Entry, exit and trading profits: A look at the trading strategies of a proprietary trading team

Journal of Empirical Finance 12 (2005) 629 – 649 www.elsevier.com/locate/econbase

Entry, exit and trading profits: A look at the trading strategies of a proprietary trading team Ryan Garvey a,b,*, Anthony Murphy c,1 a b

A.J. Palumbo School of Business Administration, Duquesne University, Pittsburgh, PA. 15282, United States John F. Donahue Graduate School of Business, Duquesne University, Pittsburgh, PA. 15282, United States c University College Dublin, Belfield, Dublin 4, Ireland Accepted 20 October 2004 Available online 19 September 2005

Abstract We examine the behavior of a 15 strong proprietary stock trading team and show how consistent intraday trading profits were generated. The team, who worked for a large US direct access trading firm, executed over 96 thousand trades in 3 months in 2000. Profitable intraday trading occurred in an anonymous dealer capacity, on both long and short positions, especially when volume and price volatility were higher. The traders rapidly entered long (short) positions when the number of dealers and size become greater on the bid (offer) side of the spread. Profits were taken early against the trend. D 2005 Elsevier B.V. All rights reserved. JEL classification: G19 Keywords: Proprietary trading; Trading strategies; ECNs; Nasdaq

1. Introduction Active traders account for just under one third, a very significant portion, of the trading volume on US markets, split roughly 50:50 between proprietary (professional) and retail * Corresponding author. John F. Donahue Graduate School of Business, Duquesne University, Pittsburgh, PA. 15282, United States. Tel.: +1 412 396 4003; fax: +1 412 396 4764. E-mail addresses: [email protected] (R. Garvey), [email protected] (A. Murphy). 1 Tel.: +353 1 716 8212; fax: +353 1 283 0068. 0927-5398/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jempfin.2004.10.002

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day traders.2 Firms that cater to active traders continue to attract new customers (McGinn, 2003), and bbrokerage firms are spending feverishly to attract and retain this lucrative segment of the marketQ (Patel, 2002). The principle characteristic that distinguishes an active trader from the traditional investor is their mind-set. Many active traders hold stocks for minutes or hours, seldom overnight, closing out positions for small profits. Despite the importance of active traders, blittle is known about their trading strategiesQ (Barber and Odean, 2001). In this paper, we focus on active proprietary traders. Garvey and Murphy (2004a) highlight several differences between retail and proprietary traders. First, retail traders trade their own capital and bear all of their profits or losses. Proprietary traders, who are hired by a firm, trade the firm’s capital. They usually pay for their losses and receive a percentage of their net profits. Second proprietary traders pay very low commissions. On average, their round trip commissions can be less than 10% of the commissions paid by retail traders. This leads to differences in trading behavior (Garvey and Murphy, 2004b). Third, retail traders need not be licensed while proprietary traders are required to pass the Series 7 licensing exam. Fourth, retail traders are governed by strict margin requirements whereas proprietary traders often receive preferential margin treatment.3 Finally, retail traders receive no formal training, whereas proprietary traders are supervised and generally receive continuous advice and training regarding trading strategies and techniques. The purpose of our paper is to examine the behavior of a proprietary stock trading team and show how consistent intraday trading profits were generated. Understanding how professionals trade is of interest to academics, practitioners, and regulators alike for several reasons. First, professional traders can have a large effect on stock prices because they continuously trade large amounts. Small retail investors, the focus of a lot of research in behavioral finance, generally have a much smaller effect on prices. Second, professional traders are a significant supplier of liquidity on alternative trading systems, such as Electronic Communication Networks (ECNs).4 Third, the professional traders we examine were able to generate consistent intraday trading profits in, what are generally considered to be, highly efficient markets. We look at the relationship between the trade and market conditions and profitable trading. Inter alia, we find that profitable intraday trading occurred in an anonymous dealer capacity, on both long and short positions, especially when volume and price volatility were higher. The proprietary traders based their trading decisions on very short-run order flow and price information and not fundamental information about the stocks they traded.

2 Bear Stearns (2003) finds that 30,000 active traders, individuals conducting more than 25 trades per day, account for 32% of Nasdaq/NYSE share volume. 3 This treatment is subject to the firm’s agreement with the clearing broker, provided minimal net capital requirements are adhered to. Firms typically engage in a joint back office (JBO) arrangement with the clearing broker in order to avoid margin regulations. Under these arrangements, credit can be extended for up to 100% of the purchase price of securities. These margin privileges are discussed in SEC (2000) and in Section 17 of the NASD Manual. 4 ECNs account for nearly half of the trading volume in Nasdaq listed stocks, or two and half times as much as Nasdaq’s own trading system (Borrus and Dwyer, 2003).

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We also look at when the proprietary traders entered and exited a position. The traders entered a long (short) position when there were more dealers and size at the inside bid (offer) than the inside offer (bid). Price changes before and after an opening trade show that the traders were usually not the first to recognize a short-run price trend. However, they responded fast enough to the trend so that they were able to profit from it. If a trade was profitable, the traders usually closed out the position early, before the trend reached its peak. When faced with a rapidly moving, adverse price trend, the traders tended to close out the position quickly using a marketable limit order. The outline of the paper is as follows. In Section 2, we review previous research and discuss the contribution of our paper. In Section 3, we describe our unique dataset; how we matched it up with the Nastraq quote and inside datasets and how we used it to calculate profits per round trip. Our analysis is in three parts. In Section 4, we examine the overall strategy of the traders, as well as profits of the individual traders and profits on the twenty most heavily traded stocks. In Section 5, we discuss when, where and how the traders were most profitable. Trade entry and exit strategies are examined in Section 6. Finally, we conclude in Section 7.

2. Previous research A small literature looks at the trading profits and strategies of traders using the Small Order Execution System (SOES) for their trading; see Harris and Schultz (1997, 1998) and Battalio et al. (1997). An SOES order is a market order that attempts to execute against Nasdaq market maker quotes, setting the inside spread.5 Before the implementation of the SEC order handling rules in 1997, individual day traders had few order-routing options. The SOES order was the preferred method of choice, and thus, day traders became known as SOES bandits. Where we differ from Harris and Schultz (1998) is that we examine proprietary trading strategies in a hybrid as opposed to a dealer market. As a result of the new order-handling rules introduced in 1997, the structure of Nasdaq changed from a dealer market, where market makers compete with each other for order flow, to a hybrid market. ECNs were developed and integrated into the national market system. In this hybrid market, active traders can place their own limit orders on ECNs and compete with market makers for order flow. As a result of these changes, the trading strategies of day traders also changed. Harris and Schultz (1998) found that SOES bandits tended to generate profits by consuming liquidity, through SOES or SelectNet, on both opening and closing positions. Our findings are the opposite of these. The proprietary traders profited by providing liquidity (competing for order flow) on ECNs, in particular the Island ECN. A majority of both opening and closing positions occurred by bidding/ offering through ECNs, as opposed to trading with market makers. Recognizing the differences in market structures faced by proprietary day traders operating in a hybrid market and SOES bandits operating in a dealer market is important

5

Nasdaq’s implementation of its new trading platform, Supermontage, resulted in the phasing out of SOES orders around the end of 2002.

