Gallagher, Gardner, Swan ('13) - Gov

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JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 48, No. 2, Apr. 2013, pp. 427458COPYRIGHT 2013, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195

doi:10.1017/S0022109013000203

Governance through Trading: InstitutionalSwing Trades and Subsequent FirmPerformance

David R. Gallagher, Peter A. Gardner, and Peter L. Swan

Abstract

Using unique daily fund-manager trade data, we examine the role of institutional tradingin influencing firm performance. We show that short-horizon informed trading by multi-ple institutional investors effectively disciplines corporate management. Our focus is onshort-term swing trades, sequences with three phases (e.g., buy-sell-buy). We find swingtrades increase stock price informativeness, are profitable after costs, and improve marketefficiency. This increase in stock price informativeness is associated with subsequent firmoutperformance. Trades are most beneficial with optimal stock holdings that reflect theinformation acquisition incentives of investors as well as liquidity costs.

I. Introduction

This paper demonstrates that institutional investors can exert governance

through trading a firms shares. In particular, we show that informed order se-

quences by institutional investors are associated with improved stock price infor-

mativeness 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, [email protected], Centre for International Finance and Regulation, Univer-sity of New South Wales, Sydney NSW 2502, Australia, Macquarie Graduate School of Management,Macquarie University, and Capital Markets CRC Limited; Gardner, [email protected],Plato Investment Management Limited, 60 Margaret St, Sydney 2000, Australia; and Swan,[email protected], Australian School of Business, University of New South Wales, SydneyNSW 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,Andre 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 Associ-ation Conference in Bergen, Norway, China International Finance Conference, 2009, Financial In-tegrity Research Network Research Day, University of Sydney Microstructure Meeting, University ofWestern Australia, University of Queensland, University of Adelaide, Reserve Bank of Australia, andAustralasian Finance and Banking Conference. We gratefully acknowledge research funding from theAustralian Research Council (DP0346064).

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428 Journal of Financial and Quantitative Analysis

better fundamental value translates into better managerial actions and higher sub-

sequent performance.

Neither standard models of governance nor previous empirical studies con-

sider 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 cor-

porate 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 stockprice more sensitive to these actions, stock-incentivized managers exert more ef-

fort on behalf of shareholders, thereby improving performance. We refer to this

phenomenon as governance through trading. This term, governance, acknowl-

edges that these investor trades do indeed improve firm value, rather than simply

exiting a stock position that could be nondisciplinary in nature. This alternate the-

ory 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 badactions and rise more rapidly in response to good actions as stock price informa-

tiveness 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 dataon blockholders below 5%.

In order to identify our informed order sequences, we use a unique and pro-

prietary data set of daily institutional trades of fund managers, including trans-

action costs and executing broker details, as well as their month-end portfolio

holdings. These unique data enable us to specify each funds 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 Securitiesand 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 funds

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


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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 gover-

nance 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., comprehen-

sive 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 thereis 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 mi-

crostructure models of multiple informed traders in receipt of the same (or

correlated) signals, with 3 sequences executed through signed order flow: buy-

sell-buy (BSB) or sell-buy-sell (SBS) trade packages. The justification for fo-

cusing 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 information-

based trading, since the investor may simply be making a strategic decision toalter 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 se-

quences 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 trad-ing patterns have no value to the ultimate investor (Dow and Gorton (1997)). As

we demonstrate below, these trade sequences are indeed profitable after transac-

tion costs, in support of the informed-trader microstructure literature such as Kyle

(1984), (1985), (1989) and ruling out agency failure. Notably, an important fea-

ture of our unique data is that they contain the investment managers 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 in-

formativeness is the decline in the relative spread as informed trading drives pricetoward 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


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establishing that such trading is informed. Returns are generally highly statisti-

cally significant, and this remains true even after transaction costs. Third, swing-

trading 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 orig-

inal spread were 1%, then each additional manager would reduce the spread by

0.0092%.

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 managers actions having a more sensitive effect

on stock price when stock price is more informative and with the governance-through-trading models of Holmstrom and Tirole (1993), Admati and Pfleiderer

(2009), Edmans (2009), and Edmans and Manso (2011). For example, the pres-

ence of a swing-trade sequence in the previous month is associated with an ap-

proximately 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 differ-

ent from 0, but that is also significantly lower at the 1% level than the firm perfor-mance 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 sig-

nificant future increases in firm performance. Seventh, swing-trading profitabil-

ity improves in the percentage size of the investment managers initial holdings

until the manager reaches an optimal holding size of 2.96% of shares outstand-

ing. 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 initialstock holdings to apparent future outperformance over the following year is op-

timal 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 devel-

ops 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 risk-

neutral insider trading in the presence of random noise traders and competitive

risk-neutral market makers, and by introducing a crucial concept, stock price in-

formativeness. Admati and Pfleiderer (1988), Holden and Subrahmanyam (1992),


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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 incorpo-

rating the firm manager into Kyles (1985) original single informed-trader frame-

work. As stock price becomes more informative of the managers 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 understand-

ing 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 problemin a framework that differs from Admati and Pfleiderer (2009) in that informa-

tion acquisition is endogenous.3 An important conceptual advance in Admati and

Pfleiderer (2009) and Edmans (2009) is recognizing that most institutional in-

vestors (blockholders) do not directly intervene in an effort to influence manage-

rial 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 un-

restricted short-selling, block size is irrelevant to information acquisition, as in

