
the matching procedure, daily data from February 2000 through December 
2000 were used and averages were taken over this sample period. Share 
price was measured by the mean value of the daily closing price and 
volatility by the average of the logarithm of the high-low intraday 
price range. The number of trades was measured by the average daily 
number of trades, and average trade size was measured as the average 
daily trading volume.[Footnote 71] These factors have different 
measurement units, implying that they could not be directly converted 
into a single measure of similarity. To develop a combined measure of 
similarity we first had to standardize the measures of all factors so 
that their average values and differences in their averages were 
measured on comparable scales. Once standardized measures of averages 
and differences were developed, we were able to sum the four 
measurements into a total measure of similarity and identify matched 
pairs of stocks. Comparability was assured because all averages and 
differences were divided by the standard deviation of the measure of 
each factor on the NYSE. 

Our matching algorithm was similar to those described in Chung et al. 
(2004) and Van Ness et al. (2001). To obtain a matching sample of NYSE 
and NASDAQ stocks, we first calculated the following combined measure 
of similarity--the composite match score (CMS)--for each NYSE stock 
using our entire sample of NASDAQ stocks. The CMS is defined as: 

[See PDF for formula]

[End of figure]

in which the superscripts N and T refer to NYSE and NASDAQ, 
respectively, and Y sub v N and Y sub v T represent one of the four 
stock attributes for each--in which i denotes the NYSE stock and j 
denotes the NASDAQ stock being matched. In the matching algorithm, each 
of the attributes v was weighted equally. Unlike the matching 
algorithms in the two aforementioned papers, we divided each stock 
attribute difference by the sample standard deviation of that attribute 
for the entire NYSE sample--denoted as small sigma Y sub v N--in order 
to create unit less measures that were normalized relative to the 
overall NYSE attributes. 

Ultimately, for each NYSE stock we selected the NASDAQ stock with the 
smallest CMS. Chung et al. (2004) used a sequential matching algorithm 
as is common in the literature. To start, they considered an NYSE stock 
and computed its CMS with all NASDAQ stocks; they matched that NYSE 
stock to the NASDAQ stock with the lowest CMS. Then they considered the 
next NYSE stock, but the NASDAQ stock that matched the prior NYSE stock 
was no longer considered among the possible universe of matches for 
this or any subsequent NYSE stock. The outcome of this type of 
algorithm is path dependent--the order in which the NYSE stocks are 
taken influences the ultimate list of unique matches. We employed 
another method that avoided this path dependence--ensuring an optimal 
match for each stock--but also allowed for the possibility of 
duplicate, nonunique NASDAQ matches. For the 981 NYSE-listed stocks, 
there were 293 NASDAQ stocks that provided the best matches.[Footnote 
72] We chose the 300 best CMS matched pairs, which consisted of 300 
NYSE and 186 unique NASDAQ stocks.[Footnote 73] Of these 186 NASDAQ 
stocks, 114 were best matches for one NYSE-listed stock, 45 were best 
matches for two NYSE- listed stocks, 19 were best matches for three 
NYSE-listed stocks, 5 were best matches for four NYSE-listed stocks, 1 
was a best match for five NYSE-listed stocks, and 2 were best matches 
for seven NYSE-listed stocks. In the subsequent analysis, each NASDAQ 
stock was weighted according to the number of best matches it yielded. 
For example, if a NASDAQ stock provided the best match for two NYSE-
listed stocks, it was counted twice in the overall averages for NASDAQ. 

Characteristics of Our Sample Stocks: 

The pairings resulting from the CMS minimization algorithm were well 
matched. The average share price for the 300 NYSE-listed (NASDAQ 
matching) stocks was $19.66 ($19.56), the average daily volume was 
132,404 (127,107), the average number of trades per day was 121 (125), 
and the measure of daily volatility was 0.018 (0.018). In terms of 
average share price, the 300 matching-pair stocks were fairly 
representative of the full sample of matching stocks, as well as of the 
potential sample universe of stocks, as illustrated in table 15 and 
figure 15. However, the resulting matched-pairs sample tended to have 
more lower-priced stocks. 

Table 15: Price Characteristics of NYSE-Listed and NASDAQ Stocks: 

Potential universe of stocks: Number; 
NYSE: 981; 
NASDAQ: 1,361. 

Potential universe of stocks: Average (median) share price; 
NYSE: $29.20 (24.07); 
NASDAQ: $25.27 (17.54). 

Potential universe of stocks: Percent priced below $25; 
NYSE: 52%; 
NASDAQ: 65%. 

Potential universe of stocks: Percent priced below $50; 
NYSE: 87%; 
NASDAQ: 89%. 

Full sample of matching stocks: Number; 
NYSE: 981; 
NASDAQ: 263. 

Full sample of matching stocks: Average (median) share price; 
NYSE: $29.20 (24.07); 
NASDAQ: $26.60 (22.27). 

Full sample of matching stocks: Percent priced below $25; 
NYSE: 52%; 
NASDAQ: 52%. 

Full sample of matching stocks: Percent priced below $50; 
NYSE: 87%; 
NASDAQ: 91%. 

300 matched-pair sample of stocks: Number; 
NYSE: 300; 
NASDAQ: 186 unique. 

300 matched-pair sample of stocks: Average (median) share price; 
NYSE: $19.66 (16.55); 
NASDAQ: $19.56 (15.94). 

300 matched-pair sample of stocks: Percent priced below $25; 
NYSE: 75%; 
NASDAQ: 74%. 

300 matched-pair sample of stocks: Percent priced below $50; 
NYSE: 96%; 
NASDAQ: 98%. 

Source: GAO analysis of TAQ data. 

Note: Share price was measured as the average daily closing price from 
February 2000 through December 2000. Of the 186 NASDAQ stocks, 114 were 
best matches for one NYSE-listed stock and the remainder were best 
matches for multiple NYSE-listed stocks. In the analysis, each NASDAQ 
stock was weighted according to the number of best matches it yielded. 

[End of table]

Figure 15: Distribution of Average Daily Closing Prices for Full Sample 
of Matching Stocks and 300 Matched-Pairs Sample: 

[See PDF for image] 

Note: Share price was measured as the average daily closing price from 
February 2000 through December 2000. There were 981 NYSE-listed stocks 
and 293 matching NASDAQ stocks in the all matching stocks sample. There 
were 300 NYSE-listed and 300 (186 unique) matching NASDAQ stocks in the 
matched pairs sample. Each NASDAQ stock was weighted according to the 
number of best matches it yielded. 

[End of figure] 

In terms of average daily trading volume, the matched-pairs sample 
underrepresented higher-volume stocks, which likely biased our results 
toward reporting larger spreads (see table 16 and fig. 16). 

Table 16: Volume Characteristics of NYSE-Listed and NASDAQ Stocks: 

Potential universe of stocks: Number; 
NYSE: 981; 
NASDAQ: 1,361. 

Potential universe of stocks: Average (median) daily volume; 
NYSE: 689,811 (190,070); 
NASDAQ: 607,687 (125,627). 

Potential universe of stocks: Percent below 150,000 shares; 
NYSE: 45%; 
NASDAQ: 54%. 

Potential universe of stocks: Percent below 500,000 shares; 
NYSE: 69%; 
NASDAQ: 82%. 

Full sample of matching stocks: Number; 
NYSE: 981; 
NASDAQ: 263. 

Full sample of matching stocks: Average (median) daily volume; 
NYSE: 689,811 (190,070); 
NASDAQ: 511,980 (185,787). 

Full sample of matching stocks: Percent below 150,000 shares; 
NYSE: 45%; 
NASDAQ: 48%. 

Full sample of matching stocks: Percent below 500,000 shares; 
NYSE: 69%; 
NASDAQ: 75%. 

300 matched-pair sample of stocks: Number; 
NYSE: 300; 
NASDAQ: 186. 

300 matched-pair sample of stocks: Average (median) daily volume; 
NYSE: 132,404 (74,188); 
NASDAQ: 127,107 (73,204). 

300 matched-pair sample of stocks: Percent below 150,000 shares; 
NYSE: 73%; 
NASDAQ: 75%. 

300 matched-pair sample of stocks: Percent below 500,000 shares; 
NYSE: 97%; 
NASDAQ: 97%. 

Source: GAO analysis of TAQ data. 

Note: Volume was measured as the average daily trading volume from 
February 2000 through December 2000. Of the 186 NASDAQ stocks, 114 were 
best matches for one NYSE-listed stock and the remainder were best 
matches for multiple NYSE-listed stocks. In the analysis, each NASDAQ 
stock was weighted according to the number of best matches it yielded. 

[End of table]

Figure 16: Distribution of Average Daily Trading Volume for Full Sample 
of Matching Stocks and 300 Matched-Pairs Sample: 

[See PDF for image] 

Note: Volume was measured as the average daily trading volume from 
February 2000 through December 2000. There were 981 NYSE-listed stocks 
and 293 matching NASDAQ stocks in the all matching stocks sample. There 
were 300 NYSE-listed and 300 (186 unique) matching NASDAQ stocks in the 
matched pairs sample. Each NASDAQ stock was weighted according to the 
number of best matches it yielded. 

