Governance through Trading: Institutional Swing Trades and Subsequent Firm Performance

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1 JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 48, No. 2, Apr. 2013, pp COPYRIGHT 2013, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA doi: /s Governance through Trading: Institutional Swing Trades and Subsequent Firm Performance David R. Gallagher, Peter A. Gardner, and Peter L. Swan Abstract Using unique daily fund-manager trade data, we examine the role of institutional trading in influencing firm performance. We show that short-horizon informed trading by multiple institutional investors effectively disciplines corporate management. Our focus is on short-term swing trades, sequences with three phases (e.g., buy-sell-buy). We find swing trades increase stock price informativeness, are profitable after costs, and improve market efficiency. This increase in stock price informativeness is associated with subsequent firm outperformance. Trades are most beneficial with optimal stock holdings that reflect the information acquisition incentives of investors as well as liquidity costs. I. Introduction This paper demonstrates that institutional investors can exert governance through trading a firm s shares. In particular, we show that informed order sequences by institutional investors are associated with improved stock price informativeness and corporate outperformance over the next 12 months. We find that the greater the strength of a particular institutional trade sequence and the larger the number of participants, the more prices reflect fundamental value. In turn, Gallagher, david.gallagher@cifr.edu.au, Centre for International Finance and Regulation, University of New South Wales, Sydney NSW 2502, Australia, Macquarie Graduate School of Management, Macquarie University, and Capital Markets CRC Limited; Gardner, peter.gardner@plato.com.au, Plato Investment Management Limited, 60 Margaret St, Sydney 2000, Australia; and Swan, peter.swan@unsw.edu.au, Australian School of Business, University of New South Wales, Sydney NSW 2052, Australia. We thank an anonymous referee for excellent insights and helpful comments and Doug Foster for his detailed feedback. We also thank Renee Adams, Jonathan Cohn, Alex Edmans (to whom we owe particular thanks), David Feldman, Ron Giammarino, Jarrad Harford, Craig Holden, André Levy, Paul Malatesta (the editor), Ernst Maug, Mark Seasholes, Jianfeng Shen, Lesley Walter, Terry Walter, Geoff Warren, John Wei, and seminar participants at the European Finance Association Conference in Bergen, Norway, China International Finance Conference, 2009, Financial Integrity Research Network Research Day, University of Sydney Microstructure Meeting, University of Western Australia, University of Queensland, University of Adelaide, Reserve Bank of Australia, and Australasian Finance and Banking Conference. We gratefully acknowledge research funding from the Australian Research Council (DP ). 427

2 428 Journal of Financial and Quantitative Analysis better fundamental value translates into better managerial actions and higher subsequent performance. Neither standard models of governance nor previous empirical studies consider trading as a method of governance. Rather, they argue that concentrated blockholders attempt to exercise voice (Hirschman (1970)), either through direct intervention including extracting private benefits of control (e.g., Zwiebel (1995), Barclay and Holderness (1989), and Laeven and Levine (2007)) or through corporate governance channels, such as voting or activism (e.g., Shleifer and Vishny (1986), Admati, Pfleiderer, and Zechner (1994), Maug (1998), and Kahn and Winton (1998)). In contrast to this conventional view, authors such as Admati and Pfleiderer (2009), Edmans (2009), and Edmans and Manso (2011) have recently advanced the threat of exit as a mechanism enhancing firm value (also known as the Wall Street walk ). Specifically, they claim that informed trades drive stock prices to fundamentals, dependent on corporate managerial actions. With stock price more sensitive to these actions, stock-incentivized managers exert more effort on behalf of shareholders, thereby improving performance. We refer to this phenomenon as governance through trading. This term, governance, acknowledges that these investor trades do indeed improve firm value, rather than simply exiting a stock position that could be nondisciplinary in nature. This alternate theory represents a new channel, enabling empirical tests to determine how effective are multiple blockholders trades on correlated signals of private information in driving future corporate performance. This performance enhancement is due to better managerial motivation. Stock prices fall more severely in response to bad actions and rise more rapidly in response to good actions as stock price informativeness improves. Governance through trading credibly rewards (penalizes) the stock-incentivized manager, who ex ante has greater incentive to put in effort by means of costly hidden actions. What is unusual about this theory, as opposed to the traditional theory, is the relative lack of empirical studies that attempt to provide detailed empirical tests. To fill this void requires not only uniquely rich data but also identification of unusual trading sequences that form the basis of our event studies. Our data are ideal for testing the theories for two reasons: they are high in frequency, permitting identification of sequences, and they include data on blockholders below 5%. In order to identify our informed order sequences, we use a unique and proprietary data set of daily institutional trades of fund managers, including transaction costs and executing broker details, as well as their month-end portfolio holdings. These unique data enable us to specify each fund s trade quantities, trade types (buy/sell), and the post-transaction costs performance of each trade. The data also enable finely detailed examination of every possible pattern of daily trades by every identifiable trader (fund manager). This high frequency contrasts with the minimum 3-month portfolio holdings window observable from Securities and Exchange Commission (SEC) Section 13f filings in the United States. Hence, we know the entire portfolio of each fund manager (trader), the size of a fund s individual holdings in each stock, the number of simultaneous traders within the sample for every time period, and the magnitude of their daily trade frequency. In addition, traditional empirical studies on blockholders use databases that define the blockholder as a 5% shareholder. This is appropriate for studies on

3 Gallagher, Gardner, and Swan 429 governance through voice, as a 5% stake may be necessary for a blockholder to exert control. However, control is not necessary for blockholders to exert governance through trading. Our database allows us to study blockholders with stakes below 5% that standard studies miss, but that may still engage in governance. Our study provides an opportunity to depart from most existing empirical analyses of firm blockholders that focus on various forms of control. Our paper aims to examine this governance mechanism by recognizing short-term patterns in the underlying trade orders that reveal information. This requires examining daily institutional investor trades, because investors break larger orders into smaller pieces ( trades ). We then reconstruct the underlying orders (i.e., comprehensive signed order flow) designated as packages identified to individual fund managers. We define a trade package sequence, following Chan and Lakonishok (1995), which captures all trades over multiple days in the same direction for each stock, and not more than 5 business days apart. We close the trade package if there is a reversal trade in the same stock. This becomes the 1st trade in the next trade package. We designate swing trades as trading order sequences derived from microstructure models of multiple informed traders in receipt of the same (or correlated) signals, with 3 sequences executed through signed order flow: buysell-buy (BSB) or sell-buy-sell (SBS) trade packages. The justification for focusing on these order sequences is as follows. When a blockholder does trade, one cannot utilize a single buy or sell order as an indicator of informationbased trading, since the investor may simply be making a strategic decision to alter holding size in response to altering influence opportunities, for example, by permanently exiting a stock. Similarly, a buy-sell or sell-buy sequence may simply be correcting a strategic error and need not indicate any intention to earn short-term trading profits. In contrast, the more peculiar BSB or SBS sequences undertaken over relatively short horizons (i.e., intra-quarter) are unlikely to have happened simply by chance. These patterns imply either the presence of private information on which the investment manager earns trading profits or some agency or other failure by the investment manager, for example, giving the appearance of doing something as an active manager even though these trading patterns have no value to the ultimate investor (Dow and Gorton (1997)). As we demonstrate below, these trade sequences are indeed profitable after transaction costs, in support of the informed-trader microstructure literature such as Kyle (1984), (1985), (1989) and ruling out agency failure. Notably, an important feature of our unique data is that they contain the investment manager s transaction costs, enabling us to distinguish between these two explanations. Having studied the profitability of swing trading, we next analyze its impact on stock price informativeness. Our proxy for the theoretical concept of price informativeness is the decline in the relative spread as informed trading drives price toward fundamentals, as attested to by the microstructure literature on asymmetric information contained in the spread (e.g., Lin, Sanger, and Booth (1995)). In this paper, we show first that short-term swing trades make up a sizable 39% of all fund-manager trades by volume. Second, short-term swing trading is profitable, with one swing sequence yielding on average a 2.72% return prior to transaction costs with an excess return of 0.90% after transaction costs, thereby

