Journal of Banking & Finance 35 (2011) 3383–3399

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Journal of Banking & Finance

journal homepage: www.elsevier .com/locate / jbf

Institutional trading and share returns

F. Douglas Foster a, David R. Gallagher b,c,⇑, Adrian Looi d

a School of Finance, Actuarial Studies and Applied Statistics, The Australian National University, Canberra, ACT 0200, Australiab Macquarie Graduate School of Management, Sydney, NSW 2109, Australiac Capital Markets CRC Limited, Sydney, NSW 2000, Australiad Marshall Wace, London, United Kingdom

a r t i c l e i n f o

Article history:Received 8 March 2010Accepted 26 May 2011Available online 30 May 2011

JEL classification:G23

Keywords:Trading behaviorInformed tradingMarket impactInstitutional trading

0378-4266/$ - see front matter � 2011 Published bydoi:10.1016/j.jbankfin.2011.05.018

⇑ Corresponding author at: Macquarie Graduate SchNSW 2109, Australia. Tel.: +61 2 9850 9975; fax: +61

E-mail addresses: doug.foster@anu.edu.au (F.Dmgsm.edu.au (D.R. Gallagher), adrianlooi77@gmail.co

1 Market impact studies documenting the effect ofinclude Chan and Lakonishok (1995), and Chiyachanta

2 See for example, Chan and Lakonishok (1993, 1995and Chiyachantana et al. (2004).

3 See Lakonishok et al. (1992), Nofsinger and Sias, (1

a b s t r a c t

Using a unique database of daily transactions from Australian equity managers, we investigate the rela-tion between institutional trading and share returns. The 34 institutional investors included in our sam-ple exhibit a statistically and economically significant ability to predict large capitalization share returnsfor the ten days following their trades. Detailed analysis indicates that investment manager style isimportant in understanding the link between institutional trading and stock returns. The contemporane-ous relation between institutional trading and returns depends on trade size, broker use, and investmentstyle. We find growth-oriented managers are momentum traders, while style-neutral and value manag-ers are contrarian.

� 2011 Published by Elsevier B.V.

1. Introduction sample of Australian institutional investors and find that links be-

Professional fund managers, as significant holders of equities,have the capacity to influence share returns and trading volume.Given the enormous value of assets under their management, theseprofessional investors not only comprise a large percentage of dailytrading volume but also have access to a wide pool of resources togather costly information and develop expertise. As such, key insti-tutional investors have the capacity to move prices both directlythrough their own trading, as well as indirectly by influencingthe trading decisions of other market participants who may ob-serve their actions.1 The literature shows individual institutionaltrades have a permanent price impact,2 suggesting that in aggregate,researchers should expect to observe active fund managers movingprices through trading. Additionally, research examining changesin the periodic holdings of fund managers indicate that increases(decreases) in holdings are contemporaneously correlated withincreasing (decreasing) stock prices.3 We use detailed data from a

Elsevier B.V.

ool of Management, Sydney,2 9850 9942.

. Foster), david.gallagher@m (A. Looi)trade activity on stock pricesna et al. (2004).), Keim and Madhavan (1997),

999), and Wermers (1999).

tween institutional investor trades and stock returns are morenuanced than current literature suggests.

Although our sample is limited in a number of ways (time per-iod covered, number of funds, shares universe, and size of localmarket), our study provides new and interesting insights, whilebeing consistent with a significant body of prior work. Using dailyinstitutional investor trade data, we find no contemporaneous rela-tion between stock returns and aggregate manager trading basedon either the number of managers buying (selling), or the total vol-ume of their purchases (sales). While this result at first seemscounterintuitive, it can be reconciled with prior studies in thatthe contemporaneous price reaction depends on a fund manager’sinvestment style. Value managers are contrarian and may act asprice stabilizers; they provide liquidity to the market during peri-ods of high volatility through buying on weakness and selling onstrength. Hence, value manager trading yields a negative relationwith contemporaneous stock returns. Conversely, growth manag-ers tend to buy (sell) shares whose price is rising (falling), sogrowth managers trading is positively correlated with contempora-neous stock returns. In aggregate, the net contemporaneous effectof both value and growth manager trading can be inconsequential.

We explore possible price stabilization by closely examiningvalue manager trading activity, and find their ability to obtain anegative correlation with contemporaneous stock returns requiresunstable (or highly volatile) intraday stock prices. Therefore,when measuring the overall average market impact of value

3384 F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399

managers we must take into account market volatility (as a proxyfor the likelihood of a price stabilization trade), or we may intro-duce a downward bias to the average market impact estimate.We find the relation between market impact and volatility de-pends on manager style – the market impact incurred by growthmanagers has little relation with volatility, while for value man-agers, high volatility is associated with a negative market impactfrom trade.

Our findings also have important implications for the study ofinstitutional ownership and stock returns. Empirical studies docu-ment a contemporaneous relation between changes in institutionalholdings and stock returns (on a monthly, quarterly or yearly basis)and imply: (1) institutional traders push prices in the direction oftheir trade through their permanent market impact (price pres-sure); (2) institutional investors are intra-period momentum trad-ers, buying as prices rise during the month, thereby inducing apositive monthly contemporaneous relation; or (3) institutionsare able to predict intra-period stock returns. Without more fre-quent trading data, distinguishing between these three competinghypotheses is difficult. However, with daily trading data testingeach hypothesis is relatively straightforward. We show that aggre-gate manager trading volume is not correlated with contemporane-ous stock returns, rejecting the price pressure hypothesis. We alsoshow that momentum trading depends on investment style:growth managers are momentum traders, while value managersare not. This weakens the intra-period momentum trading hypoth-esis since not all managers are momentum traders. Finally, weshow our sample of fund managers are able to predict future stockreturns, supporting the third hypothesis that manager trading con-tains intra-period information. Hence, our sample suggests thedocumented contemporaneous relation between periodic changesin fund manager holdings and stock returns may be due to fundmanager trading on intra-period information.

Finally, we observe the manner in which investment managerschoose to process their trades. For example, we know which broker(using an established broker identification number through theExchange) was used to facilitate the order. Analysis of the ordersubmission corroborates our breakdown of liquidity and informa-tion-related trades. We argue that when a single fund managersplits their order across many brokers, they are more likely doingso because they have an informed basis for their trade, and thereare likely to be longer-term price consequences. Further, when asingle broker manages a number of similar orders from a rangeof fund managers, it may be a consequence of the broker solicitingliquidity to offset a prior trade. If this is the case, we expect transi-tory price reactions to these trades as the liquidity need is met.Both of these interpretations are confirmed by our data.

The remainder of the paper is organized as follows. Section 2provides a brief background and outlines foundations for ourstudy. Section 3 presents a description of the data and provides ba-sic statistics. Section 4 outlines our research design while Section 5reports the results. Section 6 provides a summary.

5 For example, the liquidity effect is explored in Stoll (1978) and Grossman and

2. Background

Our research is related to studies examining the link betweenchanges in institutional holdings and stock returns; however, weexamine this issue with more detailed (daily) data than previousstudies.4 Prior studies document a strongly positive contemporane-ous relation between changes in institutional ownership and stockreturns on a monthly, quarterly or yearly basis (Grinblatt et al.,1995; Nofsinger and Sias, 1999; Wermers, 1999; Sias et al., 2006).

4 Some recent studies that examine trading and return effects with high frequencydaily data for investors include Keswani and Stolin (2008) and Yan and Zhang (2009).

We explore four main explanations for this result and outline howour work adds to each literature in Sections 2.1–2.4.

2.1. Price pressure

Institutions may push prices in the direction of their trades. Ifactive institutional traders trade on the premise of superior infor-mation, this price pressure may be a result of the information re-vealed through trading. Alternatively, active institutional tradersmay induce a counter-party to trade by offering a liquidity fee,thereby shifting the counter-party away from their preferredinventory positions, which could have a liquidity impact on prices.While we expect such liquidity impacts to be short-lived, sustainedaggregate institutional trading (such as when several large institu-tions transact large trade packages over many days) may create acontemporaneous monthly relation.

The debate between the liquidity and information effects ofinstitutional trading has a long history.5 The empirical researchoverwhelmingly rejects the liquidity hypothesis (Holthausen et al.,1990; Lakonishok et al., 1992). Using data similar to ours, Chanand Lakonishok (1993, 1995) document a positive open-to-trademarket impact for purchases, followed by price continuation ratherthan reversal (even after taking trading packages into account),which supports the information rather than liquidity hypothesis.However, for sales, they document reversal rather than continuation,suggesting liquidity rather than information motivations dominatesales. Prior work has used the observed market impact to determinethe relative strengths of information versus liquidity effects. We takea different approach and develop measures to track the informationcontent of manager trades (versus the potential liquidity impactthey may have on prices).

Liquidity effects are likely to be related to the volume of sharestraded – in inventory models, the liquidity premium demanded byliquidity suppliers is related to the total volume of demandedliquidity rather than the number of traders demanding liquidity(Stoll, 1978; Grossman and Miller, 1988). To proxy for informationeffects we consider the unanticipated number of fund managersbuying or selling on each day. If an institutional investor is tradingbecause of a particular view about future returns, they may be un-able to defer transactions as competition from other fund manag-ers, or public announcement of information would both serve tolimit discretion. From microstructure models we expect theseforces to be especially striking when information is highly corre-lated and when the insight is fully revealed through a public signalin the near future (Foster and Viswanathan, 1996). Accordingly, ifwe see a number of mutual fund managers trading in the samemanner on the same day, we argue that it is more likely that themotive for trade is information-based.6

Our research shows, in aggregate, neither the number of fundstrading, nor the volume of shares purchased and sold by institu-tions is correlated with contemporaneous stock returns. However,we show that the number of value managers purchasing has a neg-ative contemporaneous effect, while that of growth managers ispositive.

Consistent with prior empirical research, our findings supportthe information rather than the liquidity hypothesis. However,we make one important qualification: growth managers pushprices in the direction of their trade due to information; howevervalue managers often act as price stabilizers incurring negativemarket impact for the service of supplying liquidity. Further, we

Miller (1988).6 This method of breaking down the information and liquidity effect by volume and

number of institutions trading is also consistent with the prior empirical work; seeSias et al. (2006).

8

F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399 3385

show the intensity of this price stabilization behavior is positivelyrelated to the level of price instability (or volatility).

2.2. Momentum

Institutions may engage in intra-period momentum trading. Ifinstitutions purchase (sell) as prices rise (fall) during the month,then the monthly contemporaneous relation between changes ininstitutional ownership and stock returns will be positive. A com-mon view of market efficiency is that historical stock returnsshould not be a predictor of future returns, so historical returnsshould not provide systematic ways to build portfolios that outper-form a passive benchmark. However, a number of empirical studiesdocument significant abnormal returns to both contrarian andmomentum trading (DeBondt and Thaler, 1985, 1987; Lo andMacKinlay, 1988; Chan et al., 1996; Jegadeesh and Titman, 2001;George and Hwang, 2004). We contribute to the literature onmomentum trading by documenting the relation between institu-tional trading and short-term past stock returns. Overall, our sam-ple of active Australian equity managers are short-term (over10 days) contrarian traders; however when we partition by invest-ment style, we find that growth oriented investment managers(growth managers and growth-at-a-reasonable-price (GARP) man-agers) are momentum traders, while style neutral and value man-agers are contrarian.