Holmstrom and Tirole (1993), or information acquisition and thus block size isexogenous, 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 informa-

tion 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,

1In Faure-Grimaud and Gromb (2004), price informativeness is relevant, but not because it im-proves the managers incentive to perform (there is no managerial action) but because it encouragesother investors to make value-adding interventions.

2First, the manager may take an action that is bad from the perspective of shareholders but thatprivately benefits the manager. Second, the manager may take action that is good for shareholders butfor which the manager incurs a private cost. The authors show that it can be credible to threaten exit inthe 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.3In his model of the threat of exit, the manager who fails to meet earnings targets is adversely im-pacted. The manager therefore cares about short-term pricing. However, if the manager does more tobenefit shareholders by making long-term intangible investments, this can adversely affect the short-term public signal. For example, the firm might report low earnings, but the public is not aware thatthis is due to the manager making the correct value-maximizing long-term investment in invisibleintangible assets. An informed large blockholder can help to overcome the resulting managerial my-opia by not selling when the public receives the bad signal if they are able to discern the managerslong-term investment (good private signal).


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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 (penal-

izes) 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 in-

formed traders to firm value, with their main prediction being that subsequent

outperformance increases in price informativeness. A determinant of price infor-

mativeness 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 andMansos (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 ad-

vantage. 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 explana-

tion is not due simply to liquidity. Larger holdings by institutional investors meangreater 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 approx-

imately 2% per annum, even when not trading at all, most probably because of

better stock selection. McCahery, Sautner, and Starks (2011) provide a strong en-

dorsement 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 longer-

term sequences that do not alter in sign, contrary to our BSB or SBS swing se-

quences. 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


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returns. In contrast, swing trades are relatively neutral with respect to net hold-

ings, which change little and thus can encapsulate an aggressive informed trading

story while also being inconsistent with simple optimism or pessimism infor-

mational 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 signifi-

cant 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 firms 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 guidanceas to when one should measure these determinants of stock price informativeness,

for example, the number of simultaneous traders or even stock price informative-

ness 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 in-

formed 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 pro-

portion 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.

4By contrast, Dow and Gorton (1997) explain allegedly excessive trading (from a control per-spective) by institutional investors with respect to delegated portfolio management, in terms of clientsunable to distinguish between managers simply doing nothing and actively doing nothing by trad-ing to excess (e.g., by churning). While possibly less pejorative, we believe the term swing tradesis a more apt description.


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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 postselection biases.5 Hence, our 1st hypothesis, stemming from the informed-trader

models of Kyle (1984), (1985), (1989), Holden and Subrahmanyam (1992), Fos-

ter 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 man-agers 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 com-

petitive 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 multi-

ple traders in receipt of the same signal, such as Kyle (1984) and Admati andPfleiderer (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 prof-

itability. These dynamic models even question our 1st hypothesis, H1, as they pre-

dict 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 in-

vestors trading simultaneously when institutional investors execute swing trades.

It is an implication of the microstructure literature (e.g., Kyle (1984), Admatiand Pfleiderer (1988), and Edmans and Manso (2011)) that market resiliency (the

inverse of Kyles (1985) market impact, ) is increasing in the number of in-formed traders trading simultaneously. Hence, our 4th hypothesis asserts that

5It is critical to exclude ex post selection bias, sometimes known as look-ahead bias, whenassessing the profitability or otherwise of swing-trade sequences relative to other sequences or indi-vidual trades. Bias arises if the definition of a trading sequence such as buy-sell-buy uses informationto define the 3rd sequence that was not available at the times of the 1st and 2nd sequences. For ex-

ample, suppose a manager buys a stock when it is below his target price, sells it when it rises abovehis target price, and buys it again when it falls below his target price. A simple crude computationof profitability summed over each stage and based on the prices at each stage would falsely find thatthis sequence was profitable, whereas the 2nd successful buy was unknown at the time of the initialbuy followed by a sell trade. Hence, one reaches a false inference of trading prowess. We over-come this bias when making comparisons by marking all possible trade sequences, such as a singlebuy or buy followed by a sell, or a swing-trade sequence to-market at the end of the same3-month horizon so that the profitability of every sequence is computed precisely on the same uniformbasis with no ex post selection bias.