4 430 Journal of Financial and Quantitative Analysis establishing that such trading is informed. Returns are generally highly statistically significant, and this remains true even after transaction costs. Third, swingtrading profitability diminishes by 24 basis points (bp) for each additional swing trader. This is supportive of the multiple informed-trader microstructure models of Admati and Pfleiderer (1988), Holden and Subrahmanyam (1992), and Foster and Viswanathan (1993), (1996). Fourth, such trading activity produces improved pricing efficiency in the form of lower bid-ask spreads. For each additional fund manager trading simultaneously, the spread reduces by 92 bp. Hence, if the original spread were 1%, then each additional manager would reduce the spread by %. Fifth, we also find that higher price informativeness in the form of lower spreads is associated with higher subsequent long-term firm performance. This result is consistent with the firm manager s actions having a more sensitive effect on stock price when stock price is more informative and with the governancethrough-trading models of Holmstrom and Tirole (1993), Admati and Pfleiderer (2009), Edmans (2009), and Edmans and Manso (2011). For example, the presence of a swing-trade sequence in the previous month is associated with an approximately 3% excess return over the next year, with the decline in the spread playing an important role. After controlling for the presence of a swing trade, there is still an apparent improvement of 67 bp for each 10% reduction in spread due to swing trades. Sixth, when fund managers do not trade at all, there is around 2% subsequent firm performance improvement that is significantly different from 0, but that is also significantly lower at the 1% level than the firm performance improvement after swing trades of 4.62%. This result does not rule out the effectiveness of voice but does suggest that swing trades are associated with significant future increases in firm performance. Seventh, swing-trading profitability improves in the percentage size of the investment manager s initial holdings until the manager reaches an optimal holding size of 2.96% of shares outstanding. Finally, investors who have larger holdings are more likely to swing trade, and to swing with greater magnitude, in keeping with the greater profitability of their swing trades. The optimal size of initial holdings from a swing likelihood and magnitude perspective is between 2.3% and 2.5%. The contribution of initial stock holdings to apparent future outperformance over the following year is optimal when it reaches 1.94% and is thus less than the nearly 3% optimal holding from the swing trading profitability perspective. The following section provides background to the study and reviews the literature, and then Section III develops 7 underlying hypotheses. Section IV provides a description of the data and institutional arrangements affecting the investment management process for our sample of institutional investors. Section V presents the empirical results, and in Section VI we present our conclusions. II. Background and Literature Kyle (1984), (1985) makes a seminal contribution by modeling informed riskneutral insider trading in the presence of random noise traders and competitive risk-neutral market makers, and by introducing a crucial concept, stock price informativeness. Admati and Pfleiderer (1988), Holden and Subrahmanyam (1992),

5 Gallagher, Gardner, and Swan 431 and Foster and Viswanathan (1993), (1996) all show that multiple informed traders improve price informativeness compared to a single informed trader, with more rapid release of private information. None of the theoretical contributions discussed so far make the important link between stock price informativeness, managerial effort, and subsequent firm performance. Holmstrom and Tirole (1993) take the important 1st step of incorporating the firm manager into Kyle s (1985) original single informed-trader framework. As stock price becomes more informative of the manager s actions when the trader spends resources to gain a better signal of future value, firm performance improves. 1 Two important recent contributions to this literature add to our understanding of governance through trading. First, Admati and Pfleiderer (2009) provide a model based on a single large exogenously informed blockholder faced with agency problems. 2 Second, Edmans (2009) likewise poses an agency problem in a framework that differs from Admati and Pfleiderer (2009) in that information acquisition is endogenous. 3 An important conceptual advance in Admati and Pfleiderer (2009) and Edmans (2009) is recognizing that most institutional investors (blockholders) do not directly intervene in an effort to influence managerial decisions (e.g., mutual funds). Furthermore, Edmans (2009) recognizes that these investor classes typically do not short-sell. He demonstrates that now the incentive to acquire information is increasing in block size, whereas with unrestricted short-selling, block size is irrelevant to information acquisition, as in Holmstrom and Tirole (1993), or information acquisition and thus block size is exogenous, as in Admati and Pfleiderer (2009). This might suggest that block size in Edmans (2009) is unbounded, but since the blockholder needs to credibly exit when in receipt of a bad signal, this imposes a liquidity requirement in the form of a sufficient number of liquidity traders. Consequently, the optimal blockholder size is finite with an interior solution, and the impact on future performance is concave in block size, as we empirically demonstrate. A second set of predictions arising from Edmans (2009) is that the information release by blockholder trading gives rise to future firm outperformance that is also concave in the initial blockholder size, as we also empirically demonstrate. Since swing trades reduce the information asymmetry content of the spread, 1 In Faure-Grimaud and Gromb (2004), price informativeness is relevant, but not because it improves the manager s incentive to perform (there is no managerial action) but because it encourages other investors to make value-adding interventions. 2 First, the manager may take an action that is bad from the perspective of shareholders but that privately benefits the manager. Second, the manager may take action that is good for shareholders but for which the manager incurs a private cost. The authors show that it can be credible to threaten exit in the 1st disciplinary problem even though this could be costly to the blockholder. They also show that threat of exit can actually exacerbate the 2nd problem. 3 In his model of the threat of exit, the manager who fails to meet earnings targets is adversely impacted. The manager therefore cares about short-term pricing. However, if the manager does more to benefit shareholders by making long-term intangible investments, this can adversely affect the shortterm public signal. For example, the firm might report low earnings, but the public is not aware that this is due to the manager making the correct value-maximizing long-term investment in invisible intangible assets. An informed large blockholder can help to overcome the resulting managerial myopia by not selling when the public receives the bad signal if they are able to discern the manager s long-term investment (good private signal).