[End of figure] 

Filtering and Manipulation of Trade and Quote Data: 

Once we had defined our stock sample, to undertake the subsequent 
analysis we first had to filter the trades and quotes data for each 
sample stock, which involved discarding records with TAQ-labeled errors 
(such as canceled trade records and quote records identified with 
trading halts), identifying and removing other potentially erroneous 
quotation and trade records (such as stale quotes or trade or quote 
prices that appeared aberrant), as well as simply confining the data to 
records between 9:30 a.m. and 4 p.m. We also had to determine the 
national best bid and offer quotes in effect at any given moment from 
all quoting market venues--the NBBO quotation. In general, for a given 
stock the best bid (offer) represents the highest (lowest) price 
available from all market venues providing quotes to sellers (buyers) 
of the stock. 

The NBBO quotes data for a given stock were used to compute quoted bid- 
ask spreads, quote sizes, and share prices, as well as intraday price 
volatility for that stock on a daily basis. They were also used 
independently to document any quote clustering activity in that stock. 
The trades' data for a particular stock were used to analyze daily 
price ranges and trade execution price clustering. For each stock, the 
trades and NBBO quotes data were used to compute effective bid-ask 
spreads, which rely on both quotes and trades data. 

The TAQ Consolidated Quotes (CQ) file covers most activity in major 
U.S. market centers but does not include foreign market centers. A 
record in the CQ file represents a quote update originating in one of 
the included market centers: Amex, the Boston Stock Exchange, the 
Chicago Stock Exchange, electronic communication networks (ECN) and 
alternative trading systems (ATS), NASDAQ, the National Stock Exchange, 
NYSE, the Pacific Stock Exchange, and the Philadelphia Stock 
Exchange.[Footnote 74] It does not per se establish a comprehensive 
marketwide NBBO quote, however. A quote update consists of a bid price 
and the number of shares for which that price is valid and an offer 
price and the number of shares for which that price is valid. In 
general, a quote update reflects quote additions or cancellations. The 
record generally establishes the best bid and offer prevailing in a 
given market center. Normally, a quote from a market center is regarded 
as firm and valid until it is superseded by a new quote from that 
center--that is, a quote update from a market center supersedes that 
market center's previous quotes and establishes its latest, binding 
quotes. 

Specifying the NBBO involved determining the best bid and offer quotes 
available--at a particular instant, the most recent valid bids and 
offers posted by all market centers were compared and the highest bid 
and the lowest offer were selected as the NBBO quotes. The national 
best bid (NBB) and national best offer (NBO) are not necessarily from 
the same market center or posted concurrently, and the bid and offer 
sizes can be different. Bessimbinder (2003) outlined a general method 
for determining the NBBO. First, the best bid and offer in effect for 
NYSE-listed stocks among individual NASDAQ dealers (as indicated by the 
MMID data field) was assessed and designated as the NASDAQ bid and 
offer. Then, the best bid and offer in effect across the NYSE, the five 
regional exchanges, and NASDAQ were determined and designated as the 
NBBO quotations for NYSE-listed stocks. For NASDAQ stocks, quote 
records from NASDAQ market makers reflect the best bid and offer across 
these participants (collectively classified as "T" in the TAQ data). 
Competing quotes are issued from other markets (e.g., the Pacific Stock 
Exchange) as well as NASDAQ's SuperMontage Automated Display Facility, 
which reflects the quotes from most ECNs. We required additional 
details in constructing the NBBO, since quote records from competing 
market makers and market centers can have concurrent time stamps and 
there can be multiple quotes from the same market center recorded with 
the same time stamp. Moreover, identical bid or offer prices can be 
quoted by multiple market makers. To address these complications, we 
relied on language offered in SEC's Regulation NMS proposal, which 
defined the NBBO by ranking all such identical bids or offers first by 
size (giving the highest ranking to the bid or offer associated with 
the largest size) and then by time (giving the highest ranking to the 
bid or offer received first in time). In our algorithm, the NBB (NBO) 
is located by comparing the existing bids (offers) from all venues. The 
NBBO is updated with each instance of a change in the NBB or NBO. 

General Analysis Techniques: 

Each NBBO quotation was weighted by its duration (i.e., the time for 
which it was effective) and used to compute a sample week time-weighted 
average NBBO quotation for the relevant market, which was reported on a 
volume-weighted (relative to total sample market trading volume) basis. 
Ultimately, these averages were compared across markets and across pre 
and postdecimalization periods. The same general techniques were used 
in computing effective spreads, which were determined by comparing 
trade executions with NBBO quotations. For analysis of trades data 
(e.g., in computing price ranges), a simple average over all stocks in 
a given market was computed. In analyzing volatility, intraday returns 
were measured for each stock based on continuously compounded 
percentage changes in quotation midpoints, which were recorded between 
10 a.m. and 4 p.m. The standard deviation of the intraday returns was 
then computed for each stock, and the cross-sectional median across all 
stocks was taken. In assessing clustering, the frequencies of trades 
and quotes at pennies, nickels, dimes, and quarters were determined for 
each market on an aggregate basis. 

In reporting any differences between the pre-and postdecimalization 
sample periods in the trade execution cost and market quality measures 
that we analyzed, statistical significance was assessed based on cross- 
sectional variation in the stock-specific means. With the exception of 
volatility measures, statistical significance was assessed using a 
standard t-test for equality of means. Since average volatility 
measures do not conform well to the t-distribution, median volatility 
was reported for each market and the Wilcoxon rank sum test used to 
assess equality. 

Measuring Trade Execution Costs and Other Market Quality Components 
with TAQ Data: 

TAQ data allowed us to study variables that are based on trades and 
quotes but did not allow us to study any specific effects on or make 
any inferences regarding orders or institutional trading costs. This is 
an important limitation because the transition to decimal pricing may 
have impacted retail traders, whose generally smaller orders tend to be 
executed in a single trade, differently than institutional traders. Use 
of TAQ data implicitly assumes that each trade record reflects a unique 
order that is filled, so our analysis failed to address any impact of a 
change in how orders are filled and the costs associated with this. We 
reported the pre-and postdecimalization behavior of quoted bid-ask 
spreads and effective spreads. Beyond measures of trade execution cost, 
market quality is multidimensional. Possible adverse effects of 
decimalization on market quality included increased trade execution 
costs for large traders, increased commissions to offset smaller bid- 
ask spreads, slower order handling and trade executions, decreased 
market depth, and increased price volatility. The TAQ data allowed 
measurement of quotation sizes and price volatility, which we reported. 
We also analyzed quote clustering, which reflects any unusual frequency 
with which prices tend to bunch at multiples of nickels, for example. 
We generally presented our results on an average basis for a given 
market in the pre-and postdecimalization periods; we also reported the 
results for sample stocks grouped by average daily trading volume. 

Calculating Quoted Bid-Ask Spreads as a Simple Measure of Trading 
Costs: 

Average pre-and postdecimalization bid-ask spreads were calculated in 
cents per share and basis points (that is, the spread in cents relative 
to the NBBO midpoint) using the NBBO quote prices. The average spread 
was obtained in the following way. First, each NBBO quote for a given 
stock was weighted by the elapsed time before it was updated--its 
duration--on a given day of a sample week relative to the total 
duration of all NBBO quotes for that stock in that sample week. Next, 
the duration-weighted average over the five trading days in that sample 
period for that stock was used to compute the average across all stocks 
in a given market for that week; ultimately, a volume-weighted average 
was computed. For the twelve-sample week period, a volume-weighted 
average was also computed. 

Calculating Effective Bid-Ask Spreads as a Better Measure of Trading 
Costs: 

The effective bid-ask spread--how close the execution price of a trade 
is relative to the quote midpoint--is generally considered to be the 
most relevant measure of trade execution cost, as it allows measurement 
of trades that execute at prices not equal to the bid or ask. In 
keeping with standard practice, we measured the effective spread for a 
trade as twice the absolute difference between the price at which a 
trade was executed and the midpoint of the contemporaneous NBBO quote. 
Suppose for example that the NBB is $20.00 and the NBO is $20.10, so 
that the NBBO midpoint is $20.05. If a trade executes at a price of 
$20.05 then the effective spread is zero because the trade executed at 
the midpoint of the spread--the buyer of the stock paid $0.05 per share 
less than the ask price, while the seller received $0.05 per share more 
than the bid price. If a trade executes at $20.02 with the same NBBO 
prices, the effective spread is $0.06--the buyer of the stock paid 
$0.08 per share less than the ask price, while the seller received 
$0.02 per share more than the bid price. Effective spreads were 
computed in cents per share and in basis points. 

Measuring Quotation Sizes: 

Smaller quote sizes could reflect a decrease in liquidity supply, which 
in turn could be associated with increased volatility. The size of each 
NBBO quote was weighted by its duration and used to compute a volume- 
weighted average over each sample week as well as across all sample 
weeks. 

Measuring Intraday Return Volatility: 

A reduction in the tick size could lead to a decline in liquidity 
supply, which in turn could create more volatile prices. Intraday 
returns were measured for each stock based on continuously compounded 
percentage changes in quotation midpoints, which were recorded on an 
hourly basis between 10 a.m. and 4 p.m. The continuously compounded 
return over 6 hours, from 10 a.m. to 4 p.m., was also computed. The 
standard deviation (a measure of dispersion around the average) of the 
intraday returns was then computed for each stock, and the cross- 
sectional median (the middle of the distribution) was taken over all 
stocks in a given market. 

