Has Decimalization Hurt Institutional Investors? An Investigation into Trading Costs and Order Routing Practices of Buy-side Institutions

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1 Has Decimalization Hurt Institutional Investors? An Investigation into Trading Costs and Order Routing Practices of Buy-side Institutions Sugato Chakravarty Purdue University West Lafayette, IN (765) Venkatesh Panchapagesan* Washington University St. Louis, MO (314) Robert A. Wood University of Memphis Memphis, TN (901) Current Draft: May 28, 2003 Comments Welcome. Acknowledgements: Chakravarty is the corresponding author. We thank an anonymous referee, Amber Anand, Mark Carhart, Tarun Chordia, Amy Edwards, Rick Green (editor), Christine Jiang, Tim McCormick, George Sofianos and the seminar participants at NASDAQ, Goldman Sachs, Washington area Finance Conference, and NBER Market Microstructure meetings for valuable comments. We also thank the Plexus group, especially Vinod Pakianathan and Wayne Wagner, for providing us with the data and for related conversations. The usual disclaimer applies. * - currently on assignment to the Nasdaq Economic Research

2 Abstract We examine the effect of decimalization on institutional investors using proprietary data. We find no evidence that decimalization has increased trading costs for institutions. In fact, institutional trading costs appear to have declined by about 23 basis points (or, roughly 5 cents per share) after decimalization. In economic terms, this decrease roughly translates to an average monthly savings of $133 million in institutional trading costs. Estimations involving robust multivariate techniques that condition on order, manager and market characteristics yield roughly similar reductions as well. Our results are surprising in light of an oft-repeated, and increasingly louder, complaint among professional traders that liquidity is hard and expensive to find in a post-decimal trading milieu. Though there is significant change in order routing practices overall, we find an increased usage of alternate brokers (represented by ECNs and crossing networks such as Instinet) for easy-to-fill (i.e., smaller) orders and full service and independent research brokers for orders that are difficult to fill (i.e., larger size orders). Our results differ from those reported around the transition of the minimum tick size from eighths to sixteenths. These findings have important regulatory implications, especially in light of growing support among Wall Street firms to replace decimals with nickels.

3 1. Introduction After much debate, the New York Stock Exchange (NYSE), from January 29, 2001, started quoting and trading its listed issues in dollars and cents (decimalization) as opposed to increments of a sixteenth of a dollar (sixteenths). The debate centered on the possible effect of decimal prices on liquidity and trading costs for investors. While retail investors welcomed the idea of trading in finer price increments, institutional investors were more concerned about its adverse impact on liquidity. 1 Much of their concern appeared to be justified in the light of earlier findings on the US equity markets move to the sixteenths as well as on results based on the introduction of decimal pricing in other markets. 2 These results indicate that spreads both quoted and effective decrease following a reduction in tick size but so does the market depth. While both retail and institutional investors gain from a reduction in spreads, institutional investors are more likely to face the brunt of a reduction in market depth. Jones and Lipson (2001) who investigate institutional trading costs around the change (in June of 1997) of minimum tick size from $⅛ to sixteenths find that realized execution costs of institutions increased following that changeover. They conclude by stating,. institutional traders should, as a group, regard skeptically any proposal for a further reduction in minimum price increments. It is not surprising, therefore, that institutions were reluctant to embrace decimalization. 3 Now that decimalization has been in place for a while, many on Wall Street would like to see it repealed. In a recent television interview, SEC chairman William Donaldson may have also added further fuel to the issue with this specific quote: "I think the whole issue of decimalization needs to be looked at again. The spreads have narrowed, but the total cost of transactions, I suspect, has increased. And I suspect the liquidity that used to be in the market has been severely dampened by these very narrow spreads." 1 For example, a recent letter to the Security Industry Association (SIA) claims: The execution of large orders has been hampered by reduced depth of the Exchange s limit order book and by increased instances of market participants stepping ahead of orders by increments of as little as one penny, Similarly, the Head Trader at Zurich-Kemper is quoted as saying: The net effect has been for the institutional trader to lose control of his/her order flow, since no effective tools exist in the NYSE listed market to reach the market efficiently. 2 See, for example, Bacidore (1997), Bollen and Whaley (1998), Ricker (1998), Ronen and Weaver (1998), Goldstein and Kavajecz (2000), and Jones and Lipson (2001). Bacidore, Battalio and Jennings (2001), Chakravarty, Van Ness and Wood (2002), and Chung, Van Ness and Van Ness (2001) present similar results following the introduction of decimal pricing for a select group of pilot NYSE stocks before the market-wide switchover in January See Decimal Move Brings Points Of Contention From Traders, (Wall Street Journal, February 12, 2001, p. C1) and Deals & Deal Makers: Grasso Says NYSE Must Stick to Penny As Trade Increment, ((Wall Street Journal, March 22, 2001, p. C18). 1