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because it connects our study to existing research on ECNs. Research shows that both ECN quotes (Huang, 2002) and ECN trades (Barclay et al., 2003) are highly informative. Unfortunately, these studies have not shown the source of these quotes and trades, the reason being that traders on ECNs are anonymous. Almost anyone can trade through an ECN, including buy-side traders, individual investors, market makers, and broker–dealers. However, using our proprietary trading dataset, we can show who is responsible for some of these informative quotes and trades.6 In addition, we are able to identify some of the profitable trading strategies employed by the traders. For instance, we examine behavioral decisions such as when to open and close a position, as well as market trends before and after opening and closing a trade. The entry and exit decision results are, possibly, the most novel aspect of our paper.

3. The data, data matching and profit calculations The core data for this study are the trading records of a 15 strong proprietary trading team at a large, well known, US direct access broker. The trading firm was randomly selected for this study. The firm, which is a member of the National Association of Security Dealers (NASD), caters to both proprietary and retail traders. The data were obtained on site directly from the brokerage house database, ensuring data completeness and eliminating any possibilities of data tampering.7 The data cover the period March 8 through June 13, 2000.8 During this period, the US stock markets were open for 68 days and there were two market holidays. In total, the data consist of 96.3 thousand trades and 58.8 thousand round trips, involving approximately 119 million shares. The 15 traders accounted for approximately 0.1% of Nasdaq share and dollar volume. Eleven proprietary traders worked for the firm on March 8th, while thirteen traders worked for the firm on June 13th. In total, only 15 proprietary traders were employed by the firm in the 3-month sample period. The data are in the form of a transaction database. For every trade, we know the identity of the trader, the time the order was filled on the relevant exchange, the order type (limit order, stop limit order etc.), the action taken (buy, sell, short, or cover), the volume, the price, the location of the trade, the contra party on the trade, and the number of parties on the other side of the trade. The average trader in our study traded 115 times per day. The average trade involved about 1200 or so shares, costing over $42 each and was valued at about $51 thousand. The average closing trade size was a little smaller, at 1000 or so shares. Round trips involved a mean (median) average absolute price change of 8 12 (6 14 ) cents and duration of 3 14 (1 14 ) min. The traders primarily traded Nasdaq stocks using limit orders on the Island ECN. 6

Our data sample covers much the same time period and many of the same stocks examined by Huang (2002) and Barclay et al. (2003). 7 We were provided with the data shortly after contacting the firm. We did not request data from other trading firms and we did not know whether the traders were actually profitable until we analyzed the trades. 8 The sample runs for approximately 3 months because the firm changed their computer systems on March 8, 2000, and could not readily retrieve trading records for earlier periods.

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Table 1 ECN trades and the NBBO (20 stocks)

ECN Buys ECN Sells

Below best bid (%)

At the best bid (%)

Within the NBBO (%)

At the best ask (%)

Above the best ask (%)

No. of trades (000s)

14.2 2.8

46.4 3.8

29.7 26.7

5.6 51.1

4.0 15.6

23.9 29.1

This table shows where ECN orders executed in relation to the national best bid and offer (NBBO). Each ECN trade on the 20 most heavily traded stocks was matched with the corresponding inside spread taken from the Nastraq inside quote dataset. A majority (over 90%) of buys occurred below the best offer while a majority (over 93%) of sells occurred above the best bid.

Over 64% of the traders volume were executed by placing bids/offers through Island, 32% were executed by routing marketable limit orders to a market maker or ECN based on their level II quotes, 3% were sent to the floor of the NYSE/AMEX, and 1% was executed by placing bids/offers on an ECN other than Island.9 We linked the proprietary trader data to the Nastraq inside quote and dealer quote datasets, which contain all the inside quotes and individual dealer quotes during the intraday. The matching was based on the execution time (in seconds) for each trade. We were then able to relate the trade to market conditions (inside spread, depth, each market participant’s quote etc.) at the time of the trade. For the twenty most heavily traded stocks, we matched the prices of ECN trades with the inside quotes from Nastraq. The results in Table 1 show that the traders were able to obtain favorable (buys below the best offer and sells above the best bid) executions on over 92% of their ECN trades.10 Of course, not all of trades went through an ECN. When rapid upward or downward trends emerged, the traders paid for liquidity in order to ensure execution. We calculated gross (before commission) round trip profits or losses on a proper last in, first out basis. For every stock in a trader’s account, we matched opening trades with the subsequent closing trade(s) in the same day.11 The traders did not always open and close positions with two trades. Traders often laid off part of an open position or combined a closing transaction with an opening transaction. Regardless of whether the traders opened, closed, or simultaneously opened and closed a position, we searched forward in time each day until the opening position was closed out, keeping track of accumulated inventory and the corresponding prices paid or received. We were able to match all but 0.1% of the 96.3 thousand trades in this manner. We did not have actual commission data, so we could not calculate net (after commission) profits per round trip. However, we know that the firm typically charged the 15 proprietary traders between $1.50 and $3.00 commission per trade, depending on how 9 Nine ECNs were registered to trade Nasdaq stocks at the time of this study. Island and Instinet were by far the two most active ECNs in terms of quoting activity and share volume. During 2000, 53 billion shares were matched over the Island ECN, representing approximately 12% of Nasdaq trades and 6% of Nasdaq volume (based on discussions with Tim McCormick of the NASD). 10 These results are consistent with the results of researchers, such as Barclay et al. (1999), who find that the changes to the order handling rules in 1997 dramatically reduced trading costs. 11 Proprietary traders rarely hold positions overnight because of the increased price risk in doing so.