Measuring Daily Price Range: 

As another measure of price volatility, we also considered how a 
stock's daily price range (i.e., the highest and lowest prices at which 
trades were executed) may have changed following the implementation of 
decimal pricing, as the claim has been made that prices have been 
moving to a greater degree during the day after decimalization. We 
computed the equal-weighted average of each stock's daily price range 
and then computed the average over all stocks in a given market. To 
account for potentially varying price levels across the pre and 
postdecimalization sample periods, we computed the price range in both 
cents per share as well as relative to the midpoint of the first NBBO 
quote for each day. 

Measuring Trade and Quote Clustering: 

Decimalization provides a natural experiment to test whether market 
participants prefer to trade or quote at certain prices when their 
choices are unconstrained by regulation. Theory suggests that if price 
discovery is uniform, realized trades should not cluster at particular 
prices. The existence of price clustering following decimalization 
could suggest a fundamental psychological bias by investors for round 
numbers and that there may be only minor differences between the 
transactions prices that would prevail under a tick size of 5 cents 
relative to those observed under decimal pricing.[Footnote 75] For 
quotes, according to competing hypotheses in the literature, clustering 
may be due to dealer collusion, or it may simply be a natural 
phenomenon--as protection against informed traders, as compensation for 
holding inventory, or to minimize negotiation costs.[Footnote 76] For 
our analysis, we computed the frequency of trade executions and quotes 
across the range of price points, but we did not attempt to determine 
the causes of any clustering. 

Efforts to Assess Reliability of TAQ Data: 

Consistent with generally accepted government auditing standards, we 
assessed the reliability of computer-processed data that support our 
findings. To assess the reliability of TAQ data, we performed a variety 
of error checks on data from a random sample of stocks and dates. This 
involved comparing aggregated intraday data with summary daily data, 
scanning for outliers and missing data. In addition, since the TAQ 
database is in widespread use by researchers and has been for several 
years, we were able to employ additional methods for discarding 
potentially erroneous data records following widely accepted methods 
(e.g., we discarded quotation information in which a price or size was 
reported as negative). We assessed the reliability of our analysis of 
the TAQ data by performing several executions of the programs using 
identical and slight modifications of the program coding. Program logs 
were also generated and reviewed for errors. 

[End of section]

Appendix III: Measurement of Institutional Investors' Trading Costs in 
Basis Points Shows Decline since Decimal Pricing Implemented: 

As discussed in the body of this report, institutional investors' 
trading costs are commonly measured in cents per share and basis points 
(bps). Cents per share is an absolute measure of cost based on 
executing a single share. Basis points--measured in hundredths of a 
percentage point--show the absolute costs relative to the stock's 
average share price. For example, for a stock with a share price of 
$20, a transaction cost of $.05 would be 0.25 percent or 25 bps. Costs 
reported in terms of basis points can show changes resulting solely 
from changes in the level of stock prices--if the price of the $20 
stock falls to $18, the $.05 transaction cost would now be almost 0.28 
percent or 28 bps. However, many organizations track costs using basis 
points, and in this appendix we present the results of our 
institutional trading cost analysis in basis points. 

Analysis of the multiple sources of data that we collected generally 
indicated that institutional investors' trading costs had declined 
since decimal prices were implemented. Specifically, NYSE converted to 
decimal pricing on January 29, 2001, and NASDAQ completed its 
conversion on April 9, 2001. We obtained data from three leading firms 
that collect and analyze information about institutional investors' 
trading costs. These trade analytics firms (Abel/Noser, 
Elkins/McSherry, and Plexus Group) obtain trade data directly from 
institutional investors and brokerage firms and calculate trading 
costs, including market impact costs (the extent to which the security 
changes in price after the investor begins trading), typically for the 
purpose of helping investors and traders limit costs of 
trading.[Footnote 77] These firms also aggregate client data so as to 
approximate total average trading costs for all institutional 
investors. Generally, the client base represented in aggregate trade 
cost data is sufficiently broad based that the firm's aggregate cost 
data can be used to make generalizations about the institutional 
investor industry. 

Although utilizing different methodologies, the data from the firms 
that analyze institutional investor trading costs uniformly showed that 
costs had declined since decimal pricing was implemented. Our analysis 
of data from the Plexus Group showed that costs declined on both NYSE 
and NASDAQ during the 2 year period after these markets converted to 
decimal pricing. Plexus Group uses a methodology that analyzes various 
components of institutional investor trading costs, including the 
market impact of investors' trading.[Footnote 78] Total trading costs 
declined by about 32 percent for NYSE stocks, falling from about 82 bps 
to 56 bps (fig. 17). For NASDAQ stocks, the decline was about 25 
percent, from about 102 bps to about 77 bps. As can be seen in figure 
17, the decline in trading costs began before both markets implemented 
decimal pricing, which indicates that other causes, such as the 3-year 
declining stock market, in addition to decimal pricing, were also 
affecting institutional investors' trading during this period. An 
official from a trade analytics firm told us that the spike in costs 
that preceded the decimalization of NASDAQ stocks correlated to the 
pricing bubble that technology sector stocks experienced in the late 
1990s and early 2000s. An official from another trade analytics firm 
explained that trading costs increased during this time because when 
some stocks' prices would begin to rise, other investors--called 
momentum investors--would begin making purchases and cause prices for 
these stocks to move up even faster. As a result, other investors faced 
greater than usual market impact costs when also trading these stocks. 
In general, trading during periods when stock prices are either rapidly 
rising or falling can make trading very costly. 

Figure 17: Total Trading Costs from a Trade Analytics Firm for NYSE and 
NASDAQ Stocks, 1999-2004 (basis points): 

[See PDF for image] 

Note: Data are reported quarterly. After a phase-in period, all NYSE 
stocks were trading with decimal prices by January 29, 2001, and all 
NASDAQ stocks were converted by April 9, 2001. 

[End of figure] 

According to our analysis of the Plexus Group data, all of the decline 
in trading costs for NYSE stocks and NASDAQ stocks were caused by 
decreases in the costs resulting from market impact and delay for 
orders.[Footnote 79] Together, the reduction in these two components 
accounted for 29.1 bps or all of total decline, with delay costs 
representing 20.6 bps (or about 71 percent) in the approximately 2 
years following the implementation of decimal pricing and 1-cent ticks 
on the NYSE. However, commissions increased 3 bps, which led total 
trading costs to decline 26.1 bps (fig. 18). 

Figure 18: Trading Cost Components from One Trade Analytics Firm for 
NYSE and NASDAQ, 2001-2003 (basis points): 

[See PDF for image] 

Note: Data are from first quarter 2001 to second quarter 2003 for NYSE 
and second quarter 2001 to second quarter 2003 for NASDAQ. 

[End of figure] 

Figure 18 also shows that market impact and delay costs account for all 
declines to total NASDAQ trading costs. For example, market impact and 
delay costs declined 40.9 bps between the second quarter of 2001 and 
the second quarter of 2003. However, overall trading costs declined by 
only 24.4 bps, which is 16.5 bps less than declines in market impact 
and delay costs. According to Plexus Group data, overall costs would 
have declined further if not for increases to commission costs for 
NASDAQ stocks, the only cost component that increased after NASDAQ 
converted to decimal pricing and 1-cent ticks. As shown in figure 18, 
commissions that market intermediaries charged for trading NASDAQ 
stocks increased 16.5 bps from the second quarter of 2001 to the second 
quarter of 2003. Industry representatives told us these increases 
reflect the evolution of the NASDAQ brokerage industry from trading as 
principals, in which the compensation earned by market makers was 
embedded in the final trade price, to that of an agency brokerage 
model, in which broker-dealers charge explicit commissions to represent 
customer orders in the marketplace.[Footnote 80]

Analysis of data from the other two trade analytics firms from which we 
obtained data, Elkins/McSherry and Abel/Noser, also indicated that 
institutional investor trading costs varied but declined following the 
decimalization of U.S. stock markets in 2001. Because these two firms' 
methodologies do not include measures of delay, which the Plexus Group 
data shows can be significant, analysis of data from these two firms 
results in trading cost declines of a lower magnitude than those 
indicated by the Plexus Groupdata analysis. Nevertheless, the data we 
analyzed from Elkins/McSherry showed total costs for NYSE stocks 
declined about 20 percent between the first quarter of 2001 and year- 
end 2004 from about 29 bps to about 24 bps. Analysis of Abel/Noser data 
indicated that total trading costs for NYSE stocks declined 25 percent 
from 20 bps to 15 bps between year-end 2000 and 2004 (fig. 19). 

Figure 19: Total Trading Costs from Two Trade Analytics Firms for NYSE 
Stocks, 2001-2004 (basis points): 

[See PDF for image] 

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 
and the fourth quarter of 2004; Abel/Noser data are year-end totals for 
1998-2004. 

[End of figure] 

Our analysis of these firms' data also indicated that total trading 
costs declined in basis points for NASDAQ stocks or were flat. For 
example, our analysis of the Elkins/McSherry data showed that total 
trading costs for NASDAQ stocks dropped by roughly 13 percent, from 
about 38 bps to about 32 bps between the second quarter of 2001 when 
that market decimalized to year-end 2004. Analysis of the Abel/Noser 
data indicated that total trading costs increased nearly 5 percent for 
NASDAQ stocks during that period, increasing from 21 bps to 22 bps 
(fig. 20). This increase in trading cost can possibly be explained by 
the approximately 50 percent decline in average share price over the 
period. 