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H4A. Pricing efficiency, as captured by lower bid-ask spreads, is increasing in

the number of institutional investors who are trading simultaneously when insti-

tutional investors execute swing trades.

Given a direct connection between price informativeness and trade aggres-siveness, 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 dimin-

ishing rate.

Our 5th hypothesis stems from the postulated role of stock price informative-ness 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 dis-

cussed 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 firms subsequent outperfor-

mance.

There are other sequences besides swing trades exhibiting trade reversals,

potentially indicating information. Others include buy-sell and sell-buy within ourshort-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 tradi-

tional 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 de-terminants of informativeness (i.e., the number of informed blockholders choosing

to trade (H5Sub2) and the magnitude of swing trading (H5Sub3)) will add ex-

planatory 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 toH5Sub2), and dummy variables for blockholders that choose not to trade should

reveal that price informativeness and swing trading are irrelevant. Only the dummyvariables 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 pre-

vious periods when they did trade. In point of fact, our sample of investment


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manager blockholders only hold a portion of the larger stocks making up the

Standard & Poors (S&P) 200 and S&P 300 Index Returns and display average

outperformance of approximately 2% per annum. Thus, the greater the impor-

tance 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 statesH7A. The larger the investment managers 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 identifica-

tion 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-pooledAustralian 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 Consult-

ing, and it contains historical information from January 2, 1994, to June 30, 2002.

The data fields obtained for daily trading activities include the date of execu-

tion, 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 Indus-

try Research Centre of the Asia-Pacific) Limited provided access to the Aspect


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Huntley Fin Analysis accounting database for the calculation of book-to-market

ratios.

Our sample of actively managed institutional Australian equity funds em-

ployed in this study comprises 38 funds from 30 unique active institutions. Bench-

marking 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 sam-

ple 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 2130, and from 14 managers from outside the largest

30 (of funds under management as of December 31, 2001). The sample also in-

cludes 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 ownedby Standard & Poors). However, if the data that fund managers provide is repre-

sentative 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 inthe Portfolio Analytics Database (PAD) sample. In Panel A (Panel D), we measure the average number of stocks (indexweight) each month where individual managers in the PAD sample hold over 0.5%5% of the total market capitalization ofa company. In Panel B, we measure the combined holding of all managers in our sample. Panel C (Panel E) measures theaverage number of stocks (index weight) each month in which our managers have engaged in swing trading when theyhold 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 25 33 44 69 128 209Median 25 33 47 74 111 182StDev 10 12 16 23 47 81Minimum 2 2 2 9 25 46Maximum 43 52 69 113 225 385

Panel B. Number of Stocks Held by All Sample Managers Holding Over x%

Mean 30 42 58 86 125 157Median 33 46 56 78 126 156StDev 12 17 25 32 36 36Minimum 2 2 2 13 29 48Maximum 49 67 99 136 179 208

(continued on next page)

6A pertinent question is how large does a database have to be to gain status as representative ofthe population? Perhaps the most famous database used for studies of trading behavior is the largeU.S. discount brokerage set of individual households utilized by Barber and Odean (2000). Thesehouseholds 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 toBarber and Odean, our database coverage is very large indeed.


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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 2.4 3.4 5.0 8.1 16.8 30.5Median 2.0 3.0 4.0 8.0 15.0 25.0StDev 2.2 2.7 3.6 5.5 12.0 22.5Minimum 1.0Maximum 11.0 15.0 16.0 22.0 47.0 85.0

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 requir-

ing 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. Trans-action 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 managers gross return to obtain


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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 sig-

nificant 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-SortedInstitutional 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 prior6-month portfolio turnover level (the ranking period). Then we compute average statistics for each quartile (according totheir 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 an-nualize these statistics and calculate over all semiannual periods. *, **, and *** indicate significance at the 90%, 95%, and99% confidence intervals, respectively.

Gross NetGross Excess Transaction Net Excess

Avg. Turnover Return Return CS CT AS Costs Return ReturnFractile No. (%/year) (%/year) (%/year) (%/year) (%/year) (%/year) (%/year) (%/year) (%/year)

Top 25% 6.6 114.6 9.49 3.07 1.77 1.40 6.32 0.80 8.13 1.712nd 25% 5.9 77.8 10.33 3.92 2.92 0.97 6.44 0.54 9.23 2.823rd 25% 6.3 62.2 9.93 3.40 2.40 0.52 7.01 0.46 8.91 2.38Bottom 25% 5.6 44.3 9.83 3.27 2.00 0.74 7.09 0.41 8.86 2.30

Top-Bottom 25% 6.1 70.3*** 0.34 0.20 0.23 0.66 0.77 0.39*** 0.73 0.59

All Funds 6.1 76.1 9.88 3.40 2.26 0.92 6.70 0.56 8.76 2.28

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 witha 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

7One might question whether these trade sequences are due to fund inflows and outflows. As thesefunds are institutional in nature, small regular fund flows that would cause these trade sequences areunlikely; however, for robustness, we conservatively removed all buy (sell) trades in months wherethere were fund inflows (outflows). This removed 12% of trades in our sample but yielded similar re-sults. In addition, our later finding of swing-trade profitability supports our suggestion that these tradesequences are not the result of fund flows, which we would not expect to be predictive of individualstock returns.