2.3. Return predictability

Institutional traders may possess private information subse-quently impounded in prices after their trades. However, studiesfind a positive correlation between institutional trading and futurestock returns (Daniel et al., 1997; Wermers, 1999; Nofsinger andSias, 1999; Sias et al., 2006; Dorn et al., 2008).7 These findings areconsistent with institutional traders possessing superior informa-tion. A combination of contemporaneous price impact and momen-tum institutional trading may also give the appearance that currentinstitutional trading predicts future stock returns. That is, currentinstitutional trading may simply be predicting future institutionaltrading (De Long et al., 1990). Our study contributes to the literatureby measuring the impact of information while controlling for institu-tional trading volume on a daily basis. We find that, after controllingfor potential liquidity-based price impacts, institutions have signifi-cant power in predicting future stock returns.

2.4. Price destabilization

Our study also contributes to the literature on price destabiliza-tion. De Long et al. (1990) build a model of positive-feedback trad-ers and rational speculators. Positive-feedback traders purchase(sell) as prices rise (fall); however, in doing so, their market impactcauses prices to rise (fall) even further, which in turn providesthem with even more motivation to continue purchasing (selling).In their model, the interaction between rational speculators andpositive-feedback traders increases stock price volatility.

Most empirical studies confirm that institutions engage in posi-tive-feedback trading (Grinblatt et al., 1995; Wermers, 1999).Badrinath and Wahal (2002) however, decompose changes in hold-ings into entry and exit positions and find momentum trading forentry positions, but contrarian trading when exiting. We documentthe relation between daily past returns and institutional trading(partitioned by investment style) and show that value managersare deeply contrarian, while other managers are momentum trad-ers. This finding weakens the argument that institutions as a whole

7 For exceptions see Chan and Lakonishok (1995) and Cai and Zheng (2004).

are price destabilizing, since only a portion of managers engage inpositive-feedback trading. Further, we show that value managerspotentially stabilize prices during periods of high volatility.

Evidence of value manager price stabilization has importantimplications for the market impact literature. Keim and Madhavan(1997) investigate the role of investment style on market impactand find technical and index managers demand immediacy morethan value managers and incur higher market impact costs. Simi-larly, Chan and Lakonishok (1993, 1995), show that value manag-ers on average incur negative open-to-trade market impact costswhile growth managers incur positive costs, presumably due tomore patient trading. This is consistent with our findings thatthe contemporaneous relation between value manager tradingand stock returns is negative. Our research suggests that whenmeasuring the average market impact of value managers, it isimportant to acknowledge that some trades may be done withthe intention of incurring negative market impact as payment forliquidity provision.

Additionally, when fund managers trade, they compete forliquidity, but the degree of competition depends on the styleof the manager involved. We find value managers are often sup-plying liquidity rather than demanding it. Therefore while ex-pected market impact costs can be aggravated by the existenceof other growth managers, other value managers do not increasemarket impact costs because they are less often competing foravailable liquidity. Other studies have considered the effect ofcompetition for liquidity. Chiyachantana et al. (2004) examineinstitutional trades from 37 countries over bull and bear periodsand find market impact costs are higher for purchases duringbull periods, and vice versa for sells. Their explanation is thatduring bull periods, demand for buyer-initiated liquidity is high-er, which causes market impact costs for purchases to be higherthan otherwise.

3. Data

We examine the daily trading of a representative sample of ac-tive Australian equity fund managers during two calendar years forwhich complete trading records are available (2000 and 2001).While there are obvious limitations in using a sample drawn froma few investors in one country over a relatively short period, thedata allow a highly detailed glimpse into the activities of key pro-fessional investors. The particular circumstances of this market,when taken with what we know from prior studies, provide someimportant insights. For example, the Australian market is relativelysmall by global standards, and so actions of institutional investorsmay have greater importance. Also, unlike other markets, Austra-lian fund managers have, as a group, consistently outperformedpassive benchmark indices.8 This is important because the hypoth-esis that institutional traders move prices due to the information re-vealed through their trades supposes that they actually havesuperior information.

Our sample comprises 34 active Australian equity managers,sourced from the Portfolio Analytics Database. These institutionsare representative of the institutional investment managementlandscape in Australia on the basis of size of assets under manage-ment, investment style, and ownership structure (e.g., large andpublicly listed firms versus smaller boutique managers). The larg-est ten Australian investment managers account for 58% of total as-sets under management (AUD$399.9 billion out of AUD$688.9billion).

Evidence of superior manager ability in Australian equities is documented inMercer Investment Consulting surveys and from academic studies examining fundperformance (Gallagher, 2003).

Table 1Descriptive statistics. This table reports descriptive statistics for the Portfolio Analytics Database partitioned according to trade direction. Dollar trade value is the weighted averageprice of the trade multiplied by trade quantity. Trade size relative to volume is the number of shares traded as a percentage of the mean number of shares traded per day over the20 days prior. Trade size relative to shares outstanding is the number of shares traded as a percentage of the number of shares outstanding. These statistics are for the sampleperiod 2 January 2000 to 31 December 2001. Manager trading activity is defined as the number of purchases plus the number of sales made by our sample group in the sampleperiod.

Growth Value GARP Neutral

Panel A – Manager style and number of tradesNumber of managers 3 9 8 14Number of trade day observations 4551 7428 6142 12372Number of purchase day observations 2907 4435 2790 6337Number of sale day observations 1644 2993 3352 6035

Mean Stdev 25th 50th 75th

Panel B – Distribution of trade size (purchases)Dollar trade value (‘000’s) 544 1307 83 219 582Trade size relative to volume 4.92 20.35 0.46 1.52 4.59Trade size relative to SharesOutstanding 0.0080 0.0247 0.0008 0.0027 0.0081

Panel C – Distribution of trade size (sales)Dollar trade value 557 1062 570 208 72Trade size relative to volume 5.55 15.55 5.04 1.54 0.46Trade size relative to SharesOutstanding 0.0101 0.0319 0.0095 0.0029 0.0009

3386 F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399

The Portfolio Analytics Database was constructed using an ‘invi-tation approach’ where 45 institutional investment managers wererequested to provide data, with 34 providing information in ausable format. This database was constructed with the support ofMercer Investment Consulting, with periodic monthly portfolioholdings and daily trade information provided under strict confi-dentiality conditions. The database includes all transactions instocks, futures contracts, and options for each fund; we only eval-uate equity trading performance. Our study also uses a detailed re-cord of all stock price and transaction information from theAustralian Securities Exchange (ASX) (provided by the SecuritiesIndustry Research Centre of Asia–Pacific (SIRCA)). Annual reportinformation (for the book-to-market ratio) was obtained from AS-PECT Financial.

The investment managers were asked to provide informationabout their largest pooled active Australian equity funds (whereappropriate) that were open to institutional investors. These fundsare benchmarked against the S&P/ASX 200 and 300 AccumulationIndices.9 The term ‘largest’ was defined as the marked-to-marketvaluation of assets under management as at 31 December 2001,and was used as an indicative means of identifying portfolios thatwere representative of the investment manager. In addition, thelargest pooled institutional equity fund represents the manager’ssingle largest revenue source from the fund family, because fundrevenue is determined as a fixed percentage of assets under manage-ment, and fee variations are relatively small within common assetclasses.

We investigate possible survivorship and selection bias by com-paring the performance of our sample with the population ofinvestment managers, which includes non-surviving funds. Thesedata are sourced from the Mercer Investment Consulting ManagerPerformance Analytics (MPA) database. The average gross outper-formance of the average manager relative to the ASX/S&P 200 in-dex is 1.78% per annum with a standard deviation of 1.39%.10 Forour sample the mean manager outperformed the average MPAmanager, weighted by manager years, by 0.34% per annum. Whilethis indicates that our sample outperforms the industry, we find

9 The correlation between these indexes is very high (approximately 0.98). Theadditional stocks included in the S&P/ASX 300 only increases total market capital-ization by around 1 percent.

10 According to the Mercer Wholesale Investment Fee Survey, mean managementexpense ratios for 2000 and 2001 were 0.74 and 0.8 percent respectively, indicatingthat active Australian equity managers were able to outperform on both a gross andnet of fees basis.

that the magnitude of the outperformance is low compared tothe dispersion of performance. Selection bias, it appears, is not asignificant problem. In 2001, the mean return of the entire fundpopulation was 12.42% with a standard deviation of 3.8%, whilethe mean performance of our sample was 12.68% with a standarddeviation of 5.5%.

Table 1 provides descriptive statistics for the number of man-ager trades, investment style, and characteristics of the trades. Pa-nel A shows our sample comprises predominantly style-neutraland value managers. Hence, we combine growth and GARP manag-ers as ‘‘growth oriented’’ funds.

Panels B and C of Table 1 provide the distribution of trades forthe 50 largest capitalization stocks (purchases and sales, respec-tively) in terms of three measures of trade size: dollar value oftrade, trade size relative to mean daily volume and trade size rela-tive to the number of shares outstanding.

For the purpose of this study, we restrict the sample of stocksunder investigation to the largest 50 stocks, ranked by market cap-italization at the start of the sample. This restriction maintains areasonable number of manager trades per day per stock.11 As at31 December 2001, the fifty largest stocks account for 82% of totalmarket capitalization stocks in which institutional investors aremore actively engaged.

Statistics regarding manager trading in the selected stocks arepresented in Table 2. The mean number of purchasing and sellingmanagers each day in the 15 largest capitalization stocks is 1.07and 0.82, respectively. As a percentage of mean daily trading vol-ume, the average number of shares purchased and sold per dayin the 15 largest capitalization stocks is 2.83% and 2.45%, respec-tively. We standardize manager-trading activity with the samplemean and standard deviation of manager trading in each stockand find that the relative size of manager trades rises for lowercapitalization shares. Table 2 also provides statistics on the weightof the portfolio invested in stocks included in our sample of 50large capitalization stocks. On average, over 65% of the portfolioweight is allocated to these 50 stocks, indicating that our samplecovers much of the manager’s eligible universe, weighted by mar-ket capitalization.

11 We have also repeated all analysis using the fifty most active stocks (by managertrading) over the sample period and obtain very similar results. Manager tradingactivity is defined as the number of purchases plus the number of sales made in astock by the managers in the database over the sample period

Table 2Institutional trading activity. This table reports descriptive statistics for the trading activity and portfolio weights for the largest 50 stocks in our sample. We select the largeststocks based on market capitalizations on the first day of our sample period. Trade size relative to volume is the number of shares traded as a percentage of the mean number ofshares traded per day over the 20 days prior. Trade size relative to shares outstanding is the number of shares traded as a percentage of the number of shares outstanding. Themean sum of weights is the sum of the portfolio weights for all stocks in the corresponding stock size bucket, averaged over all the managers in the sample. The Weights of stocksis the average weight allocated by the average manager to stocks in that stock size bucket. Managers with zero holdings are included in the calculation of these figures. All figuresare in percent.

