Performance of trades and stocks of fund managers pinnuck

JOURNAL OF FINANCIAL AND OUANTITATIVE ANALYSIS VOL. 38, NO, 4, DECEMBER 2003COPYRIGHT 2003, SCHOOL OF BUSINESS ADMINISTRATION, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195

An Examination of the Performance of theTrades and Stock Holdings of Fund Managers:Further Evidence

Matt Pinnuck*

Abstract

Recent research has examined the performance of stocks held by U.S. mutual funds andfound they realize abnormal returns. The result is significant as it stands in contrast tothe general consensus from traditional performance studies that mutual funds do not pos-sess superior information. Employing a unique dataset, I examine the performance of themonthly stock holdings and trades of a sample of Australian fund managers. When stockholdings are observable, performance measures can be constructed that are more precisethan traditional fund manager performance measures. I find the stocks held by fund man-agers realize abnormal returns consistent with some stock selection ability across fundmanagers. Examining the performance of their individual trades, I find that the stocks theybuy realize abnormal returns whereas for sell trades I find no evidence of abnormal returns.Overall, the results suggest fund managers have the ability to select stocks that realize pos-itive abnormal returns thus providing out-of-sample support for similar recent findings forU.S. mutual funds.

I. Introduction

Traditional mutual fund performance methodology examines the actual hot-tom-line returns that investors realize from holding mutual funds. Since Jensen(1968), the general consensus from these studies is that the net return ofthe activefund manager industry does not outperform a passive benchmark.' However, incontrast to traditional performance studies, recent studies hy Daniel, Grinblatt,

* Pinnuck, [email protected]. Department of Accounting, University of Melbourne,Parkville 3010, Australia. For helpful comments and suggestions, t thank Jane Hronsky, Chris Jubb,Petko Kalev, Josef Lakonishok (associate editor and referee), Paul Malatesta (the editor), Nasser Spear,and seminar participants at the University of Melbourne, University of Otago, the AAANZ Auckland2001 conference, and the Melboume-Monash Joint Symposium. I thank Kevin Davis, tan Ramasay,and Geof Stapledon, Frank Russell Company, and the Australian tnvestment Managers Associationfor assistance with the database employed in this study.

'tn the U.S., all the recent studies also report no evidence of superior performance. Examplesare Elton, Gruber, Das, and HIavka (1993), Malkiel (1995), Gruber (1996), and Carhart (1997)). InAustralia, early studies by Bird, Chin, and McCrae (1983) and Robson (1986) employed the traditionalJensen measure and reported no evidence of superior performance. More recent studies by Hallahanand Faff (1999) and Sawicki and Ong (2000) employ both the Jensen measure and the extensions totraditional factor time-series regressions and find no evidence of selection ability in the Australianmarket.

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Titman, and Wermers (DGTW) (1997), Chen, Jegadeesh, and Wermers (2000),and Wermers (2000) take a different approach and examine the performance ofthe individual stocks held in fund manager portfolios. They report results con-sistent with fund managers having the ability to choose stocks that outperformtheir benchmarks before any expenses are deducted. As this result stands in di-rect contrast to the long-standing evidence from traditional performance studies,which suggest fund managers do not possess superior information, it is somewhatcontroversial and has not been without criticism.

In this study, I examine the performance of both the stock holdings andtrades of Australian active equity fund managers using a unique database of theirmonthly equity portfolio holdings. I contribute to the emerging literature thatexamines the performance of the stock holdings of fund managers in three mainways. First, the study provides the only out-of-sample evidence on the perfor-mance of stock holdings employing a data set that retains the essential charac-teristics of the U.S. data yet is independent of existing U.S. data sets in bothconstruction and fund manager population. ^

Second, the study examines the performance of the calendar month-end port-folio stock holdings of fund managers. An examination of month-end portfo-lios alleviates a concern with the results from prior U.S. stock holding perfor-mance studies that have only examined the performance of stocks held at calendarquarter-ends. Moskowitz (2000) argues the performance attributable to quarter-end portfolios may not be representative of the typical fund portfolio. This ison the basis there may be a systematic difference between the characteristics ofthe stock holdings in the quarter-end portfolio and the portfolio holdings in thebetween quarter month-ends, due to fund reporting biases.''

Finally, in addition to examining the performance of stock holdings, I alsoexamine the performance of the individual trades of each fund manager. Chen,Jegadeesh, and Wermers (2000) argue an examination of the trades as opposedto the holdings of each fund manager is a more powerful metric to determinethe existence of superior information. Further, an examination of trades allowsone to make some simple theoretical predictions of differential performance be-tween subgroups of trades. Assuming a valid theory, then results consistent withpredictions alleviate, to some extent, concerns regarding the robustness of theperformance benchmark employed and also provide some insights into how fundmanagers trade with superior information.