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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 splitour 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), whouse a 5-day gap definition of a package, implying that a new package begins when there is a 5-day gap between managertrades (in the same direction), or when the manager executes a trade in the opposite direction. Principal refers to the totaltraded value. The sample comprises all trades of 30 active Australian investment managers during the period January 2,1994June 30, 2002.

Nature of Buy, Sell,and Swing Packages 1 Day 23 Days 46 Days 710 Days 11+ Days

Panel A. All Buys (41,781 packages, $46.1 billion principal)

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)

principal

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)principal3 12.5% 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)

principal4 21.9% 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)

principal5 (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)

principal

Panel B. All Sells (32,609 packages, $35.4 billion principal)

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)





principal

Panel C. Swing Buys (12,698 packages, $16.8 billion principal)

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)




principal5 (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)principal

Panel D. Swing Sells (12,240 packages, $14.9 billion principal)

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)





principal

fund-manager trades, we find packages make up, on average, 90% of an aver-

age days 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.


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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 investi-

gate 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 Costsover 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-handcolumn. Return is calculated using the institutional investment managers actual average trade package price such thatthe excess return After Buy, Before Sell is calculated as the price return between the purchase and sale price that themanager actually obtained less the market return from the 1st day of the purchase to the 1st day of the sale. We modeltransaction costs as per the description in the text, and subtract from returns after purchases, but add to returns followingsales. 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 AfterBuy, Sell,

Working Days No. of Past 5 Before Before Next 5 Next 10 Next 90 Next 250Trade Allocation Trades Days Sell Buy Days Days Days Days

5 Working days 552 0.07 0.08 0.12 0.40 0.22 0.17 1.56(t-statistic) (0.31) (0.55) (0.70) (1.23) (0.57) (0.22) (1.26)

610 Working days 1,290 0.40*** 0.26** 0.31*** 0.03 0.02 1.12** 1.51*(t-statistic) (3.04) (1.97) (2.64) (0.13) (0.08) (2.12) (1.95)

1121 Working days 2,326 0.95*** 0.68*** 0.62*** 0.36** 0.33* 0.04 0.42(t-statistic) (9.94) (5.10) (5.18) (2.36) (1.91) (0.11) (0.71)

12 months 1,936 1.28*** 1.54*** 0.83*** 0.32** 0.43** 0.38 0.55(t-statistic) (12.03) (8.16) (5.52) (2.08) (2.40) (0.95) (0.83)

23 months 856 1.11*** 1.04*** 0.86** 0.24 0.09 0.17 0.50(t-statistic) (6.14) (2.64) (2.18) (0.98) (0.30) (0.24) (0.46)

All trades < 3 months 6,960 0.89*** 0.82*** 0.59*** 0.28*** 0.26** 0.36 0.63*(t-statistic) (15.47) (9.27) (7.40) (3.19) (2.57) (1.62) (1.81)

Panel B. Sell-Buy-Sell Swing Trade

After AfterSell, Buy,

Working Days No. of Past 5 Before Before Next 5 Next 10 Next 90 Next 250Trade Allocation Trades Days Buy Sell Days Days Days Days

5 Working days 501 0.40 0.16 0.38 0.32 0.26 1.14 1.72(t-statistic) (1.53) (0.91) (1.64) (0.89) (0.65) (1.26) (1.28)

610 Working days 1,399 0.29** 0.30*** 0.41*** 0.48*** 0.49** 0.03 0.80(t-statistic) (2.42) (2.91) (3.53) (2.70) (2.35) (0.06) (1.04)

1121 Working days 2,125 0.54*** 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)

12 months 1,586 0.52*** 0.99*** 0.58*** 0.45** 0.42** 0.27 0.13(t-statistic) (4.18) (4.62) (2.84) (2.34) (1.96) (0.60) (0.18)

23 months 735 0.77*** 1.10** 1.16*** 0.47 0.22 0.03 0.26(t-statistic) (4.82) (2.53) (3.02) (1.51) (0.51) (0.04) (0.23)

All trades < 3 months 6,346 0.50*** 0.58*** 0.55*** 0.42*** 0.41*** 0.32 0.52(t-statistic) (8.51) (6.61) (6.46) (4.60) (3.75) (1.35) (1.42)


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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 hypothesisH1. In total, the net excess

return to 5 days after the 2nd purchase is 1.69% (0.82 ( 0.59) + 0.28). 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) + 0.55 (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, buy-

sell, 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 byfocusing solely on reversed trades. Were we to ignore the profitability or otherwise

of simpler nonreversed trades, we could artificially inflate the apparent profitabil-

ity 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 dif-

ference 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 excessprofit 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 non-

swing 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 managers actions indi-

cate access to valuable information about future firm performance that displayssequences 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


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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 swing-

trading 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 atwindow close. Buy (Sell) Only refers to nonreversed trades within the 3-month window. Buy-Sell (Sell-Buy) Only refersto trades that are reversed once only during the 3-month window. The sequence Buy-Sell-Buy (Sell-Buy-Sell) refersto 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 parenthe-ses are t-statistics.