6 432 Journal of Financial and Quantitative Analysis irrespective of the nature of the swing trade, we show that the more negative the change in the spread, the greater the subsequent firm outperformance. With multiple traders and a correlated signal, too much trading occurs from the standpoint of any single investor faced with unwanted competition, as shown in the models of Kyle (1984) and Admati and Pfleiderer (1988). A larger number of informed traders is associated with greater trading volumes and thus higher price informativeness. This informed trading activity credibly rewards (penalizes) the manager whose stock holdings are exogenously given and whose hidden actions are likely to be personally costly if beneficial to firm value. From these important conceptual advances made by Kyle (1984) and Admati and Pfleiderer (1988), the work of Edmans and Manso (2011) links multiple informed traders to firm value, with their main prediction being that subsequent outperformance increases in price informativeness. A determinant of price informativeness is the number of informed blockholders trading simultaneously. Several empirical papers shape our method and hypotheses. Bennett, Sias, and Starks (2003) find that changes in institutional demand affect future prices, indicating that institutional investors possess information; however, their study does not address whether specific trading patterns incorporate information. Sias, Starks, and Titman (2006) find evidence to suggest that institutional investors possess better information, on average, and that security prices incorporate their information when they trade. In particular, they find the number of institutional traders plays an important role in determining quarterly returns, even though some of these traders are relatively small, supportive of our as well as and Edmans and Manso s (2011) focus on the number of informed traders. Yan and Zhang (2009) find that short-term trading by institutional investors forecasts future returns, while long-term investor trades do not, making clear that institutional investors obtain information for short-term trading purposes. Hence, it makes sense for these short-term investors to exploit their informational advantage. Boehmer and Kelley (2009) show that, even apart from trading, it is institutional ownership outside the 5 most concentrated institutional investors that is particularly associated with greater pricing efficiency. Moreover, the explanation is not due simply to liquidity. Larger holdings by institutional investors mean greater information acquisition, as we confirm. Boehmer and Kelley also find that multiple informed traders who are more competitive and less concentrated drive these efficiency gains, even in periods when no trading occurs. Their conclusions are consistent with what we find: Institutional investors outperform by approximately 2% per annum, even when not trading at all, most probably because of better stock selection. McCahery, Sautner, and Starks (2011) provide a strong endorsement of the role of institutional trading on stock price informativeness with their finding that 80% of responding institutional investors are willing to vote with their feet by selling their shares. The existing empirical literature illustrates why it is vital to rule out longerterm sequences that do not alter in sign, contrary to our BSB or SBS swing sequences. Parrino, Sias, and Starks (2003) find that sales by large institutional investors can predict forced chief executive officer (CEO) turnover, and Chen, Harford, and Li (2007) find that independent institutional investors change their holdings well in advance of extremely bad or good acquisition announcement

7 Gallagher, Gardner, and Swan 433 returns. In contrast, swing trades are relatively neutral with respect to net holdings, which change little and thus can encapsulate an aggressive informed trading story while also being inconsistent with simple optimism or pessimism informational stories exemplified by these prior studies. Bharath, Jayaraman, and Nagar (2013) take a different perspective than ours on blockholder numbers and price informativeness by providing what they term indirect tests of the predictions of Admati and Pfleiderer (2009) and Edmans (2009). They examine the impact of exogenous liquidity shocks on the interaction between blockholder ownership and firm value. Consistent with their hypothesis, they find that positive liquidity shocks (e.g., decimalization) enhance blockholder value. In a similar vein, Edmans, Fang, and Zur (2013) find that activist hedge funds are more likely to acquire blocks in liquid stocks as investors exercising governance through trading. Moreover, such listings lead to positive and significant announcement effects on the stocks in question. III. Hypotheses A. The Link between Stock Price Informativeness and Trade Sequences As shown in the basic model of Edmans and Manso ((2011), Prop. 13), stock price informativeness, and thus the firm s future stock price, must be increasing in the number of simultaneous informed traders and the volatility of the underlying signal received by informed traders. However, this contention gives little guidance as to when one should measure these determinants of stock price informativeness, for example, the number of simultaneous traders or even stock price informativeness itself. For example, should one measure this whenever there is a single trade, or whenever there is a special sequence of trades or underlying orders indicating precise informational advantage? The more precise are signals received by informed traders, the more informative is the stock price. A core contribution of the current paper is in establishing the critical role of these special trade sequences (swing trades) that generate stock price informativeness and are associated with spread reductions and subsequent outperformance. B. Relative Significance of Swing Trades We first establish that swing-trading activity forms a very significant proportion of all investment manager trades. 4 If they only contributed to investment manager trades trivially, they would be hardly worth mentioning. Far from large shareholdings being predominantly stable, as is required for the effective exercise of the blockholder voice proposition, short-term swing trading is a major activity that forms a very significant portion of overall stock turnover. 4 By contrast, Dow and Gorton (1997) explain allegedly excessive trading (from a control perspective) by institutional investors with respect to delegated portfolio management, in terms of clients unable to distinguish between managers simply doing nothing and actively doing nothing by trading to excess (e.g., by churning ). While possibly less pejorative, we believe the term swing trades is a more apt description.

8 434 Journal of Financial and Quantitative Analysis C. The Hypotheses For the trading view of investor monitoring to be substantive, investment managers must be in receipt of valuable information. Thus, swing trading must be profitable to undertake, even after accounting for trading costs and ex post selection biases. 5 Hence, our 1st hypothesis, stemming from the informed-trader models of Kyle (1984), (1985), (1989), Holden and Subrahmanyam (1992), Foster and Viswanathan (1993), Admati and Pfleiderer (1988), (2009), and Edmans (2009) among others, asserts that H1. Swing trades are profitable on a net basis after accounting for transaction costs and ex post selection bias. Following Edmans (2009), our associated hypothesis asserts that H2. Net profitability after transaction costs is increasing in the investment manager s initial holdings but at a sufficiently diminishing rate, such that an optimal interior solution exists. An additional requirement, from the perspective of synchronized informed trading based on swing trades, is that investment managers are subject to competitive pressure from similar investment managers wanting to trade in the same direction, due to some correlation in the signal. The greater is the number of investors trading, the lower the value of any such signal is to each investor. Only single-period or static versions of microstructure models with multiple traders in receipt of the same signal, such as Kyle (1984) and Admati and Pfleiderer (1988), actually support this monotonicity hypothesis, since dynamic models such as Holden and Subrahmanyam (1992) and Foster and Viswanathan (1993) predict that as few as 2 competing traders suffice to eliminate trading profitability. These dynamic models even question our 1st hypothesis, H1, as they predict zero profitability for 2 or more traders. A step fall between 1 and 2 investors as in the dynamic theory is not sufficient. Hence, our 3rd hypothesis asserts that H3. Institutional investor trading profitability is declining in the number of investors trading simultaneously when institutional investors execute swing trades. It is an implication of the microstructure literature (e.g., Kyle (1984), Admati and Pfleiderer (1988), and Edmans and Manso (2011)) that market resiliency (the inverse of Kyle s (1985) market impact, λ) is increasing in the number of informed traders trading simultaneously. Hence, our 4th hypothesis asserts that 5 It is critical to exclude ex post selection bias, sometimes known as look-ahead bias, when assessing the profitability or otherwise of swing-trade sequences relative to other sequences or individual trades. Bias arises if the definition of a trading sequence such as buy-sell-buy uses information to define the 3rd sequence that was not available at the times of the 1st and 2nd sequences. For example, suppose a manager buys a stock when it is below his target price, sells it when it rises above his target price, and buys it again when it falls below his target price. A simple crude computation of profitability summed over each stage and based on the prices at each stage would falsely find that this sequence was profitable, whereas the 2nd successful buy was unknown at the time of the initial buy followed by a sell trade. Hence, one reaches a false inference of trading prowess. We overcome this bias when making comparisons by marking all possible trade sequences, such as a single buy or buy followed by a sell, or a swing-trade sequence to-market at the end of the same 3-month horizon so that the profitability of every sequence is computed precisely on the same uniform basis with no ex post selection bias.