^tn respect of institutional investors more generally, there is some contrasting evidence. Lakon-ishok, Shieifer, and Vishny (1992) as part of a study examining the performance of the pension fundindustry briefly examine the performance of the trades of pension funds. Except for those pensionfunds that follow a growth style, they found no evidence of superior information.

^The empirical evidence for the Australian capital market and fund industry population is con-sistent with the U.S. data in regard to the following two key characteristics. First, the best ex antepredictors of cross-sectional patterns in common stock retums in the Australian capital market aresize, book-to-market, and momentum. Second, the traditional time-series factor models report noevidence of superior performance by the Australian fund management industry. Citations for thisAustralian evidence are provided in the text.

''Moskowitz (2000) argues that fund reporting biases such as window dressing operations or tax-motivated trading may result in quarter-end reported portfolio holdings being systematically differentfrom intervening monthly portfolio holdings not reported.

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I find the following results in this paper. First, the results reported are consis-tent with the stocks held by fund managers on average realizing abnormal returns.Second, when I examine fund manager trades, consistent with my prediction, Ifind stocks that are purchased by fund managers on average realize abnormal re-turns whereas stocks sold do not. Third, when I classify stocks by size, I find thatthere is a greater probability of fund managers possessing superior informationfor large relative to small stocks. Overall, both the existence and magnitude ofthe abnormal returns give support to the conclusion from DGTW (1997) that fundmanagers do possess superior information.

However, while fund managers may realize abnormal returns on their hold-ings or trades, this, as Wermers (2000) discusses, does not imply that they deliversuperior net returns to investors. To consider whether the benefits of any superiorinformation fund managers may possess is delivered to unit holders, I also ex-amine the performance of the net return realized by the unit holders. The resultssuggest that the superior returns from a fund manager's stock holding are not de-livered to unit holders. There are a number of possible reasons for this such astransactions costs, management fees, and poor market timing decisions.

The remainder of the paper is set out as follows. In the next section, I discussthe units of observation I employ. Section III sets out the performance evaluationmethodology employed. The construction of the database is discussed in SectionIV. The characteristics of the stocks are examined in Section V. Empirical find-ings are presented in Section VI. Section VII examines the performance of thenet return delivered to unit holders. The conclusion is presented in Section VIIL

II. Units of Observation for Performance Measurement

In this paper, I examine the performance of each fund manager y using twodistinct units of observation: i) stock holdings and ii) trades. An examinationof the performance of stock holdings measures the performance return on eachstock (' held in the fund manager's portfolio at each month-end t. The portfolioperformance of fundy at time t is then simply the value-weighted performance ofall stocks held. The weight of security i in the portfolio of the fund managery' attime t is measured as

en w- — " '•''

i=\

where P,, is the price of stock i at time t, Hy, is the number of shares held by fundmanager^ in stock / at time t, and A is the number of different stocks held by eachfund manager,^

^Where a fund manager atso holds option contracts, 1 replaced each actual option position for acompany in the portfolio with an instantaneously equivalent position of the underlying ordinary shares.This was approached by computing the delta for each option contract held, enabling me to determinethe number of ordinary shares that must be bought/sold in order to have the same exposure to a smallmovement in the share price as the option contracts held. For call options, the delta is computed usingthe partial derivative of the Black-Scholes model modified for dividends and early exercise. For putoptions, as there is no closed-form valuation solution, I numerically compute each options delta usingthe numerical procedures of the Cox-Rubinstein binomial pricing model.


I also examine the subsequent abnormal performance of the stocks a fundmatiager trades, specifically the stocks they buy or sell. This is motivated by Cheti,Jegadeesh, and Wermers (2000) who argue the trade of a stock is more likelyto represent a signal of private information than the passive decision of holdingthe existing position in the stock. They suggest a fund manager may continueto hold a stock for reasons other than future abnormal performance because ofthe frictions involved in trading such as trading costs, as well as more implicitcosts such as the triggering of a capital gains tax event through a sale. As aconsequence of these frictions, the return on holdings may not reveal the trueprivate information possessed by fund managers. Thus, trades may provide morepowerful evidence of the information fund managers possess about future returns.