3rd largest rank 35–50 2nd largest rank 16–34 1st largest rank 1–15

Mean Stdev Mean Stdev Mean Stdev

Panel A – Distribution of trades partitioned by stock rankNumber of purchasing managers 0.34 0.58 0.59 0.77 1.07 1.04Number of selling managers 0.34 0.60 0.53 0.73 0.82 0.99Trade size relative to volume (purchases) 4.52 18.18 3.57 10.50 2.83 6.16Trade size relative to volume (sales) 4.14 16.22 4.03 11.33 2.45 6.88Trade size relative to SharesOutstanding (purchases) 0.0073 0.0250 0.0059 0.0189 0.0047 0.0100Trade size relative to SharesOutstanding (sales) 0.0093 0.0378 0.0065 0.0175 0.0038 0.0091

Panel B – Distribution of manager weights and overweightsSum of weights of stocks within rank bucket 6.1534 4.7010 14.8020 6.0730 44.4080 11.9710Weights of stocks within rank bucket 0.3846 0.9132 0.7791 1.3342 2.9605 2.7364

Sum of overweights of stocks within rank bucket 1.2744 4.7010 3.2452 6.0730 5.4511 11.9710Overweights of stocks within rank bucket 0.0797 0.9132 0.1708 1.3342 0.3634 2.7364

F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399 3387

4. Research design

If institutional investors possess superior private information,their trading on information may have a contemporaneous effecton stock returns as their insights are revealed through trading. Thismeans that a fund manager has a strong incentive to match theirinformed trading to available market liquidity. A skilled investortrades aggressively when the share price is unlikely to rise (fall)from their purchases (sales).12 If this is the case, then professionaltrades may have a relatively small share price impact, irrespectiveof the underlying trade motive. To gain a better understanding ofthese tradeoffs we need to posit a motive for institutional tradesfrom transaction histories. This requires information about thetrades of other potentially informed traders.

Researchers have considered the effects of competition amonginformed investors where the information is identical (perfectlypositively correlated) or merely related. When there is a largenumber of trading periods (when the information is long-lived incalendar time or trading is continuous), they predict dramaticchanges to the intensity of trade by informed investors, price re-sponses and expected profits to informed traders depending onthe correlation between informed trader beliefs. Examples of thiscan be found in studies by Foster and Viswanathan (1996), andBack et al. (2000). With identical information and ‘‘near’’ continu-ous trading, for example, we expect to find very aggressive trade bythe informed investors, low total expected profits from the infor-mation, and low market liquidity in response to their actions. Thisis markedly different from the case of a single informed investor.For cases that do not assume identical information, Foster andViswanathan (1996) show that there is initially strong competitionamong informed traders when the conditional correlation betweentheir information is positive.13 These insights are consistent withevidence in empirical studies such as Sias et al. (2006), who suggestthat the impact of informed trading is related to the number of trad-ers rather than their trading volume.

Consequently, when we have a number of potentially informedtraders all buying (selling) the same stock on the same day, it ismore likely that (i) they have positive (negative) information aboutthe company’s future share price, and (ii) that their information is

12 There is a basic question of whether we see any discretionary trading among fundmanagers. Some evidence consistent with discretionary liquidity trading is thattrading volume from our sample of fund managers is significantly lower on Mondaythan any other trading day of the week.

13 See Figs. 5 and 6 in Foster and Viswanathan (1996).

positively correlated or is expected to last for a limited amount oftime. Of course, there are other forms of less correlated (initially orconditionally, after some trading by a number of differentially in-formed traders) information flows that we will not detect with thisapproach. Gallagher et al. (2010) document trading among oursample of managers around earnings announcements and findbehavior consistent with information motives for trade, wheninformation is about to be made public. Bozcuk and Lasfer (2005)document information effects from only the larger institutionaltrades on the London Stock Exchange that can be consistent withgreater information flows from trade when institutional investorschoose not to exercise discretion.

We use total trading volume, expressed as a percentage of meandaily trading volume, as a proxy for trades with an ambiguous mo-tive, or those for which we cannot reject as being liquidity moti-vated (the fund manager is comfortable increasing tradeintensity with the expectation that the market will accommodatethe transaction). To be more certain that there is an informationmotive for a transaction, we require that such trades occur whenmore than one of the investment firms in our sample is makingsimilar trades (e.g., buying) in the same stock on the same day.To explore further the motive for trade and the consequences forsubsequent share returns, we also consider the style of the fundmanager. Our results are clearly limited by our sample; eventhough we do not have the transactions of all institutional manag-ers, incorporating the actions of some other managers allows us togenerate insights that are both consistent with, and extend, thework of others.

To investigate the relation between institutional trading andstock returns we examine three temporal orderings: (a) the influ-ence of past stock returns on current institutional trading, (b) thecontemporaneous impact of fund trading on stock returns, and(c) the ability of professional investors to forecast future stock re-turns (the influence of past trading on current returns). We devel-op regression tools for each of these three settings.

4.1. Past stock returns and institutional trading

If the stock market is efficient, historical returns have no predic-tive power. Consequently, a rational stock trading strategy shouldnot rely on historical stock returns. Accordingly, our null hypothe-sis is that past stock returns have no influence on institutionaltrading. However, a large body of empirical research (Lo andMacKinlay, 1988; Jegadeesh and Titman, 1993; Chan et al., 1996)shows that past stock returns do have predictive power. Studies

3388 F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399

investigating the role of historical stock returns on institutionaltrading appear to reject the null hypothesis (Grinblatt et al.,1995; Nofsinger and Sias, 1999; Cai and Zheng, 2004).

To test our null hypothesis, we regress standardized institu-tional trading (purchasing and selling separately) against laggedstock returns, lagged institutional trading, and the lagged valuesof aggregate shares traded by the institutions in the sample. Wealso include a number of control variables: the market return,book-to-market ratio, size, and momentum. If past stock returnsdo not influence the trading decisions of institutional traders, theslope coefficients of lagged stock returns will not be statisticallydifferent from zero.

We include lagged institutional trading to investigate the extentto which institutional trading is serially correlated. Serially corre-lated trading could occur for a number of reasons: temporally cor-related information, herding, or because fund managers may tradeover several days in order to reduce market impact (Chan andLakonishok, 1995). Indeed, Fong et al. (2011) show that followersimitate the trades of leaders and that such activity leads to signif-icant profits for the leader and early followers.14 To isolate theeffect of past returns on institutional trading, lagged values of aggre-gate shares traded by sample institutions are included.

We test our hypotheses with a panel data model, where we al-low the coefficients on the control variables to vary according toeach stock in the sample:

ys;t ¼Xz¼10

z¼1

bj;zRs;t�z þ bkMgrBuyt�1 þ blMgrSellt�1

þ bmSharesBuyt�1 þ bnSharesSellt�1 þXS

s¼1

ba;s:ds

þXS

s¼1

bb;s:dsMarkett þXS

s¼1

bc;s:ds:SIZEt

þXS

s¼1

bd;s:ds:BMRatiot þXS

s¼1

be;s:ds:Momentumt þ es;t ð1Þ

The dependent variable ys,t is one of four variables: MgrBuyt,MgrSellt, SharesBuyt, and SharesSellt. MgrBuy is the standardizednumber of managers purchasing stock s on day t. MgrSell is thestandardized number of managers selling stock s on day t. We stan-dardize by subtracting the mean and dividing by the standard devi-ation of the institutional trading variable particular to each stockover the sample period. SharesBuy is the total number of sharesbought by managers in stock s on day t divided by the mean dailyvolume calculated over the prior 20 days (approximately a tradingmonth). SharesSell is the total number of shares sold by managersin stock s on day t divided by the mean daily volume calculatedover the prior 20 days.15 Rs,t is the return on stock s on day t.

Explanatory variables include lags of the dependent variables aswell as risk control variables. Market is the return on the value-weighted portfolio of all stocks listed on the ASX300 (the largest300 stocks on the exchange, not to be confused with the ASX/S&P 300 which is an index constructed by Standard and Poor’s)on day t. Size, is the value weighted return on a portfolio of stockscomprised of the largest quintile of stocks (as ranked by marketcapitalization) in the ASX300 less the value weighted return on aportfolio of stocks comprised of the smallest quintile of stocks in

14 Venezia et al. (2011) find that both amateur and professional investors exhibitherding behavior, yet it is less pronounced for professional investors. Chuang andSusmel (2011) find that individuals and institutions trade aggressively, and thisactivity depends on states such as market condition, stock size, risk, and momentumeffects.

15 In unreported regressions, we re-estimate our results using alternative measuresof volume traded by managers, including the number of shares purchased/solddivided by the number of shares outstanding. The results are qualitatively similar.

the ASX300. BMRatio is the value weighted return on a portfolioof stocks comprised of the largest quintile of stocks (as ranked bybook to market ratio) in the ASX300 less the value weighted returnon a portfolio of stocks comprised of the smallest quintile of stocksin the ASX300. Momentum is the value-weighted return on a port-folio of stocks comprised of the largest quintile of stocks (as rankedby stock return calculated over the last 130 days) in the ASX300less the value weighted return on a portfolio of stocks comprisedof the smallest quintile of stocks in the ASX300. d is an indicatorvariable for each stock.16

Since lagged values of the dependent variable in Eq. (1) are in-cluded as explanatory variables, ordinary least squares estimatesare inefficient and inconsistent. We therefore employ a two-stepprocedure suggested by Hatanaka (1974) to compute our esti-mates. The severity of the inconsistency depends on the numberof periods in the panel. Nickell (1981) shows that for a small num-ber of time periods the bias can be quite severe. However in oursample we have over 500 daily observations compared to only50 securities in each panel. Consequently, the degree of inconsis-tency is likely to be small.

4.2. Contemporaneous stock returns and institutional trading

We expect the contemporaneous relation between institutionaltrading and share returns to be influenced by liquidity and infor-mation effects. If institutional trades are small or do not requirethe provision of additional liquidity, we expect no contemporane-ous link between institutional trade and stock prices. This wouldbe the case, for example, if institutional trades have a temporaryliquidity impact that is dissipated by the end of the trading day.Hence, our first null hypothesis is that there is no contemporane-ous association between the volume of trades and excess shareprice returns.

It is possible, however, that liquidity effects may stretch beyondthe day on which the trade was made; i.e. the trade impact has be-come permanent. If there were an information motive to the trade,we would expect the market price to shift as a liquidity effect thatthen extends beyond the current day. We use the number of insti-tutions purchasing or selling to proxy for the information contentof a trade. This gives us another null hypothesis: contemporaneousstock returns are not related to the number of institutions purchas-ing or selling. Hence we can decompose any price impact intoliquidity and information components with the model given inEq. (2).

Rs;t ¼Xz¼10

z¼0

bk;zMgrBuyt�z þXz¼10

z¼0

bl;zMgrSellt�z

þXz¼10

z¼0

bm;zSharesBuyt�z þXz¼10

z¼0

bn;zSharesSellt�z

þXS

s¼1

ba;s:ds þXS

s¼1

bb;s:dsMarkett þXS

s¼1

bc;s:ds:SIZEt

þXS

s¼1

bd;s:ds:BMRatiot þXS

s¼1

be;s:ds:Momentumt þ es;t ð2Þ

where the dependent variable Rs,t is the stock return on day t instock s and other variables are defined as above. Note that Eq. (2)explicitly considers the possibility that institutional trading maybe positively auto-correlated.