9 Gallagher, Gardner, and Swan 435 H4A. Pricing efficiency, as captured by lower bid-ask spreads, is increasing in the number of institutional investors who are trading simultaneously when institutional investors execute swing trades. Given a direct connection between price informativeness and trade aggressiveness, a variant on this hypothesis is H4B. Pricing efficiency is also improving in the magnitude of swing trades. Since Edmans (2009) proposes that trades by institutional investors with higher initial holdings in a stock are more likely to contain information, an additional variant is H4C. Pricing efficiency is improving in initial managerial holdings at a diminishing rate. Our 5th hypothesis stems from the postulated role of stock price informativeness in the governance-through-trading models of Holmstrom and Tirole (1993), Admati and Pfleiderer (2009), Edmans (2009), and Edmans and Manso (2011) and the critical link between stock price informativeness and swing trades discussed in Section III.A. It states that H5. The greater the pricing efficiency as measured by the spread reduction brought about by swing trades, the greater should be the firm s subsequent outperformance. There are other sequences besides swing trades exhibiting trade reversals, potentially indicating information. Others include buy-sell and sell-buy within our short-term 3-month horizon but indicate less intensive short-term trading activity without the peaks and troughs generated by swing trades. We test the subsidiary hypothesis that these sequences result in lower subsequent firm outperformance due to smaller information content (H5Sub1). Additional subsidiary hypotheses arise from the possibility that our traditional microstructure spread measure of informativeness does not fully capture the change in price for a given change in stock fundamentals, for which there may not exist any perfect empirical proxy. Hence, we add subsidiary hypotheses: Even after controlling for our spread measure of informativeness, two of the determinants of informativeness (i.e., the number of informed blockholders choosing to trade (H5Sub2) and the magnitude of swing trading (H5Sub3)) will add explanatory power, as these will be imperfectly correlated with our informativeness measure. If blockholders govern exclusively by exercising voice, then a regression of subsequent outperformance on our spread measure of informativeness (due to H5), the number of swing trades after controlling for informativeness (due to H5Sub2), and dummy variables for blockholders that choose not to trade should reveal that price informativeness and swing trading are irrelevant. Only the dummy variables for blockholders choosing not to trade should be relevant. Alternatively, if price informativeness and trading sequences matter, performance should also depend on the spread reduction (according to H5). However, blockholders who neither exercise voice nor trade in a particular month may still outperform the market due to the long-lasting effects of stock selection skills exercised in previous periods when they did trade. In point of fact, our sample of investment

10 436 Journal of Financial and Quantitative Analysis manager blockholders only hold a portion of the larger stocks making up the Standard & Poor s (S&P) 200 and S&P 300 Index Returns and display average outperformance of approximately 2% per annum. Thus, the greater the importance of trading sequences is relative to blockholders that choose not to trade, the more price informativeness matters relative to voice or even simple stock-picking ability for whatever reason. Hence, our 6th hypothesis asserts that H6. If investment managers do not trade, there will be evidence of significant firm (baseline) outperformance. When swing trading does reduce spreads, additional induced outperformance significantly exceeds this baseline. Edmans (2009) shows theoretically that the larger the stockholding in a given stock, the greater the incentive to gather information for long-only investors. Thus, the likelihood of informed swing trades increases in initial holding size as none of our sample of investment managers short-sell. The 7th hypothesis states H7A. The larger the investment manager s initial stake in a stock, the greater the likelihood of that manager undertaking swing trades, H7B. The greater is the number of swing trades, and H7C. The greater is the magnitude of the swing trades. IV. Data We develop empirical tests of the Admati and Pfleiderer (2009), Edmans (2009), and Edmans and Manso (2011) governance through trading hypotheses, together with tests of the associated Kyle (1984), (1985), (1989), Admati and Pfleiderer (1988), Holden and Subrahmanyam (1992), and Foster and Viswanathan (1993), (1996) microstructure models. As already noted, this requires identification of the number of agents trading, inclusive of very detailed short-term trading data, replete with trader identities and detailed transaction costs. The Portfolio Analytics Database (PAD) contains proprietary information pertaining to the daily trades and portfolio holdings of Australian investment managers in the domestic equities asset class. Thus, it does not focus solely on large blockholders. We asked investment managers to provide information for their 2 largest institutional-pooled Australian equity funds and to exclude index fund managers. PAD obtains historical month-end portfolio holdings and daily trading data for Australian equity managers with the support of Mercer Investment Consulting, and it contains historical information from January 2, 1994, to June 30, The data fields obtained for daily trading activities include the date of execution, Australian Securities Exchange (ASX) stock code, name, quantity traded, daily weighted average price of the trade, explicit transaction costs (brokerage) incurred, and even the identity of the broker. We received a complete data set of all trades and holdings for that period (including equities, convertibles, options, etc.). We supplemented our database with stock price data sourced from the ASX Stock Exchange Automated Trading System (SEATS). SEATS contains all trade information for stocks listed on the ASX, with stock-specific data such as prices, returns, market capitalization, and spreads. Index changes to the S&P/ASX 300 Index Return are also located in the SEATS database. SIRCA (Securities Industry Research Centre of the Asia-Pacific) Limited provided access to the Aspect

11 Gallagher, Gardner, and Swan 437 Huntley Fin Analysis accounting database for the calculation of book-to-market ratios. Our sample of actively managed institutional Australian equity funds employed in this study comprises 38 funds from 30 unique active institutions. Benchmarking of all Australian equity funds in this database occurs to either the S&P/ASX 200 or S&P/ASX 300 Index Returns. This database provides a sample that is representative of the Australian investment management industry. It includes data from 6 of the 10 largest fund managers, from 6 of the next 10, from 4 of those managers ranked 21 30, and from 14 managers from outside the largest 30 (of funds under management as of December 31, 2001). The sample also includes 6 boutique firms that manage less than $A100 million each. Many previous papers, including Brands, Brown, and Gallagher (2005), utilize PAD. Our data set constitutes around 10% of funds under management in the asset class as reported by the fund performance-monitoring firm ASSIRT (now owned by Standard & Poor s). However, if the data that fund managers provide is representative of their entire Australian operations, as the fund managers claim, then the effective coverage is over 50%, as 12 of the top 20 fund managers provide us with data. 6 In Table 1, we summarize the holdings according to the number of stocks held by individual managers and the combined holdings for all 30 managers in the PAD sample. Panel B shows the latter. It can be seen from Panel A that if we adopt the standard definition of a blockholder owning 5% or more of a stock, TABLE 1 Descriptive Statistics of Substantial Manager Stock Positions Table 1 presents a number of descriptive statistics concerning the blockholder status of the investment managers in the Portfolio Analytics Database (PAD) sample. In Panel A (Panel D), we measure the average number of stocks (index weight) each month where individual managers in the PAD sample hold over 0.5% 5% of the total market capitalization of a company. In Panel B, we measure the combined holding of all managers in our sample. Panel C (Panel E) measures the average number of stocks (index weight) each month in which our managers have engaged in swing trading when they hold over 0.5% 5% of the total market capitalization of a company. Description 5% 4% 3% 2% 1% 0.5% Panel A. Number of Stocks Individual Managers Hold Over x% Mean Median StDev Minimum Maximum Panel B. Number of Stocks Held by All Sample Managers Holding Over x% Mean Median StDev Minimum Maximum (continued on next page) 6 A pertinent question is how large does a database have to be to gain status as representative of the population? Perhaps the most famous database used for studies of trading behavior is the large U.S. discount brokerage set of individual households utilized by Barber and Odean (2000). These households collectively made 3 million trades. By contrast, a recent study by Kelley and Tetlock (2013) utilizes a data set of U.S. 225 million household trades (75 times larger). In comparison to Barber and Odean, our database coverage is very large indeed.