4 In the wake of the above comment, NYSE chairman Dick Grasso, in an interview with the L.A. Times and the Washington Post, suggested instituting a new pilot program in which a few chosen stocks would be traded at 5- cent intervals across all markets to test the impact of nickel increments on liquidity and trading costs. 4 But the furor over decimalization, and its possible impact on institutional profits, begs the question of whether institutions were indeed adversely impacted by it. If liquidity has indeed dried up, as opined by institutional investors (and apparently endorsed by some senior administrators and regulators), it should be reflected in higher trading costs following decimalization. Accordingly, this paper seeks to investigate the state of institutional trading costs following decimalization. However, unlike retail investors, it is difficult to measure trading costs for institutions. Institutions often need to transact large quantities of shares in the market and, frequently, have to wait for several days before their order gets filled. Sometimes these orders do not get filled on time and sometimes they do not get filled at all. Determining the impact of missed trades or delayed trades is highly subjective and depends largely on the investment horizon of the institution. While commissions paid by institutions are easy to identify, estimation of both price impact of their completed trades and opportunity costs of their missed trades requires us to make assumptions that may, or may not, be valid. Moreover, unlike retail investors, institutions use multiple brokers who fill orders with varying speeds and costs that make order routing an important determinant of their cost of trading. The issue of order routing has become quite important in recent years, as alternative (and mostly electronic) trading systems have risen by providing features such as speed and anonymity to attract traders away from traditional trading systems. Any change in the trading landscape (such as decimalization) that favors one type of broker over another is bound to alter the way institutions route their orders and hence impact their trading costs. To further compound the issue, institutional trading costs often mask payments for services explicit and implicit that their executing brokers may provide (See Schwartz and Steil (2002)). This makes it almost impossible to separate the real trading costs from observed trading costs. We use proprietary data from Plexus Group, a well-known firm that advises buy-side institutional investors on trading costs. The data provide us with detailed information on institutional orders and trades for all Plexus clients. In 2001, Plexus handled $4.5 trillion in institutional equity trades, or roughly a fifth of dollar trading volume in US equity markets. 5 Using a sample of institutional trades in NYSE-listed stocks, we estimate trading costs before and after decimalization. We estimate direct costs, namely commission and price impact, as well as examine indirect cost drivers such as the number of days and number of brokers used that 4 See Wall Street Rethinks the Penny Revolution, (The Washington Post, May 18, 2003, p. F01). 5 See and 2001 NYSE Fact Book. 2

5 could impact the overall cost of trading for institutions. We compute our trading cost measures also during the first quarter of 2000 to highlight the role played by market conditions on institutional trading. Our main findings can be summarized as follows. We find no evidence of increased trading costs for institutions following decimalization. Our conclusion is robust to the sample selection method, the benchmark price for computing price impact, and the estimation techniques used in the analyses. Specifically, we find that institutional trading costs have declined by about 23 basis points (or roughly 5 cents per share) after decimalization. In economic terms, this decrease translates to an average monthly savings of roughly $133 million in institutional trading costs. We find little, if any, shift with regard to the kinds of stocks traded after decimalization versus before. However, the average size in terms of shares (in dollars) of an institutional order has fallen from 42,381 shares ($1.7M) to 36,209 shares ($1.4M) following decimalization. Though smaller order sizes could also lower costs, our robust multivariate techniques show a decline in trading costs (of about 28 basis points) even after controlling for order, manager and market characteristics. 6 Moreover, we find no evidence that institutions are trading more frequently since the average number of trading decisions per day has also fallen from 1,958 to 1,886 following decimalization. Our results appear to be in sharp contrast to that reported by Jones and Lipson (2001) who document an overall (statistically significant) increase of about 17 basis points in direct trading costs following the move to sixteenths. Though direct trading costs have declined following decimalization, indirect determinants of institutional trading cost, such as the time to fill a decision (especially the relatively larger size decisions), appear to have gone up, providing some credence to claims that decimalization has been detrimental to large investors. Unfortunately our data are unable to capture the full effect of such indirect costs and, consequently, we do not address these issues in our analysis. Though we find little meaningful change in the order routing practices overall, we find an increased usage of alternate brokers, represented by ECNs and crossing networks such as Instinet and Posit, for orders that are relatively easy to fill (i.e., smaller orders), and full service brokers for orders that are difficult to fill (i.e., larger size orders). These changes go hand in hand with reduction in costs of smaller orders executed through alternate brokers and reduction in costs of larger orders routed through full service brokers following decimalization. Interestingly, full service brokers are getting a lesser share of smaller orders despite executing them at a low cost suggesting that other parameters, such as speed, may be as important to institutions as costs 6 A recent study by Werner (2002) investigates (and reports findings similar to ours) institutional trading costs for the top 100 Nasdaq stocks that were routed to Nasdaq (sell-side) dealers only. An internal study by Plexus (2001) also supports our findings. 3

6 in routing decisions. There is a decline in the usage of soft dollar brokers that mirrors an increase in the usage of independent research brokers suggesting that institutions are increasingly seeking separation of execution quality from other broker services such as free research. Overall, an important implication of our findings is that institutions are increasingly cost conscious in a post-decimal world, sending relatively smaller orders through automated execution systems while routing the more difficult decisions to full service and independent research brokers. It, therefore, appears that both electronic networks and traditional brokers could exist side by side as they cater to distinct needs of institutional investors. Our findings should provide comfort to regulators facing criticism, mostly by practitioners and some academics, related to the possibility of a drop in liquidity supply in a post-decimal trading milieu. The remainder of the paper is as follows. Section 2 discusses the related literature while Section 3 discusses the data and provides some descriptive statistics. Section 4 provides multiple ways to measure transactions costs and provides univariate analyses of transactions costs on various partitions of the data. Section 5 examines the order routing decisions by institutions. Section 6 extends the analyses to a multivariate examination and performs robustness checks on our conclusions. Section 7 concludes with a discussion. 2. Related Literature While there has been a longstanding interest among financial economists on the impact of the equity trading process on stock prices, the last decade has seen an impressive amount of research being conducted in documenting institutional execution costs under a variety of circumstances. What makes this area of research interesting is the fact that institutions trade large quantities and they trade often, which makes them significantly impact prices. There appear to be three streams of research involving institutional price impact studies. The first stream investigates the determinants of such price impacts. Thus, for example, Chan and Lakonishok (1995) report that institutional trading impact and trading cost are related to firm capitalization, relative decision size, identity of the management firm behind the trade and the degree of demand for immediacy. Keim and Madhavan (1997) focus on institutional investment styles and its impact on their trading costs. They report that trading costs increase with trading difficulty and that these costs vary with factors like investment styles, order submission strategies and exchange listing. Nelling (1996) argues that the process of shopping the block, or wanting to trade large sizes, affects stock prices, especially in smaller capitalization stocks and for seller initiated trades. The second stream of research focuses on the location of trading including upstairs versus downstairs markets as well as across U.S. equity markets. For example, Keim and Madhavan (1996) investigate a sample of upstairs trades in the NYSE and report that price movements (up to four weeks prior to the trade date) are 4