Figure 20: Total Trading Costs from Two Trade Analytics Firms for 
NASDAQ Stocks, 2001-2004 (basis points): 

[See PDF for image] 

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 
and the fourth quarter of 2004; Abel/Noser data are year-end totals for 
1998-2004. 

[End of figure] 

Similar to Plexus Group data analysis, our analysis of the 
Elkins/McSherry and Abel/Noser data also indicated that reductions to 
market impact costs accounted for a vast proportion of overall 
reductions for NYSE stocks (fig. 21).[Footnote 81] Analysis of the 
Elkins/McSherry data indicated that by declining 7.6 bps during this 
period, reduced market impact accounted for 95 percent of total cost 
trading declines. The 3 bps reduction in market impact costs identified 
in the Abel/Noser data represented the entire total trading cost 
reductions for NYSE stocks. 

Figure 21: Trading Cost Components from Two Trade Analytics Firms for 
NYSE Stocks, 2001 and 2004 (basis points): 

[See PDF for image] 

Note: Abel/Noser does not account for exchange fees as a component of 
trading cost. For Elkins/McSherry, we obtained first quarter 2001 data 
and fourth quarter 2004. For Abel/Noser, we obtained data from the end 
of 2000 and 2004. 

[End of figure] 

Reductions to market impact costs explain virtually the entire decline 
to total trading costs captured by the Elkins/McSherry data for NASDAQ 
stocks and all of the Abel/Noser data for NASDAQ stocks. For 
Elkins/McSherry and Abel/Noser, such costs would have produced even 
larger total declines had commissions for such stocks not increased 
since 2001. Market impact costs declined 22.3 bps (about 64 percent) 
according to our analysis of the Elkins/McSherry data and 14 bps (about 
74 percent) according to analysis of the Abel/Noser data (fig. 22). 
However, during this period, commissions charged on NASDAQ stock trades 
included in these firms' data increased by 16.9 bps, marking 
approximately a sixfold increase in commissions as measured by 
Elkins/McSherry and by 15 bps or about a fifteenfold increase according 
to Abel/Noser. 

Figure 22: Trading Cost Components from Two Trade Analytics Firms for 
NASDAQ Stocks, 2001 and 2004 (basis points): 

[See PDF for image] 

Note: Abel/Noser does not account for exchange fees as a component of 
trading cost. For Elkins/McSherry, we obtained first quarter 2001 data 
and fourth quarter 2004. For Abel/Noser, we obtained data from the end 
of 2000 and 2004. 

[End of figure] 

Data from a fourth firm, ITG, which recently began measuring 
institutional trading costs, also indicates that such costs have 
declined. This firm began collecting data from its institutional 
clients in January 2003. Like the other trade analytics firms, its data 
is similarly broad based, representing about 100 large institutional 
investors and about $2 trillion worth of U.S. stock trades. ITG's 
measure of institutional investor trading cost is solely composed of 
market impact costs and does not include explicit costs, such as 
commissions and fees, in its calculations. Although changes in ITG's 
client base for its trade cost analysis service prevented direct period 
to period comparisons, an ITG official told us that its institutional 
investor clients' trading costs have been trending lower since 
2003.[Footnote 82]

[End of section]

Appendix IV: Additional Analysis Using Trade and Quotes Data: 

As part of our analysis of the Trade and Quotes database, we also 
examined how quoted and effective spreads changed as a percentage of 
stock prices and also examined whether the extent to which quotes 
clustered on particular prices changed since decimal pricing began. In 
addition to measuring spreads in cents per share, spreads are also 
frequently measured in basis points, which are 1/100 of a percent. We 
found that spreads generally declined when measured in basis points 
similar to our analysis measured in cents. Reporting spreads in basis 
points potentially accounts for changes in the general price level of 
our sample stocks, which could impact our results reported in cents per 
share. We found that both quoted and effective spreads generally 
declined when measured relative to quote midpoints as they did when 
measured simply in cents (see tables 17 and 18). 

Table 17: Average Quoted Spreads Before and After Decimalization, 2000- 
2004 (basis points): 

Stocks by average daily volume of shares traded: High; 
NYSE quoted spread: Average spread in basis points before decimals: 
49.3; 
NYSE quoted spread: Average spread in basis points after decimals: 
16.0; 
NYSE quoted spread: Percent change: -68%; 
NASDAQ quoted spread: Average spread in basis points before decimals: 
40.9; 
NASDAQ quoted spread: Average spread in basis points after decimals: 
13.0; 
NASDAQ quoted spread: Percent change: -68%. 

Stocks by average daily volume of shares traded: Medium; 
NYSE quoted spread: Average spread in basis points before decimals: 
71.8; 
NYSE quoted spread: Average spread in basis points after decimals: 
19.5; 
NYSE quoted spread: Percent change: -73%; 
NASDAQ quoted spread: Average spread in basis points before decimals: 
72.4; 
NASDAQ quoted spread: Average spread in basis points after decimals: 
22.4; 
NASDAQ quoted spread: Percent change: -69%. 

Stocks by average daily volume of shares traded: Low; 
NYSE quoted spread: Average spread in basis points before decimals: 
125.7; 
NYSE quoted spread: Average spread in basis points after decimals: 
32.6; 
NYSE quoted spread: Percent change: -74%; 
NASDAQ quoted spread: Average spread in basis points before decimals: 
127.9; 
NASDAQ quoted spread: Average spread in basis points after decimals: 
36.6; 
NASDAQ quoted spread: Percent change: -71%. 

Stocks by average daily volume of shares traded: All stocks; 
NYSE quoted spread: Average spread in basis points before decimals: 
78.4; 
NYSE quoted spread: Average spread in basis points after decimals: 
25.1; 
NYSE quoted spread: Percent change: -68%; 
NASDAQ quoted spread: Average spread in basis points before decimals: 
82.0; 
NASDAQ quoted spread: Average spread in basis points after decimals: 
27.2; 
NASDAQ quoted spread: Percent change: -67%. 

Source: GAO analysis of TAQ data. 

Note: Quoted spreads in the table represent the volume-weighted average 
quoted spread (i.e., stocks and weeks with more total trading volume 
have greater weight) as a percentage of the midpoint of the prevailing 
quotes over 12 sample weeks during the predecimals period (February 
2000-January 2001) and 12 sample weeks during the postdecimals period 
(April 2001-November 2004) for our sample of stocks. Stocks were 
segregated by volume according to the following categories: 

* High volume stocks were those in our sample of stocks with average 
daily trading volumes exceeding 500,000 shares. 

* Medium volume stocks were those in our sample of stocks with average 
daily trading volumes between 100,000 and 499,999 shares. 

* Low volume stocks were those in our sample of stocks with average 
daily trading volumes of less than 100,000 shares. 

[End of table]

Table 18: Average Effective Spreads Before and After Decimalization, 
2000-2004 (basis points): 

Stocks by average daily volume of shares traded: High; 
NYSE effective spreads: Average spread in basis points before decimals: 
47.8; 
NYSE effective spreads: Average spread in basis points after decimals: 
29.4; 
NYSE effective spreads: Percent change: -38%; 
NASDAQ effective spreads: Average spread in basis points before 
decimals: 51.1; 
NASDAQ effective spreads: Average spread in basis points after 
decimals: 25.3; 
NASDAQ effective spreads: Percent change: -51%. 

Stocks by average daily volume of shares traded: Medium; 
NYSE effective spreads: Average spread in basis points before decimals: 
61.8; 
NYSE effective spreads: Average spread in basis points after decimals: 
26.5; 
NYSE effective spreads: Percent change: -57%; 
NASDAQ effective spreads: Average spread in basis points before 
decimals: 70.8; 
NASDAQ effective spreads: Average spread in basis points after 
decimals: 30.4; 
NASDAQ effective spreads: Percent change: -57%. 

Stocks by average daily volume of shares traded: Low; 
NYSE effective spreads: Average spread in basis points before decimals: 
99.4; 
NYSE effective spreads: Average spread in basis points after decimals: 
38.3; 
NYSE effective spreads: Percent change: -61%; 
NASDAQ effective spreads: Average spread in basis points before 
decimals: 112.4; 
NASDAQ effective spreads: Average spread in basis points after 
decimals: 39.0; 
NASDAQ effective spreads: Percent change: -65%. 

Stocks by average daily volume of shares traded: All stocks; 
NYSE effective spreads: Average spread in basis points before decimals: 
65.3; 
NYSE effective spreads: Average spread in basis points after decimals: 
29.4; 
NYSE effective spreads: Percent change: -55%; 
NASDAQ effective spreads: Average spread in basis points before 
decimals: 73.4; 
NASDAQ effective spreads: Average spread in basis points after 
decimals: 32.5; 
NASDAQ effective spreads: Percent change: -56. 

Source: GAO analysis of TAQ data. 

Note: Effective quoted spreads (the difference between the price at 
which a trade is executed and the midpoint between the prevailing 
quoted bid and ask prices) in the table represent the volume-weighted 
average effective spread (i.e., stocks and weeks with more total 
trading volume have greater weight) as a percentage of the midpoint of 
the prevailing quotes over 12 sample weeks during the predecimals 
period (February 2000-January 2001) and 12 sample weeks during the 
postdecimals period (April 2001-November 2004) for our sample of 
stocks. Stocks were segregated by volume according to the following 
categories: 

* High volume stocks were those in our sample of stocks with average 
daily trading volumes exceeding 500,000 shares. 