12 438 Journal of Financial and Quantitative Analysis TABLE 1 (continued) Descriptive Statistics of Substantial Manager Stock Positions Description 5% 4% 3% 2% 1% 0.5% Panel C. Number of Stocks Swing Traded by Individual Managers Where Holding Over x% Mean Median StDev Minimum 1.0 Maximum Panel D. Index Weight of Stocks Individual Managers Hold Over x% Mean 2.8% 6.0% 12.8% 29.9% 51.7% 68.5% Median 1.2% 2.5% 4.2% 18.0% 61.9% 77.8% StDev 2.8% 7.9% 15.4% 26.3% 28.7% 19.7% Minimum 0.1% 0.1% 0.2% 2.1% 6.3% 30.1% Maximum 13.6% 34.9% 59.3% 80.0% 86.7% 88.9% Panel E. Index Weight of Stocks Swing Traded by Individual Managers Where Holding Over x% Mean 0.3% 0.4% 0.7% 1.5% 6.2% 26.0% Median 0.1% 0.2% 0.4% 1.0% 4.5% 19.5% StDev 0.4% 0.6% 0.9% 1.3% 6.8% 22.5% Minimum 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% Maximum 2.4% 2.9% 3.6% 4.5% 34.1% 82.7% then on average, only 25 stocks qualify. At the other extreme, the mean number of stocks held where the fund manager owns 0.5% or more is 209. In Panel C, we present the number of swing stocks within each category. The mean number of stocks swing traded in the blockholder category is only 2.4. It rises to 30.5 in the 0.5% or more category. Hence, our sample of investment managers does not consist typically of blockholders, according to the standard definition requiring 5% minimum holding. Moreover, we find that highly informed stock swing trading is most likely to occur in stocks where our managers hold just under 3% of the shares on issue; thus, our sample incorporates bias against finding highly informed swing-trading activity, as this optimal holdings level lies toward the maximum holdings in our sample. V. Results A. Significance of Stock Trading and Swing Trades In this section, we determine the nature and significance of equity stock trading by our sample of actively managed institutional equity funds and the significance of swing trading within this overall pattern of trading. We employ the method of Wermers (2000), who decomposes equity mutual fund returns into the transaction costs incurred and net returns after transaction costs. We calculate overall turnover as the average of buys and sells during a certain period. Transaction costs are calculated using explicit brokerage costs provided by managers. Missing brokerage costs for trades comprise less than 8.6% of our trades by value (21.5% by number). Encountering this problem with just 4 investment managers, we use regression coefficients from investment managers with complete data to estimate the brokerage costs for the remaining managers (see Appendix A). We subtract the total transaction costs from a fund manager s gross return to obtain

13 Gallagher, Gardner, and Swan 439 the net return and express excess returns relative to the S&P/ASX 300 Index Return. Table 2 reports descriptive statistics on overall turnover and transaction costs for the entire sample of investment funds. The average annual turnover rate is 76%. Investors who are more active incur significantly higher transaction costs. This activity does not appear to penalize investors, as there is no statistically significant net return penalty. In fact, an overarching aim of this paper is to provide a satisfactory rationale for otherwise puzzling trading (from a control perspective) and, in particular, swing-trading activity. TABLE 2 Descriptive Statistics of Institutional Investor Sample Showing the Turnover-Sorted Institutional Investor Return Decomposition Table 2 provides a decomposition of Australian institutional investor returns contained in the Portfolio Analytics Database (PAD). At the end of each semiannual period from June 1999 to June 2002, we rank all funds in the database by their prior 6-month portfolio turnover level (the ranking period). Then we compute average statistics for each quartile (according to their prior portfolio turnover) over the following 6 months. These statistics are calculated using monthly manager positions: portfolio turnover, gross return, gross excess return (over the S&P/ASX 300 Index Return), transaction costs, net return (net of transaction costs), and net excess return (over the S&P/ASX 300 Index Return). The characteristic selectivity (CS), characteristic timing (CT), and average style (AS) are determined following the methodology of Wermers (2000). We annualize these statistics and calculate over all semiannual periods. *, **, and *** indicate significance at the 90%, 95%, and 99% confidence intervals, respectively. Gross Net Gross Excess Transaction Net Excess Avg. Turnover Return Return CS CT AS Costs Return Return Fractile No. (%/year) (%/year) (%/year) (%/year) (%/year) (%/year) (%/year) (%/year) (%/year) Top 25% nd 25% rd 25% Bottom 25% Top-Bottom 25% *** *** All Funds Table 3 provides descriptive statistics of overall trades and swing trades for our sample of investment managers. We report as all buys and all sells swing trades commencing with either a buy (i.e., BSB ) or commencing with a sell (i.e., SBS ) that complete within a 3-month horizon. 7 Swing trades make up 33.5% (38.9%) of our investment manager trades (trade volume), with 65.8% of these trades occurring in the largest stocks, compared with 52.6% of all trades. This establishes our initial finding: Short-term swing trades comprise a significant portion of the overall trading volume. Moreover, trading volume is itself quite significant. When we analyze the number of days over which these trades are completed, we find similar percentages comparing swing trades with all institutional trades in our database (see Appendix B). When we analyze all 7 One might question whether these trade sequences are due to fund inflows and outflows. As these funds are institutional in nature, small regular fund flows that would cause these trade sequences are unlikely; however, for robustness, we conservatively removed all buy (sell) trades in months where there were fund inflows (outflows). This removed 12% of trades in our sample but yielded similar results. In addition, our later finding of swing-trade profitability supports our suggestion that these trade sequences are not the result of fund flows, which we would not expect to be predictive of individual stock returns.