7 significantly positively related to trade size consistent with information leakage. More importantly, and related to our work, they point out the importance of the choice of pre-trade benchmark prices in estimating institutional price impact. Madhavan and Cheng (1997) compare execution costs in both upstairs and downstairs markets and find the economic benefits of upstairs trading are small for the average-sized block trade. 7 Chan and Lakonishok (1997) compare institutional trade execution costs across the NYSE and the NASDAQ and report that, after appropriate controls, costs are lower on NASDAQ (NYSE) for smaller (larger) firms. Jones and Lipson (1999) compare institutional execution costs across major U.S. Exchanges and find that execution costs (including commissions) are indistinguishable across these exchanges. The third, and emergent, stream of research, and that to which the current paper belongs, investigates the impact of minimum tick size reductions on institutional trade execution costs. For example, Jones and Lipson (2001) investigate institutional trading costs around the changeover in minimum tick-size from eighths to sixteenths in the NYSE in June They find that realized execution costs in their sample of firms increase after the changeover and conclude that smaller tick sizes may actually reduce market liquidity. While we both use institutional order data from Plexus, there are some important differences in both the content and the scope of the data that we examine. First, the environment under which institutions operate have changed since the period covered by the Jones and Lipson study that might have a direct bearing on the trading costs reported by them. For example, there has been a repeal of NYSE Rule 390 which allows NYSE members to trade outside an exchange, as well as the explosive growth in electronic trading systems. Second, there has been a growing importance of worked orders orders that take longer than a day to fill or filled using more than one broker. While worked orders comprised only 14 percent of all orders in the Jones and Lipson sample, they represent about 30 percent in our sample which suggests that institutional strategies, including order routing practices, may have changed since the move to sixteenths. Interestingly, Jones and Lipson report a reduction of 5.9 bp in the costs of trading worked orders (see their Table 5, p. 265) that is consistent with our findings following decimalization. Last, but certainly not least, unlike Jones and Lipson, we focus more on longer term Plexus clients who traded both before and after decimalization. This enhances our ability to tease out effects related to decimalization from other issues such as the entry and exit of firms into the Plexus clientele. Though we believe that the composition of Plexus clientele could have an important effect on our results, we also examine the impact of decimalization on all Plexus clients as a robustness check of our overall conclusion. 7 In addition, Bessembinder and Venkatraman (2002) investigate upstairs trading on the Paris Bourse, while Booth, Lin, Martikainen, and Tse (2001) do so for the Helsinki Exchange and Smith, Turnbull and White (2001) examine upstairs trading in the Toronto Exchange. 5

8 3. Data Before formally describing our data, it is important to understand how buy-side institutions trade. Each institutional client employs many portfolio managers who collectively manage its assets. The trading process begins with the stock selection by the portfolio manager. Thereafter, a trading decision is made that comprises of all buy/sell order activity that occurs within a certain period of time for that stock. The portfolio manager then makes one or more releases to the trader who works for the manager against that decision. The trader, in turn, releases one or more orders to either one or many brokers (called broker releases). The broker may then execute each release with one or more trades. This entire process is well recorded in Plexus data except at the final stage when brokers may elect to aggregate their trades before reporting them back to Plexus. This limits our ability to infer whether the number of trades have exploded since decimalization, a common complaint among institutional traders. 3.1 Plexus Data Our data contain information on all orders and trades in NYSE-listed stocks of Plexus clients over the period November 28, 2000, to January 26, 2001 (period before decimalization, or simply BEFORE ), and over the period January 30 March 31, 2001 (period after decimalization, or simply AFTER ). 8 We choose this period so that it straddles the date when all stocks went to decimal pricing (January 29, 2001) such that we have roughly an equal number of days before and after decimalization. All stocks, except those that were part of the pilot programs, traded in sixteenths before January 29, The NYSE introduced decimal trading in a small group of stocks, including active stocks such as Fedex, through its pilot programs starting in August The pilot programs were designed to provide investors, including institutional investors, the opportunity to learn and operate under the new environment. We ignore the pilot stocks and concentrate on the overwhelming majority of the NYSE-listed stocks that started trading in decimals only from January 29, We also exclude trading decisions taken before January 29 but were completed after to keep our analysis clean and simple. To highlight the role of market conditions on institutional trading, we also examine trading decisions over the period January 30 March 31, 2000 (Q1 8 Though we are the first to use Plexus data to examine institutional trading following decimalization, many researchers have used it to examine a wide variety of topics related to institutional trading behavior (see Keim and Madhavan (1996, 1997), Conrad et al. (2001a, 2001b), and Jones and Lipson (1999, 2001)). 9 See Chakravarty, Wood and Van Ness (2003) for the list of stocks that were included in these pilot rounds. 6