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they executed their order.12 The traders then kept 50% or more of their net trading profits. We were not told how net losses were distributed, but we believe that the traders bore all of the net losses. However, most of the traders in our data set were highly profitable. We also know that the traders were hired and fired on the basis of their performance. The firm would not reveal how much capital it provided to the proprietary team. However, we suspect that it was very little.13

4. Trading profits The 15 traders generated a total of $1.4 million profits before commissions. Table 2 shows the distribution of gross profits, before commissions, for all 58.8 thousand round trip transactions. Average profits per round trip trades were relatively small. Mean and median gross profits per round trip were $24.3 and $18.8 respectively. 62% of the round trips resulted in positive gross profits, 10% in zero gross profits and 28% in gross losses. Approximately 30% of round trips were for gross profits of between one cent and $50, while only 8% generated gross profits of $150 or more. Occasionally, the traders realized large trading profits—the largest was about $3200. However, more often then not, they traded frequently using very short-term information, earning relatively small profits per round trip. Frequent trading, combined with generally correct market timing decisions, led to sizeable profits at the end of the day. The distribution of holding times and mean round trip profits before commissions by duration are set out in Table 3. These clearly show that the traders were profiting from short-run movements in prices rather than fundamentals. Table 4 shows the gross trading profit on the 20 most heavily traded equities. These stocks accounted for over 90% of the traders’ volume and about 92% of their gross profits. The proprietary traders generally traded large capitalization Nasdaq stocks. Fifteen of the top 20 traded stocks were members of the Nasdaq 100 composite index during our time period. Dell and WorldCom were by far the most heavily traded, accounting for over 55% of all round trips. Mean and median gross profits per round trip trade were significantly different from both zero and the upper bound estimate of commissions per round trip ($4.92) in just about every case.14 Why did the traders prefer large capitalization, Nasdaq technology stocks? We believe that the proprietary traders were attracted to these stocks for several reasons. Large cap, Nasdaq tech stocks are liquid, have small spreads and tend to exhibit greater price volatility than many other stocks. As we show later on, greater price volatility is associated with higher profitability. The size of the companies also ensures that there are many market makers, which has advantages in terms of information, small spreads and liquidity. Proprietary traders often base their trading decisions on information gleaned from the 12

The firm earned between $0.14 and $0.29 million in commission from the proprietary traders over the sample period. The commission charge covered all execution costs as well as the costs associated with running a proprietary trading desk (clearing services, technology, connectivity fees etc.). 13 The SEC (2000) found that proprietary trading programs are highly leveraged during the intraday. 14 The $4.92 figure is obtained by taking the maximum commission (the number of trades times $3 commission per trade) and dividing it by the number of round trips.

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Table 2 The distribution of round trip trading profits Gross profit/loss per round trip

Number of round trips (000s)

Share of all round trips (%)

N$150.00 $100.01–$150.00 $50.01–$100.00 $00.01–$50.00 $0 $00.01– $50.00 $50.01– $100.00 $100.01– $150.00 V$150.00 Profitable Zero Profit Unprofitable

5.0 3.9 9.8 17.6 5.9 7.3 4.3 2.0 3.0 36.3 5.9 16.6

8 7 17 30 10 12 7 3 5 62 10 28

This table shows the distribution of gross trading profits, i.e., profits exclusive of commissions/trading costs, for the 58.8 thousand round trips in the dataset.

quote updates of the larger market makers, who make a market in large cap stocks. Spreads are tighter, ceteris paribus, the more market makers there are. This is important when bpaying the spreadQ with marketable limit orders.15 In addition, multiple market makers provide liquidity, the ability to buy or sell an asset quickly and in large volume without substantially affecting prices. This is essential for proprietary traders, who trade larger volumes, moving in and out of stocks in a few minutes. Table 5 shows, for each trader, the number of days they traded, the number of round trips, average profit per round trip as well as their gross trading profits. The number of days traded varies. For example, only one trader traded every day. Differences in the daily number of round trip trades per trader suggest that trading profits were realized in a number of different ways. Traders 3 and 13 both traded 65 days, yet trader 3 closed out almost 6800 more trades. Trader 1, who only traded 1 more day than trader 2, closed out about 3300 more trades. Some traders preferred trading larger sized trades every few seconds (often referred to as a grinding strategy), while others preferred trading a smaller number of shares every few minutes (often referred to as spread or range trading). Average gross profits per round trip are significantly different from zero for 13 out of the 15 traders. Twelve of the traders are clearly profitable after allowing for commissions. The large differences in average profit per round trip and total trading profits means that some traders were more skilled, or just luckier, at trading than others. Given that all the traders worked for the same firm, one could argue that their daily profits or average round trip profits ought to be highly correlated with each other. If we find high correlations amongst the daily returns or, more formally, if the first principal component accounted for most of the variation in daily returns, then one could argue that we are looking at a single trading style as opposed to a range of independent trades styles.

15

The traders used marketable limit orders for 39% of their opening trades and 23% of their closing trades.

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Table 3 Round trip durations and gross profits Duration of round trip

Share of all round trips (%)

Mean gross profit per round trip

Less than 1 min 1 to 2 min 2 to 3 min 3 to 4 min 4 or more min All durations

42.8 22.6 11.6 7.0 16.0 –

$30.5 $25.1 $22.4 $14.6 $12.3 $24.3

This table shows the distribution of round trip durations and mean gross profit per round trip by duration for all of 58.8 thousand round trips in the dataset.

The average correlation amongst the daily profits is about a quarter (mean = 0.26, median = 0.25, first quartile = 0.14 and third quartile = 0.40), which is not very supportive of the single trading style hypothesis.16 Almost all of the principal components of the correlation matrix of daily profits are significantly different from zero. The first principal component accounts for just over one third and the first five account for about 70% of the variation in daily returns across traders. Similar results were obtained when we looked at average profits per round trip. These results suggest that we are looking at a range of trading styles. However, we believe that we can identify important characteristics of profitable trades. Although the traders were highly profitable overall, is there any benchmark against which we can measure their relative performance? Unfortunately, the answer is no. We do not know the amount of trading capital that the firm put up for each trader, nor do we know their initial inventory levels. This means that we cannot estimate a benchmark or normal level of trading profits. However, the trading profits were obtained during a bearish market. Nearly all of the stocks traded were down in value over our sample period. The Nasdaq composite and the Nasdaq 100 composite indices, which include the stocks which the day traders traded the most, were down 23% and 14% respectively during our 68-day trading period. Dell and WorldCom fell by approximately 2% and 6% over this period. Clearly the traders in our sample did not benefit from an upward trending market. Neither did they particularly benefit from going short in a downward trending market. As we show in the next section, the traders traded off of very short-run volatility in prices rather than fundamentals. Did the traders profit by taking advantage of the more stringent requirements placed on Nasdaq market makers? Proprietary traders, who trade in a similar fashion to market makers, are not required to register to make a market in a stock and post bid and offer quotes at all times. However, these requirements on market makers are not very onerous. It is very easy and cheap for a market maker to enter or exit a market. A market maker can enter a stock following a 1-day registration period and exit from a stock having given half an hour’s notice. Although market makers are required to post both a bid and an ask but this does not mean they are required to trade. In fact, Ellis et al. (2002) find that most registered market

16

Missing values restricted the analysis to 13 traders. We could not include two traders who only traded 24 out of the 68 days in our sample.