* Medium volume stocks were those in our sample of stocks with average 
daily trading volumes between 100,000 and 499,999 shares. 

* Low volume stocks were those in our sample of stocks with average 
daily trading volumes of less than 100,000 shares. 

[End of table]

We also analyzed the extent to which quote and trade execution prices 
cluster at particular price points, a phenomenon known as clustering. 
Clustering, particularly on multiples of nickels, dimes, and quarters, 
has been well documented by various researchers, and various reasons 
are cited to explain why all possible price points are not used with 
equal frequency. We extended the general body of research to include 
how clustering may have changed after decimalization, but we do not 
attempt to explain its causes. We generally found that prices tend to 
cluster on certain price points--especially on nickel, dime, and 
quarter multiples--but this tendency has been lessening over time. We 
provide examples of clustering in national best bid quote prices 
recorded for our sample of NYSE-listed stocks, but the same general 
features were found in national best offer quote and trade execution 
prices for both NYSE-listed and Nasdaq stocks. Figure 23 illustrates 
quote price clustering (using national best bid prices) over our entire 
postdecimalization sample period, which included 12 sample weeks from 
April 2001 through November 2004. Prices are observed generally 
clustering at nickel increments. 

Figure 23: Quote Clustering After Decimalization, 2001-2004: 

[See PDF for image] 

Notes: Quote clustering in the figure represents the frequency with 
which each national best bid quote price point, from zero cents to 99 
cents, was used by all of the NYSE-listed stocks from our matched-pairs 
sample over the 12 sample weeks during the postdecimals period (April 
2001-November 2004). While not included in this appendix, similar 
results were generally obtained for both NYSE-listed and Nasdaq stocks 
using national best offer quote and trade execution prices. 

[End of figure] 

We also analyzed how clustering may have changed over time. Using the 
same data as above, we separated the data by sample week. Our results, 
displayed in figure 24, depict a general decline in the use of price 
increment multiples of a nickel. This may suggest that traders have 
been adapting their strategies to the penny environment and are 
becoming increasingly comfortable with using various price points, 
which may be a result of the increased use of electronic trading. It 
may also be the case that traders are making use of the finer price 
grid to gain execution priority. 

Figure 24: Quote Clustering After Decimalization, by Sample Week, 2001- 
2004: 

[See PDF for image] 

Notes: Quote clustering in the figure represents the frequency with 
which each national best bid quote price point, from zero cents to 99 
cents, was used by all of the NYSE-listed stocks from our matched-pairs 
sample over the 12 sample weeks during the postdecimals period (April 
2001-November 2004). The notation y.x0 indicates any price for which 
the second decimal place is a zero (e.g., $5.20); similarly, the 
notation y.x9 indicates any price for which the second decimal place is 
a nine (e.g., $5.29). While not included in this appendix, similar 
results were generally obtained for both NYSE-listed and Nasdaq stocks 
using national best offer quote and trade execution prices. 

[End of figure] 

[End of section]

Appendix V: GAO Contacts and Staff Acknowledgments: 

GAO Contacts: 

Richard J. Hillman, (202) 512-8678: 

Staff Acknowledgments: 

In addition to the individuals named above, Cody Goebel, Emily 
Chalmers, Jordan Corey, Joe Hunter, Austin Kelly, Mitchell Rachlis, 
Carl Ramirez, Omyra Ramsingh, Kathryn Supinski, and Richard Vagnoni 
made key contributions to this report. 

[End of section]

Glossary of Terms: 

Ask price (offer/sell price): 

The lowest price at which someone is willing to sell a security at a 
given time. 

Basis point: 

A basis point is equal to 1/100 of 1 percent. 

Bear market: 

A market in which stock prices decline over a sustained period of time. 

Best execution requirement: 

The obligation of broker-dealers to seek to obtain the best terms 
reasonably available under the circumstances for customer orders. 

Bid-ask spread: 

The difference between the price at which a market maker is willing to 
buy a security (bid) and the price at which the firm is willing to sell 
it (ask). The spread narrows or widens according to the supply and 
demand for the security being traded. The spread is what the market 
maker retains as compensation (or income) for his/her effort and risk. 

Bid price (buy price): 

The highest price at which someone is willing to buy a security at a 
given time. 

Block trade: 

Represents the purchase or sale of (1) a large quantity of stock, 
generally 10,000 shares or more or (2) shares valued at $200,000 or 
more in total market value. 

Broker: 

An individual or firm who acts as an intermediary (agent) between a 
buyer and seller and who usually charges a commission. 

Bull market: 

A market in which stock prices rise over a sustained period of time. 

Call option: 

A contract granting the right to buy a fixed amount of a given security 
at a specified price within a limited period of time. 

Commission: 

A fee paid to a broker for executing a trade based on the number of 
shares traded or the dollar amount of the trade. 

Dealer: 

An individual or firm in the business of buying and selling securities 
for his or her own account (principal) through a broker or otherwise. 

Decimalization/decimal pricing: 

The quoting and trading of securities in dollars and cents ($2.25) 
instead of fractions ($8 1/8). 

Delay cost: 

A type of market impact cost that occurs as the result of changes in 
the price of the stock being traded during the time institutional 
investors' portfolio mangers direct their traders to buy and sell stock 
and the moment these orders are released to brokers. 

Effective spread: 

Measures the trading costs relative to the midpoint of the quoted 
spread at the time the trade occurred. It is defined as twice (to 
reflect the implied roundtrip cost) the difference between the trade 
price and the midpoint of the most recent bid and ask quotes. It 
reflects the price actually paid or received by customers. It is 
considered a better measure of execution costs than quoted spreads 
because orders do not always execute exactly at the bid or offer price. 

Electronic Communication Network (ECN): 

An electronic trading system that automatically matches buy and sell 
orders at specified prices. It is a type of alternative trading system-
-an automated market in which orders are centralized, displayed, 
matched, and otherwise executed. 

Exchange: 

An organized marketplace (stock exchange) in which members of the 
exchange, acting both as brokers and dealers, trade securities. Through 
exchanges, brokers and dealers meet to execute orders from individual 
and institutional investors and to buy and sell securities. 

Floor-based (or auction) market: 

Is a stock exchange (like the American Stock Exchange and the New York 
Stock Exchange) where buyers and sellers meet through an intermediary-
-called a specialist. A specialist operates in a centralized location 
or "floor" and primarily matches incoming orders to buy and sell each 
stock. There is only one specialist designated for a firm or several 
firms who is assigned to oversee the market for those stocks. 

Floor broker: 

A member of an exchange who is an employee of a member firm and 
executes orders, as agent, on the floor of the exchange for their 
clients. 

Inside spread (inside quote): 

The highest bid and lowest offer being quoted among all the market 
makers competing in a security. 

Intermarket linkage system: 

An electronic trading linkage between the major exchanges (stock and 
option) and other trading centers. The system allows brokers to seek 
best execution in any market within the system. 

Institutional investor: 

An organization whose primary purpose is to invest its own assets or 
those held in trust by it for others and typically buys and sells large 
volumes of securities. Examples of such organizations include mutual 
funds, pension funds, insurance companies, and charitable 
organizations. 

Limit order: 

An order to buy or sell a specified number of shares of a security at 
or better than a customer-specified price. Limit orders supply 
additional liquidity to the marketplace. A limit order book is a 
specialist's record of unexecuted limit orders. 

Liquidity: 

The ease with which the market can accommodate large volumes of 
securities trading without significant price changes. 

Listed stock: 

The stock of a company that is listed on a securities exchange. 

Market depth: 

The numbers of shares available for trading around the best bid and ask 
prices. 

Market impact: 

The degree to which an order affects the price of a security. 

Market maker: 

A dealer that maintains a market in a given security by buying or 
selling securities at quoted prices. 

Market order: 

An order to buy or sell a stated amount of a security at the best price 
available when the order reaches the marketplace. 

NASDAQ Stock Market (NASDAQ): 

A market for securities traded "over-the-counter" through a network of 
computers and telephones, rather than on a stock exchange floor. NASDAQ 
is an electronic communications system in which certain NASD member 
broker-dealers act as market makers by quoting prices at which they are 
willing to buy or sell securities for their own accounts or for their 
customers. NASDAQ traditionally has been a "dealer" market in which 
prices are set by the interaction of dealer quotes. 

National best bid and offer (NBBO): 

Defined as the highest bid and lowest ask across all U.S. markets 
providing quotes for an individual stock. 

Order Handling Rules: 

SEC rules that require (1) the display of customer limit orders that 
improve certain over-the-counter (OTC) market makers' and specialists' 
quotes or add to the size associated with such quotes (Rule 11Ac1-4 
(Display Rule)); (2) OTC market makers and specialists who place priced 
orders with ECNs to reflect those orders in their published quotes 
(Quote Rule); and (3) OTC market makers and specialists that account 
for more than 1 percent of the volume in any listed security to publish 
their quotations for that security (Mandatory Quote Rule). 

Opportunity cost: 

The cost from delaying execution to lessen market impact, or not be 
able to make the execution at all, or abandoning part of it because the 
market has turned against the strategy. 

Price improvement: 

Occurs when an order is executed at better than the quoted price. 

Put option: 

A contract granting the right to sell a fixed amount of a given stock 
at a specified price within a limited period of time. 

Quote: 

The highest bid to buy and the lowest offer to sell any stock at a 
given time. 