14 440 Journal of Financial and Quantitative Analysis TABLE 3 Descriptive Statistics of Institutional Investment Manager Trades Table 3 measures the percentage of our institutional investor buy, sell, swing-trading buy and swing-trading sell trades (trade volume figures are in parentheses), split by stock size quintile, and the number of package days over which we split our trade. We define swing trades as the following trade sequences: i) purchase, sale, purchase and ii) sale, purchase, sale, completed over a period of less than 3 months. Packages are defined following Chan and Lakonishok (1995), who use a 5-day gap definition of a package, implying that a new package begins when there is a 5-day gap between manager trades (in the same direction), or when the manager executes a trade in the opposite direction. Principal refers to the total traded value. The sample comprises all trades of 30 active Australian investment managers during the period January 2, 1994 June 30, Nature of Buy, Sell, and Swing Packages 1 Day 2 3 Days 4 6 Days 7 10 Days 11+ Days Panel A. All Buys (41,781 packages, $46.1 billion ) All Buys 61.9 (25.3) 13.5 (14.4) 13.2 (18.0) 6.0 (14.6) 5.4 (27.8) 1 (small) 7.0% of packages, 1.9% of 69.1 (43.9) 10.7 (12.8) 10.7 (14.9) 5.1 (11.7) 4.4 (16.8) 2 5.5% of packages, 2.0% of 65.9 (37.9) 12.5 (15.6) 11.4 (12.7) 4.8 (14.1) 5.4 (19.6) % of packages, 9.1% of 61.5 (26.4) 13.7 (12.2) 12.9 (17.1) 6.2 (13.0) 5.7 (31.3) % of packages, 17.3% of 60.5 (24.5) 13.9 (13.8) 13.8 (19.4) 6.1 (16.2) 5.7 (26.1) 5 (large) 53.1% of packages, 69.7% of 61.0 (24.4) 13.7 (14.8) 13.6 (17.9) 6.2 (14.5) 5.5 (28.4) Panel B. All Sells (32,609 packages, $35.4 billion ) All Sells 61.9 (27.7) 15.2 (16.5) 12.3 (18.6) 5.9 (14.6) 4.7 (22.6) 1 (small) 7.7% of packages, 2.1% of 66.5 (44.1) 12.2 (12.5) 11.4 (14.7) 5.4 (12.7) 4.5 (16.0) 2 5.6% of packages, 2.0% of 62.5 (31.0) 14.7 (13.4) 11.9 (20.0) 6.2 (13.5) 4.7 (22.1) % of packages, 8.2% of 59.5 (32.9) 15.4 (14.0) 13.5 (20.2) 6.3 (12.9) 5.3 (20.0) % of packages, 18.3% of 59.4 (23.0) 15.5 (16.6) 12.3 (18.3) 7.1 (16.5) 5.7 (25.6) 5 (large) 52.1% of packages, 69.4% of 62.2 (27.5) 15.6 (17.0) 12.4 (18.6) 5.5 (14.5) 4.3 (22.4) Panel C. Swing Buys (12,698 packages, $16.8 billion ) All Buys 58.9 (19.3) 13.7 (14.4) 14.8 (17.7) 6.2 (14.5) 6.4 (34.1) 1 (small) 3.0% of packages, 0.6% of 71.1 (36.7) 9.0 (10.1) 12.4 (16.6) 4.7 (27.0) 2.8 (9.6) 2 2.9% of packages, 0.8% of 71.4 (42.0) 9.0 (19.1) 10.2 (11.7) 5.2 (9.5) 4.2 (17.7) 3 9.3% of packages, 8.4% of 57.9 (16.9) 15.3 (12.1) 14.0 (16.4) 6.3 (11.9) 6.5 (42.7) % of packages, 13.6% of 58.4 (19.8) 13.3 (14.1) 14.6 (17.9) 6.7 (17.2) 7.0 (31.0) 5 (large) 65.8% of packages, 76.6% of 58.0 (19.1) 14.0 (14.7) 15.2 (17.8) 6.2 (14.2) 6.6 (34.2) Panel D. Swing Sells (12,240 packages, $14.9 billion ) All Sells 61.2 (23.7) 15.9 (16.2) 12.6 (20.2) 5.9 (16.6) 4.4 (23.3) 1 (small) 3.0% of packages, 0.7% of 67.8 (56.3) 13.7 (9.2) 10.3 (7.5) 5.9 (14.4) 2.3 (12.6) 2 3.0% of packages, 0.9% of 64.4 (28.0) 13.1 (12.8) 9.6 (12.3) 8.0 (18.1) 4.9 (28.8) 3 9.1% of packages, 5.9% of 63.8 (28.9) 13.6 (13.3) 12.5 (20.9) 6.2 (14.2) 3.9 (22.7) % of packages, 13.8% of 59.7 (21.0) 15.8 (16.3) 12.6 (18.6) 7.2 (20.6) 4.7 (23.5) 5 (large) 65.9% of packages, 78.7% of 60.8 (23.4) 16.5 (16.5) 12.8 (20.6) 5.5 (16.1) 4.4 (23.4) fund-manager trades, we find packages make up, on average, 90% of an average day s trading volume. This suggests active institutional investors are stealth traders who frequently split up trades over multiple days to limit information revealed via trading and to reduce market impact.

15 Gallagher, Gardner, and Swan 441 B. Profitability of Swing Trades and Ex Post Selection Bias Governance through trading requires investors to observe a signal of future stock value and to trade profitably based on this signal (H1). We now investigate whether the swing-trade sequences identified in Table 3 meet our criteria. In Table 4, we measure the excess return for fund-manager swing trades, that is, 3 successive trade packages (defined according to Chan and Lakonishok (1995)) made up of a purchase (sale), sale (purchase), and purchase (sale). Completion of 3 successive trades must take place within 3 months (or the period shown on the left-hand side of Table 4). For example, a trading sequence whereby a manager purchases, then sells, then sells again (after a break of more than 5 days, so that the trades are not packaged) before finally purchasing, would not be included, TABLE 4 Performance of Swing Trades Using Manager Trade Prices after Transaction Costs over Short- and Long-Term Horizons Table 4 measures excess stock return (over the S&P/ASX 300 Index Return) around the following trade sequences: i) purchase, sale, purchase and ii) sale, purchase, sale. These trade sequences occur over the interval in the left-hand column. Return is calculated using the institutional investment manager s actual average trade package price such that the excess return After Buy, Before Sell is calculated as the price return between the purchase and sale price that the manager actually obtained less the market return from the 1st day of the purchase to the 1st day of the sale. We model transaction costs as per the description in the text, and subtract from returns after purchases, but add to returns following sales. All figures (not in parentheses) are in percentages. *, **, and *** indicate significance at the 90%, 95%, and 99% confidence intervals, respectively. The numbers in parentheses are t-statistics. Panel A. Buy-Sell-Buy Swing Trade After After Buy, Sell, Working Days No. of Past 5 Before Before Next 5 Next 10 Next 90 Next 250 Trade Allocation Trades Days Sell Buy Days Days Days Days 5 Working days (t-statistic) (0.31) (0.55) (0.70) (1.23) (0.57) (0.22) (1.26) 6 10 Working days 1, *** 0.26** 0.31*** ** 1.51* (t-statistic) (3.04) (1.97) (2.64) (0.13) (0.08) (2.12) (1.95) Working days 2, *** 0.68*** 0.62*** 0.36** 0.33* (t-statistic) (9.94) (5.10) (5.18) (2.36) (1.91) (0.11) (0.71) 1 2 months 1, *** 1.54*** 0.83*** 0.32** 0.43** (t-statistic) (12.03) (8.16) (5.52) (2.08) (2.40) (0.95) (0.83) 2 3 months *** 1.04*** 0.86** (t-statistic) (6.14) (2.64) (2.18) (0.98) (0.30) (0.24) (0.46) All trades < 3 months 6, *** 0.82*** 0.59*** 0.28*** 0.26** * (t-statistic) (15.47) (9.27) (7.40) (3.19) (2.57) (1.62) (1.81) Panel B. Sell-Buy-Sell Swing Trade After After Sell, Buy, Working Days No. of Past 5 Before Before Next 5 Next 10 Next 90 Next 250 Trade Allocation Trades Days Buy Sell Days Days Days Days 5 Working days (t-statistic) (1.53) (0.91) (1.64) (0.89) (0.65) (1.26) (1.28) 6 10 Working days 1, ** 0.30*** 0.41*** 0.48*** 0.49** (t-statistic) (2.42) (2.91) (3.53) (2.70) (2.35) (0.06) (1.04) Working days 2, *** 0.40*** 0.44*** 0.35** 0.45** 0.89** 1.88*** (t-statistic) (5.64) (3.36) (3.60) (2.38) (2.55) (2.16) (2.89) 1 2 months 1, *** 0.99*** 0.58*** 0.45** 0.42** (t-statistic) (4.18) (4.62) (2.84) (2.34) (1.96) (0.60) (0.18) 2 3 months *** 1.10** 1.16*** (t-statistic) (4.82) (2.53) (3.02) (1.51) (0.51) (0.04) (0.23) All trades < 3 months 6, *** 0.58*** 0.55*** 0.42*** 0.41*** (t-statistic) (8.51) (6.61) (6.46) (4.60) (3.75) (1.35) (1.42)