I measure Trade,/; as the change in the weight of stock i from the beginningto the end of month t in fund manager/s portfolio,

(2) Tradey, = Wy,-M^',_,,

where wy, is as defined by (1) and H^'_I is defined as

i=\

where the weights at time t- \ given by (3) refiect the portfolio holdings at f - 1that are evaluated at the same end-of-month prices as weight, Wy;. The Trademetric in equation (2) therefore measures the difference between two differentportfolios (at t and t — \), which are evaluated at the same end-of-month prices.Therefore, Wy-, differs from Wyv-1 only because of trading from t — \ to t. Intu-itively, the latter value is the value of the starting portfolio if no trading took placeduring the month.*

I categorize these trades as either purchases or sales (where purchase stocksare all stocks with a positive Trade measure). I then construct purchase and saleportfolios and analyze their returns with the performance evaluation methods doc-umented in Section III.

III. Performance Evaluation Methodology with ObservablePortfolio Weights

This section shows how I construct the DGTW characteristic-matching per-formance measure for this study. To address concerns that any results are due tothe benchmark employed and not superior information, I employ two specifica-tion checks. First, I employ a performance evaluation methodology proposed byGrinblatt and Titman (GT) (1993) that does not require an arbitrary model of ex-pected returns. Second, I develop some simple a priori predictions of differentialperformance between different classes of stocks and trades. Results consistentwith the predictions alleviate, to some extent, concerns regarding the benchmarkemployed.

*Both holdings Wy, and Wjj,— \ are evaluated at the same prices so that there are no spurious pricechange effects, allowing me to separate trades from price momentum effects.

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A, The DGTW Characteristic-Matching Performance Measure

The DGTW performance measure for each fund is simply obtained by mul-tiplying the portfolio stock weights by the abnormal returns. The abnormal re-turn is calculated by subtracting the benchmark-matched portfolio return fromthe stock's return. Formally, the DGTW performance measure for fund managerj in month t is defined as

(4) DGTW,, =

where w,-,,-1 is the portfolio weight for stock / at the end of month t— l,Ri^,is themonth t return of stock j , and R,'''~ is the month t return of the characteristic-based benchmark portfolio that is matched to stock i during month r - 1,

Two different characteristic-based benchmarks are constructed. One set ofbenchmark portfolios is constructed to represent the stock characteristics of sizeand book-to-market, A second set of benchmark portfolios is constructed to repre-sent the characteristics of size, book-to-market, and momentum. The two bench-marks allow performance to be measured both with and without an adjustmentfor momentum. The benchmark portfolios are constructed in a similar manner toDGTW (1997),^

B, The GT (1993) Measure of Performance

The measure, developed by GT (1993) (hereafter the GT measure) uses thepast portfolio weights of a given mutual fund to calculate a benchmark returnfor the evaluation period. The advantage of the GT measure for the abnormalreturn calculation is that it does not adjust retums according to a particular asset-pricing model. With this measure, the benchmark used to adjust the gross returnof the portfolio of fund manager^ for its risk in a given month t is the month f'sreturn earned by the portfolio holdings 12-months prior to month f's holdings.More formally the GT portfolio performance measure I employ for month t canbe expressed as

(5) GT, =( = 1 1=1

where /?,, is the security return on / from date ttot+l. Wu is the portfolio weightof security / at date t. W,,,-i2 is the portfolio weight of security i at date t - 12. Tis the number of periods,

'The size and book-to-market benchmark-based portfolios are constructed as follows. Beginningin December 1989 and each following December 31, each stock in the AGSM Price Relative File thatsatisfied the data requirements, is placed into size and book-to-market portfolios. The composition ofeach portfolio is determined by each December sorting of the universe of stocks into quintiles basedon each firm's market value of equity. Then, firms in each size quintile are further sorted into quartilesbased on their book-to-market ratio. This yields 20 benchmark portfolios. The average number offirms in each portfolio is 32, The size, book-to-market, and momentum benchmark-based portfoliosare constructed by sorting firms in each of the 20 size and book-to-market portfolios into a furtherthree portfolios based on their preceding 12-month return calculated to the end of November, Thisgives a total of 60 size, book-to-market, and momentum portfolios. The average number of firms ineach portfolio is 10,


Under the null hypothesis of no superior information, the changes in weightsfrom the prior period are uncorrelated with current returns. In this case, themeasure converges to zero. Under the alternate hypothesis that a fund manageris informed, the measure converges to the average eovarianee between R „ and{Wi, - Wi,,-x2). Expression (5) will be positive for informed investors and zerofor uninformed.

C. Performance Predictions for Different Classes of Stocks and Trades

In this section, I develop some simple a priori predictions of differentialperformance among subgroups of stocks to provide some insight into the cross-sectional variation in performance and to also provide some assurance any find-ings of superior performance are not due to a misspecified benchmark. As dis-cussed by Kothari and Warner (2001), a well-specified performance measureshould not indicate abnormal performance where none is predicted to exist.