9 2000). 10 Our main analysis is done using trades in all stocks by 32 Plexus clients who traded in all the three periods we examine - AFTER, BEFORE and Q By examining all stocks, we allow for the possibility that the type of stocks traded by institutions may have changed following the move to decimals. 11 Our approach of focusing on Plexus clients who traded over the three periods addresses an important limitation of earlier studies that use Plexus data in that they fail to recognize changes in the composition of Plexus clientele over time. It is difficult to interpret changes in institutional trading behavior when the population of institutions that are examined changes as well. However, to ensure the robustness of our conclusions, we also redo our analysis using trades in all stocks by all Plexus clients in our data. 12 This ensures that our results are comparable with results from previous studies. For each trading decision, the data include a) the stock to be traded and the date the decision was made; b) the desired number of shares to be bought or sold; c) whether the decision was to buy or sell; d) the dates when the individual components of the trading decision were released to the executing broker; e) the dates and prices at which the various components of the decision were filled; f) the commissions in dollars per share; g) the volume weighted average trade price for the stock on each of the days a component of the decision was filled; h) the manager submitting the orders as belonging to one of three trading styles: value, diversified or growth; and i) the different brokers a trading decision is released to. The identification of the underlying manager s style behind each trading decision is significant because it enables us to get a glimpse of transactions costs as a function of the aggressiveness of an order. For example, value managers are investors whose trading strategy is based on identification of undervalued stocks with a decidedly longer-term perspective and could be termed patient investors. Growth managers, on the other hand, are expected to have a shorter investment horizon and buy and sell stocks based less on company fundamentals and more on short-term price appreciation. They are similar to technical traders. Diversified managers are expected to lie in between growth and value managers and have elements of both in their investment strategy. Also included in this category are institutions that follow quantitative styles, including indexing, that are neither momentum nor value-based. In terms of their willingness to bear price impact as well 10 Specifically, the S&P 500 Index increased by 3 percent in the first quarter of 2000 but fell by 5.7% in the last quarter of 2000 and the twenty-six days in January prior to decimalization. It continued to fall in the first quarter of 2001 by as much as 15 percent after stocks started trading in decimals. 11 In an earlier version, we had focused only on stocks that were traded in all the three periods we examine. 12 We thank an anonymous referee for encouraging us to do so. 7

10 as in their desire for immediacy, it is reasonable to expect growth (value) managers to be most (least) aggressive, with diversified managers falling in between. It should be emphasized here that this style classification is made by Plexus and not by the institutions themselves. We have little reason to doubt the integrity of their classification given their experience and standing in the business of institutional trading cost measurement. The data also provide us with a clear picture of the order routing strategies (through the choice of broker types) adopted by institutions before and after the move to decimals. We discuss the issue of order routing in greater detail later in the paper. 3.2 Descriptive Statistics Table 1 provides summary statistics of our data. It is designed to provide the backdrop with which to examine the research questions addressed in the paper. Panel A provides an overall summary while Panel B provides a glimpse of the types of stocks traded across the three periods of interest classified by manager style. From Panel A, it is clear that institutional trading activity had declined following decimalization. The average daily dollar value (total dollar value) of institutional trading decisions was down from $3.3 billion ($136.7 billion) to $2.6 billion ($116.2 billion). This drop is fueled by a decrease in the number of decisions as well as by the average size of each decision. While decimalization may have little to do with the former, clearly it could have impacted the latter given the increased need to protect oneself from an increased risk of front running in a decimal environment. Interestingly, the levels after decimalization are similar to the levels a year before though clearly the market conditions were quite different. This suggests that institutional trading activity is robust to changes in overall market conditions. There is little change in the percentage of trading decisions filled completely (over 98%) across the three periods. Plexus clients are also marginally net buyers in all the three periods of our sample. In Panel B, we take a closer look at the types of stocks traded by institutions and whether there were any changes in them over the three periods. We further classify the types of stocks traded by managerial style: Value, Diversified and Growth. There were 8 Value managers, 15 Diversified managers and 9 Growth managers in our sample. Overall we find little difference in the composition of stocks traded by managers with different styles. Not surprisingly, both Diversified and Growth managers trade far more in large cap stocks than Value managers who specialize in picking smaller and more out-of-favor stocks. However, our results reveal no change in the type of stocks traded by buy-side institutions following decimalization. Our summary statistics reveal significant changes in institutional trading activity around decimalization that may have a direct bearing on their trading costs. We investigate trading costs in greater detail in the next section. 8