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Table 4 Round trip profits by stock Stock

No. of traders

Share of Nasdaq volume (%)

No. of round trips (000s)

Mean gross profit per round trip

Median gross profit per round trip

Total gross profits (000s)

DELL WCOM ORCL PAGE TCLN ERICY GBLX ATML MSFT CSCO COMS NOVL ADCT ICOM CNTR MCLL PMTC IFMX EGRP ICGE All other stocks

12 14 8 9 8 10 11 3 12 9 7 11 6 6 4 3 7 10 8 3 15

1.8 2.0 0.4 2.8 2.7 0.7 0.5 1.8 0.2 0.1 0.4 0.7 1.0 0.8 1.7 1.3 0.3 0.3 0.2 0.4 –

15.6 16.4 2.7 1.6 1.4 2.0 2.4 1.7 1.8 1.0 1.0 1.0 0.8 0.3 0.3 0.4 0.4 0.4 0.6 0.3 6.4

$27.1*+ $17.6*+ $39.7*+ $42.1*+ $65.9*+ $26.0*+ $19.9*+ $35.7*+ $11.0*+ $6.9 $20.5*+ $23.5*+ $14.6**+ $50.9*+ $54.3*+ $33.4*+ $25.2*+ $19.2*+ $15.4*+ $38.6*++ $18.7*+

$18.9*+ $18.8*+ $12.2*+++ $25.0*+ $23.4*+ $20.5*+ $21.5*+ $15.6*+ $15.6*+ $5.6* $7.8*+ $16.1*+ $11.4* $14.8*+++ $19.3*+ $12.5*++ $23.4*+ $23.1*+ $14.1*+ $23.3*++ $12.5*+

$423.3 $297.5 $107.1 $68.7 $94.3 $52.3 $46.9 $59.4 $19.4 $6.8 $20.5 $22.8 $11.7 $15.8 $17.8 $11.9 $9.1 $7.0 $8.6 $11.1 $119.4

This table shows, for the 20 most heavily traded stocks, the number of traders that traded the stocks, the percentage of Nasdaq share volume accounted for by the traders, the number of round trip transactions, mean profit per round trip, and the total gross trading profits. Stocks are listed in descending order, based on the total number of shares traded over the sample period. A t-test (sign rank test) is used to determine if the mean (median) profit is significantly different from zero and $4.92, the estimated cost of trading per round trip. *, **, ***Significantly different from zero at the 1%, 5%, and 10% levels respectively. + ++ +++ , , Significantly different from $4.92 at the 1%, 5%, and 10% levels respectively.

makers are there only in name.17 Therefore, we believe that it is highly unlikely that differences in exchange requirements accounted for the profitability of these traders. If profitable trading was not the result of an upward or downward trending market or differences in exchange requirements, how then did our day traders earn such high profits? In discussions with traders at the firm, they claimed to base their trading decisions on information derived from market maker quote updates, while gauging overall supply and demand in the market. This is very similar to what profitable SOES bandits did (Harris and Schultz, 1998). We return to this issue later on in Section 6 of the paper, when we examine dealer quote activity when positions were opened and closed. Market makers have private information concerning the large orders they are working for themselves, their clients, and other market participants based on the trading requests they receive. The proprietary 17 Whether or not a market maker trades depends on the aggressiveness of quotes and on their involvement in such practices as preferencing, whereby a smaller dealer pays larger dealers cash or provides service for the privilege of executing their orders.

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Table 5 Round trip profit by trader Trader ID

No. of days traded

No. of round trips (000s)

Mean gross profit per round trip

Median gross profit per round trip

Total gross profits (000s)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Total

67 66 65 68 67 60 66 54 65 44 24 47 65 24 54 836

9.4 6.2 7.7 5.8 4.8 3.9 7.7 2.3 1.1 3.3 0.7 2.4 1.7 0.6 1.2 58.8

$32.6*+ $37.6*+ $29.9*+ $27.0*+ $24.3*+ $26.8*+ $14.9*+ $25.8*+ $27.4*+ $9.2*+ $36.6*+ $4.4* $3.9 $7.6*++ $2.2 $24.3*+

$20.5*+ $13.2*+ $12.5*+ $24.9*+ $20.5*+ $23.4*+ $20.2*+ $0.0 $23.4*+ $16.6*+ $31.2*+ $9.0* $7.8*+++ $18.8*+ $14.1*+ $18.8*+

$306.4 $233.4 $229.3 $155.7 $116.8 $103.5 $115.6 $58.9 $31.2 $30.8 $25.8 $10.8 $6.6 $4.3 $2.6 $1431.6

This table shows the number of days traded, number of round trip transactions, mean and median gross (before commissions) profit per round trip and total gross profits for each trader. See the notes to Table 3. *, **, ***Significantly different from zero at the 1%, 5%, and 10% levels respectively. + ++ +++ , , Significantly different from $4.92 at the 1%, 5%, and 10% levels respectively.

traders in our sample did not have this private order flow information, but they were able to monitor the quote updates of Nasdaq market makers in real-time and identify profitable short-run price movements.18

5. The when, where and how of higher profitability In order to examine the factors associated with higher levels of profitability, we regressed the 58,835 round trip gross trading profits per share on a set of relevant explanatory variables, all of which are dummy variables. The regression results are a useful summary of the consistent patterns or regularities, which we find when we examine the performance of the traders. These regularities are captured by the coefficient estimates and standard errors on the explanatory variables in the regression. In addition, the regression results help us isolate the net or ceteris paribus effects of the individual explanatory variables on trading profitability. The explanatory variables are listed in Table 6. They consist of indicators for the time of day, high volume and high volatility day, closing trade size, long or short position, and 18 In a Wall Street Journal article (Ip, 2000), Kenneth Pasternack, former CEO of Knight Securities, Nasdaq’s largest market maker, stressed the informational advantages provided by the order flow data. bWe’re smarter than the market in aggregate and we’re able, therefore, to make a determination whether the stock will go up or down.Q To take advantage of this order flow information, Knight employed approximately 393 traders. Harris and Schultz (1998) also note the information advantage that market makers posses.