Quote flickering: 

Where a given price quote is only visible for a brief moment on the 
display screen. 

Quoted spread: 

Measures the cost of executing a simultaneous buy and sell order at the 
quoted prices. It is the simplest measure of trade execution cost (or 
trading cost). 

Retail investor: 

One who trades securities for himself/herself or who gives money to any 
institution, such as a mutual fund, to invest for himself/herself. 

Securities and Exchange Commission: 

The federal regulatory agency created by the Securities Exchange Act of 
1934 that is responsible for ensuring investor protection and market 
integrity in the U.S. securities markets. 

Specialists: 

Members of an exchange who handle transactions on the trading floor for 
the stocks for which they are registered and who have the 
responsibility to maintain an orderly market in these stocks. They do 
this by buying or selling a stock on their own accounts when there is a 
temporary disparity between supply and demand for the stock. 

Stepping ahead/penny jumping: 

The practice of improving the best price by a penny or less in an 
attempt to gain execution priority. 

Stock: 

A financial instrument that signifies an ownership position in a 
company. 

Tick size (or minimum price increment): 

The smallest price difference by which a stock price can change (up or 
down). 

Trade-through: 

The execution of a customer order in a market at a price that is 
inferior to a price displayed (or available) in another market. 

Trading cost: 

The cost for executing the trade (brokerage commission, fees, market 
impact). 

Transparency: 

The degree to which trade and quotation information (price and volume) 
is available to the public on a current basis. 

Volatility: 

A measure of the fluctuation in the market price of a security. 

Volume: 

The number of shares traded in a security or an entire market during a 
given period--generally on a daily basis. It is a measure of liquidity 
in a market. 

Volume weighted average price (VWAP): 

A trading benchmark used to evaluate the performance of institutional 
traders. It is the average price at which a given day's trading in a 
given security took place. VWAP is calculated by adding up the dollars 
traded for every transaction (price times shares traded) and then 
dividing by the total shares traded for the day. The theory is that if 
the price of a buy trade is lower than the VWAP, then it is a good 
trade. The opposite is true if the price is higher than the VWAP. 

[End of section]

Related GAO Products: 

Securities Markets: Preliminary Observations on the Use of Subpenny 
Pricing. 
[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-04-968T] 
Washington, D.C.: July 22, 2004. 

Securities Pricing: Trading Volumes and NASD System Limitations Led to 
Decimal-Trading Delay. 
[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO/GGD/AIMD-00-319] 
Washington, D.C.: September 20, 2000. 

Securities Pricing: Progress and Challenges in Converting to Decimals. 
[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO/T-GGD-00-96] 
Washington, D.C.: March 1, 2000. 

Securities Pricing: Actions Needed for Conversion to Decimals. 
[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO/T-GGD-98-121] 
Washington, D.C.: May 8, 1998. 

(250195): 

FOOTNOTES

[1] A stock is a security that signifies an ownership position in a 
company. An option contract provides the purchaser the right to buy 
(call) or sell (put) a fixed amount of a given security at a specified 
price within a limited period of time. 

[2] For option contracts priced $3 and above, the tick size was reduced 
from 1/8 of a dollar (12.5 cents) to 10 cents, and the tick size for 
contracts priced below $3 was reduced from 1/16 of a dollar (6.25 
cents) to 5 cents. 

[3] As used in this report, retail investors are individuals who buy or 
sell securities for their own accounts. 

[4] Institutional investors are entities, such as mutual funds, 
insurance companies, pension plans, or charitable organizations, that 
invest on behalf of themselves or others. Such investors typically have 
large pools of assets and buy and sell securities in large quantities 
or blocks. The stock markets classify block trades as those involving 
10,000 shares or more. 

[5] This analysis used the Trade and Quotes (TAQ) database maintained 
by the New York Stock Exchange. The TAQ contains records of all trades 
and price quotes from all U.S. exchanges and the NASDAQ Stock Market. 

[6] In general, for a given stock transaction the best bid (ask) price 
represents the highest (lowest) price available from all U.S. market 
venues providing quotes to sellers (buyers) of the stock. This is known 
as the national best bid and offer, or NBBO. 

[7] Securities Exchange Act Release No. 37619A (Sept. 6, 1996), 61 FR 
48290 (Sept. 12, 1996). 

[8] For example, the Chartered Financial Analyst Institute, which sets 
standards for investment professionals, issued guidelines on trade 
management that emphasize the need for investment managers to seek to 
achieve best execution for their clients. In addition, a top SEC 
examination official noted in a speech in 2002 that firms should 
increase their efforts to better ensure that the broker-dealers they 
use are achieving the best executions for their trades. 

[9] SEC, Division of Market Regulation: Order Directing the Exchanges 
and NASD to Submit a Decimalization Implementation Plan, Exchange Act 
Release No. 42360 (January 28, 2000), 65 Fed. Reg. 5003 (2000). 

[10] Another component of small investors' trading costs is the 
commission they pay to broker-dealers for executing their trades. The 
move to decimal pricing was not expected to change retail commissions 
and thus have not been included in our analysis of retail investor 
costs. 

[11] For this analysis, we selected pairs of NYSE and NASDAQ stocks by 
matching stocks with similar trading and stock characteristics. By 
generating these pairs, we attempted to prevent our results from being 
influenced by the differences between stocks' characteristics so as to 
better isolate the impact of decimal pricing alone. By selecting pairs 
of NYSE and NASDAQ stocks, our sample may be biased because the 
smallest NASDAQ stocks are not generally comparable in characteristics 
to NYSE stocks; this bias may tend to overstate the benefits of 
decimalization such as reductions in spreads and thus caution should be 
used in generalizing our results. However, our matched pairs also 
tended to underrepresent stocks with higher daily trading volume, which 
likely would bias our results toward understating spread reductions. 

[12] For example, the effective spread for a trade executed for an 
investor at a price of $10.03 for stock that was purchased when the bid-
ask prices were $10.01 (bid) and $10.03 (ask) would be 2 cents per 
share. 

[13] Hendrik Bessembinder, "Trade Execution Costs and Market Quality 
after Decimalization," Journal of Financial and Quantitative Analysis, 
vol. 38, no. 4, 760. 

[14] Market capitalization is a company's share price multiplied by the 
number of shares outstanding. 

[15] New York Stock Exchange, Inc., Decimalization of Trading on the 
New York Stock Exchange: A Report to the Securities and Exchange 
Commission, September 7, 2001. For the predecimalization sample period, 
the NYSE used the 19 trading days from August 1, 2000, through August 
25, 2000. The postdecimalization sample period is composed of the 21 
trading days in June 2001. 

[16] The NASDAQ Stock Market, Inc., The Impact of Decimalization on the 
NASDAQ Stock Market: Final Report to the SEC, June 11, 2001. The 
predecimalization sample period NASDAQ used included the 2 weeks before 
and the 2 weeks after the final date all NASDAQ securities had 
converted to penny increments on April 9, 2001. 

[17] ITG, another trade analytics firm, did not begin to measure 
institutional investors' trading costs until January 2003, after the 
implementation of decimal pricing and 1-cent ticks. 

[18] Specifically, Abel/Noser captures data from about 50 large 
investment management firms that in some years represent over 500 
institutional investors and well over 1,000 unique portfolio managers. 
In addition, Abel/Noser claims its data represent nearly $3 trillion in 
principal traded each year. Elkins/McSherry captures trade data from 
about 1,400 investment managers and 2,000 brokers worldwide, capturing 
about 20 percent of all dollars traded on NYSE and NASDAQ. The Plexus 
Group collects data from money managers representing as many as 100 
institutional investors. 

[19] To measure market impact costs, the Plexus Group compares a 
proprietary benchmark stock price to the average price an investor 
receives. The Plexus Group benchmark attempts to show the price at 
which the order for a particular stock should be executed. The firm 
calculates this expected price using trade data of its clients for the 
two quarters preceding the date of the trade under study and takes into 
account variables such as trade size, liquidity, and the direction of 
stock price movement. 

[20] Delay costs are market impact costs that occur between the time 
institutional investors' portfolio managers direct their traders to buy 
or sell stock and the moment these orders are released to brokers. The 
amount that the stock's price changes during this period is the cost of 
delaying the order. An order may be delayed for a number of reasons-- 
for instance, because it could affect prices in the market too much. 
See Plexus Group, The Official Icebergs of Transaction Costs, 
Commentary #54, January 1998. 

[21] As principals, NASDAQ market makers had earned revenue from 
spreads by buying shares at the bid price from investors and selling 
those same shares to other investors at the higher ask price. 

[22] These two firms analyze market impact costs by comparing their 
clients' trades to the volume-weighted average price (VWAP) of the 
particular stocks traded. The VWAP represents the average price at 
which a particular stock traded on a specific trading day and is 
calculated by adding up the dollars traded for every transaction (price 
times shares traded) and then dividing by the total number of shares 
traded for the day. The closer an investor's average price is to the 
VWAP, the lower the calculated market impact costs. 

[23] We do not present the specific analysis of ITG's data because the 
firm's client base for its trade cost analysis grew significantly after 
it first began offering this service, including the addition of some 
larger clients with sophisticated trading operations that contributed 
to the overall decline measured by the firm. 

[24] According to an academic recognized as an expert in financial 
markets' use of information technology, studies based on direct 
measurements of institutional trading costs, such as data compiled by 
trade analytics firms and exchanges, lead to more reliable calculations 
of trading costs than do studies that rely on indirect determinants. 