I predict the informed trades of a fund manager are more likely to be pur-chases than sales. This is based on two arguments that have been presented inthe literature. First, it has been observed fund managers are in general long onlyinvestors (i.e., they only hold assets in non-negative amounts). It has been shownanalytically by Saar (2001) and argued by Chan and Lakonishok (1993) and Keimand Madhavan (1995) that being a long only investor creates a situation in whichit is optimal for fund managers to predominately engage in searching for stockswhose price is expected to rise.^ To purchase the stock, they rebalance their port-folios to sell stocks that do not fit this description. Ideally, they will sell stockswhose price they expect to go down. However, as fund managers can only sellstocks they already hold, they have a limited number of alternatives. Thus, theymay have to sell stocks for which they simply expect the price to go nowhere. Asa consequence, buy trades are more likely to be motivated by information and selltrades to be motivated by portfolio rebalancing.'"

The second reason for buys being more informative than sells is that analystsare a source of information for fund managers. It has been argued by McNicholsand O'Brien (1997) and others that analysts have greater incentives to issue "buy"recommendations than "sell" because the former generate greater trading volume.Furthermore, it is argued that analysts avoid sell recommendations for fear oflosing access to management as a source of information.''

^This is a characteristic of the portfolio holdings of the fund managers in this sample. Saar (2001)observes most mutual funds do not sell short as a matter of policy because it involves the risk ofunlimited losses if the stock price goes up and the charters of many mutual funds explicitly restrict theusage of short sales.

'This is because the information search for bad news is restricted to the limited available alterna-tives in the portfolio, tn contrast, the search for good news can be among the many potential assets tobuy.

'"tt is important to note that this argument does not suggest that fund managers never possessprivate information with respect to bad news. The argument simply suggests it is more likely that thetypical buy trade rather than the typical sell trade reveals private information.

" A number of papers provide empirical evidence that can be interpreted as being consistent withinstitutional investors possessing good but not bad news. Chen, Jegadeesh, and Wermers (2000) haveprovided evidence consistent with the aggregated buys but not sells realizing abnormal returns. Chanand Lakonishok (1993) in an examination of intraday price impact of institutional block trades foundthat buys but not sells have a permanent price impact. They interpret this as being consistent with

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Standard models of informed trade (i.e., Kyle (1985)) show that, ceterisparibus, there is a positive relationship between trade size and abnormal retums.I therefore examine the differential performance among trades of different size. Itshould, however, be recognized that the relationship between trade size and abnor-mal retums is significantly more complex tban that presented. Standard modelsof informed trade show the relationship also depends on stock liquidity, infor-mation precision, and risk aversion. Therefore, the evidence with respect to theperformance of different sized trades is descriptive only and does not represent anexamination of a specific hypothesis.

Finally, I consider firm size as a partitioning variable. Based on the argu-ments of Atiase (1985) and Bhushan (1989), I predict the incentive for infor-mation search may be greater for large firms for a number of reasons. First, tominimize the risk of underperformance of the market index, they will hold largefirms in the portfolio. Second, for larger firms, per unit trading costs are lower,liquidity higher, and aggregate trading profits for a given change in share priceare greater. This discussion suggests, due to the differential incentives for infor-mation search, fund managers possess more precise information for large than forsmall firms.

IV. Data

A. Construction of Database

My data consists of monthly observations on the equity portfolio holdingsof 35 Australian active equity fund managers from January 1990 to December1997. All the portfolios are fund products where the objective is to outperform themarket. The portfolios have 24-72 months of data. The monthly equity holdingsdata over the period were obtained from two sources. First, data was sourced froma collaborative project between the University of Melboume and the AustralianInvestment Managers' Association (AIMA). Secondly, portfolio holding data wasobtained from Frank Russell Company, which maintains a database of portfolioholdings of Australian fund managers.

Table 1 shows the number of fund managers in botb the sample and popu-lation in each year from 1990-1997. The sample represents on average 72% ofthe population over the time period examined. Table 1 also summarizes the ag-gregated dollar value of fund manager equity holdings over the sample period,indicating that the sample represents a large fraction of the total value of equityholdings of the Australian funds' population. Therefore, the sample, notwith-standing what may appear to be a small number of funds relative to a typicalU.S. study, can be taken as representative of tbe Australian funds managementindustry.'^

traders having good but not bad news. At a market level Hong, Lirti, and Stein (1999) provide evidencethat bad news is incorporated into prices more slowly than good news. They conjecture that this isconsistent with economic agents such as fund managers gathering good but not bad news.

'^The sample only includes surviving funds as at the date of database establishment. Survivorshipbias is therefore likely to affect the results in this paper. The potential impact of survivorship bias isdiscussed in Section VI.