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Table 6 Trading profit by trade and market conditions No. of round Mean gross Mean gross Median gross Total gross trips (000s) profit per share profit per round trip profit per round trip profit (000s) Time Before the open 9:30–10:00 a.m. 10:00–10:30 a.m. 10:30–11:00 a.m. 11:00–11:30 a.m. 11:30–12:00 p.m. 12:00–12:30 p.m. 12:30–1:00 p.m. 1:00–1:30 p.m. 1:30–2:00 p.m. 2:00–2:30 p.m. 2:30–3:00 p.m. 3:00–3:30 p.m. 3:30–4:00 p.m. After the close

0.1 6.5 6.7 5.7 4.8 4.1 3.2 2.8 2.7 3.5 4.4 4.8 5.5 4.0 0.0

3.5c 4.6c 3.2c 2.4c 2.4c 2.3c 2.7c 2.6c 3.1c 2.8c 2.5c 2.4c 2.7c 3.2c 4.5c

$32.5* $41.0* $27.2* $20.4* $20.1* $22.1* $21.8* $18.4* $27.8* $21.8* $19.7* $17.1* $24.8* $24.7* $13.3

$20.9* $31.2* $21.9* $18.2* $18.0* $18.1* $13.3* $13.4* $14.5* $13.2* $15.6* $15.2* $16.9* $18.8* $0.0

$2.6 $267.3 $181.4 $115.5 $95.7 $90.3 $70.3 $52.2 $75.2 $76.7 $87.0 $82.6 $135.5 $99.6 $0.3

Nasdaq daily volume High (34 days) 31.9 Low (34 days) 26.9

3.4c 2.3c

$29.7* $17.9*

$20.8* $15.3*

$950.9 $481.6

Nasdaq daily volatility High (34 days) 31.6 Low (34 days) 27.2

3.1c 2.6c

$26.3* $22.0*

$18.8* $15.8*

$832.0 $599.6

Closing trade size Trade b 1000 25.4 1000 V Trade b 2000 25.5 Trade z 2000 7.9

3.6c 2.5c 1.9c

$15.4* $27.3* $43.5*

$11.7* $31.2* $46.9*

$391.5 $698.1 $342.0

Opening position Long Short

33.2 25.6

3.2c 2.5c

$27.8* $19.8*

$20.8* $15.6*

$924.0 $507.6

Order routing method Island 43.1 Marketable limit 12.9 Listed 2.5 Other ECNs 0.3

5.0c 4.4c 3.8c 5.3c

$47.9* $54.1* $24.2* $20.2*

$31.2* $31.3* $0.0* $6.2*

$2,063.0 $698.7 $61.3 $6.0

This Table relates trade and market conditions to the number of round trips and gross profitability per round trip. There were 58.8 thousand round trips. * Significantly different from zero at the 1% level.

order-routing method on the closing trade. Within trading hours, time of day is split into half-hour periods. Volume is calculated by taking the total share volume traded on Nasdaq each day. The 68-day sample period is then split in two into higher and lower volume days. Volatility is calculated by taking the difference in the Nasdaq composite index daily

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high and low values divided by the opening value of the index. The sample period is then divided into higher and lower volatility days. Closing trade size is grouped into closing trades of under 1000 shares, between 1000 shares and 1999 shares, and 2000 or more shares. Position refers to an opening long or short intraday position. Order routing is based on round trips that occurred on the Island ECN, on an ECN other than Island, on marketable limit orders and on the specialist exchanges. Data relating the number of round trips, mean and median profitability per round trip etc. to the trade and market conditions indicators are presented in Table 6. 5.1. Volume and volatility are essential The traders’ intraday volume, shown in Table 6, is consistent with market-volume trading patterns. It is heavy at the open, light during midday, and picks back up again towards the end of the day. In addition, the traders are more profitable during peak hours of trading, especially in the opening half-hour. Trading profitability steadily decreases during the day to a low in the 1:00 to 1:30 p.m. period and then picks back up again towards the end of the day. The results indicate how important the open is for profitable intraday trading. These findings are confirmed by the regression results in Table 7. For example, the estimated coefficient on the 9.30 to 10 a.m. period is large, positive, and statistically significant. It is the only time period coefficient that is both positive and statistically significant. If traders are more profitable during peak trading times during the day, then their profitability should also be higher when daily share volume is higher. To test this, we split our 68-day sample period in half into high and low volume days. In Table 6, the mean gross profit per round trip trade is $29.7 on high volume days as opposed to $17.9 on low volume days. Furthermore, the estimated high volume regression coefficient in Table 7 is positive and highly significant. One reason why the traders preferred, and were generally more profitable trading in, high volume periods is because they frequently sold rather than consumed liquidity. During these periods, they were able to execute more orders and profit from the spread. Higher volume often translates into higher price volatility. Therefore, we anticipate finding higher trading profits when Nasdaq volatility is higher. In any case, we know that the traders were trading off of very short-run price volatility. Table 6 shows that there were 4.4 thousand more trades, and that mean profit per round trip was $4.3 higher, on high volatility days. This raw effect also shows up in the regressions results in Table 7 when we control for other relevant factors. The estimated high volatility regression coefficient is positive and highly significant. 5.2. The trade-off between trade size and trading profits Average (mean and median) closing volumes were around 1000 shares.19 Table 6 shows that gross profits per round trip increased with closing volume/trade size. Mean gross profits per round trip were $15.4 for less than 1000 shares, $27.3 for between 1000 19

The 10th, 25th, 75th and 90th closing volume percentiles are 200, 500, 1000 and 2000 shares respectively.

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Table 7 Closing profit per share regression results Explanatory variable

Coefficient estimate

Standard error

t-Statistic

Intercept

2.843

0.722

3.94

Time 9:30–10:00 a.m. 10:00–10:30 a.m. 10:30–11:00 a.m. 11:00–11:30 a.m. 11:30–12:00 p.m. 12:00–12:30 p.m. 12:30–1:00 p.m. 1:00–1:30 p.m. 1:30–2:00 p.m. 2:00–2:30 p.m. 2:30–3:00 p.m. 3:00–3:30 p.m.