Past studies examining the role of liquidity versus informationin share returns overwhelming reject a pure liquidity explanation

16 In measuring stock returns, we use the midpoint of the closing bid and ask, ratherthan the last trade as at the close. We reproduce our results using the closing priceand do not find any significant difference (not reported).

ble 3stitutional trading and past stock returns. This table reports regression estimates of the following regression equation: ys;t ¼

Pz¼10z¼1 bj;zRs;t�z þ bkMgrBuyt�1 þ blMgrSellt�1 þ bmSharesBuyt�1 þ bnSharesSellt�1 þ

PSs¼1ba;sdsþ

Ss¼1bb;sdsMarkett þ

PSs¼1bc;sdsSIZEt þ

PSs¼1bd;sdsBMRatiot þ

PSs¼1be;sdsMomentumt þ es;t The dependent variable y is one of four variables: MgrBuy, MgrSell, SharesBuy, and SharesSell. MgrBuy is the standardized number of managers

rchasing stock s on day t, although we leave out the subscripts. MgrSell is the standardized number of managers selling stock s on day t. We standardize by subtracting the mean and dividing by the standard deviation of thestitutional trading variable particular to each stock over the sample period. R(t) is the return on stock s on day t, SharesBuy is the total number of shares bought by managers in stock on day t divided by the mean daily volumelculated over the prior 20 days. SharesSell is the total number of shares sold by managers in stock s on day t divided by the mean daily volume calculated over the prior 20 days. Market is the return on the value weighted portfolio of allocks listed on the ASX300 (the largest 300 stocks on the exchange) on day t. Size is the value weighted return on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by market capitalization) in the ASX300 less thelue weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. BMRatio is the value weighted return on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by book toarket ratio) in the ASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. Momentum is the value weighted return on a portfolio of stocks comprised of the largestintile of stocks (as ranked by stock return calculated over the last 130 days) in the ASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. d is an indicator variable forch stock. These results are for the sample period 2 January 2000 to 31 December 2001. Only trades in the largest 50 stocks (based on market capitalizations on the first day of our sample period) are included. We report the sum of thegged coefficients as follows, (t-1: t-10) is the sum of the lags from t-1 to t-10.

Panel A – R-squaredR-squared 0.1371 0.1392 0.1097 0.1120Adjusted R-squared 0.1271 0.1293 0.0994 0.1017

Variable MgrBuy MgrSell SharesBuy SharesSell

Coefficient t-stat Coefficient t-stat Coefficient t-stat Coefficient t-stat

Panel B – Regression estimatesMgrBuy(t-1) 0.5193 35.53*** 0.0155 2.16** 0.0025 7.61*** 0.0000 �0.07MgrSell(t-1) �0.0001 �0.01 0.4753 33.83*** �0.0006 �1.82* 0.0020 6.27***

Ret(t-1: t-5) �2.0298 �3.15*** 1.3350 2.12*** �0.0603 �2.41*** �0.0130 �0.42Ret(t-1: t-10) �2.6908 �2.88*** 0.8793 0.96 �0.0978 �2.63*** �0.0685 �1.50SharesBuy(t-1) 0.5336 6.58*** �0.2803 �3.34*** 0.3836 25.34*** �0.0057 �0.92SharesSell(t-1) �0.0951 �1.18 0.7748 9.81*** �0.0066 �1.26 0.4103 28.68***

* Statistical significance at the 10% levels.Statistical significance at the 5% levels.

* Statistical significance at the 1% levels.

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(Holthausen et al., 1990; Lakonishok et al., 1992). If information ef-fects dominate, we expect contemporaneous stock returns to bepositively (negatively) correlated to the contemporaneous numberof institutions purchasing (selling), rather than their contempora-neous aggregate volume of shares purchased (sold).

4.3. Past institutional trading and stock returns

Eq. (2) also contains information on the link between currentreturns and past institutional trades. Hence, we can documentthe influence of prior (as many as ten trading days) institutionaltrades on current returns.17 If past institutional trades continue tohave impact, we expect that they are more likely to be informationmotivated; evidence fund managers are able to anticipate future re-turns. We expect liquidity influences not to persist, and informationinfluences to be relatively permanent. Hence, we expect stock re-turns to be positively (negatively) correlated with lagged numbersof institutions purchasing (selling). Further, we expect stock returnsto be unrelated to lagged institutional trading volume.

Eqs. (1) and (2) form a recursive system of equations that can beestimated with OLS. Since daily stock returns are likely to be auto-correlated, we create and use as dependent and independent vari-ables return innovations, that is, the residuals obtained from fittingan AR(1) model to the return series of each security. We also notethat each stock is likely to have a different residual variance induc-ing heteroskedasticity. We, therefore, estimate the residual vari-ance for each stock individually, and transform the variables inthe regression accordingly.18 The inclusion of dummy variables inexpressions (1) and (2) controls for stock fixed effects.19

5. Results

5.1. Influence of stock returns on institutional trading

The estimated regression coefficients from Eq. (1) are reportedin Table 3. To conserve space we do not report the coefficients ofrisk control factors since there is an intercept, a market, a size, abook-to-market, and a momentum slope estimate for each of the50 stocks in our sample. We report lagged coefficients as sums:the sum of coefficients up to lag 5 and the sum of coefficients upto lag 10.

17 We use a lag length of 10 for included dependent variables in our tests. Inunreported results, we consider lags of up to 20 (essentially a full trading month) andthe results do not change significantly. We find the influence of variables with lagsgreater than 10 are not statistically different from zero.

18 In unreported results, we test the robustness of our standard error estimates byconducting a boot-strap experiment. We form numeric standard errors by randomlyreconstructing the independent variables to build an empirical distribution for thecoefficients. For example, for each stock, we divide the manager buying variable intoblocks of 4 (in order to retain any autocorrelation structures present in the managerbuying variable) and randomly reassign the order of the blocks. We then re-estimatethe regression with the randomly assigned blocks and note the coefficient of managerbuying. We conduct this trial 500,000 times to build up a density for the coefficient ofmanager buying. Our results show that the reported regression results using OLS givesimilar results to the numerically generated estimates. We also find similar resultswhen we use heteroskedasticity consistent estimators on the ‘raw’ unweighted data.We have also re-estimated our results using a series of stock-by-stock regressionsrather than a panel data model, and find similar results for the mean coefficients andt-statistics.

19 An alternative specification would be to drop the dummy variables and reportclustered standard errors. Our approach is discussed in Petersen (2009) (see Section4.2 on page 464 and simulation evidence reported in Table 5, Panel A, Column I onpage 462). Further, we find estimated correlations between independent variablesand correlations in residuals with our specifications are both small, ensuring theirproduct is near zero. This means that there are no substantive differences betweenusing dummy variables and reporting either least squares or clustered standarderrors (Petersen, 2009; expressions (6) and (3)). The use of least square dummyvariables in this setting is consistent with the discussion in Greene (2008).

The results show that institutions are contrarian traders inaggregate. From the table, we observe that the sum of the coeffi-cients of lagged stock returns from t-1 to t-10 is �2.6908 and0.8793 for purchases and sales, respectively. This implies that a fall(rise) in price over the last 10 days induces institutions to purchase(sell). As an example of how this might be interpreted, considershares in BHP Billiton, a well-known, large capitalization stock.The mean number of managers purchasing on any given day inour sample is 1.67, with a standard deviation of 1.47. For a 20% fallin the price of BHP (over and above any autocorrelation effects)over 10 days, for example, the model predicts that on average,one manager will be induced to buy (�2.6908 � �0.2 � 1.47 = 0.79managers).

Our finding that institutions are, on average, contrarian tradersis consistent with Gompers and Metrick (2001) and Cohen et al.(2002). However, it is inconsistent with Grinblatt et al. (1995),Nofsinger and Sias (1999), and Cai and Zheng (2004). There areperhaps two explanations for differences with this second groupof papers: frequency of data and value versus equal-weighting ofthe underlying share positions. The frequency of data in theseother studies varies from monthly to annual, while our data is dai-ly. Institutions may have a positive feedback trading strategy overlonger-term horizons, while trading in a contrarian fashion overthe short-term. Chan and Lakonishok (1995) find that for institu-tional purchases using daily trading data on a value-weighted ba-sis, institutions are momentum traders. On a simple-weightedbasis, however, institutions are contrarian. For sales, they find thatinstitutions are contrarian on both a value- and simple-weightedbasis. Our findings are consistent with Chan and Lakonishok(1995) as our regression framework does not value-weightobservations.

Finally, a striking feature of the results in Table 3 is that institu-tional trading is serially correlated. Lagged institutional purchasing(selling) is highly positively correlated with current institutionalpurchasing (selling), indicating that institutional trading is associ-ated with similar future transactions. This may be caused by a vari-ety of factors: institutions may have serially correlatedinformation, they may be herding, or institutions may be purchas-ing or selling trade packages over several days in order to reducemarket impact (Chan and Lakonishok, 1995). In unreported results,we find similar results when we repeat the analysis using tradepackages as defined by Chan and Lakonishok (1995) – there is per-sistence in transactions beyond what we can attribute to multi-daytrade packages.

In practical terms, highly significant coefficients on lagged insti-tutional trading show that an increase in the number of institu-tions purchasing, yields an increase in institutions purchasing inthe future. In the case of BHP, if we observe three managers pur-chasing more than average in one day (three more managers rep-resents roughly two standard deviations above the mean since thestandard deviation is 1.47), we expect an increase of one managerpurchasing (2 � 0.5193 = 1.04) over the next day. These results areconsistent with studies that show institutions ‘‘herd’’ (Grinblattet al., 1995; Nofsinger and Sias, 1999; Wermers, 1999; Sias et al.,2006). Our results are also consistent with Sias (2004), who findsinstitutional trading is related more to lagged institutional tradingthan past stock returns.

5.2. Contemporaneous price impact of institutional trading

For an institutional investor’s transaction to have a contempo-raneous price impact on stock returns through the informationcontent of their trades, we expect stock returns on day t to be pos-itively (negatively) correlated with the number of institutions pur-chasing (selling) on day t. However, if the institutional investor hasa contemporaneous price impact due to the liquidity pressures

20 This has empirical support from Fong et al. (2011), who find multiple brokertrades generate significantly higher returns over the subsequent twelve months.

F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399 3391

placed on liquidity providers, we expect that stock returns on day tshould be positively (negatively) correlated with the number ofshares purchased (sold) on day t by the manager.

We test these hypotheses using Eq. (2) with results reported inthe column headed ‘‘Basic’’ in Table 4. The results indicate thatthere is no significant contemporaneous effect. The coefficient ofmanager buying from our estimate of Eq. (2) is �0.0002, while thatof selling is 0.0001, neither statistically significant at conventionallevels. This result is surprising since we expect institutional tradesto have some informational impact, especially in light of our find-ings in the next section indicating that managers are able to fore-cast stock returns. There are a number of reasons why we do notfind a contemporaneous effect, and we explore these possibilitiesin Section 5.4.