11 4. Institutional Trading Costs As mentioned before, there are several factors that make capturing trading costs for institutional investors harder than for retail investors. Traditional measures like bid-ask spreads are highly inappropriate as institutional trades are often large and take days to fill. Moreover, the true costs to an institutional trader include administrative costs of working an order as well as the opportunity costs of missed trades. In this section, we present changes in general cost drivers such as the time to fill a decision that impact the administrative costs of trading for institutions as well as changes in costs that can be more explicitly measured, namely commissions and price impact, around decimalization. 4.1 Changes in General Cost Drivers Figures 1(a) and 1(b) present a graphical view of the average time to fill a decision and the average number of brokers 13 employed to do so classified by decision complexity: easy, moderate and difficult. In particular, decision complexity is based on the decision volume as a percentage of the average daily dollar volume in September Our relative measure makes it easier to compare cost drivers over time, especially when average decision size has been falling. This follows because a 10,000 share order (say) may be easy to execute during a period when average daily volume is 500,000 shares relative to when the average daily volume is 50,000 shares. Thus, if average daily volume declined along with average decision sizes (as is the case in the periods we examine), then even smaller decision sizes could be difficult to execute. We classify decisions below the 33 rd percentile (0-0.39%) as easy decisions; decisions between the 33 rd and 66 th percentile ( %) as moderate, and decisions above the 66 th percentile (>3.06%) as difficult. We find little difference in the time to fill an easy decision over the three periods we examine. While it took, on average, 1.71 days to fill an easy decision in Q1-2000, it takes 1.77 days and 1.80 days during BEFORE and AFTER, respectively. The moderate decisions take slightly longer (2.65 days) to execute AFTER relative to BEFORE (2.33 days) though considerably shorter than in Q (3.21 days). In contrast, while it takes 5.63 days to fill a difficult decision in the BEFORE period, it takes 6.62 days to fill a similar decision in the AFTER period, smaller than 7.17 days taken in Q We find lesser variation in the number of brokers used across the three periods. Thus, for example, while the institutions use, on an average, 1.55 brokers BEFORE and 1.60 brokers AFTER for easy decisions, they use 4.57 brokers and 5.09 brokers over the same periods for the more difficult decisions. In sum, institutions appear to have to wait longer and use more brokers to fill their decisions after 13 Note that here we use the number of individual brokers in our data, and not the broker types (as used later in the analysis) that were used to fill a decision. 9

12 decimalization than before, but whether this increased time of order execution affected their direct trading costs remains an empirical question. We address this question below. 4.2 Changes in Commission and Price Impact Unlike commissions, the price impact of a trade the deviation of the transaction price from the unperturbed price that would prevail had the trade not occurred is arguably more difficult to measure. Much depends on the proper identification of the unperturbed price. In particular, our measure should be such that it is least influenced by the trade itself. Keim and Madhavan (1996) discuss the importance of this issue in great detail. We use two measures that are popular with both academics and practitioners alike for the unperturbed price: (1) the closing price of the stock on the day prior to the trading decision (Measure I); and (2) the valueweighted average price (VWAP) across all trades, both by the institution as well as by others, over all days over which the decision was executed (Measure II). 14 Most studies on institutional trading costs use Measure I for it is the purest form of the unperturbed price one could measure. Though its importance is diminished by the fact that traders often can and do trade after hours, it still remains the most popular benchmark to measure price impact. Perold (1988) popularized the use of Measure I (also called implementation shortfall) while Berkowitz, Logue and Noser (1988) popularized Measure II based on the notion that no single trader can influence the value-weighted average price of all trades during a day. It is widely used in practice both for cost measurement and trader evaluation though its use can eliminate incentives to seek out better executions. It should be underscored that VWAP costs should come out systematically lower than costs measured with other benchmarks (like benchmarking against previous day s close) because typically brokers/institutional money managers are aware that they are being evaluated against it and always trade as closely as possible to the VWAP. Our price impact measure is computed as the percentage deviation of the value weighted average trade price for each decision from the unperturbed price. We multiply this deviation by 1 if the decision is a sale to ensure that it measures trading costs appropriately for buy and sell orders. Table 2 presents average commissions and price impact of institutional trades immediately before and after decimalization as well as one year before decimalization. We present costs both in terms of dollars per share and in basis points. This gives us a better picture of trading costs across periods when price levels have changed as well. If costs remain the same while prices fall from one period to the next, our basis point measure may look different even though the 14 For robustness, we also use other variants such as the VWAP across all trades on the first day after the decision was made; VWAP across all trades on the day of the last fill; the average of the closing price prior to the day of the decision; and the last fill price. Our results remain qualitatively similar regardless of the benchmark used. 10

13 underlying cost has not changed. The layout of Table 2 is as follows. We present costs for the full sample first (Panel A) and then partitioned by market capitalization (Panel B), Decision Complexity (Panel C) and Manager Style (Panel D). In the overall sample, trading costs have come down after decimalization both in terms of costs per share and in basis points. In particular, using Measure I, total costs (including commissions) AFTER (BEFORE) are $0.39/share ($0.44/share), while using Measure II, costs AFTER (BEFORE) are $0.06/share ($0.09/share). Both cost measures I and II show a statistically significant decline following decimalization although these costs are still refreshingly smaller than during the highs of Q ($0.89/share using Measure I). The story is the same with costs expressed in basis points. That is, total trading costs have declined to bp (AFTER) from bp (BEFORE) using Measure I and this decline is statistically significant. Similar numbers using Measure II are bp (AFTER) from bp (BEFORE). As before these costs are still smaller than during Q ( bp using Measure I). Hence, there is unambiguous evidence that institutional trading costs, however sliced, did not increase following decimalization. Since the emergent story in the overall sample using both cost measures (and in dollars per share) is the same, in subsequent Panels B, C, and D, we formally provide cost measures using only Measure I and in basis points. 15 The stocks in our sample below the 33 rd percentile in market capitalization (0 - $0.35B) are defined as small stocks; stocks with market capitalization between the 33 rd and 66 th percentile ($0.35B - $1.6B) as medium size stocks; and stocks above the 66 th percentile (>$1.6B) as large size stocks. 16 In panel B, we see that the decline in total trading costs permeates across stocks of all capitalizations. Thus, for example, total cost of trading in small stocks declines from bp to bp; in medium stocks it declines from bp to bp; and in large stocks it declines from to bp. Commissions increase across the board with the greatest increase in the small cap stocks. However, in spite of the increases, they are still significantly lower relative to Q In Panel C, we present results by decision complexity. Recall that we use decision size relative to the average daily volume in September 2000 to determine the difficulty in filling an institutional order. By this definition, easy trades cost less (down to 7.87 bp from bp) as do the difficult trades (94.83 bp from bp); moderate trades cost about the same. Once again, the AFTER numbers are all lower than the corresponding Q numbers. 15 The remaining cost calculations are available on request. 16 Market capitalization of the stocks in our sample is computed on the basis of their closing prices on the last trading day of September,