[25] Sugato Chakravarty, Venkatesh Panchapagesan, and Robert A. Wood. 
"Has Decimalization Hurt Institutional Investors? An Investigation into 
Trading Costs and Order Routing Practices of Buy-Side Institutions" 
(Unpublished study: May 28, 2003). 

[26] Ingrid M. Werner, "Execution Quality for Institutional Orders 
Routed to NASDAQ Dealers Before and After Decimals, Study Prepared for 
the Fisher College of Business, The Ohio State University" (October 20, 
2003). 

[27] Nicolas P.B. Bollen and Jeffrey A. Busse, "Tick Size, Trading 
Costs, and Mutual Fund Performance" (Unpublished study: 2004). 

[28] The authors constructed a synthetic benchmark that mimics the 
stock holdings and expense ratios of the actual mutual funds they 
studied. Because the benchmark portfolio has zero trading costs by 
construction, the difference between the return on the benchmark and 
the actual funds was the authors' measure of trading cost. 

[29] This ranking was published in Institutional Investor, vol. 38, no. 
7 (July 2004). The firms we interviewed represented a broad cross 
section of the institutional investor community, including 
representatives of the four largest money managers in the United States 
in 2003, four large public pension plan administrators, two large hedge 
funds, and other large, mid-size, and small money managers with assets 
under management ranging from about $2 billion to $500 billion. 

[30] Market microstructure is the study of the process of how the 
trading of securities affects prices, volumes and trader behavior. 

[31] See Hendrik Bessembinder, "Trade Execution Costs."

[32] New York Stock Exchange, Inc., Decimalization of Trading on the 
New York Stock Exchange, A Report to the Securities and Exchange 
Commission, 9. Also see NASDAQ Stock Market, Inc., The Impact of 
Decimalization on The NASDAQ Stock Market: Final Report to the SEC, 33. 

[33] NASDAQ used the average quoted spread from January 2001, before 
the market converted to decimal pricing, to study the cumulative number 
of shares that were displayed before and after decimalization. 

[34] In a related matter, on April 6, 2005, the SEC Commission adopted 
Regulation NMS (National Market System), which included a ban on 
quotations in increments of less than one penny (known as subpenny 
pricing) for stocks priced $1 and above. In prior GAO work, we found 
that quoting in subpenny increments resulted in more instances of 
traders "stepping ahead" of large limit orders. For additional 
information on subpenny pricing, see GAO's testimony Securities 
Markets: Preliminary Observations on the Use of Subpenny Pricing, GAO- 
04-968T (Washington, D.C.: July 22, 2004). 

[35] An order that specifies a particular price at which it can be 
executed is called a limit order. Limit orders are required to be 
executed at the specified price or better. Limit orders provide 
liquidity to markets. 

[36] Lawrence Harris, Decimalization: A Review of the Arguments and 
Evidence, USC Working Paper (Los Angeles, Calif.: Apr. 3, 1997), i. 

[37] Ingrid M. Werner, 17 and 26. 

[38] Data on the volume of trades executed on NASDAQ for this period 
was not comparable to that from NYSE because trades in NASDAQ stocks 
were increasingly being executed outside this market. The declining 
trading volumes being reported by NASDAQ were the result of alternative 
trading venues, such as ECNs, executing increasing portions of volume 
in NASDAQ shares but reporting these trades outside the NASDAQ trade 
reporting system. For example, trades executed by the Island ECN were 
previously reported to NASDAQ and were included in NASDAQ's total 
trading volume statistics. However, in 2002 Island began reporting its 
trades instead through the Cincinnati Stock Exchange (now called the 
National Stock Exchange), which caused a reduction of over 20 percent 
in trades that NASDAQ reported as being executed within its market. 

[39] See The Tabb Group, Institutional Equity Trading in America: A Buy-
Side Perspective. (Westborough, Mass.: April 2004), 32. 

[40] For example, in 2001 SEC approved the establishment of the 
Archipelago Exchange as the stock trading facility of the Pacific 
Exchange. See SEC, PCX Rulemaking: Order Approving Proposed Rule Change 
by the Pacific Exchange, Inc., as Amended, and Notice of Filing and 
Order Granting Accelerated Approval to Amendment Nos. 4 and 5 
Concerning the Establishment of the Archipelago Exchange as the 
Equities Trading Facility of PCX Equities, Inc., Exchange Act Release 
No. 44983 (October 25, 2001), 66 Fed. Reg. 55225 (2001). 

[41] Justin Shack, "The Orders of Battle," Institutional Investor, vol. 
38, no. 11, November 2004, 82. 

[42] Regulation NMS was originally proposed for public comment in 
February 2004. Exchange Act Release No. 49325 (Feb. 26, 2004), 69 Fed. 
Reg. 11126 (2004). The SEC extended the period for comment and issued a 
supplemental release regarding Regulation NMS in May 2004. Exchange Act 
Release No. 49749 (May 20, 2004), 69 Fed. Reg. 30142 (2004). SEC 
reproposed a revised Regulation NMS in December 2004. Exchange Act 
Release No. 50870 (Dec. 16, 2004), 69 Fed. Reg. 77424 (2004). Changes 
to the trade through rule (now known as the Order Protection Rule) are 
to be implemented for a limited number of stocks beginning April 10, 
2006, and for all National Market System stocks by June 12, 2006. 

[43] These rules include the Limit Order Display Rule (SEC Rule 11Ac1- 
4) and the Quote Rule (SEC Rule 11Ac1-1). Rule 11Ac1-4 mandated that 
public limit orders for all NASDAQ securities should be reflected in 
the best bid and offer disseminated by that market. Rule 11Ac1-1, 
states that market makers may not post one quote on NASDAQ and a 
different quote on an alternative quote dissemination system (i.e., 
ECN). These rules are known as the Order Handling Rules. 

[44] The Tabb Group, Institutional Equity Trading, 29. 

[45] Shack, "Orders of Battle," 82. 

[46] SIA has approximately 600 members. SIA members include most of the 
largest U.S. broker-dealers. 

[47] These filings are the Financial and Operational Combined Uniform 
Single (FOCUS) reports. 

[48] A bull market is a market in which stock prices rise over a 
sustained period of time. 

[49] Lawrence Harris and Venkatesh Panchapagesan, "The Information 
Content of the Limit Order Book: Evidence from NYSE Specialist Trading 
Decisions," Journal of Financial Markets, Vol. 8 (2005). 

[50] This fee is one charged for trade recording and is assessed at 
$0.0025 per share in the trade with a minimum charge of $0.0075 and the 
maximum of $0.15. For example, a trade executed for 10,000 shares would 
be charged the maximum of $0.15. However, if this trade is broken into 
two executions of 5,000 shares each, each trade would be charged 
$0.125, or a total of $0.25, illustrating how more trades could lead to 
higher clearing costs. 

[51] Over-the-counter stocks are those not listed on exchanges. 
NASDAQ's National Market System includes the largest, most actively 
traded stocks. 

[52] An IPO is the first sale of stock by a private company to the 
general public. This process is often called "going public" and 
represents the primary market. The secondary market for stocks is the 
market where securities are traded after they are initially offered in 
the primary market. 

[53] The four option exchanges whose studies we obtained include the 
American Stock Exchange, Chicago Board Options Exchange, Pacific 
Exchange, and Philadelphia Stock Exchange. 

[54] Quotes "flicker" on trading information screens when the prices of 
underlying stocks are changing too rapidly. 

[55] The first multiple listing of an options contract occurred in 
February 1976 when the Chicago Board Options Exchange multilisted 
options on the stock of the Boise Cascade Corporation, which had 
previously been listed only by the Philadelphia Stock Exchange. 

[56] In September 2000, both the Department of Justice and SEC reached 
a settlement with the American Stock Exchange, Chicago Board Options 
Exchange, Pacific Exchange, and Philadelphia Stock Exchange with 
respect to alleged anticompetitive activities and the failure to 
adequately enforce compliance with their own rules. 

[57] SEC, Office of Compliance Inspections and Examinations and Office 
of Economic Analysis, Special Study: Payment for Order Flow and 
Internalization in the Options Markets, December 2000. The quoted 
spread is the difference between the displayed bid and ask prices and 
generally measures retail trading costs, since retail investors 
typically conduct transactions at these prices. The effective spread 
measures the trading cost relative to the midpoint of the quoted spread 
at the time the trade occurred. The lower the effective spread, the 
lower the cost to investors. 

[58] See Patrick De Fontnouvelle, Raymond P.H. Fishe, and Jeffrey H. 
Harris, "The Behavior of Bid-Ask Spreads and Volume in Options Markets 
During the Competition for Listings in 1999," The Journal of Finance, 
vol. 58, no. 6, (December 2003); Battalio, Robert, Brian Hatch and 
Robert Jennings, "Toward a National Market System for U.S. Exchange- 
Listed Stock Options," The Journal of Finance, vol. 59, no. 2, (April 
2004). 

[59] These systems include the American Stock Exchange's ANTE, the 
CBOE's Hybrid Trading System, the Pacific Exchange's PCX Plus, and the 
Philadelphia Stock Exchange's XL. The two electronic-based option 
exchanges are BOX and ISE. 