Year

19901991199219931994199519961997

TABLE

Sample and Population of Equity

No. ofFunds

2223242837404348

Population

AggregateTNA

($Mill)

7601,2581,3942,3502,5983,0534,4354,401

1

Fund Managers in Australia

Sample

No. ofFunds

1415171932353528

AggregateTNA

(SMill)

507898

100218732154274538532904

Sample as% of Population

No. ofFunds(%)

6365716886878158

AggregateTNA(%)

6771717982898666

Table 1 sfiows the number of active equity funds in both the sample and the Australian population from 1990 to 1997 as ofJanuary 31 each year. The population is active Austraiian equity fund managers. The table aiso shows the dollar amountof total net assets (TNA) in $AUS million.

V. Stock Characteristics of Aggregate Mutual FundHoldings

This section presents some descriptive evidence in relation to the average in-vestment style of the sampled fund managers, I approach this, in a manner similarto Chan, Chen, and Lakonishok (2002), by examining some key investment stylecharacteristics of the stocks the sampled fund managers prefer to hold. First, Iexamine whether the fund manager prefers to hold large or small stocks wheresize is measured by market capitalization as at the beginning of the calender year.Second, I investigate whether the fund manager favors value stocks (high book-to-market ratio) or growth stocks (low book-to-market ratio). In addition, I alsoexamine the characteristics of the fund manager's stock holding with respect toprior stock returns (12-month return ending one month prior to holding), volatil-ity (standard deviation of monthly returns over the 36-month interval ending threemonths prior to holding date), and liquidity (annual trading volume in the firm'sstock in the year immediately preceding holding date, divided by the average totalnumber of shares outstanding for the year).

At the end of each financial year, all available domestic stocks listed on theAustralian Stock Exchange (recorded in the Australian Graduate School of Man-agement (AGSM) price relative file) are ranked in ascending order by the relevantcharacteristic (i.e,, book-to-market, size) and given a percentile ranking from zero(for the lowest ranked firm) to one (for the highest ranked firm), I then use theholdings of each fund manager y at 30 June each year to compute the weightedaverage of the percentile rankings over all stocks in the portfolio at that point intime. The weight of a stock is the proportion of the portfolio's value investedin the stock. This metric is then averaged across time for fund manager j andthen averaged across all fund managers in the sample to provide the reported re-sults. As explained by Chan, Chen, and Lakonishok (2002), the characteristicrank score for a stock is that stock's percentile rank on that characteristic rela-tive to all stocks covered by the AGSM database. The average rank score acrossall stocks is 0,5, As a consequence, an average fund manager rank score greater(less) than 0,5 indicates a tilt toward (away from) a particular characteristic. To

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provide the fund manager stock preferences with a basis of comparison, I use as abenchmark the All Ordinaries Accumulation Index, which I assume to representthe average weights of the hypothetical average investor. ' The portfolio averagecharacteristic for the index is computed as for the funds and is simply the capital-ization weighted average of the rank scores for the stocks in the index. The resultsare reported in Table 2.

TABLE 2

Characteristics of Stocks Held by Fund Managers

Rank

Size Book-lo-Market Momentum Volatility Liquidity

Fund manager 0.95 0.38 0.60 0.20 0.70Ali Ordinaries Index 0.96 0.40 0.58 0.19 0.64

The Table 2 time period is June 1990 to June 1997. For each fund, at every finanoiai year-end, weighted average char-aoteristios (in percentiie rankings) are caicuiated across ali stocks heid in a fund's portfolio. The characteristics are: size(equity market capitaiization), book-to market vaiue of equity, past three-year stock return beginning three and one-haifyears ago and ending six months ago, and the most recent past one-year stock return. The Ail Ordinaries AccumulationIndex is used as a benchmark portfoiio, and represents the totai of aii stocks iisted on the Austraiian Stock Exchange.To caicuiate the overaii average characteristic of the index and the aggregate fund portfolio, aii domestic equity stocksare ranked by the reievant characteristic and assigned a score from zero (iowest) to one (highest). The portfolio averagefor the index is the capitalization-weighted average of these rank scores across aii stocks in the index; the average forthe fund portfoiio is the weigfited average across stocks in the aggregated portfolio of ail funds, with weights giveh bythe vaiue of the fund's hoidings of the stock. Based on its portfoiio characteristic, a fund is assigned to one of 10 groupsdetermined by the decile breakpoints of ail domestic stocks in the index.