14 In Panel D, we see an interesting trend. Value managers trading costs have gone up following decimalization (35.61 bp from 5.40 bp) while growth managers costs have declined significantly over the same period ( bp from bp). Diversified managers whose trading styles encompass both growth and value managers styles also show an increase in trading costs following decimalization (43.71 bp from bp). Almost all costs are significantly lower relative to those during Q with the exception of value managers costs where there has been a significant increase following the move to decimals. This could be due to value managers increased use of alternative trading systems an issue we will address in greater detail later. In terms of commissions, the trend is toward an increase following decimalization. We believe this trend would be more visible in Nasdaq where several big brokerage houses have reverted to commissions to ensure revenue generation in recent times. 17 Overall, our results, through the various partitions related to stock size, degree of order execution difficulty and managerial investing style, indicate quite convincingly a significantly declining trend (both in dollar terms and in basis points) in institutional trading costs following decimalization. A reasonable interpretation of our findings is that institutions are doing significantly better after decimalization than before. It also appears that some of the decline in institutional trading costs may be related to market reforms and related activities unrelated to the event of decimalization. It is also possible that much of our results are driven by changes in the way institutions themselves use brokers following decimalization. We examine this issue in the following section. 5. Order Routing Decisions by Institutions 5.1 Usage Frequency by Broker Type The decision of the kind of brokers to route their trading decisions to is an important one for institutional money managers as it impacts the explicit and implicit trading costs incurred by them especially given the size of their orders. Conrad, Johnson and Wahal (2001a) explore routing decisions through soft dollar brokers, in particular, who are known to provide non-execution services such as data or macroeconomic forecasts in exchange for commissions. Here we provide a detailed look at frequency of usage, as well as trading costs, associated with the entire range of brokers, as provided by Plexus, around decimalization. In particular, Plexus classifies brokers into the five major categories See Nasdaq Traders Stumbling over Decimals, (Wall Street Journal, May 25, 2001, p. C1). 18 There is a sixth category, Execution Brokers, that was rarely used by Plexus clients during our sample period. 12

15 1. Alternate Brokers: This group includes electronic SuperDOT entry desks of full service brokers as well as crossing networks and ECNs. Examples include ITG-Posit and Instinet. 2. Full-Service Brokers: This group includes brokers who provide a menu of services including execution, portfolio management and data to their clients. Examples are Goldman Sachs, Merrill Lynch and Morgan Stanley. 3. Research Brokers: These brokers execute trades and provide in-house research, usually separately, to their clients. Examples include Hambrecht Quist, SG Cowen Securities, and Zacks. 4. Soft-dollar Brokers: They provide free research and other services usually in exchange for trades directed their way. Examples include Capital Institutional Services, Lynch, Jones and Ryan, and Pershing Securities. 5. Non-classified: A catch-all category used by Plexus to classify brokers it cannot place in the four above categories for a given decision. Brokerage arms of banks and insurance companies are usually classified in this group. We present the dollar value-weighted frequency of order routing decisions by institutions across the five broker types around decimalization in Table 3. Panel A lists the frequency of order routing decisions as well as average order sizes in the overall sample, while Panel B investigates the frequency of order routing decisions by further partitioning on the degree of difficulty of the decision for each broker type; Panel C does so by manager s investment style; and Panel D looks at the broker routing decision partitioned by order size. In particular, the three trade size classes are 0-1,000 shares (small), 1,001 10,000 shares (medium), and >10,000 shares (large). From Panel A, we see a small (but statistically significant) increase in the frequency of usage of alternate brokers following decimalization (from 4.15% to 5%) while the average order size executed through them has declined significantly (from 6,984 shares to 4,723 shares) over the same period. By contrast, a slightly different picture emerges for both the full service and independent research brokers, both of whom are being used more frequently, but with little changes in the size of orders routed to them, AFTER relative to BEFORE. Institutions also seem to route fewer orders to soft dollar brokers following decimalization (from 4.56% to 3.33%). Panel B presents evidence that alternate brokers are increasingly getting the easy to execute orders from institutions in a post decimal world. Specifically, alternate brokers are getting about double the percentage of easy orders AFTER relative to BEFORE (39.26% from 21.88%) and even significantly greater than the percentage going to them at the height of the boom in Q By contrast, both full service and independent research brokers are getting a significantly higher percentage of the difficult orders following decimalization. 13