Profitability of Trade Sequences

Excess ReturnSwing 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 0.37 0.49** 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% ofthe average managers 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., 0.0315 percentage points). For the 4 most active funds, the contribu-

tion 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


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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 se-quences: 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 returnafter 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 short-

term trades do not detract value, but rather result from superior information on

the part of institutional investors, in support ofH1.

Next, we test Edmans (2009) conjecture giving rise to H2: that there exists

an optimal size of our managers holding due to information increasing in initial

holdings and the existence of liquidity constraints. We do this by including the

managers percentage holding as well as the squared value of the managers per-centage 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 coeffi-

cients 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


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TABLE 6

Impact of Multiple Institutional Investors Swing Trading the Same Stock on Short-TermTrade Profitability Utilizing Regression Analysis

In Table 6, we regress the package profitability against a number of variables. The swing trades must have the followingtrade sequences: i) purchase, sale, purchase or ii) sale, purchase, sale. Profitability is calculated as the excessreturn (overthe S&P/ASX 300 Index Return) earned after the 1st trade, minus the excess return earned after the 2nd trade, plus thereturn earned in the 5 days following the 3rd trade. The independent variables include the number of different managerstrading sequences completed over the previous month (Number of Investment Managers Trading in the Same Month), themaximum percentage deviation in stock holdings (from peak to trough), the managers percentage holdings of the stock,and the managers percentage holdings squared. Control variables include log(Market Capitalization), Book-to-MarketRatio, and 6m Momentum measures, a dummy variable equal to 1 if the swing sequence was Buy-Sell-Buy (rather thanSell-Buy-Sell), as well as the average change in manager weight over the previous month. *, **, and *** indicate significanceat the 90%, 95%, and 99% confidence intervals, respectively. The numbers in parentheses are t-statistics.

Model

Variable 1 2 3 4

Constant 0.0020 0.0059 0.0041 0.0031

(t-statistic) (0.24) (0.65) (0.49) (0.38)

log(Market Capitalization) 0.0006 0.0005 0.0006 0.0006(t-statistic) (1.57) (1.24) (1.64) (1.46)

Book-to-Market Ratio 0.0030 0.0031 0.0030 0.0032(t-statistic) (1.46) (1.49) (1.47) (1.55)

6m Momentum 0.0048 0.0046 0.0033 0.0061(t-statistic) (1.00) (0.95) (0.68) (1.27)

Net Invest. Mgr Change in Position 0.0203***(t-statistic) (5.58)

Number of Invest. Mgrs Trading in Same Month 0.0023*** 0.0024*** 0.0024*** 0.0023***(t-statistic) (4.87) (4.99) (4.98) (4.93)

Buy-Sell-Buy Dummy 0.0065*** 0.0065*** 0.0018 0.0064***(t-statistic) (3.33) (3.34) (0.83) (3.31)

Holdings Percentage Deviation 0.0038(t-statistic) (1.14)

Mgr % Holdings 2.6243***(t-statistic) (8.34)

Mgr Squared % Holdings 44.3681***(t-statistic) (5.77)

No. of obs. 13,046 13,046 13,046 13,046R2 0.30% 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 mightexplain this. Informed insider trading conforms to the model when subjects know

the number of agents beforehand, but fails when the number of insiders is un-

known (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 prof-

itability 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


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investors trading simultaneously increases. This then constitutes H4A, predict-

ing 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 pre-

viously, this spread reduction is related to, but not identical with, the more funda-

mental but not strictly observable price informativeness concept.

We calculate the relative time-weighted bid-ask spread for the ith stock andtth period using intraday SEATS data provided by SIRCA based on the formula

Spreadi, t =

n

j=1

(Aski,j Bidi,j) Timei,j

n

j=1

(Aski, j + Bidi, j)2

Timei,j

.