Table 2 shows fund managers have a strong preference for large stocks. Theaverage size rank for the portfolio of stocks held is 0.95. This rank average forthe fund managers is similar to the index rank average of 0.96, suggesting thatfund managers tend to concentrate their portfolio in the same large-sized stocksas the index. Fund managers also have a marginal preference for growth stocks,as indicated by an average book-to-market rank of 0.38. This is slightly moreconcentrated toward growth than value stocks compared to the All Ordinaries Ac-cumulation Index (average rank 0.40). The average momentum rank is 0.6, whichis slightly greater than the index consistent with fund managers holding past win-ners. The liquidity rank of 0.7 is consistent with the prediction that fund managerstend to hold more liquid rather than less liquid stocks. Finally, the volatility rankof 0.2 suggests fund managers prefer less risky stocks. In summary, the basicfinding is that fund managers prefer to hold large, liquid growth stocks. The re-sults also suggest that fund managers hold portfolios, in respect of the attributesexamined, similar to the All Ordinaries Accumulation Index. This is consistentwith the industry practice of minimizing tracking error from a market benchmark.These findings are similar to those reported for the U.S. mutual fund industry byChan, Chen, and Lakonishok (2002).

VI. Performance Evaluation: Results

This section discusses the results of each of the two performance evaluationmethods set out in Section III applied to the holdings and trades of fund managers.To determine the statistical significance of the benchmark-adjusted performance

'^This is the Australian capital market equivalent of the S&P 500.


for the entire sample or a subsample, I follow DGTW (1997) and compute t-statistics based on the time-series portfolio of funds in the sample. Specifically, Icalculate the benchmark-adjusted performance on an equally weighted portfolioof funds, existing at a point in time, for each of the t months in the database, I thencompare the mean of the resulting t values to its time-series standard error to con-struct the f-test,'•* Note that all performance results are reported as a percentagereturn per month,

I present performance measures for the portfolio of holdings and trades of thefund manager as of each month-end (month 0) for each of the next six months.That is, I compute separate performance estimates for each event month frommonth+1 through month+6. As an example for portfolio holdings at March 31the performance estimates for month+1 represents the abnormal return on thestocks in the month of April, The performance estimate for month+2 representsthe abnormal return on the March 31 stocks in the month of May, and so on.

The reason for having six separate event months for each fund manager isthat it is unclear over what time period the superior information potentially pos-sessed by the fund manager will be revealed to the market. If fund managers havesuperior information that is revealed to the market within one month, the month+1measure provides the most power. However, if information is incorporated intomarket prices more slowly, then month+3, +4, +5, or month+6 may have morepower,

A. Performance Evaluation Results of Holdings

Table 3 presents performance results using the DGTW (1997) measure foran equally weighted portfolio of fund managers. Performance results after ad-justment for the benchmark return from size and book-to-market portfolios arehereafter referred to as DGTW alpha (1), Performance results after adjustmentfor the benchmark return from size, book-to-market, and momentum portfoliosare hereafter referred to as DGTW alpha (2), The DGTW alpha (1) results showthe average fund has a significant positive selectivity measure in the first month(month+1) after the holding measurement date and close to traditional signifi-cance levels in month+2 (f-statistic of 1,87), The magnitude of the results, 0,24%in month+1, is economically significant. The reported results for DGTW alpha (2)show that the average fund, after adjusting its performance for the size, book-to-market, and momentum characteristics of its stocks still has a significant positiveselectivity measure in month+1. The lower magnitude of the results in month+1(0,16%) relative to the results reported for DGTW alpha (1) is consistent withfund managers benefiting from momentum in retums.

Table 3 also presents performance results using the GT (1993) measure foran equally weighted portfolio of fund managers. The results for the entire sampleshow that the average GT performance is significantly positive in each of the threemonths (month+1 through +3) after the holding measurement date,

'••it is important to note that as the reported (-tests are all based on time-series estimates of standarderrors it is possible they may be misspecified due to inter-temporal dependence between the residualsfrom this time-series. This concern is however alleviated to some extent as there was no evidence ofcorrelation between the residuals at monthly lags of one through six.

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TABLE 3

Performance Estimates for Fund Managers' Stock Holdings (in % return per montfi)

Event Time

GT performance measure

DGTW alpha (1)

DGTW alpha (2)

fvlonth 0

0.69(8.2)***

0.60

0.51(7.07)-"

fvlonth4.1

0.20(3.11)*"

0.24(3.1)*"

0.16(2.25)**

Month-^2

0.20(3.08)***

0.18(1.87)-

0.11(1.05)

Month+3

0.16(2.24)**

0.12(1.28)

0.07(0.79)

Month+4

0.08(1.20)

0.08(0.94)

0.01(0.09)

Month+5

0.11(1.43)

0.00(0.70)

0.00(0.01)

Month+6

0.34(1.57)

0.11(1.01)

0.00(-0.35)