Turning to the liquidity hypothesis, if managers are adept athiding the information content of their trades, the contemporane-ous effect of manager trading volume should not be statisticallysignificant. We find no evidence of a contemporaneous effect forpurchases or sales. One should note, however, that while we finda zero contemporaneous effect when measured over the close-to-close period, these results do not say anything about the marketimpact costs incurred by each manager individually. Chan and Lak-onishok (1993), for example, show that managers experience mar-ket impact costs when measured against open or closing pricebenchmarks.

5.3. Ability of institutional traders to forecast future stock returns

Consistent with studies showing that institutional traders pos-sess valuable private information (Daniel et al., 1997; Nofsingerand Sias, 1999; Sias et al., 2006), the lagged values of the numberof institutions purchasing (selling) reported in Table 4 are signifi-cantly related to stock returns. Further, the correlation betweenlagged aggregate institutional trading volume and stock returnsis not as statistically significant as the number of institutions pur-chasing (selling). So, our information measure of fund activity isable to predict future returns, while the liquidity measure of fundactivity cannot. This suggests that, as a group, institutional investortrades predict future stock returns over and above any associatedliquidity effects. In unreported computations, we have found theseresults to be robust over the specification of the liquidity effect(aggregate institutional trading relative to mean daily trading vol-ume or the number of shares outstanding) and persist indepen-dently of trade package specification (indicating forecastingability past the end of the package).

In terms of economic significance, the predictive power of insti-tutional trading can be significant. According to Table 4, using the‘‘Basic’’ specification with BHP, an increase in the number of man-agers purchasing of three over the average (which is two standarddeviations) has a total effect on returns of 0.42% over the following10 days (0.0014 � 3 = 0.0042). The effect for sales is not quite asstrong, with the sum of the lag coefficient of manager selling being�0.0011 (although still statistically different from zero at the 1% le-vel) as compared to 0.0014 for purchases. While these figures mayseem small in magnitude, an annual alpha of 2% is approximately0.08% over 10 trading days.

5.4. Contemporaneous effects and the forecasting power ofinstitutional trading

In Section 5.2 we noted that transactions from our sample ofinstitutional investors have no contemporaneous effect on stockprices, whether through the information content of their tradesor through a liquidity effect. There may be a number of reasonswhy we do not find a contemporaneous effect. One possibility isthat we may be aggregating many different types of trades in the

MgrBuy variable that have very different contemporaneous effects.That is, the investment style of the fund manager may cause themto trade in very different circumstances, leading to distinct con-temporaneous effects.

5.4.1. Trade characteristicsPerhaps the most obvious trade characteristic is trade size.

Large trades relative to funds under management (FUM) shouldbe indicative of high information content. Large trades are morelikely to be information motivated since a trader with more valu-able information can profit more from the information by makinga larger trade. Easley and O’Hara (1987) suggest that larger tradeshave the capacity for greater market impact than small trades, andhence we expect the contemporaneous effect of large trades to ex-ceed that of other trades. Small trades are more likely to be liquid-ity motivated, perhaps motivated by redemptions or applications.Edelen (1999) shows mutual funds engage in significant unin-formed or liquidity-motivated trading.

To account for trade size, we divide our sample of institutionaltrades into two groups: (large sized) the top quartile of tradesranked by standardized relative trade size, and (others) all othertrades. We define trade size relative to both the market capitaliza-tion of the stock and the funds under management:

Tradesize ¼ price� quantitybmkweight � FUM

ð3Þ

where, bmkweight is the market capitalization of the stock as at thetime of trade divided by the total value of the largest 300 stocks onthe exchange. FUM is the total dollar value of holdings under man-agement in the fund. The relative trade size is then standardizedacross all the trades made by our sample of institutions in each par-ticular stock. We use the standardized relative trade size of eachtransaction to split the sample according to the two groups outlinedabove. We use this measure of relative trade size, rather than ameasure relative to mean daily trading volume, because it scalesaccording to manager size as well as share capitalization.

We form two variables (and their lags) of institutional tradingcorresponding to the number of manager purchases and salesmade on day t in each stock s and the number of large sized pur-chases and sales made on day t in each stock s. These variablesare standardized according to the mean and standard deviation rel-evant for each stock in the sample.

Another factor that may be contributing to a zero contempora-neous effect for manager trading is that managers may be maskingtheir trades. One such tactic is to use multiple brokers to trade thesame stock on the same day. The converse, multiple managersusing the same broker in the same stock on the same day may indi-cate a broker providing price and time sensitive information tomany managers at the same time.20 Hence we expect days wheremany managers use the same broker to have a positive contempora-neous relation between trading and returns.

We regress the midpoint close-to-close return on the standard-ized number of institutions purchasing and selling in the largetrade size category and the non-large trade size category as wellas the number of managers trading multiplied by two sets of indi-cator variables: MultiBKR, set to one if a manager has used morethan one broker to trade, and MultiMgr, set to one if the same bro-ker has purchased or sold for many managers. We also include therisk control variables for market, stock size, book-to-market andmomentum. Results from this analysis are presented in the columnheaded ‘‘TradeCharacteristics’’ in Table 4.

Table 4Institutional trading, contemporaneous effects, forecasting, and trade characteristics. This table reports regression estimates of the following regression equation: Rs;t ¼

Pz¼10z¼1 bk;zMgrBuyt�z þ

Pz¼10z¼1 bl;zMgrSellt�z þ

Pz¼10z¼1 bm;zSharesBuyt�z

þPz¼10

z¼1 bn;zSharesSellt�z þPz¼10

z¼1 bo;zLRGMgrBuyt�z þPz¼10

z¼1 bp;zLRGMgrSellt�z þPz¼10

z¼1 bq;zMultiBKR�MgrBuyt�q þPz¼10

z¼1 br;zMultiMGR�MgrSellt�z þPS

s¼1ba;sds þPS

s¼1bb;sdsMarkett þPS

s¼1bc;sdsSIZEt þPS

s¼1bd;sdsBMRatiot þPS

s¼1be;sdsMomentumt

þes;t The dependent variable Rs,t is the return stock s on day t. MgrBuy is the standardized number of managers purchasing stock s on day t of the mid-sized trade size. MgrSell is the standardized number of managers selling stock s on day t of themid-sized trade size. LRGMgrBuy is the standardized number of managers purchasing stock s on day t of the large sized trade size. LRGMgrSell is the standardized number of managers selling stock s on day t of the large sized trade size. Tradesize is defined by the dollar value of the trade divided by the weight of the stock in the ASX300, further divided by funds under management. Large sized trades are those larger than the 75th percentile of trade ranked by relative trade size.MultiBKR is an indicator variable set to unity when a manager on day t uses more than one broker to purchase or sell stock s. MultiMgr is an indicator variable set to unity when a broker on day t services more than one manager to purchase orsell stock s. We standardize by subtracting the mean and dividing by the standard deviation of the institutional trading variable particular to each stock over the sample period. R(t) is the return on stock s on day t. SharesBuy is the total numberof shares bought by managers in stock s on day t divided by the mean share trading volume calculated over the prior 20 days. SharesSell is the total number of shares sold by managers in stock s on day t divided by the mean share tradingvolume calculated over the prior 20 days. Market is the return on the value weighted portfolio of all stocks listed on the ASX300 (the largest 300 stocks on the exchange) on day t. Size is the value weighted return on a portfolio of stockscomprised of the largest quintile of stocks (as ranked by market capitalization) in the ASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. BMRatio is the value weightedreturn on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by book to market ratio) in the ASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300.Momentum is the value weighted return on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by stock return calculated over the last 130 days) in the ASX300 less the value weighted return on a portfolio of stockscomprised of the smallest quintile of stocks in the ASX300. d is an indicator variable for each stock. These results are for the sample period 2 January 2000 to 31 December 2001. Only trades in the largest 50 stocks (based on marketcapitalizations on the first day of our sample period) are included. We report the sum of the lagged coefficients as follows, (t-1: t-10) is the sum of the lags from t-1 to t-10.

Panel A – R-squaredR-squared 0.1455 0.1505Adjusted R-squared 0.1340 0.1364

Variable Basic TradeCharacteristics

Coefficient t-stat Coefficient t-stat

Panel B – Regression estimatesMgrBuy(t) �0.0002 �1.13 �0.0003 �2.33***

MgrBuy(t-1: t-5) 0.0011 4.60*** 0.0010 3.69***

MgrBuy(t-1: t-10) 0.0014 4.89*** 0.0013 3.84***

MgrSell(t) 0.0001 1.04 0.0003 2.09***

MgrSell(t-1: t-5) �0.0011 �4.30*** �0.0013 �4.66***

MgrSell(t-1: t-10) �0.0011 �3.73*** �0.0013 �4.16***

MgrBuyLRG(t) 0.0002 1.79*

MgrBuyLRG(t-1: t-5) �0.0001 �0.56MgrBuyLRG(t-1: t-10) 0.0002 0.45MgrSellLRG(t) �0.0003 �2.44***

MgrSellLRG(t-1: t-5) 0.0003 1.21MgrSellLRG(t-1: t-10) 0.0004 1.11MgrBuy�MultiBroker(t) 0.0008 1.28MgrBuy�MultiBroker(t-1: t-5) 0.0025 1.97**

MgrBuy�MultiBroker(t-1: t-10) 0.0017 0.98MgrSell�MultiBroker(t) �0.0005 �0.86MgrSell�MultiBroker(t-1: t-5) 0.0004 0.27MgrSell�MultiBroker(t-1: t-10) 0.0021 1.12MgrBuy�MultiMgr(t) 0.0014 2.24***

MgrBuy�MultiMgr(t-1: t-5) 0.0016 1.14MgrBuy�MultiMgr(t-1: t-10) 0.0002 0.12MgrSell�MultiMgr(t) �0.0007 �1.12MgrSell�MultiMgr(t-1: t-5) 0.0002 0.17MgrSell�MultiMgr(t-1: t-10) 0.0004 0.20SharesBuy(t) �0.0022 �1.42 �0.0027 �1.62SharesBuy(t-1: t-5) �0.0008 �0.29 �0.0006 �0.20SharesBuy(t-1: t-10) �0.0031 �0.88 �0.0035 �0.99SharesSell(t) �0.0025 �1.48 �0.0030 �1.75*

SharesSell(t-1: t-5) �0.0036 �1.13 �0.0034 �1.03SharesSell(t-1: t-10) �0.0046 �1.19 �0.0040 �0.99

* Statistical significance at the 10% levels.** Statistical significance at the 5% levels.

*** Statistical significance at the 1% levels.

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We find that the contemporaneous return effect of the numberof managers for large trades is significantly larger than that forother trades. The coefficient for large purchases is 0.0002, and issignificant at the 10% level. This result lends support to the infor-mation hypothesis of price impact. A significant permanent effectis non-existent for large sized trades, however. The additional con-temporaneous return effect (through the number of managers) of alarge sale is larger in magnitude and more significant than thatfound for a large purchase.

These results are consistent with Chan and Lakonishok (1993,1995), who find market impact costs are positively correlated withtrade size (relative to mean daily trading volume). Care must be ta-ken in comparing our results, however, since our definition of tradesize is relative to both funds under management, and stock capital-ization. We expect that our measure is more related to informationwhile that of Chan and Lakonishok (1993, 1995) is more related toliquidity (since their definition of trade size is relative to mean dai-ly trading volume).