16 Also consistent with the above discussion, the percentage of orders going to soft dollar brokers has declined sharply across the board. The increase in patronage of independent research and full service brokers and the decline of use of soft dollar brokers might suggest that institutions are increasingly favoring unbundling of research and other services from execution quality making it easier to evaluate trading costs. The increased patronage of alternate brokers while the average order size handled through them declines would also point toward institutions being increasingly cost conscious in a post-decimal world sending relatively smaller orders through these systems for quick and cheap execution. By contrast, institutions appear to be sending the more difficult decisions to the full service and independent research brokers. From this it is clear that there are distinct reasons why ECNs could coexist with traditional floor brokerage even for NYSE-listed stocks. While alternate systems guarantee fast executions at relatively low cost, full-service brokers provide the experience and expertise of working a large order that is often crucial to institutions. In Panel C, we see that, following decimalization, value managers are sending a significantly higher (lower) percentage of their orders through alternate (full service) brokers. Value managers are also using the independent research brokers more AFTER relative to BEFORE while significantly avoiding sending orders through soft dollar brokers in the wake of decimalization. At the other extreme, growth managers are using alternate brokers significantly less AFTER relative to BEFORE. But unlike value managers, their usage of full service and independent research brokers appear to be statistically similar following decimalization relative to BEFORE. However, they too appear to be avoiding soft dollar brokers after decimalization. Diversified managers trade execution behavior appears to lie in between the value and growth managers, as we would expect. Finally, in Panel D, we provide broker usage by shares routed: 0-1,000 shares (small size orders); 1,001-10,000 shares (medium size orders); and > 10,000 shares (large size orders). Our results here are broadly in line with results from other panels. Overall, we can reasonably conclude that there appears to be a significantly increased awareness toward cost savings in a post decimal world. Alternate execution systems are widely favored for executing easy orders while full service and independent research brokers are increasingly getting more of the difficult orders following decimalization. Institutions also appear determined to separate execution services from other services, such as research, that brokers offer. Our findings suggest that institutions are actively managing their brokers, something that may not have been widely practiced in 1997 following the move to sixteenths. 5.2 Trading Costs by Broker Type Evaluating trading costs by broker type is a tricky exercise. This is because brokers specialize in handling different types of order flow. Some orders may be easy to fill even though they may be large while 14

17 smaller orders may sometimes be difficult to execute under certain market conditions (like buy orders in a rising market or sell orders in a falling market). Moreover, orders are often times routed to electronic markets, such as Instinet, only after ensuring that adequate liquidity is available which is often not possible for orders routed to traditional brokers. Though it is difficult to control for all order flow characteristics in measuring trading costs, we use a simple yardstick that is well recognized by traders size of orders routed. We hope to capture the relative advantages of different brokers by examining their trading performance of different size orders. In Table 4, we present our results on total trading costs computed by using Measure I and classified by broker types and, within each broker type, by order sizes (small, medium and large) categorized as before. All our cost measures represent dollar value-weighted averages within each size category. Full service brokers show a decline in trading costs across the board for small, medium and large size orders although the decline is statistically significant for small (from bp BEFORE to bp AFTER) and large size trades (from bp to bp) only. The combination of upstairs market presence and their clout on the trading floor appears to make it attractive for institutions to use full service brokers following decimalization. For alternate brokers, costs have declined in small and medium trade size classes although this decrease is not statistically significant. For large size trades, however, costs have increased significantly following decimalization. Traders generally do not send large orders to electronic systems unless they are sure to trade. Given that displayed liquidity may have declined following decimalization in these systems, it is not surprising that large trades incur high costs. However, these systems provide fast executions that may explain why institutions continue to route large orders there despite high costs. Finally, independent research and soft dollar brokers display a significant change in their trading costs, but only in large size trades, with trading costs increasing for independent research brokers and decreasing for soft dollar brokers following decimalization. Given that several factors influence the choice of the broker, our univariate results only present part of the picture. We therefore present a more robust multivariate analysis below. 6. Multivariate Analysis 6.1 A Simple Regression Model of Total Trading Costs In this section, we present a simple regression model that controls for order characteristics and market conditions that could influence changes in institutional trading costs as well. Specifically, we estimate the following model for each stock that has been traded by Plexus clients around decimalization: TC i, j = α i + β i Afterj + δ i X i, j + ε i, j (1) 15

18 where TC i,j is the total cost (commissions plus price impact) of executing order j (in basis points or dollar per share) in stock i. We compute price impact using the closing price prior to the day the decision was made (Measure I) as our benchmark. After j is an indicator variable equal to one if order j is released after decimalization on January 29, 2001, (the variable of interest), and X i,j is a vector of control variables associated with order j in stock i that are related to order, manager and market characteristics. For the purpose of these regressions, we do not include data from the period Q For ease of comparison, our control variables are the same as those used in Jones and Lipson (2001, Table 6) who, in turn, use variables similar to those used in Keim and Madhavan (1997). More specifically, these variables are: Buy, an indicator variable that equals one if the decision was to buy; NYSEVol, the total NYSE market volume in billions of dollars on the day before the decision was made; LogDecisionSize, the logarithm of decision size in shares; Growth, an indicator variable that is one if the decision was made by a growth manager and zero otherwise; Value, an indicator variable that is one if the decision was made by a value manager and zero otherwise; Lag2DayRet, the average of two-day return percentage prior to the decision date multiplied by 1 if the decision was to sell; InvPrice, the inverse of the price at close on the day before the decision was made; and Range, the percentage of the difference between the high and low price over the price at close on the day prior to the decision date. As discussed in both Jones and Lipson (2001) and Keim and Madhavan (1997), these variables control for order characteristics and market conditions quite well. Table 5 presents the cross-sectional means of the coefficients from individual stock wise regressions in the whole sample. We estimate the regressions where the dependent variable, the total trading cost associated with a decision, is expressed in basis points as well as in dollars per share. To have enough degrees of freedom to run our firm-by-firm regressions, we require each firm to have arbitrarily at least 20 trading decisions. The After dummy is negative, but statistically insignificant, implying that decimalization appears to not have significantly impacted institutional trading costs. Among the control variables, LogDecisionSize is positive and significant implying that larger orders cost more to execute; Growth managers are paying more in trading costs while value managers costs have not significantly changed over the sample period. This finding is not surprising given the aggressive trading style of growth managers relative to value managers. InvPrice is positive and significant implying that lower priced stocks cost more to transact. Finally, Range is negative and significant implying that executions are more expensive in volatile markets. In sum, we find, after controlling for other factors, a marginal decrease in trading costs after decimalization that is not statistically significant. The implication is that after controlling for the standard determinants of institutional execution costs, the act of decimalization itself has not had a significant impact on trade execution costs. Most importantly, there is no evidence of an increase in institutional trading costs following decimalization. This flies in the face of popular wisdom claiming that liquidity has dried up outside 16