Table 3 reports three performance measures for the equally weighted time-series portfolio of funds in the sampie. TheGT performance measure is caicuiated by subtracting the time (return of the portfolio heid at month ( — 13 from thetime (return of the portfoiio heid at ( - 1. To compute the DGTW aipha (1) and DGTW aipha (2) benchmark-adjustedreturn for a given stock during a given month, the buy-and-hoid return on a value-weighted portfolio of stocks having thesame size, book-to-market value of equity characteristics (and momentum for DGTW aipha (2)) as the stock is subtractedfrom the stock's buy-and-hold return during the month. Each fund manager's DGTW aipha (1) and (2) measure, for agiven month, is then computed as the portfolio-weighted benchmark-adjusted return of the individuai stocks in the fundsportfoiio (normalizing so that the weights of all stocks add to one). The performance estimates for each performancemeasure for event months from Months 1 through MonthH.6 for portfolios with weights based on the fvlonth-i-O hoidings ofthat stock by the fund manager are reported, (-statistics based on the time-series standard deviation are in parentheses.***, *', and * indicate significance at the 1%, 5%, and 10% two-taii ieveis, respectiveiy.

I also examine performance results for a value-weighted portfolio of fundmanagers. The weights for each calendar month were hased on the value of theassets under management as of January 1 each year. In results not reported, allthree performance metrics, GT, DGTW alpha (1), and DGTW alpha (2), are pos-itive and statistically significant in the first month after the holding measurementdate, although DGTW alpha (2) is now significant at a lower level of confidence.

B. Performance Evaluation Results: Trades

Table 4 presents the performance evaluation results for the trades of fundmanagers. I focus the discussion on the implications of the DGTW alpha (2) re-sults for the performance ofthe fund manager. ' The buy stocks have statisticallysignificant positive abnormal returns for two months after the holding measure-ment date.'* The magnitude ofthe results for buy trades in month+1 of 0.36% islarger than the comparable DGTW performance result for holdings in month+1of 0.16%. This is consistent with fund managers holding stocks beyond the timehorizon for which they provide positive abnormal returns. The reason for this,as suggested by Chen, Jegadeesh, and Wermers (2000), may be to avoid hightransaction costs or a capital gains tax event that could accompany a stock sale.

None of the reported abnormal returns for stocks sold are statistically sig-nificantly different from zero. The absence of statistically significant negative

"The reported results for DGTW alpha (1) are similar in all respects to DGTW alpha (2) excepttfiey are of slightly greater magnitude.

"in the portfolio formation month, denoted month 0, there is no evidence of the trades realizingpositive abnormal returns attributable to superior information. More specifically, the returns on thesell trades are greater than those on the buy trades. The reason for this can probably be attributed toa combination of i) fund managers on average being momentum investors, and ii) the negative first-order autocorrelation in monthly returns to Australian stocks (Gaunt and Gray (2001)). Therefore, inthe month of the trade, if fund managers sell stocks that performed poorly in the prior month, thesestocks would outperform the stocks bought by an economically significant magnitude.


TABLE 4

Performance Estimates for Fund Manager Trades (in % return per month)

Panel A. Gross Returns

Buys (Trades > 0)

Sells (Trades < 0)

Buys less Sells

Panel B. DGTW alpha (1)

Buys (Trades > 0)

Sells (Trades < 0)

Buys less Sells

Panel a DGTW alpha (2)

Buys (Trades > 0)

Sells (Trades < 0)

Buys less Sells

Month 0

145(3.5)"-

2.30(4.3)"-

-0.85(2.17)"

0.66(3.6)"-

1.38(4.47)"-

-0.72(2.57)"

0.62(4.28)"'

0.90(7.59)'"

-0.28-(1.72)

Month+1

1.26(3.0)"-

0.78(2.42)"

0.48(1.68)

0.45(3.29)"-

0.13(0.96)

0.32(1.50)

0.36(2.63)"-

0.07(0.54)

0.29(1.74)

Month+2

1.13(3.04)"'

0.92(2,20)"

0.21(2.34)"

0.36(2,9)"*

0,00(0,13)

0,36(2,10)"

0,32(2.67)"*

-0,02(0,12)

0,33(2,71)"*

Event Month

Month+3

1,25(2,9)***

0,97(2,42)**

0,28(1.42)

0,25(1,70)

0,00(0.10)

0,25(1.10)

0,22(1.55)

-0,14(0.93)

0.33(2,06)**

Month+4

1,08(2,65)***

0,99(2,40)"

0,09(0,51)

0,06(0,53)

0,02(0,19)

0,04(0,23)

0,00(0,41)

0,02(0,13)

-0,02(0,45)

Month+5

1.14(2.83)***

1,00(2,55)**

0,14(0,79)

0,13(0,91)

0,03(0,27)

0.01(0.56)

0.04(0,33)

0,03(0,22)