There is evidence of an information masking effect through theuse of multiple brokers. When managers use many brokers to pur-chase shares, we find that over the next five days autocorrelation-adjusted stock returns rise by 0.25% (significant at the 5% level) perstandard deviation of manager buying. This incremental informa-tion advantage, however, appears to be short-lived, dissipating to0.17% (not significant) over ten days. Interestingly, this incremen-tal information effect does not come at an increased contempora-neous cost. We find no evidence of a significant differencebetween the contemporaneous effects of manager buying (selling)on days when a manager has used multiple brokers. This suggestsfund managers benefit from the higher information content of theirpurchases if they mask their trades using multiple brokers, withoutcausing adverse changes to stock prices on the day they trade.21

When multiple managers use the same broker we find thatthere is a positive incremental contemporaneous effect (statisti-cally significant at the 1% level) for purchases. This suggests thatwhen brokers provide information to many of their institutionalclients, prices adjust on the same day. We find no evidence of anincremental information effect over the following 5 or 10 days;perhaps information revealed by brokers is fully impounded instock prices on the same day and may be about temporary liquidityneeds, rather than long-term share valuations.

5.4.2. Investment styleAnother possible factor influencing the contemporaneous im-

pact of institutional trading is investment manager style. Valuemanagers, for example, aim to purchase (sell) stocks at a cheap(an expensive) price relative to fundamentals. Consequently, theybehave as price stabilizers, purchasing or selling when prices devi-ate from fundamentals. Alternatively, momentum managers maypurchase on strength to continue to boost share prices to matchperceived valuations.

To investigate the influence of investment style on the contem-poraneous effects of institutional trading, we first examine theinfluence of past stock returns on institutional trading, partitionedby investment style. We do so by regressing the standardized num-ber of purchases and sales made on day t in stock s by managers ofthe same style, on past stock returns, and lagged institutional trad-ing according to investment style. We present separate results forpurchases and sales in Table 5.

21 There may be other reasons for using multiple brokers; for example, a managerwith a large time critical order may employ several brokers to ensure the order isfilled quickly, however we control for the impact of large trades by including the tradesize variable. Therefore, the coefficient of the broker variables should capture theincremental effect of using multiple brokers over and above any effect due to timecritical large trades.

The results in Table 5 show that investment style has a strongeffect on the relation between past stock returns and institutionaltrading. Style-neutral purchases (sells) are significantly negatively(positively) related to past stock returns. We find growth managersare momentum traders, with past stock returns positively corre-lated (although not statistically significant) with purchases andnegatively correlated with sells (significant at the 5% level). Valuemanagers are strongly contrarian, with past stock returns nega-tively correlated with value manager purchases and positively cor-related with value manager sells (both are statistically significantat the 1% level).

All investment styles are highly positively serially correlatedwith trading activity of their own style, although not necessarilywith the trading activity of other styles. For example, value man-ager purchases are negatively correlated with lagged growth man-ager purchases. Most institutional trading is uncorrelated withlagged values of aggregate shares purchased or sold by ourmanagers.

We investigate the influence of investment style on the contem-poraneous effects and on the forecasting ability of institutionaltrading by regressing stock returns on the standardized numberof style-neutral, growth, and value managers purchasing and sell-ing on day t in stock s and their lagged values. We also includethe risk control variables as discussed above. The results of thestyle regressions are reported in Table 6. We find that lagged val-ues of the number of institutions purchasing are positively relatedto stock returns (there is a negative relation for sales), and are ro-bust to value and growth styles. Consistent with prior studies, wefind that value managers have superior forecasting ability relativeto growth managers.

The results also show that the contemporaneous effect of insti-tutional trading depends on investment style. Style-neutral pur-chasing and selling has a statistically significant impact, butgrowth manager selling does not. The contemporaneous effect ofvalue managers is much stronger than style-neutral or growthmanagers and is of the opposite sign. This result seems counter-intuitive; according to both the liquidity and information hypoth-eses, we expect that as more managers purchase, stock returnswould rise on that day. However, if value managers are stabilizingprices (e.g. selling when they perceive prices have risen above fun-damental levels) the contemporaneous effect of their trades onstock prices will be negative. This follows the traditional notionof profitable stabilization as in Friedman (1953). During a supplyshock (many investors wishing to sell for liquidity reasons), valuemanagers may provide liquidity to the market, stabilize prices, andrequire a discount for the service they provide as counterparty pur-chasers. Put differently, value managers are likely to ‘buy on weak-ness and sell on strength’, which is consistent with thecontemporaneous coefficients reported in Tables 4 (Trade Charac-teristics column) and 6.

Value manager speculation (price stabilization) should be morecommon during times of relative uncertainty in the share market.To test this, we proxy for instability with lagged intra-day volatilityand investigate the interaction between lagged volatility and valuemanager trading. We use lagged volatility because value managerscannot observe price instability directly, instead they must inferinstability from historical data. In unreported results, we confirmthat volatility is highly serially correlated, suggesting that informa-tion about lagged volatility is useful in determining currentinstability.22

If value managers profit from volatile prices, we would observemore aggressive buying (selling) on weakness (strength) during

22 Ideally we would use the volatility of prices immediately prior to value managertrades, however we do not know the exact time of day when each manager trades, sowe use the one-day lag of volatility as a proxy.

Table 5Institutional trading, past stock returns and investment style. This table reports regression estimates of the following regression equation: ys;t ¼

Pz¼10z¼1 bjRs;t�z þ bkNeutralBuyt�1 þ blNeutralBuyt�1 þ bmGrowthBuyt�1þ

bnGrowthBuyt�1 þ boValueBuyt�1 þ bpValueBuyt�1 þPS

s¼1ba;sds þPS

s¼1bb;sdsMarkett þPS

s¼1bc;sdsSIZEt þPS

s¼1bd;sdsBMRatiot þPS

s¼1be;sdsMomentumt þ es;t The dependent variable y is one of six variables: NeutralBuy, NeutralSell,GrowthBuy, GrowthSell, ValueBuy and ValueSell. NeutralBuy is the standardized number of neutral managers purchasing stock s on day t, although we leave out the subscripts. NeutralSell is the standardized number of neutralmanagers selling stock s on day t. GrowthBuy is the standardized number of growth managers purchasing stock s on day t. GrowthSell is the standardized number of growth managers selling stock s on day t. ValueBuy is the standardizednumber of value managers purchasing stock s on day t. ValueSell is the standardized number of value managers selling stock ‘s’ on day ‘t’. We standardize by subtracting the mean and dividing by the standard deviation of theinstitutional trading variable particular to each stock over the sample period. R(t) is the return on stock s on day t, SharesBuy is the total number of shares bought by managers in stock s on day t divided by the mean daily volumecalculated over the prior 20 days. SharesSell is the total number of shares sold by managers in stock s on day t divided by the mean daily volume calculated over the prior 20 days. Market is the return on the value weighted portfolio of allstocks listed on the ASX300 (the largest 300 stocks on the exchange) on day t. Size is the value weighted return on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by market capitalization) in the ASX300 less thevalue weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. BMRatio is the value weighted return on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by book tomarket ratio) in the ASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. Momentum is the value weighted return on a portfolio of stocks comprised of the largestquintile of stocks (as ranked by stock return calculated over the last 130 days) in the ASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. d is an indicator variable foreach stock. These results are for the sample period 2 January 2000 to 31 December 2001. Only trades in the largest 50 stocks (based on market capitalizations on the first day of our sample period) are included. We report the sum of thelagged coefficients as follows, (t-1: t-10) is the sum of the lags from t-1 to t-10.

Panel A – R-squaredR-squared (BUYS) 0.1286 0.0608 0.1313Adjusted R-squared (BUYS) 0.1184 0.0498 0.1211R-squared (SELL) 0.1062 0.0693 0.1488Adjusted R-squared (SELLS) 0.0957 0.0584 0.1388

Variable Neutral Value Growth

Coefficient t-stat Coefficient t-stat Coefficient t-stat

Panel B – Regression estimates (BUYS)NeutralMgrBuy(t-1) 0.5087 37.25*** �0.0026 �0.39 �0.0039 �0.59NeutralMgrSell(t-1) 0.0066 1.02 0.0007 0.11 0.0130 2.00**

ValueMgrBuy(t-1) �0.0034 �0.52 0.3914 21.52*** �0.0006 �0.10ValueMgrSell(t-1) �0.0169 �2.55** �0.0247 �3.61*** 0.0158 2.37***

GrowthMgrBuy(t-1) 0.0013 0.19 �0.0061 �0.85 0.5620 39.07***

GrowthMgrSell(t-1) 0.0070 0.98 0.0059 0.81 �0.0100 �1.44Ret(t-1: t-5) �0.0930 �0.14 �4.9891 �7.12*** 0.5412 0.81Ret(t-1: t-10) �0.2437 �0.25 �6.4709 �6.33*** 0.8226 0.85SharesBuy(t-1) 0.3254 3.98*** 0.2711 3.10*** 0.4646 5.50***

SharesSell(t-1) �0.0256 �0.32 �0.0840 �1.00 �0.1147 �1.43

Panel C – Regression estimates (SELLS)NeutralMgrBuy(t-1) 0.0029 0.44 0.0122 1.86** 0.0175 2.72***

NeutralMgrSell(t-1) 0.4292 29.35*** 0.0071 1.09 0.0000 0.00ValueMgrBuy(t-1) 0.0007 0.11 �0.0228 �3.43*** �0.0112 �1.73*

ValueMgrSell(t-1) 0.0006 0.08 0.4015 24.17*** �0.0103 �1.59GrowthMgrBuy(t-1) 0.0212 3.03*** 0.0281 4.00*** �0.0116 �1.71*

GrowthMgrSell(t-1) �0.0189 �2.65*** 0.0095 1.33 0.5236 39.09***

Ret(t-1: t-5) 1.7002 2.58*** 3.8728 5.70*** �2.1673 �3.31***

Ret(t-1: t-10) 2.0555 2.14*** 5.0408 5.10*** �3.6719 �3.84***

SharesBuy(t-1) �0.1833 �2.24*** �0.1425 �1.67* �0.0324 �0.39SharesSell(t-1) 0.3843 4.70*** 0.0464 0.57 0.7794 9.85***

* Statistical significance at the 10% levels.** Statistical significance at the 5% levels.