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 RelativeTime-Weighted Spread Utilizing Regression Analysis

In Table 7, we regress the change in (models 13) and actual (models 45) relative time-weighted spread against anumber of variables before and after swing trades. The dependent variable for models 13 is the percentage change inspread, and for models 45 it is the spread after a swing-trade package. The swing trades must have the following tradesequences: i) purchase, sale, purchase or ii) sale, purchase, sale. The independent variables include variables equal to thenumber of different managers swing trading sequences completed over the previous month, the maximum percentagedeviation in stock holdings (from peak to trough), the managers percentage holdings of the stock, and the managerspercentage holdings squared. Control variables include stock size (log(Market Capitalization)), Book-to-Market Ratio, and6m Momentum, the spread before the swing package, as well as the average change in manager weight over the previousmonth. *, **, and *** indicate significance at the 90%, 95%, and 99% confidence intervals, respectively. The numbers inparentheses are t-statistics.

Model

Variable 1 2 3 4 5

Constant 0.0127 0.0014 0.0123 0.2459*** 0.2471***(t-statistic) (0.81) (0.08) (0.78) (19.55) (19.59)

Size (log(Market Capitalization)) 0.0017** 0.0014* 0.0015** 0.0062*** 0.0064***(t-statistic) (2.31) (1.80) (1.99) (11.15) (11.58)

Book-to-Market Ratio 0.0001 0.0001 0.0003 0.0078*** 0.0082***(t-statistic) (0.02) (0.02) (0.08) (2.75) (2.90)

6m Momentum 0.1477*** 0.1484*** 0.1477*** 0.0648*** 0.0643***(t-statistic) (16.34) (16.40) (16.33) (9.83) (9.76)

Net Invest. Mgr Change in Position 0.0105* 0.0101 0.0104 0.0042 0.0041(t-statistic) (1.66) (1.59) (1.64) (0.91) (0.90)

No. of Invest. Mgrs Using Swing Trades 0.0068*** 0.0071*** 0.0067*** 0.0126*** 0.0127***(t-statistic) (7.49) (7.69) (7.38) (18.11) (18.24)

Holdings Percentage Deviation 0.0111*

(t-statistic) (1.77)Mgr % Holdings 2.2327*** 2.8328***(t-statistic) (3.74) (6.49)

Mgr Squared % Holdings 53.3276*** 58.5357***(t-statistic) (3.66) (5.50)

Spread Before 0.7583*** 0.7554***(t-statistic) (173.71) (172.50)

No. of obs. 13,033 13,033 13,033 13,033 13,033R2 2.37% 2.39% 2.48% 78.16% 78.23%


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activity, the most reasonable expectation consistent with our findings is that for-

merly private information is now public. Sources of new private information to

keep spreads wide have dried up, hence spreads now narrow.

Table 7 reports the results, after controlling for a variety of factors including

stock size, book-to-market ratio, and momentum. We find in support ofH4A that

the greater the number of investors trading simultaneously in the stock, the greater

the reduction in the spread. The average percentage reduction per extra manager

is 0.92% when taken over the 5 sets of estimates. We also find weak evidence

for H4B that the larger the expected degree of trader aggressiveness in the form

of a higher percentage deviation of stock holding from the highest to lowest, the

greater the percentage reduction in the excess spread. Lastly, we find there is no

optimal size of a managers holding to reduce spread, as the greater the holdings

(above 2.09%), the greater the reduction in spread. Spread continues to diminish

in the square of the investment managers initial holdings, even though there arelimits on the incentive to acquire information.

E. Impact of Swing Trading (Nontrading) on Subsequent Firm

Performance

In this section, we determine whether the improvement in price sensitivity to

the managers action caused by informed short-term trades improves subsequent

firm performance (i.e., H5). To confirm the unique nature of our swing-trading

sequence in indicating informed trading, we create 2 dummy variables of alter-native short-term trades, that is, (i) where an investor buys ((ii) sells) and then

sells (buys) within the next 3 months, without buying (selling) again (H5Sub1).

If swing-trading sequences are unique in terms of their ability to improve future

performance, we expect these dummy variables to be less significant than our

swing-trading dummy variable. To test H5Sub2 and H5Sub3, we add the num-

ber of blockholder traders and the percentage deviation of holdings to our spread

reduction measure as measures of trader aggressiveness that our spread reduction

proxy for price informativeness may not fully capture.

We regress subsequent firm performance over the next 12 months againstthese short-term trading variables together with controls. These control variables

consist of the Carhart (1997) risk factors as well as the change in fund-manager

weight. We include these in order to isolate the influence of short-term trading

behavior on firm performance. Due to the overlapping nature of the monthly ob-

servations and 12-monthly excess stock returns in this regression, we calculate t-

statistics using robust standard errors, accounting for clustering in the 12-monthly

excess stock return variable. This removes the effect of the overlapping sample in

biasing t-statistics.