[60] Competitive Developments in the Options Markets, Exchange Act 
Release No. 49175 (Feb. 3, 2004), 69 Fed. Reg. 6124 (2004). In this 
release, SEC also sought comments on a variety of issues, including 
payment for order flow, internalization, and specialist participation 
guarantees. Payment for order flow is an arrangement under which a 
broker is paid to route its customer orders to a particular market for 
execution. Internalization occurs when a brokerage firm fills a 
customer's order from the broker's own inventory of securities without 
exposing the order to the market. Specialist participation guarantees 
offer these intermediaries a percentage of the order flow from a 
particular options exchange for providing liquidity, depth, and 
continuity in that market. 

[61] An actively-traded stock like International Business Machines 
(IBM) may have thousands of options available for trading. For example, 
if IBM's stock price is around $100, options granting the right to buy 
or sell the stock are likely trading with strike prices of $90, $95, 
$100, $105, $110, etc., and each of these prices will have separate 
options expiration dates (months). Simultaneously, trading will also be 
occurring in both call options and put options using the same strike 
prices and expiration months. 

[62] Throughout, "decimalization" reflects the transition from 
fractional pricing (that is, pricing generally in sixteenths of a 
dollar) to decimal pricing (that is, pricing in round cents) and, more 
significantly, the 84 percent reduction in the minimum price increment, 
or tick, from one-sixteenth of a dollar to 1 cent. Decimalization was 
fully implemented on the NYSE on January 29, 2001, but not until April 
9, 2001, on NASDAQ. An event study is the analytical framework used to 
measure the economic effect of an event, such as the transition from 
fractional to decimal pricing. 

[63] Hendrik Bessimbinder, 2003, "Trade Execution Costs and Market 
Quality after Decimalization," Journal of Financial and Quantitative 
Analysis 38(4), 747-777; and K. Chung, B. Van Ness, and R. Van Ness, 
2004, "Trading Costs and Quote Clustering on the NYSE and NASDAQ after 
Decimalization," Journal of Financial Research 27(3), 309-328. 
Bessimbinder (2003) performed an event study analysis of the impact of 
decimalization on several trade execution cost and market quality 
measures using a sample of NYSE-listed and NASDAQ stocks in a 
predecimalization period and a postdecimalization period. While not an 
event study, Chung et al. (2004) focused on the differences in several 
trade execution cost and market quality measures between a sample of 
NYSE-listed and NASDAQ stocks in the month after decimalization; they 
also analyzed quote clustering, which is the tendency for quotes to 
"cluster" at certain price points, such as nickels and dimes. 

[64] Stocks were grouped by volume according to the following 
categories: 

High volume stocks were those in our sample of stocks with average 
daily trading volumes exceeding 500,000 shares (the maximum was less 
than 1.6 million shares). 

Medium volume stocks were those in our sample of stocks with average 
daily trading volumes between 100,000 and 499,999 shares. 

Low volume stocks were those in our sample of stocks with average daily 
trading volumes of less than 100,000 shares. 

[65] For example, since an order can often be filled through a number 
of trade executions, and use of TAQ data implicitly assumes that each 
trade record reflects a unique order that is filled, our analysis 
failed to address any impact of a change in how orders are filled and 
the costs associated with this. 

[66] Since there are important structural differences between the NYSE 
and NASDAQ markets and the stocks listed on each, a general analysis of 
the effect of an event on both markets could yield biased results if 
the stock samples are not chosen carefully. For this reason, 
researchers analyzing the impact of decimalization on the NYSE and 
NASDAQ usually employ a matched-pairs analysis. For our analysis, a 
matched pair consisted of one NYSE-listed stock and one NASDAQ stock 
(among all NASDAQ stocks) that provided the closest match to it in 
terms of characteristics related to trading activity, such as share 
price and average daily trading volume, which are generally thought to 
explain variation in bid-ask spreads, among other things. By matching 
the stocks on these characteristics, a matched-pairs analysis attempts 
to isolate the effect of an event on the different markets by 
considering how it affects groups of analogous stocks. 

[67] Bessimbinder (2003) separated the pre and postdecimalization 
periods as the 3 weeks before January 29, 2001, and from April 9, 2001 
through August 31, 2001. Chung et al. (2004) considered only May 2001, 
as their focus was not on a pre-versus postdecimalization comparison. 
Other studies, both from researchers and exchanges, examining 
decimalization often selected a 1-month or shorter period sometime 
shortly before decimalization and an analogous period sometime after 
decimalization for comparison. 

[68] Despite the two "event dates" for the NYSE and NASDAQ, our 
analysis incorporated calendar-period comparisons rather than event- 
time comparisons (for example, 1 month following decimalization on the 
NYSE compared with 1 month following decimalization on NASDAQ). We 
believed that it was reasonable to assume that the lag time between 
full decimalization on the NYSE and NASDAQ would not lead to any 
sizeable learning discrepancies between the markets since NASDAQ market 
participants were able to observe NYSE activity over this period. 

[69] NASDAQ stock symbols are four to five letters in length. A fifth 
letter in a NASDAQ stock symbol indicates, among other things, share 
class or unusual circumstances such as bankruptcy or delayed SEC 
filing. 

[70] While Bessimbinder (2003) used only market capitalization as the 
matching criterion, Chung et al. (2004) used five stock attributes-- 
share price, number of trades, trade size, return volatility, and 
market capitalization. In the absence of market capitalization data, we 
followed Van Ness et al. (2001) and used four variables. Researchers 
have generally found that overall results are similar regardless of the 
matching variables used. 

[71] The reported number of trades on NYSE is not directly comparable 
to that reported on NASDAQ due to interdealer trading on NASDAQ. NASDAQ 
volume has been estimated to be exaggerated by 30 percent to 50 percent 
relative to NYSE volume. As with Chung et al. (2004), we counterbalance 
the discrepancy by including trades in NYSE-listed stocks that occur 
outside of the NYSE, reflecting activity at regional exchanges and 
elsewhere, rather that incorporating an "inflation factor."

[72] Of the 293 NASDAQ matches, 127 were best matches for one NYSE- 
listed stock, 64 were best matches for two NYSE-listed stocks, 28 were 
best matches for three, 13 were best matches for four, 16 were best 
matches for five, 7 were best matches for six, 11 were best matches for 
seven, 1 was a best match for eight, 6 were best matches for nine, 3 
were best matches for ten, and 17 were best matches for 11 to 26 NYSE 
stocks. 

[73] Relative to the marginal cost in terms of computing resources and 
analysis time, the marginal benefit of increasing the number of matched 
pairs was limited, as the top 400 (500) matched pairs consisted of 400 
(500) NYSE stocks and 215 (235) NASDAQ matching stocks. 

[74] In the TAQ CQ file, NASDAQ dealers and ECNs are collectively 
classified under "T" as the source market for quotations for NYSE- 
listed issues. The market maker identification (MMID) data field 
provides an additional classification layer among NASDAQ dealers and 
ECNs. For example, "TRIM" denotes "Trimark," a NASDAQ dealer, while 
"BRUT" denotes the BRUT ECN. "CAES" is the acronym for "Computer 
Assisted Execution System," which is a NASDAQ system that allows its 
members to quote NYSE-listed stocks. The National Securities Clearing 
Corporation provides a listing of NASDAQ market makers and their MMIDs 
in the Member Directory at www.nscc.com. The Boston Stock Exchange, the 
Chicago Stock Exchange, the National Stock Exchange, the Pacific Stock 
Exchange, and the Philadelphia Stock Exchange are regional exchanges. 

[75] This was explored in a working paper, D. Ikenberry and J. Weston, 
2003, "Clustering in U.S. Stock Prices after Decimalization."

[76] This was explored in Chung et al. (2004). 

[77] ITG, another trade analytics firm, did not begin to measure 
institutional investors' trading costs until January 2003, after the 
implementation of decimal pricing and 1-cent ticks. 

[78] To measure market impact costs, the Plexus Group compares a 
proprietary benchmark stock price to the average price an investor 
receives. The Plexus Group benchmark attempts to show the price at 
which the order for a particular stock should be executed. The firm 
calculates this expected price using trade data of its clients for the 
two quarters preceding the date of the trade under study, and takes 
into account variables such as trade size, liquidity, and the direction 
of stock price movement. 

[79] Delay costs are a type of market impact cost that occur between 
the time institutional investors' portfolio managers direct their 
traders to buy or sell stock and the moment these orders are released 
to brokers. The amount that the stock's price changes during this 
period is the cost of delaying the order. An order may be delayed for a 
number of reasons--for instance, because it could affect prices in the 
market too much. Plexus Group, The Official Icebergs of Transaction 
Costs, Commentary #54, January 1998. 

[80] For example, NASDAQ market makers previously could earn revenue 
trading as principals by buying shares at the bid price from investors 
and selling those shares to other investors at the higher ask price, 
thus earning the difference or spread amount as compensation. 

[81] These two firms analyze market impact costs by comparing their 
clients' trades to the volume-weighted average price (VWAP) of the 
particular stocks traded. The VWAP represents the average price at 
which a particular stock traded on a specific trading day and is 
calculated by weighting each trade's price according to the proportion 
of shares of a specific stock it represents on a given day. The closer 
an investor's average price is to the VWAP, the lower the calculated 
market impact costs. 

[82] We do not present the specific analysis of ITG's data because the 
firm's client base for its trade cost analysis grew significantly after 
it first began offering this service, including the addition of some 
larger clients with sophisticated trading operations that contributed 
to the overall decline measured by the firm. 

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