*** Statistical significance at the 1% levels.

3394F.D

.Fosteret

al./Journalof

Banking&

Finance35

(2011)3383–

3399

Table 6Institutional trading, contemporaneous effects, forecasting, and investment style. This table reports regression estimates of the following regression equation:Rs;t ¼

Pz¼10z¼1 bk;zNeutralBuyt�z þ

Pz¼10z¼1 bl;zNeutralSellt�z þ

Pz¼10z¼1 bm;zSharesBuyt�z þ

Pz¼10z¼1 bn;zSharesSellt�z þ

Pz¼10z¼1 bo;zGrowthBuyt�z þ

Pz¼10z¼1 bp;zGrowthSellt�z þ

Pz¼10z¼1 bq;zValueBuyt�zþPz¼10

z¼1 br;zValueSellt�z þPS

s¼1ba;s:ds þPS

s¼1bb;s:dsMarkett þPS

s¼1bc;s :ds:SIZEt þPS

s¼1bd;s:ds:BMRatiot þPS

s¼1be;s :ds:Momentumt þ es;t The dependent variable Rs,t is the return stock son day t. NeutralBuy and NeutralSell is the standardized number of style-neutral managers purchasing and selling stock s on day t respectively. GrowthBuy and GrowthSell is thestandardized number of growth managers purchasing and selling stock s on day t respectively. ValueBuy and ValueSell is the standardized number of value managers purchasingand selling stock s on day t respectively. We standardize by subtracting the mean and dividing by the standard deviation of the institutional trading variable particular to eachstock over the sample period. R(t) is the return on stock s on day t. SharesBuy is the total number of shares bought by managers in stock s on day t divided by the mean sharetrading volume calculated over the prior 20 days. SharesSell is the total number of shares sold by managers in stock ‘s’ on day ‘t’ divided by the mean share trading volumecalculated over the prior 20 days. Market is the return on the value weighted portfolio of all stocks listed on the ASX300 (the largest 300 stocks on the exchange) on day t. Size isthe value weighted return on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by market capitalization) in the ASX300 less the value weighted return ona portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. BMRatio is the value weighted return on a portfolio of stocks comprised of the largest quintile ofstocks (as ranked by book to market ratio) in the ASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300.Momentum is the value weighted return on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by stock return calculated over the last 130 days) in theASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. d is an indicator variable for each stock. These results arefor the sample period 2 January 2000 to 31 December 2001. Only trades in the largest 50 stocks (based on market capitalizations on the first day of our sample period) areincluded. We report the sum of the lagged coefficients as follows, (t-1: t-10) is the sum of the lags from t-1 to t-10.

Panel A – R-squaredR-squared 0.1555Adjusted R-squared 0.1424

Variable Basic

Coefficient t-stat

Panel B – Regression estimatesNeutralMgrBuy(t) 0.0007 5.54***

NeutralMgrBuy(t-1: t-5) 0.0003 1.16NeutralMgrBuy(t-1: t-10) 0.0005 1.90NeutralMgrSell(t) �0.0005 �4.19***

NeutralMgrSell(t-1: t-5) �0.0001 �0.69NeutralMgrSell(t-1: t-10) �0.0001 �0.28ValueMgrBuy(t) �0.0009 �8.02***

ValueMgrBuy(t-1: t-5) 0.0010 4.54***

ValueMgrBuy(t-1: t-10) 0.0014 5.27***

ValueMgrSell(t) 0.0010 8.86***

ValueMgrSell(t-1: t-5) �0.0011 �4.98***

ValueMgrSell(t-1: t-10) �0.0010 �3.88***

GrowthMgrBuy(t) 0.0000 �0.17GrowthMgrBuy(t-1: t-5) 0.0004 1.83*

GrowthMgrBuy(t-1: t-10) 0.0004 1.42GrowthMgrSell(t) �0.0001 �0.96GrowthMgrSell(t-1: t-5) �0.0009 �3.83***

GrowthMgrSell(t-1: t-10) �0.0009 �3.39***

SharesBuy(t) �0.0017 �1.06SharesBuy(t-1: t-5) �0.0005 �0.19SharesBuy(t-1: t-10) �0.0024 �0.69SharesSell(t) �0.0021 �1.30SharesSell(t-1: t-5) �0.0056 �1.79*

SharesSell(t-1: t-10) �0.0064 �1.70*

* Statistical significance at the 10% levels.�� Statistical significance at the 5% levels.

*** Statistical significance at the 1% levels.

F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399 3395

periods of high volatility. Eq. (4) is a panel regression that allows usto measure the effects of manager style, prior volatility, and otherrisk measures on current return. Of particular interest are the coef-ficients of the interaction terms between lagged volatility and va-lue buying and selling.

ys;t ¼Xz¼10

z¼1

bk;zNonValueBuyt�z þXz¼10

z¼1

bl;zNonValueSellt�z

þXz¼10

z¼1

bm;zSharesBuyt�z þXz¼10

z¼1

bn;zSharesSellt�z

þXz¼10

z¼1

bo;zValueBuyt�z þXz¼10

z¼1

bp;zValueSellt�z

þXz¼10

z¼0

bq:Volatilityt�z�1

þXz¼10

z¼1

br;zVolatility�t�z�1ValueBuyt�z

þXz¼10

z¼1

bu;zVolatilityt�z�1 � ValueSellt�z þ es;t ð4Þ

Intra-day volatility is defined as the standard deviation of the trade-to-trade returns within each day. We use standardized intra-dayvolatility to ensure our results do not reflect cross-sectional varia-tion in volatility.

The results for the value manager regressions are reported inthe columns of Table 7 headed ‘Value’. We also compute resultsusing growth-oriented managers to determine if there is any sym-metry between value- and growth-oriented managers (these re-sults are reported in the columns headed ‘Growth’). The variableStyle in the column of variable names refers to either value orgrowth corresponding to the ‘Value’ and ‘Growth’ columnheadings.

The results show that the value manager contemporaneous ef-fect of purchasing is less negative during periods of low intra-dayvolatility, as evidenced by the statistically significant negativecoefficient of StyleMgrBUY�LagVolatility and positive coefficient ofStyleMgrSELL�LagVolatility. Thus, when prices are relatively stable(when LagVolatility is small), value manager trading is less likelyto be due to price stabilizing behavior and therefore yields a posi-tive (or less negative) contemporaneous effect. Conversely, whenprices are relatively unstable (when LagVolatiltiy is high), a portionof value manager trading is related to their price stabilizing

Table 7Institutional trading, investment style, and intra-day volatility. This table reports regression estimates of the following regression equation: Rs;t ¼

Pz¼10z¼1 bk;z

NonStyleBuyt�z þPz¼10

z¼1 bl;zNonStyleSellt�z þPz¼10

z¼1 bm;zSharesBuyt�z þPz¼10

z¼1 bn;zSharesSellt�z þPz¼10

z¼1 bo;zStyleBuyt�z þPz¼10

z¼1 bp;zStyleSellt�z þPz¼10

z¼0 bq:Volatilityt�z�1 þPz¼10

z¼1 br;z

Volatilityt�z�1 � StyleBuyt�z þPz¼10

z¼1 bu;zVolatility�t�z�1StyleSellt�z þ es;t The dependent variable Rs,t is the return stock s on day t. NonStyleBuy is the standardized number of non-value or non-growth managers purchasing stock s on day t. NonStyleSell is the standardized number of non-value or non-growth managers selling stock s on day t. StyleBuy is thestandardized number of value or growth managers purchasing stock s on day t. StyleSell is the standardized number of value or growth managers selling stock s on day t. Westandardize by subtracting the mean and dividing by the standard deviation of the institutional trading variable particular to each stock over the sample period. R(t) is the returnon stock s on day t. SharesBuy is the total number of shares bought by managers in stock s on day t divided by the mean share trading volume calculated over the prior 20 days.SharesSell is the total number of shares sold by managers in stock s on day t divided by the mean share trading volume calculated over the prior 20 days. Market is the return onthe value weighted portfolio of all stocks listed on the ASX300 (the largest 300 stocks on the exchange) on day t. Size is the value weighted return on a portfolio of stockscomprised of the largest quintile of stocks (as ranked by market capitalization) in the ASX300 less the value weighted return on a portfolio of stocks comprised of the smallestquintile of stocks in the ASX300. BMRatio is the value weighted return on a portfolio of stocks comprised of the largest quintile of stocks (as ranked by book to market ratio) in theASX300 less the value weighted return on a portfolio of stocks comprised of the smallest quintile of stocks in the ASX300. Momentum is the value weighted return on a portfolio ofstocks comprised of the largest quintile of stocks (as ranked by stock return calculated over the last 130 days) in the ASX300 less the value weighted return on a portfolio of stockscomprised of the smallest quintile of stocks in the ASX300. d is an indicator variable for each stock. These results are for the sample period 2 January 2000 to 31 December 2001.Only trades in the largest 50 stocks (based on market capitalizations on the first day of our sample period) are included. We report the sum of the lagged coefficients as follows, (t-1: t-10) is the sum of the lags from t-1 to t-10. Volatility is defined in Eq. (4).

Panel A – R-squaredR-squared 0.1573 0.1499Adjusted R-squared 0.1438 0.1363

Variable Value Growth

Coefficient t-stat Coefficient t-stat

Panel B – Regression estimatesNonStyleMgrBuy(t) 0.0005 3.82*** �0.0001 �0.63NonStyleMgrBuy(t-1: t-5) 0.0004 1.95** 0.0009 4.11***

NonStyleMgrBuy(t-1: t-10) 0.0006 2.36*** 0.0013 4.83***

NonStyleMgrSell(t) �0.0005 �4.02*** 0.0002 1.94*

NonStyleMgrSell(t-1: t-5) �0.0005 �2.36*** �0.0008 �3.34***

NonStyleMgrSell(t-1: t-10) �0.0005 �1.80* �0.0008 �2.74***

SharesBuy(t) �0.0020 �1.26 �0.0025 �1.59SharesBuy(t-1: t-5) �0.0004 �0.16 �0.0009 �0.30SharesBuy(t-1: t-10) �0.0028 �0.82 �0.0032 �0.92SharesSell(t) �0.0026 �1.59 �0.0035 �2.17**

SharesSell(t-1: t-5) �0.0039 �1.26 �0.0041 �1.31SharesSell(t-1: t-10) �0.0047 �1.25 �0.0049 �1.29StyleMgrBuy(t) �0.0009 �7.83*** 0.0000 �0.08StyleMgrBuy(t-1: t-5) 0.0010 4.49*** 0.0005 2.14**

StyleMgrBuy(t-1: t-10) 0.0013 4.99*** 0.0005 1.81*

StyleMgrSell(t) 0.0010 8.55*** �0.0002 �1.62StyleMgrSell(t-1: t-5) �0.0010 �4.89*** �0.0008 �3.49***

StyleMgrSell(t-1: t-10) �0.0010 �3.71*** �0.0008 �3.01***

StyleMgrBuy�LagVolatility(t) �0.0003 �2.95*** 0.0001 1.02StyleMgrBuy�LagVolatility(t-1: t-5) 0.0001 0.27 0.0002 0.78StyleMgrBuy�LagVolatility(t-1: t-10) 0.0005 1.98** 0.0002 0.56StyleMgrSell�LagVolatility(t) 0.0003 2.95*** �0.0001 �0.99StyleMgrSell�LagVolatility(t-1: t-5) �0.0005 �2.60*** 0.0004 1.82StyleMgrSell�LagVolatility(t-1: t-10) �0.0009 �3.39*** 0.0004 1.61LagVolatility(t) 0.0000 �0.26 �0.0001 �0.62LagVolatility(t-1: t-5) 0.0003 1.14 0.0002 1.05LagVolatility(t-1: t-10) 0.0003 1.62 0.0004 1.82*

* Statistical significance at the 10% levels.** Statistical significance at the 5% levels.

*** Statistical significance at the 1% levels.

3396 F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399

behavior and hence they are able to transact with a negative rela-tion to contemporaneous returns.