Results reported in Table 8 indicate that firm performance over the subse-quent 12 months is positively and statistically significantly related to whether

institutional investors engage in short-term swing trading (at the 1% level). The

swing-trade dummy row of Table 8 in models 12, 6, and 810 indicates an eco-

nomically significant excess return of approximately 2% to 4.6% due to the com-

pletion of a short-term swing-trading sequence in the previous month. Model 3

shows that Buy-Sell-Buy sequences are far more influential that Sell-Buy-Sell


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TABLE 8 (continued)

Impact of Short-Term Institutional Investor Trading on Subsequent Firm Perfo

Model

Variable 1 2 3 4 5 6

Sell-Buy-Sell Dummy 0.0059(t-statistic) (0.77)

No. of Invest. Mgrs Using Swing Trades 0.0034** 0(t-statistic) (2.29) (2

Holdings Percentage Deviation 0.0192** 0.0084 0(t-statistic) (2.18) (0.85) (1

Spread After / Spread Before Swing Trade 0.0671*** 0(t-statistic) (2.81) (2

SwingTradeDummy Mgr % Holdings (t-statistic)

SwingTradeDummy Mgr Squared % Holdings(t-statistic)

MgrHoldwNoSwingTradeDummy (t-statistic)

MgrHoldNoSwingTradeDummy Mgr % Holdings (t-statistic)

MgrHoldNoSwingTradeDummy Mgr Squared % Holdings(t-statistic)

MgrNoSwingTradeDummy (t-statistic)

MgrHoldwNoTradeDummy

(t-statistic)

No. of obs. 20,945 20,945 20,945 20,945 20,945 20,945 20R2 0.84% 0.85% 0.85% 0.70% 0.72% 0.85% 0


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sequences. The relative lack of statistical significance of other sequences such as

Buy-Sell and Sell-Buy shown in model 2 supports the 1st subsidiary hypothesis,

H5Sub1. Model 4 indicates that the larger is one of our determinants of price

informativeness, namely the holdings percentage deviation during a swing trade,

the higher is future return. Similarly, model 5 indicates that the other determinant,

the number of swing traders, also has a statistically significant effect on future

return.

In models 6 and 7 of Table 8, we include our proxy for stock price informa-

tiveness, namely, the percentage reduction in relative time-weighted spread over

the previous month in response to a swing trade, and thus test H5. Model 6 indi-

cates that this informativeness measure is highly statistically significant and adds

another 42 bp to firm performance for every 10% reduction in spread, even after

including the dummy variable for the swing trade that by itself indicates 3.2%

outperformance. The significance of the spread reduction remains even after re-placing the swing-trade dummy variable by the number of investment managers

using swing trades and the magnitude of the deviation in the swing trade in model

7. Hence, we find support for H5. Since the number of investment managers swing

trading remains significant but not the magnitude of the swing, we find support

for subsidiary hypothesis H5Sub2 but not H5Sub3.

Following Edmans (2009), we also include the managers percentage of

shares outstanding for each stock in the portfolio, and the squared value of the

managers percentage outstanding in models 8 and 9 of Table 8 with similar highly

statistically significant findings. We solve for the maximum firm performanceusing the coefficients in model 8 to show that the optimal manager percentage

holding that maximizes subsequent firm outperformance is 1.94%. As acknowl-

edged in H6, investment managers may possess stock selection skills or utilize

voice in addition to information that results in swing or other forms of trading. It

is therefore essential to set a baseline benchmark for swing trades that recognizes

skills in both stock selection and voice and in trading other than of the swing vari-

ety. In our sample, investment managers hold just over 50% of the available stocks

at any one time. Hence, managers could outperform the market based simply on

their portfolio choice, or by combining portfolio selection with voice.Model 9 of Table 8 is similar to model 8 in that it shows the effect of

holding size and square of holding size on the degree of outperformance, but

it now includes nontraded holdings as well as swing-traded holdings. The results

confirm that the Edmans (2009) informational advantage effect in holding size

extends to nontraded stocks. Again, we can solve for a maximum initial holding

size for nontraders that in model 9 is 2.48%. Hence, firm performance is dimin-

ishing in holding size above 2.48%, indicating that voice is unlikely to have been

successful. This is because larger holdings would normally help to overcome the

free-riding behavior of other blockholders. Thus, if voice were responsible, wewould anticipate finding increasing, not diminishing, returns to the exercise of

voice.

In model 10 of Table 8 we include dummy variables to capture 3 events when

a manager either: i) engages in swing trading, ii) trades in the stock but does not

swing trade, or iii) holds the stock but does not trade. We find that portfolio selec-

tion and voice in the absence of all trading results in baseline firm outperformance


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over the next year of 2.18% over and above the S&P/ASX 300 Index Return.

Hence, the portfolio benchmark is already a high hurdle. Trades in stocks that

represent sequences other than swing trades do not significantly outperform the

portfolio benchmark. However, swing-traded stocks outperform the index by a

considerable 4.62%, as shown by the magnitude of the swing-trade dummy vari-

able. This outperformance is significantly different at the 1% level from stocks

not swing traded.