19 the best bid and offer prices thereby leading to costlier executions especially for large institutional orders in the wake of decimalization. 19 This is an important finding that should be significant to policy makers debating the merits of this historic decision amidst demands of a rollback from the buy-side. We now turn to several robustness checks to ensure the validity of our conclusions. 6.2 Changes in the Unexplained Component of Trading Costs It is, of course, possible that the liquidity environment post-decimals has changed in a fundamentally complex manner that is unable to be captured in our admittedly simple regression framework above. In addition, it is possible that any change in trading costs around decimalization may have been a part of changes in institutional strategies that may have been in place well before decimalization. To address these issues, we adapt the intuitive approach of Jones and Lipson (2001) and examine the difference between actual and detrended expected trading costs pre- and post-decimalization. This approach is akin to creating a matched sample of orders (based on some exogenously defined criteria) over the pre-decimal and post-decimal periods and then examining the dollar weighted change in execution costs net of any possible time trend. To operationalize the above intuition, we use all pre-decimalization data (i.e., by including both the Q and BEFORE data) to estimate the following model for each stock: TC, i, j = α i + β i BEFORE j + δ i X i, j + ε i j where TC i,j is the total cost (commissions plus price impact) of executing order j (in basis points) in stock i. BEFORE j is an indicator variable equal to one if order j is released BEFORE and X i,j is a vector of control variables associated with order j in stock i that are related to order, manager and market characteristics. Each included stock has to have at least 20 completed decisions to be included in the analysis. Notice that β i represents the overall trend in trading costs between the period Q and BEFORE that is unexplained by order, manager and market characteristics. If this trend were approximately linear, then the expected change in costs per quarter would simply be β i /3, as there are three quarters between Q and BEFORE. The expected trading cost for trades AFTER would, therefore, be ˆ α + ˆ β *(4 / 3) + ˆ δ, if we assume that the time trend in the decline in trading costs continues AFTER. 20 i i i X i, j 19 To ensure the robustness of our conclusions, we also re-estimate the same model with the benchmark price being measured as the VWAP across all trades over all days over which the decision was filled (i.e., Measure II). These results are qualitatively similar and are not presented. 20 Though we report our results using the method of de-trending described above, our results remain qualitatively similar without the de-trending measure. We omit a formal presentation of these results for brevity. 17

20 The difference between the actual and the expected trading cost (or residuals) for decisions placed BEFORE and AFTER represents the trading cost unexplained by order, manager and market conditions, as well as by any time trend, that persists in our cost measures. We average these residuals using dollar-volume weights for each day in the period. If there is no change in the trading environment BEFORE and AFTER, then the residuals should be distributed identically in the two periods under examination and the weighted average change in these residuals over the two periods should be zero. This is what we test in our data. The interested reader is referred to Jones and Lipson (2001, p ) for other relevant details and justification of assumptions. Table 6 provides the results. Under each change in total trading costs by trade size, we also report the corresponding number of stock-days for which there is a relevant Plexus client trade. Under the assumption that execution costs are independent across firms and across days, the number of stock-days can be thought of as the number of independent observations in the sample. From Table 6, we see that for the overall (non-partitioned) sample, the unexpected trading cost has declined by a statistically significant 28 bp. Once again, this result is consistent with our earlier findings, and provides us with significant comfort about the robustness of our findings and resultant conclusions. Execution costs also are cheaper by 24 bp for the small orders while for the intermediate and large size orders the costs are cheaper by 21 bp and 28 bp, respectively. Upon partitioning the data by broker types, we see an increase in trading costs associated with alternate and soft dollar brokers, although the specific magnitude of the costs might have to be interpreted with some caution given the relatively small sample size (in terms of stock-days) associated with such brokers. Not surprisingly, given our dollar value-weighting scheme for computing averages, most of the increase for these broker types is driven by increases in costs for the large orders. In contrast, the cost associated with full service brokers shows a statistically significant decrease of 47 bp after decimalization. We see a decrease, ranging from 62 bp for small orders to 48 bp for large orders, in each of the three size categories for full service brokers. Independent research brokers show a decline as well though their results are hampered by smaller sample sizes. In sum, alternate and soft dollar brokers show an increase in trading costs while full service and independent research brokers show a decline in total trading costs following decimalization. 21 More importantly, our analysis of pre- and post-decimalization residuals yields results consistent with the relatively simpler regression analysis of trading costs as a function of the decimal dummy variable (After) and control variables. 21 While our univariate analysis (see Table 4) reveals that trading costs associated with soft dollar brokers appear to have declined following decimalization, the much more robust multivariate methodology where we control for a number of factors, including time trend, in fact reveal that their costs have actually increased. The latter result has to be interpreted with caution, however, given the relatively small sample size. 18