1.056 0.081 0.808 0.925 0.978 0.609 0.746 0.201 0.443 0.677 0.698 0.483

0.336 0.312 0.297 0.308 0.412 0.324 0.346 0.461 0.329 0.314 0.313 0.315

3.14 0.26 2.73 3.01 2.37 1.88 2.16 0.44 1.35 2.15 2.23 1.54

Nasdaq daily volume High volume

0.794

0.115

6.94

Nasdaq daily volatility High volatility

0.476

0.113

4.19

Closing trade size Trade size b 1000 1000 V Trade Size b 2000

0.858 0.368

0.162 0.141

5.30 2.61

Opening position Long

0.214

0.123

1.74

Order routing method Island Marketable Limit order Other ECNs

1.177 8.177 1.634

0.637 0.648 0.852

1.85 12.62 1.92

The regression is estimated using the 58.8 thousand round trips in the dataset. The dependent variable is gross profit per share, i.e., profit per before commission (in cents). All of the explanatory variables are dummy variables (see Table 6). The reference trade is a short trade of 2000 or more shares, placed after 3:30 p.m. (or outside of regular trading hours), on a low volume and volatility day, which is routed to a specialist exchange such as the NYSE. The mean and standard deviation of the dependent variable are 2.89c and 15.1c respectively. The equation standard error is 14.1c and the adjusted R 2 is 0.069. The standard errors are heteroscedastic consistent ones. A LM test of heteroscedasticity was insignificant. However, the Jarque–Bera test for normality and Ramsey’s RESET test of functional form were highly significant.

and 1999 shares, and $43.5 for more than 2000 shares. However, gross profit per share declined as size rose. Mean gross profit per share, in these three size categories, were 3.6c, 2.5c and 1.9c respectively. These results are confirmed by the regression results in Table 7. The estimated coefficient on closing round trip trades of less than 1000 shares is large, positive, and highly significant whereas that on trades of between 1000 and 2000 shares is smaller, positive and less significant.

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5.3. Short selling Profitable intraday trading occurs on both long and short positions. Of the 58.8 thousand round trips in our dataset, 33.2 thousand opened long and 25.6 thousand short. Mean profits were $27.8 for long positions and $19.8 for short positions. In Table 7, the estimated coefficient on a long position is small, positive, with a p-value of approximately 9%, indicating some small difference in trading profits between a long and short position, ceteris paribus. However, the traders are highly profitable in both cases. The preference of traders to go long rather than short, even in a downward-trending market, could be due to restrictions on short selling. On Nasdaq, a stock can only be sold short if the inside bid is up. Proprietary trading desks have sophisticated software that prevents the traders from violating this up-tick rule. 5.4. The importance of ECNs The results in Table 6 clearly indicate that profitable day trading mainly occurred on the Island ECN. Almost three quarters (73%) of the closing trades were routed to the Island ECN and over a fifth (22%) went to market makers. Mean and median round trip profits, before commission, were $47.9 and $31.2 using limit orders routed to Island. The corresponding marketable limit order losses were $54.1 and $31.2 respectively. Total gross profits were over $2 million when using Island limit orders whereas gross losses on marketable limit orders were $0.7 million. In the dataset, we are able to see the identities of the contra parties to these trades. The majority of the time, the contra parties were larger market making firms such as Morgan Stanley, Goldman Sachs, Merrill Lynch, Schwab Capital Markets, and Salomon Smith Barney. The regression results in Table 7 show the ceteris paribus effect of the closing order routing method on gross profits per share. The estimated Island coefficient is positive and statistically significant at the 10% level whereas the estimated coefficient on marketable limit orders is very large, negative, and highly significant at the 1% level. Why did the proprietary traders primarily trade (and profit) on ECNs rather than with market makers, as the SOES bandits in Harris and Schultz (1998) did? One reason is that ECNs allow traders to profit from the spread, as opposed to paying the spread. Because day traders seek to profit from small price changes, they try to avoid paying the spread as much as possible. Traders can also trade anonymously on ECNs and thus reduce the amount of information they reveal. In addition, the Island ECNs paid a rebate to suppliers of liquidity—other ECNs did not pay rebates during our sample period. As a result, a highly active trader could generate substantial revenue by placing quotes on the Island ECN as opposed to using alternative order-routing methods. This is the major reason why the traders showed a clear preference for routing their orders to Island as opposed to competing ECNs. Other reasons include Island’s liquid order book and reputation for reliability and speed of execution. Trading on the other side of the spread is one reason for the substantial losses occurred on marketable limit orders. However, this is only part of the story. In the next section, we find that, when faced with rapid moving adverse price trends, the traders tended to close out their position and cut their losses using marketable limit orders.

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6. Entry and exit point strategies By matching the proprietary dataset with the Nastraq dealer quote file, we are able to determine the number of dealers and size at the inside spread when the traders decided to enter and exit a position. The entry and exit results are displayed in Tables 8 and 9.

Table 8 Entry strategies

Marketable limit orders Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic) All ECN trades Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic) ECN trades within NBBO Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic) ECN Trades At NBBO Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic) ECN trades outside NBBO Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic)

Opening a long position (buy)

Opening a short position (sell)

No. of Mean trades no. of (000s) dealers

No. of Mean no. trades of dealers (000s)

Mean size (000s)

8.4

Mean size (000s)

5.9 7.1

13.3

3.0

3.4

3.1

3.3

7.0

10.1

4.0 (70.7)

10.0 (44.9)

9.4

4.0 ( 43.5)

6.7 ( 33.6)

9.3 6.4

10.1

4.8

6.4

4.7

6.5

5.6

7.4

1.7 (29.4)

3.6 (15.6)

4.5

0.8 ( 14.2)

1.0 ( 7.0)

3.2 7.7

14.1

3.8

4.9

4.0

5.2

7.3

10.1

3.7 (43.4)

8.9 (22.1)

3.9

3.5 ( 32.1)

5.2 ( 20.7)

5.4 5.9

7.1

5.3

7.0

5.3

7.6

5.2

6.5

0.6 (6.5)

0.5 ( 2.0)

0.9

0.1 (1.6)

0.5 (2.7)

0.8 2.5

2.9

5.7

8.6

5.4

8.2

2.2

2.6

2.9 ( 18.5)

5.3 ( 14.9)

3.5 (20.8)

6.0 (1.3)

This table shows the average number of dealers and size at the inside when the traders opened up their positions on the 20 most heavily traded stocks. ECN trades are further disaggregated into those within, at or outside the National Best Bid and Offer (NBBO). A trade is within the NBBO if the shares were bought for more than the NBB or sold for less than the NBO; it is at the NBBO if the shares were bought at the NBB or sold at the NBO; it is outside the NBBO if the shares were bought for less that the NBB or sold for more than the NBO.