23 Open-to-trade market impact is defined as the value weighted trade priceobtained during the day, divided by the midpoint of the opening bid and ask, minusone.

5.4.3. Market impact, investment style and prior tradingIf some value manager trades are price stabilizing, then we

would expect the market impact of value manager transactionsto be negatively related to return volatility. Further, such price sta-bilizing behavior would imply that value managers do not competefor liquidity with non-value managers (i.e. value managers wouldprovide liquidity). We explore these additional implications byconsidering stock returns during the day for both purchase andsale transactions with respect to differences in the investmentstyles of fund managers. In particular, we relate intraday returnsto the prior day’s activities; namely prior day return volatilityand trading from investors of different styles.

To measure the market impact of a fund manager’s trade weconsider the open-to-trade return each day.23 If there is a rise inthe share price between the market open and when a managerbought stock, we interpret this as a positive market impact, as it sug-gests that the manager used liquidity from the market to facilitatetheir trade. If there is a fall in the share price between the marketopen and when a manager bought shares we view this as a negativemarket impact, as it suggests that the manager provided liquidity tothe market to facilitate the trades of others (they bought on weak-ness). Similar interpretations are used for sell transactions formanagers.

Table 8Stock returns around institutional trading. This table reports the results of regressing the open-to-trade return, defined as the trade weighted transaction price, divided by themidpoint of the open bid and ask, minus one, on (i) one day lagged abnormal intra-day volatility and (ii) one day lagged abnormal net growth, value and neutral managerpurchasing, as well as control variables for forecast trade size (FRTS), and dummy variables for manager identification. We also include the trade-to-close regression, where thetrade-to-close is defined as the midpoint of the closing bid and ask divided by the trade weighted price, minus one. We omit stock size control variables since our sample isalready limited to the largest 50 stocks on the exchange. Abnormal intra-day volatility is measured as intra-day volatility divided by mean intra-day volatility measured from t-60to t-20. Abnormal net manager trading is measured by the number of managers of a particular style purchasing, less the number selling, divided by the mean calculated from t-60to t-20. We use lagged values of abnormal volatility and manager trading to avoid look-ahead bias.

Open-to-trade Trade-to-close

Growth Value Neutral Growth Value Neutral

Panel A – PurchasesCoefficientFRTS (%) 0.1916 �0.0065 0.9088 �0.0664 �0.0230 �0.3216VOL (%) �0.0842 �0.5479 �0.2393 0.1256 0.0811 0.0888Growth (%) 0.0102 0.0072 0.0032 0.0028 0.0007 �0.0027Value (%) 0.0007 0.0034 0.0047 0.0046 0.0031 0.0015Neutral (%) 0.0169 0.0035 �0.0003 0.0000 0.0026 0.0016

t-statFRTS 0.91 �0.02 3.50 �0.64 �0.15 �2.54VOL �1.51 �8.64 �4.85 4.60 2.69 3.69Growth 3.11 1.58 1.09 1.71 0.32 �1.89Value 0.20 0.68 1.26 2.63 1.30 0.84Neutral 3.11 0.48 �0.07 0.00 0.75 0.87

R-squaredR-squared (%) 4.0646 5.9762 2.0245 1.6200 1.5297 2.3238Adjusted R-squared (%) 3.8737 5.7240 1.7676 1.4243 1.2656 2.0676

Panel B – SellsCoefficientFRTS (%) �0.0653 0.5326 �0.8024 0.4773 0.5484 0.5531VOL (%) �0.1645 0.2802 �0.0675 �0.0315 �0.0159 �0.0597Growth (%) 0.0017 0.0137 0.0019 0.0012 0.0012 0.0027Value (%) 0.0023 0.0170 �0.0034 0.0025 0.0037 0.0082Neutral (%) 0.0172 0.0116 �0.0048 0.0058 0.0058 0.0008

t-statFRTS �0.38 1.75 �2.83 5.23 3.68 3.68VOL �3.13 3.89 �1.20 �1.11 �0.45 �2.01Growth 0.43 3.83 0.60 0.59 0.66 1.66Value 0.60 4.23 �0.92 1.21 1.88 4.10Neutral 2.63 1.99 �1.04 1.64 2.03 0.31

R-squaredR-squared (%) 3.2279 7.9546 1.9131 2.2397 3.9949 1.9187Adjusted R-squared (%) 2.9846 7.6167 1.6409 1.9939 3.6425 1.6465

F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399 3397

We regress open-to-trade returns on one-day lagged abnormalintra-day volatility, one-day lagged abnormal net purchases fromgrowth, value and neutral managers,24 control variables for forecasttrade size (FRTS),25 and manager fixed effect dummy variables.26

Abnormal intra-day volatility is measured as intra-day volatility di-vided by mean intra-day volatility measured from days t-60 to t-20.Abnormal net manager trading is measured by the number of man-agers of a particular style of purchasing, less the number selling, di-vided by the mean calculated from days t-60 to t-20.

The results of these regressions are presented in Table 8. Themarket impact incurred by value managers for both purchaseand sell transactions are strongly and negatively correlated withlagged intra-day volatility. For example, the estimate for valuemanager purchases implies that for a 10% increase in lagged abnor-mal intra-day volatility, the expected open-to-trade return for apurchase falls by 5.4 basis points. However, this result does notextend to managers of other styles. While the estimated market

24 We proxy competition for liquidity as lagged abnormal net purchasing, sincemanager trading is highly persistent with a high autocorrelation coefficient. Thissuggests that if net activity is high, then the expected level of purchasing over thesubsequent period will also be high.

25 This is measured by dollar trade value divided by the mean daily trading value(calculated over the last 20 trading days).

26 We omit stock size control variables as our sample is already limited to thelargest 50 stocks on the exchange.

impact incurred by neutral manager purchases is negatively corre-lated with lagged abnormal volatility, the magnitude is only halfthat of value managers (and the result for neutral manager sellsis of the opposite sign and is not statistically significant). Further,the open-to-trade market impact incurred by growth manager pur-chases is not statistically significantly related to lagged abnormalintra-day volatility. These results are consistent with Table 7 andsupport our hypothesis that value managers are behaving as pricestabilizers since they show that value managers are able to attainlow or even negative market impact during periods of priceinstability.

For sells, growth managers appear to be demanding liquidity(share prices are falling as they sell). These results suggest thatgrowth managers are less likely to stabilize prices and are usingliquidity, whereas value managers are most likely to provideliquidity and stabilize prices. The trade-to-close returns show thatmanagers of all styles benefit from price instability while purchas-ing (the results for sell transactions are statistically significant onlyfor neutral managers). For example, the growth manager VOL coef-ficient of 0.1256% indicates that for a 10% increase in volatility, thepost trade stock return for the average growth manager increasesby 1.2 basis points.

Finally, we consider the extent to which managers of differentstyles might compete for liquidity. To document this interactionwe consider the influence of prior day net demand for shares from

3398 F.D. Foster et al. / Journal of Banking & Finance 35 (2011) 3383–3399

each investment style on the market impact of a manager’s trade.The regression results from Table 8 indicate that growth manageropen-to-trade market impact for purchases is significantly andpositively related to growth and neutral manager purchases theprior day, suggesting some competition for liquidity (althoughthe results for growth manager sell transactions are not as signifi-cant). For example, in Panel A, the coefficient of 0.0102% for growthmanager purchases indicates that for a 1 standard deviationincrease in lagged growth manager demand, market impactincreases by 1 basis point. If both net neutral and net growth man-ager purchases increase by 10%, we estimate the market impact ofgrowth manager purchases the next day will increase by about 2.7basis points. For value managers we find that their purchases areunrelated to the prior day’s trade by managers of all investmentstyles. For value manager sells there is a significant and positiverelation between the open to trade return and prior day net pur-chases by managers of all styles. This is consistent with value man-agers providing liquidity when other managers demand it, andearning a small spread for their efforts.

6. Conclusions

Using a unique database of daily fund manager transactions, weinvestigate the relation between institutional trading and stock re-turns for the largest 50 stocks listed on the Australian SecuritiesExchange. While our sample is limited in some respects (time per-iod, shares considered, and trades of a portion of institutional man-agers) we are nevertheless able to gain deeper insights into driversand consequences of institutional trading than in prior studies. Forexample, while our sample is not exhaustive the securities in-cluded represent about 82% of total market capitalization.

Institutions in our sample are, on average, contrarian tradersover short-term horizons of about ten days. When we partitionaccording to investment style, we find that growth-orientedmanagers are momentum traders while style-neutral and valuemanagers are contrarian. This is consistent with the manager’sself-reported investment styles; that is value managers appear tobuy on weakness and sell on strength.

We also find that institutional trading is highly persistent formanagers with the same investment style. Value manager purchas-ing, for example, is strongly positively correlated with lagged valuemanager purchasing, but is weakly negatively correlated withgrowth-oriented manager purchasing. For all investment styles,the auto-correlation with lagged value of aggregate purchase orsell volume is of less importance than the lagged number of insti-tutions within their own investment style purchasing or selling.From the pattern of autocorrelation within investment styles, weconclude that institutional traders may either engage in informa-tion-based herding, or may receive serially correlated signals. Ineither case, the evidence suggests that such behavior is rational,since we find no evidence of price reversals over the ten days fol-lowing their trades.

Much of the literature finds a strong correlation betweenchanges in institutional holdings (or inferred trades) and stock re-turns in the same period. Many of these studies, however, usemonthly, quarterly or even annual data. With daily data we findthat institutional purchasing and selling is not correlated with con-temporaneous stock returns. There are several factors related tothis result. Trade characteristics such as size and broker use can af-fect the contemporaneous relation between returns and trading.Investment manager style also has a strong effect on this relation.

Lagged values of the number of institutions purchasing or sell-ing are correlated with stock returns. This suggests that institu-tional investors are able to predict returns. This finding is robustover trade size, broker use, and investment style. In general we

note that a deeper understanding of the details of investment man-ager actions and strategies provides a more nuanced view of therelation between institutional trading and share returns.

Acknowledgements

The authors gratefully acknowledge the assistance and support ofthe Securities Industry Research Centre of Asia-Pacific (SIRCA) forprovision of the ASX SEATS data, and the investment managerswho provided daily trading information used to construct the Portfo-lio Analytics Database. The identities of the participating investmentmanagers remain strictly confidential. We are indebted to seminarparticipants at the Asian FMA Annual Meetings, The Australian Na-tional University, Australasian Finance and Banking Conference, Chi-na International Conference in Finance, Chinese University of HongKong, EFA Annual Meetings, FMA (Europe) Annual Meetings, FudanUniversity, Monash University, Hong Kong Polytechnic University,Journal of Banking and Finance Conference, Nanyang TechnologicalUniversity, National University of Singapore, The University ofNew South Wales, Singapore Management University, The Univer-sity of Sydney and the UNSW Investment Management Conferencefor their comments. Detailed comments from Sohnke Bartram,Tom George, Jennifer Gippel, Sara Hartley, Petko Kalev, AmezianeLasfer, Tom Smith, Terry Walter, Geoff Warren, and Robert Whaleyimproved the paper substantially. Part of this research was com-pleted when Foster and Gallagher were at UNSW, while Foster wasa visitor at HKUST, and while Gallagher was at University of Texasat Austin and UTS.

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