16 442 Journal of Financial and Quantitative Analysis as it does not fit our criteria for a swing trade. This particular trade sequence would be classified as a purchase followed by a sale, and then a sale followed by a purchase. We calculate the return using the actual volume-weighted average price (VWAP) that the fund manager obtains for their trades, and we subtract (add) the explicit transaction costs from post-purchase (post-sale) excess return. We calculate all returns as excess returns relative to the S&P/ASX 300 Index Return, although using actual returns yields similar results. In the last 2 rows of Panel A of Table 4, we observe that fund managers profit from all trades in the BSB swing trading sequence, as they do for the SBS sequence in Panel B, which confirms hypothesis H1. In total, the net excess return to 5 days after the 2nd purchase is 1.69% (0.82 ( 0.59) ). In Panel B, fund managers profit after the initial sale and the reversing purchase, but not after the subsequent sale. The total net excess return to sales in Panel B is 0.71% ( ( 0.58) (0.42)). After partitioning these short-term trading sequences by the number of days in which they take place, we find that trade reversals over intervals of less than 5 days (a short window, indeed) are not profitable. However, trades taking place over a longer window appear to be profitable. For periods ranging up to 3 months, we not only evaluate the profitability of all reversed trades, such as the sequences buy-sell-buy, sell-buy-sell, buysell, and sell-buy, but we also evaluate all nonreversed trades that consist of simply a stand-alone buy or sell within every 3-month horizon. Hence, we are able to identify any ex post selection or look-ahead bias engendered by focusing solely on reversed trades. Were we to ignore the profitability or otherwise of simpler nonreversed trades, we could artificially inflate the apparent profitability of our trade sequences such as buy-sell-buy that include reversed trades. We evaluate the profitability of nonreversed trades by marking-to-market at the end of the 3-month period, as we do for all trade sequences. Similarly, we assess the profitability of nonreversed sale decisions by treating as a notional profit the difference between the initial sale price and the repurchase price after the lapse of 3 months, if this is even lower. If it is higher, then we attribute a notional loss to the initial sale. If the fund manager has no abnormal trading ability, then the excess profit from the ex post-selected swing-trading sequences will either be offset or more than offset by losses from the nonreversed marked-to-market trades at the end of the 3-month period. If the fund manager possesses trading ability in terms of the initial purchase or sale decision yet possesses no additional skills in terms of sequences of swing trades, then there will be no difference in profitability between swing and nonswing trades (i.e., nonreversed, single purchases or sales, or a buy followed by a sell or sell followed by a buy). Finally, if the fund manager s actions indicate access to valuable information about future firm performance that displays sequences of both good and bad news, then the profitability of the swing trades will be higher than the profitability of the nonswing trades. In Table 5, we aggregate all buys (sells) that have not been reversed, labeled Buy Only (Sell Only); trades that have been reversed only once, labeled Buy-Sell Only (Sell-Buy Only); and swing trades, labeled Buy-Sell-Buy (Sell-Buy-Sell). We find for both buys and sells that swing trades are more profitable than those

17 Gallagher, Gardner, and Swan 443 trades that managers do not reverse, as well as those trades managers reverse only once, suggesting managers do indeed have sufficient access to information to profit from swing trading. Hence, we conclude that the profitability of our swingtrading sequences is not due to ex post selection of these sequences when we consider all other possible short-term trading strategies over a 3-month window, further supporting our 1st hypothesis. TABLE 5 Aggregate Profitability of a Variety of Multiple Institutional Investor Trade Sequences Table 5 measures the return of sequences of trades over a 3-month window with each sequence marked-to-market at window close. Buy (Sell) Only refers to nonreversed trades within the 3-month window. Buy-Sell (Sell-Buy) Only refers to trades that are reversed once only during the 3-month window. The sequence Buy-Sell-Buy ( Sell-Buy-Sell ) refers to reversal of trades followed by repurchase (resale) within the 3-month window. We calculate the excess return as the difference between the stock return and the S&P/ASX 300 Index Return. All figures not in parentheses are in percentages. *, **, and *** indicate significance at the 90%, 95%, and 99% confidence intervals, respectively. The numbers in parentheses are t-statistics. Profitability of Trade Sequences Excess Return Swing Sequence Return Excess Return (aft. trans. costs) Buy-Sell-Buy 2.72*** 1.80*** 0.90*** (t-statistic) (19.27) (13.83) (6.93) Buy-Sell Only ** 0.11 (t-statistic) (1.49) (2.03) (0.44) Buy Only 0.86*** 0.25*** 0.55*** (t-statistic) (9.85) (3.06) (6.66) Sell-Buy-Sell 1.04*** 1.39*** 0.49*** (t-statistic) (6.75) (9.72) (3.44) Sell-Buy Only 0.59*** 0.67*** 0.07 (t-statistic) (3.21) (3.93) (0.41) Sell Only 0.20** 1.06*** 0.76*** (t-statistic) (2.04) (10.96) (7.87) Figure 1 displays the average excess return around all swing trades made over an interval of less than 3 months, showing that over short-term intervals, investment managers on average appear to be able to buy when the stock price is low and sell when it is high. This is as if they are able to observe a (correlated) signal of future stock price. Fund managers express different proclivities with respect to swing trading, with 4 funds executing 70% of swing trades (by number of trades). In unreported results, we complete tests using the trades of these 4 managers, as well as the trades of the remaining sample, finding the difference between these 2 partitions is minimal. There is no consistent fund-manager style or size difference. There is also no identifiable difference in the performance of these funds. However, it is possible to attribute the contribution to outperformance of these fund managers to their increased volume of swing trades. These swing trades comprise only 1.4% of the average manager s excess performance over the S&P/ASX 300 Index Return (i.e., only a very small 1.4% of the 2.25 percentage point outperformance of our sample, i.e., percentage points). For the 4 most active funds, the contribution is higher, as they comprise 2.6% of the excess performance of these funds. Note that the firm outperformance subsequent to the swing trades is not included in the computation of swing-trading profitability. While swing trades account for