Our findings on firm outperformance subsequent to swing trades provide in-

sights as to the relative performance contribution to the total return to the invest-

ment manager. Over our entire sample, direct swing-trading profitability explains

only about 3 bp of investment manager outperformance. However, subsequent

outperformance over the next year from holding these stocks adds another 38 bp

of excess return. Hence, the quotient of long- to short-term outperformance is ap-

proximately 12.67 times. Total swing-trading-related outperformance represents18% of the overall outperformance for our sample. While these figures are overall,

for funds that undertake a higher proportion of swing trading, the 1-year outper-

formance is much higher, peaking at 170 bp. For such funds, the indirect benefits

of swing trading contribute to a sizable portion of the investment managers over-

all outperformance.

F. Are Institutional Investors with Larger Holdings More Likely to Swing

Trade?

Edmans (2009) conjectures that the larger the stockholding, the greater the

incentive to gather price-sensitive information (H7A). We also extend (H7A) to

anticipate that the greater the number of swing trades (H7B) and the greater the

magnitude of these trades (H7C), the higher is the managers percentage share of

total holdings. To test these conjectures, for each 3-month period and stock, we

create a swing-trading dummy variable equal to 1 if the fund manager engaged

in swing trading (when a manager has a trading sequence i) buy-sell-buy or

ii) sell-buy-sell) during that quarter and in that stock, and 0 otherwise. For each

manager, our sample only includes those stocks in which a manager has tradedmore than twice (over the life of our sample), as it is unrealistic to expect a man-

ager to swing trade a stock the manager has never held. We regress this dummy

variable using Logit and Tobit regressions against stock and manager character-

istics calculated over the past period as well as the current period. This should

determine whether, for example, past volatility affects fund-manager swing trad-

ing or current volatility.

As established in the extensive microstructure literature, commencing with

Kyle (1984), (1985), trading aggressiveness will be higher, the larger the number

of informed traders. Hence, we include the number of swing trades as one of ourdependent variables that we wish to explain in Table 9, since we believe it captures

informed trading and thus one aspect of trade aggressiveness. Stock characteris-

tics and controls include size, book-to-market ratio, prior 3-month stock momen-

tum, stock volatility, turnover, spread, prior 3-month S&P/ASX 300 Index Return,

and stock return volatility. Measurement takes the form of ranks between 0 and 1;

hence, the smallest (largest) stock receives a value of 0 (1) for each quarter.


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TABLE 9 (continued)

Characteristics of Institutional Investor Swing Trades and Relation to Investor Perce

Model

1 2 3 4 5

Swing Swing Swing Swing Holdings Dependent Variable Dummy Dummy Number Performance Deviation Regression Methodology Logit Probit Tobit Tobit Tobit Period Calculation of Independent Variables Previous Previous Previous Previous Previous

3m Relative Bid-Ask Spread 193.28*** 96.76*** 488.63*** 23.56*** 71.77***(t-statistic) (11.30) (14.18) (13.63) (9.63) (13.65)

Stock Turnover (t-statistic)

Invest. Mgr % of Stock Holdings 182.43*** 92.01*** 434.24*** 24.80*** 57.77***

(t-statistic) (29.35) (29.21) (23.92) (18.76) (21.46)

Invest. Mgr Squared % of Stock Holdings 3,818.00*** 1,965.10*** 9,219.30*** 529.57*** 1,234.40***(t-statistic) (19.67) (22.46) (19.80) (15.42) (17.81)

Invest. Mgr Size 0.03*** 0.01*** 0.04*** 0.01** 0.01 (t-statistic) (5.65) (3.36) (2.89) (2.13) (1.59)

Growth Invest. Mgr Dummy 0.13*** 0.05** 0.12 0.01 0.01(t-statistic) (2.64) (2.09) (0.91) (0.47) (0.27)

Value Invest. Mgr Dummy 0.12*** 0.07*** 0.16 0.01 0.04**(t-statistic) (2.32) (2.98) (1.21) (0.72) (2.08)

Style-Neutral Invest. Mgr Dummy 0.11** 0.03 0.29* 0.01 0.01 (t-statistic) (1.97) (1.04) (1.94) (0.42) (0.64)

Prior 6m Invest. Mgr Performance 0.38 0.05 0.82 0.01 0.01(t-statistic) (1.10) (0.25) (0.90) (0.18) (0.02)

Prior 6m Portfolio Turnover 1.76*** 0.80*** 4.16*** 0.21*** 0.64*** (t-statistic) (21.63) (19.07) (19.52) (13.46) (19.15)

No. of obs. 8,283,100 8,283,100 8,283,100 8,283,100 8,283,100 R2 (%) 7.31 6.29 99.8 99.79 99.79


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Manager characteristics include the logarithm of fund size (log(fund size)), style

(dummy variables for growth, v

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