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Table 9 Exit strategies Closing a short position (buy)

Closing a long position (sell)

No. of Mean no. trades of dealers (000s)

No. of Mean no. trades of dealers (000s)

Marketable limit orders 5.8 Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic) All ECN trades 17.0 Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic) ECN trades within NBBO 5.9 Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic) ECN trades at NBBO 8.3 Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic) ECN trades outside NBBO 2.9 Dealers and size at the inside bid Dealers and size at the inside ask Difference (t-statistic)

Mean size (000s)

Mean size (000s)

5.9 6.2

10.9

3.8

4.3

3.5

3.5

6.0

10.5

2.7 (41.9)

7.4 (41.7)

2.2 ( 33.9)

6.2 ( 30.1)

21.7 5.0

6.1

5.8

9.3

5.6

7.3

4.8

6.4

0.6 ( 14.2)

1.2 ( 12.3)

1.0 (27.7)

2.9 (23.3)

7.3 6.0

8.9

5.0

7.5

4.8

6.0

5.9

8.9

1.2 (21.7)

2.9 (13.7)

0.9 ( 12.8)

1.4 ( 5.8)

10.5 4.9

5.2

6.1

9.2

6.0

7.8

4.8

5.9

1.1 ( 19.1)

2.6 ( 22.1)

1.3 (25.6)

3.3 (19.9)

3.8 2.9

2.9

6.6

12.9

5.7

8.6

2.7

3.1

2.8 ( 31.3)

5.7 ( 29.5)

3.9 (49.9)

9.8 (35.7)

This table shows the average number of dealers and size at the inside when the traders closed their positions on the 20 most heavily traded equities.

6.1. Opening trades and imbalances at the inside When the traders entered a long position, there was a substantial greater number of dealers and size at the inside bid than at the inside ask. For instance, when traders entered a long position via a marketable limit order there were, on average, 7.1 dealers with 13.3 thousand shares at the best/inside bid and 3.1 dealers with 3.3 thousand shares at the best/ inside ask. The corresponding figures for long positions opened via an ECN are 6.4 dealers on average at the inside bid with an average size of 10.1 thousand shares versus 4.7 dealers on average at the inside offer with an average size of 6.5 thousand shares. These differences in the number of dealers and size at the inside bid and ask are highly significant. When the

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traders entered a short position, the imbalance went the opposite way—there were significantly more dealers and size at the inside offer than at the inside bid. We segregate ECN trades by where they execute in relation to the inside spread. When the traders entered a long position, they were willing to pay more than the best bid if there was a significantly greater number of dealers and size at the inside bid rather than the ask because this suggested that the price was upward trending. When there was only a small imbalance between the number of dealers and size at the inside bid and ask, the traders tended to enter a long position at the best bid, intending to mainly profit from the spread (market making). The small number (5%) of long trades executed below the best bid, occurred when there was more demand (dealers and size) at the inside ask than at the inside bid.20 The SOES bandits in Harris and Schultz (1998) opened up positions differently. They always opened long and, nearly always, used market (SOES) orders. Perhaps more importantly, though, were the market conditions when the bandits opened up trades. Harris and Schultz (1998) suggest that there were generally more dealers on the inside offer than the inside bid, which is the opposite of our findings. In order to test the robustness of our results, we measured the trend of the market before and after each position were opened. Because the traders were highly profitable, we expect the trend to be bullish when they opened a long trade and to be bearish when they opened a short trade. The results for opening long positions in Table 10 confirm these priors.21 When the traders entered a long position, via a marketable limit order, the price rise was far greater than when they traded on an ECN. This is consistent with our findings on the number of dealers and size at the inside price. When the traders opened long with a marketable limit order, the average price rise was 1.8 cents at +15 s, 3.0 cents at +30 s, and 3.8 cents at +45 s. The traders were willing to pay for liquidity and rapid order execution, at or above the best offer, because the large size imbalance at the inside bid suggested that large price rises were likely in the short run. When the traders entered a long position through an ECN, on average, the price rose 0.1 cents at + 15 s, 0.1 cents at + 30 s, and 0.4 cents at + 45 s. The smaller size imbalance at the inside bid suggested that small price rises were likely. In this case, the traders benefited more from the spread than from the trend.22 6.2. Closing trades In the case of closing trades, the traders generally used marketable limit order when the position moved rapidly against them. Obviously, there was a tradeoff between preventing further losses and paying the spread. Table 9 shows that, when a marketable limit order was used to close a short position, on average, there were 6.2 dealers at the inside bid with 20

Possibly, the traders felt that the premium (best bid minus execution price) they received was sufficient compensation for any subsequent adverse price trend. 21 Using the Nastraq inside quote file, we calculated the midpoint price (bid + ask)/2 at time t, the time of the trade, and at times t F s where s is 15 s, 30 s, and 45 s. In Table 9, the midpoint price at time t is then subtracted from the midpoint price at time t F s. 22 When the traders entered a short position through an ECN, the subsequent price change was generally upwards rather than downwards. A majority of these trades occurred at the best offer, which suggests that the traders were seeking to profit more from the spread as opposed to the trend.

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Table 10 Price changes before and after a trade No of Mean change in bid/ask midpoint price at time t F s relative to trades (000s) midpoint price at time t P tFs P t t

45

t

30

t

15

t + 15

t + 30

t + 45

Opened a long position ECN 9.4 Marketable limit order 8.4

1.1cf* 3.6c*

0.8c* 2.7c*

0.3c* 1.4c*

0.1c* 1.8c*

0.1c* 3.0c*

0.4c 3.8c*

Opened a short position ECN 9.3 Marketable limit order 3.6

0.2c 0.2c*

0.1c* 0.7c*

0.1c* 0.9c*

0.2c* 1.4c*

0.3* 2.3*

0.8c* 2.7c*

Closed a long position ECN 21.7 Marketable limit order 5.9

3.2c 2.2c*

2.3c* 2.1c*

1.3c* 1.3c*

0.7c* 1.3c*

1.2c* 2.4c*

1.6c* 3.2c*

Closed a short position ECN 17.0 Marketable limit order 5.8

1.6c* 2.6c*

1.2c* 2.2c*

0.8c* 1.3c*

0.5c* 1.7c*

0.6* 2.8*

0.7c* 3.3*

This table shows the market trend before and after each position was opened and closed. The analysis of opening trades reveals whether or not the traders opened a long position when the market was upward trending and a short position when the market was downward trending. Mean price changes consistent with this pattern are displayed in bold. The analysis of closing trades reveals whether or not the traders closed long (short) positions when the market was still upward (downward) trending. Mean price changes consistent with this pattern are displayed in bold. All bid/ask midpoint price changes are in cents. * Significantly different from zero at the 1% level using a t test.