18 444 Journal of Financial and Quantitative Analysis FIGURE 1 Excess Return (After Transaction Costs) Around Swing Trades Figure 1 displays stock excess performance (over the S&P/ASX 300 Index Return) around the following swing-trade sequences: i) purchase, sale, purchase and ii) sale, purchase, sale. These trade sequences occur over less than 3 months. We used the VWAP as reported by the fund manager. We subtract transaction costs from (added to) the excess return after purchases (sales). 38.9% of overall manager trading volume (as measured by the dollar value), they account for a more significant 63.4% for the 4 largest swing traders. These findings elicit surprise from a control perspective, as they show that fund managers have substantial short-term turnover, which accounts for only a small (yet significant) portion of their overall excess performance. These shortterm trades do not detract value, but rather result from superior information on the part of institutional investors, in support of H1. Next, we test Edmans (2009) conjecture giving rise to H2: that there exists an optimal size of our manager s holding due to information increasing in initial holdings and the existence of liquidity constraints. We do this by including the manager s percentage holding as well as the squared value of the manager s percentage holding in the trade profitability regression analysis presented in Table 6 (model 4). We not only find support for H2 but also utilize the regression coefficients of the estimated quadratic profit function to find that the profit-maximizing percentage holding of a stock to optimize swing-trading profits is high at 2.96%. The average (standard deviation of) manager percentage holding in stocks that are swing traded is 0.48% (0.94%), so only around 0.5% of observations are above 2.96%. C. Impact of the Trader Numbers on Trading Profitability A crucial requirement of our framework, inclusive of multiple institutional investor trading, which compels managerial effort, is the receipt by symmetric investors of correlated informational signals and consequently H3, which we also test in Table 6. Incidentally, this hypothesis represents the 1st formal empirical testing of one of the major predictions of the Kyle (1984) model using actual

19 Gallagher, Gardner, and Swan 445 TABLE 6 Impact of Multiple Institutional Investors Swing Trading the Same Stock on Short-Term Trade Profitability Utilizing Regression Analysis In Table 6, we regress the package profitability against a number of variables. The swing trades must have the following trade sequences: i) purchase, sale, purchase or ii) sale, purchase, sale. Profitability is calculated as the excess return (over the S&P/ASX 300 Index Return) earned after the 1st trade, minus the excess return earned after the 2nd trade, plus the return earned in the 5 days following the 3rd trade. The independent variables include the number of different managers trading sequences completed over the previous month (Number of Investment Managers Trading in the Same Month), the maximum percentage deviation in stock holdings (from peak to trough), the manager s percentage holdings of the stock, and the manager s percentage holdings squared. Control variables include log(market Capitalization), Book-to-Market Ratio, and 6m Momentum measures, a dummy variable equal to 1 if the swing sequence was Buy-Sell-Buy (rather than Sell-Buy-Sell), as well as the average change in manager weight over the previous month. *, **, and *** indicate significance at the 90%, 95%, and 99% confidence intervals, respectively. The numbers in parentheses are t-statistics. Model Variable Constant (t-statistic) (0.24) (0.65) (0.49) (0.38) log(market Capitalization) (t-statistic) (1.57) (1.24) (1.64) (1.46) Book-to-Market Ratio (t-statistic) (1.46) (1.49) (1.47) (1.55) 6m Momentum (t-statistic) (1.00) (0.95) (0.68) (1.27) Net Invest. Mgr Change in Position *** (t-statistic) (5.58) Number of Invest. Mgrs Trading in Same Month *** *** *** *** (t-statistic) (4.87) (4.99) (4.98) (4.93) Buy-Sell-Buy Dummy *** *** *** (t-statistic) (3.33) (3.34) (0.83) (3.31) Holdings Percentage Deviation (t-statistic) (1.14) Mgr % Holdings *** (t-statistic) (8.34) Mgr Squared % Holdings *** (t-statistic) (5.77) No. of obs. 13,046 13,046 13,046 13,046 R % 0.31% 0.54% 1.04% trading data. Importantly, we find that trading profitability does not disappear with just 2 or a few traders, as in the dynamic models of Holden and Subrahmanyam (1992) and Foster and Viswanathan (1993), (1996). Experimental evidence might explain this. Informed insider trading conforms to the model when subjects know the number of agents beforehand, but fails when the number of insiders is unknown (Schnitzlein (2002)). In our sample, there is a large pool of potentially informed traders, but it must be very difficult for any individual trader to predict the number of competitors when the individual/institution decides to trade. After controlling for a variety of factors including stock size, book-to-market ratio, and momentum, we find that institutional trading profitability reduces by 24 bp for each extra investor trading a stock. If the signals were uncorrelated, then profitability would be unaffected by the number of investment managers trading. D. Effect of the Number of Institutional Investors on the Bid-Ask Spread The Kyle (1984), (1989) models, together with Holden and Subrahmanyam (1992) and Foster and Viswanathan (1993), (1996), predict that market depth should be greater and thus bid-ask spreads lower as the number of informed

20 446 Journal of Financial and Quantitative Analysis investors trading simultaneously increases. This then constitutes H4A, predicting that more informed traders acting simultaneously lower the spread. This is because trading more rapidly reduces asymmetric information the greater is the number of informed traders and the more informed is each trader. As noted previously, this spread reduction is related to, but not identical with, the more fundamental but not strictly observable price informativeness concept. We calculate the relative time-weighted bid-ask spread for the ith stock and tth period using intraday SEATS data provided by SIRCA based on the formula Spread i, t = n (Ask i, j Bid i, j ) Time i, j n. (Ask i, j + Bid i, j) 2 Time i, j j=1 j=1 In Table 7 we investigate the impact of investors swing trading the same stocks on the bid-ask spread using regression analysis. In the presence of informed traders, one expects reduced liquidity and depth due to greater adverse selection (see Heflin and Shaw (2000) for evidence). However, following intense swing-trading TABLE 7 Impact of the Number of Institutional Investors Trading Simultaneously on the Relative Time-Weighted Spread Utilizing Regression Analysis In Table 7, we regress the change in (models 1 3) and actual (models 4 5) relative time-weighted spread against a number of variables before and after swing trades. The dependent variable for models 1 3 is the percentage change in spread, and for models 4 5 it is the spread after a swing-trade package. The swing trades must have the following trade sequences: i) purchase, sale, purchase or ii) sale, purchase, sale. The independent variables include variables equal to the number of different managers swing trading sequences completed over the previous month, the maximum percentage deviation in stock holdings (from peak to trough), the manager s percentage holdings of the stock, and the manager s percentage holdings squared. Control variables include stock size (log(market Capitalization)), Book-to-Market Ratio, and 6m Momentum, the spread before the swing package, as well as the average change in manager weight over the previous month. *, **, and *** indicate significance at the 90%, 95%, and 99% confidence intervals, respectively. The numbers in parentheses are t-statistics. Model Variable Constant *** *** (t-statistic) (0.81) (0.08) (0.78) (19.55) (19.59) Size (log(market Capitalization)) ** * ** *** *** (t-statistic) (2.31) (1.80) (1.99) (11.15) (11.58) Book-to-Market Ratio *** *** (t-statistic) (0.02) (0.02) (0.08) (2.75) (2.90) 6m Momentum *** *** *** *** *** (t-statistic) (16.34) (16.40) (16.33) (9.83) (9.76) Net Invest. Mgr Change in Position * (t-statistic) (1.66) (1.59) (1.64) (0.91) (0.90) No. of Invest. Mgrs Using Swing Trades *** *** *** *** *** (t-statistic) (7.49) (7.69) (7.38) (18.11) (18.24) Holdings Percentage Deviation * (t-statistic) (1.77) Mgr % Holdings *** *** (t-statistic) (3.74) (6.49) Mgr Squared % Holdings *** *** (t-statistic) (3.66) (5.50) Spread Before *** *** (t-statistic) (173.71) (172.50) No. of obs. 13,033 13,033 13,033 13,033 13,033 R % 2.39% 2.48% 78.16% 78.23%