10.9 thousand shares and 3.5 dealers at the inside offer with 3.6 thousand shares. These imbalances suggested that prices were likely to rise quite sharply. We can confirm this by looking at the price change results before and after each trade in Table 10. The average midpoint price rises, for closing a short position using a marketable limit order, were 1.7 cents at + 15 s, 2.8 cents at + 30 s, and 3.3 cents at +45 s. When the traders were in a profitable trade, they generally closed the position on an ECN. The traders closed both long and short positions before the trend had reached its peak, in order to guarantee execution and to profit from the spread. This can be seen by observing the number of dealers and size at the inside and/or by examining the price trends subsequent to closing the positions. For example, in Table 10, prices continued to rise when a long position was closed on an ECN. Midpoint price increased, on average, by 0.7 cents at + 15 s, 1.2 cents at + 30 s, and 1.6 cents at +45 s. When a short position was closed on an ECN, price tended to continue falling—midpoint prices fell, on average, by 0.5, 0.6 and 0.7 cents 15, 30 and 45 s later. 6.3. Price trends before trades Our examination of price changes before the trades were executed suggests that the traders were not the first to recognize a trend. However, once they recognized a trend, they followed it rapidly by opening up a position so that they profited from the trend. Generally,

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Table 11 bCorrectQ (buy low, sell high) timing of trades No. of trades (000s)

Correct timing at time t relative to time t F s

40.7 40.4

69 81

t Buy Sell

45 [%]

t 76 83

30 [%]

t 84 88

15 [%]

t + 15 [%]

t + 30 [%]

t + 45 [%]

88 85

82 77

77 73

This table shows the estimated incidence of correct timing on all trades involving the 20 most heavily traded stocks. The analysis tests whether the traders buy low and sell high. In the case of a buy (sell), the timing is judged to be correct when the bid/ask quote midpoint at time t is less (greater) than or equal to the bid/ask quote midpoint at time t F s.

when the traders opened a long (short) position, the price was already upward (downward) trending. These findings are not surprising. We know that the traders continuously monitored the quotes of better informed market participants, who often initiated these price trends based on their order flow. 6.4. Correct timing Could the traders have improved their performance by buying or selling earlier or later? To see how accurate these traders were, we again looked at prices before and after the decision to enter and exit a position. In Table 11, we estimate how often the traders got their timing correct. Our analysis is based on midpoint prices – the average of the bids and asks – from the Nastraq inside quote file. With any trading strategy, the goal is to buy low and sell high. In the case of a buy (sell), we judge the timing to be correct when the midpoint price at time t is less (greater) than or equal to midpoint prices at time t F s, where s is 15, 30 or 45 s. We find that the traders were quite accurate in their trading decisions. For example, when the traders executed a buy, they were correct in doing so between 88% and 77% of the time (corresponding to buying 15 and 45 s later). Should the traders have bought sooner? We estimate that the traders were correct in buying when they did between 84% and 69% of the time (corresponding to buying 15 and 45 s earlier). The sell results are similar.

7. Conclusion We examine how a 15 strong US proprietary trading team generated over $1.4 million profit before commissions from day trading during 3 months in 2000. The team operated in a hybrid market and could route anonymous limit orders to ECNs, thus competing with market makers for orders. They traded frequently. Round trip durations and absolute price changes were small. They typically traded between 1000 and 1200 shares and earned small but regular profits averaging $24.3 per round trip. We find that profitable intraday trading occurred in an anonymous dealer capacity, on both long and short positions, especially when volume and price volatility were higher. The traders were not informed. The very short durations of their round trip trades clearly show this. Instead, the traders continuously monitored short-run price trends and

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the quote updates of better informed market participants. They were very quick to act when they spotted a short-run trend. However, the price movements before they opened a position suggest that the traders were generally not the first to spot a trend. The traders usually opened a long (short) position when the number of dealers and size was larger (smaller) at the inside bid than at the inside ask. Displayed quotes and size were good short-run indicators of future price changes. The extent of the imbalance at the inside determined whether the traders paid for liquidity and executed on the opposite side of the spread, or whether they entered a position through an ECN. In the latter case, the traders often earned a greater share of their trading profits from the spread, as opposed to the trend. The traders generally closed profitable positions using ECN limit orders, often closing out before the trend had reached a peak. When prices rapidly went against them, the traders generally closed their positions with a marketable limit order. Although highly unprofitable, the traders did cut their losses using closing marketable limit orders. Our results show that modern electronic trading systems allowed skillful proprietary traders to earn incremental profits on the Nasdaq. Whether or not consistent profitable day trading opportunities continue to exist on Nasdaq or in other hybrid markets is an interesting question.

Acknowledgements We would like to thank the CEO and other executives of the US direct access broker that provided the data for this study. References Barber, B.M., Odean, T., 2001. The Internet and the investor. Journal of Economic Perspectives 15, 41 – 54. Barclay, M.J., Christie, W.G., Harris, J.H., Kandel, E., Shultz, P.H., 1999. Effects of market reform on the trading costs and depths of Nasdaq stocks. Journal of Finance 54, 1 – 34. Barclay, M.J., Hendershott, T., McCormick, T., 2003. Competition among trading venues: information and trading on electronic communications networks. Journal of Finance 58, 2637 – 2665. Battalio, R.H., Hatch, B., Jennings, R., 1997. SOES trading and market volatility. Journal of Financial and Quantitative Analysis 32, 225 – 238. Bear Stearns, 2003, Down, but not out: Semi-pro traders endure a third straight down year, March company report prepared by Daniel Goldberg, New York, New York. Borrus, A., Dwyer, P., 2003. Nasdaq: the fight of its life. Business Week, 64 – 71 (August 11 issue). Ellis, K., Michaely, R., O’Hara, M., 2002. The making of a dealer market: from entry to equilibrium in the trading of Nasdaq stocks. Journal of Finance 57, 2289 – 2316. Garvey, R., Murphy, A., 2004a. Are professional traders too slow to realize their losses? Financial Analysts Journal 60, 35 – 43. Garvey, R., Murphy, A., 2004b. Commissions matter: the trading behavior of institutional and individual active traders. The Journal of Behavioral Finance 5, 214 – 221. Harris, J.H., Schultz, P.H., 1997. The importance of firm quotes and rapid executions: evidence from the January 1994 SOES rules change. Journal of Financial Economics 45, 135 – 166. Harris, J.H., Schultz, P.H., 1998. The trading profits of SOES bandits. Journal of Financial Economics 50, 39 – 62. Huang, R., 2002. The quality of ECN and Nasdaq market maker quotes. Journal of Finance 57, 1285 – 1320.

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