0,01(0,11)

Month+6

0,91(2.7)*"

0.92(2,10)"

-0,01(0.99)

0,00(0,06)

0,17(1.49)

-0.17(1.08)

-0.03(0,24)

0,07(0.60)

-0 ,10(0,70)

At the end of each calender month for each fund manager for each stock. I compute the Trade as the change in holdings,I classify all stocks traded for each fund manager into buys and sells (where buy stocks are all stocks with a positivetrade measure). Panel A presents the time-series weighted average raw returns for fund manager buy and seii trades.Paneis B and C present the DGTW aipha (1) and the DGTW alpha (2) performance measure for the equally weightedtime-series portfolio of fund buy and seii trades in the sampie. To compute the DGTW (1) and the DGTW (2) behchmark-adjusted return for a given stock trade during a given month, the buy-and-hoid return on a value-weighted portfoiio ofstocks having the same size, book-to-market value of equity, and momentum for DGTW (2) characteristics as the stockis subtracted from the stock's buy-and-hold return during the month. Each fund manager's DGTW measure, for a givenmonth, is then computed as the portfolio-weighted behchmark-adjusted return of the ihdividuai stock trades in the fundsportfolio (normalizing so that the weights of aii stocks add to one), Ali returns for event months from Month+1 throughMonth+6 for trades with weights based on the Month+0 trade size of that stock by the fund manager are reported. Thereturns are computed as the equaily weighted time-series portfolio of fund trades in the sample, ^statistics based on thetime-series standard deviation are in parentheses, *** . **, and * indicate significance at the 1%, 5%. and 10% two-taiilevels, respectively.

abnormal retums is consistent with the average sell trade not revealing superiorinformation about poorly performing stocks. Note that the insignificant ^-statisticwith respect to sell trades does not necessarily imply an absence of skill in pre-dicting negative retums. It simply indicates tbat any information is not apparentfrom an examination of the average sell trade.

To consider the differential performance between trades of different sizes,the buy and sell trades of fund managers are classified by size of the trade metric(2) into large, medium, and small. Table 5 presents the DGTW size, book-to-market, and momentum-adjusted retums for buy and sell trades of fund managersclassified by trade size.'^ Across all buys and sells trade size categories, fundmanagers only eam statistically significant superior performance in tbe buy largeand medium trade size category. Relative to the large trades, the medium size buytrades have smaller abnormal retums and larger standard errors. As the trade sizeincreases, it appears (approximately) that the standard errors decline and abnor-

"The performance results for DGTW alpha (1), being retums adjusted for size and book-to-marketbut not momentum characteristics, are similar in all respects to the results reported in Table 5, exceptthey are of a slightly greater magnitude.

Pinnuck 823

mal returns increase. This is consistent with a central premise from the standardmodels of informed trade that the position acquired in an information-motivatedtrade is proportional to the precision of that information.

TABLE 5

DGTW Performance Estimates for Fund Manager Trades Ciassified by Size of Trade(in % return per month)

SellsSmali trades

Medium trades

Large trades

BuysSmali trades

Medium trades

Large trades

Large buys less large sellsReturn

Month 0

-0.33(1.44)

0.22(0.94)

1.45(3.48)***

-0.50(2.58)"*

-0.09(0.61)

0.81(4.91)*"

-0.64(1.48)

Month+1

-0.17(0.75)

0.00(0.00)

0.08(0.56)

-0.08(0.34)

0.25(2.88)*"

0.38(2.54)**

0.30(1.62)

Month+2

0.33(0.88)

-0.54(0.90)

-0.06(0.51)

0.32(1.05)

0.15(0.69)

0.37(2.89)***

0.43(2.71)*"

Eveht Mohth

Month+3

-0.21(0.73)

-0.12(0.49)

-0.04(0.24)

0.38(1.48)

0.15(1.03)

0.24(1.48)

0.28(1.23)

Month+4

0.23(0.74)

-0.27(1.20)

0.00(0.03)

0.04(0.18)

-0.26(1.65)

0.00(0.06)

0.00(0.06)

Mohth+5

0.65(1.35)

-0.28(1.16)

0.00(0.06)

0.02(0.07)

0.19(0.78)

-0.01(0.08)

-0.01(0.01)

Mohth+6

-0.24(1.29)

0.12(0.67)

0.14(0.72)

0.28(1.02)

0.08(0.60)

-0.06(0.43)

-0.20(0.81)

Table 5 calculates the Trade as the change in holdings at the end of each calender month for eaoh fund manager foreach stock. All stocks traded for each fund manager are classified into buys and sells (where buy stocks are all stockswith a positive trade measure). Each group is further classified as small, medium, or large based on the size of the trade.The stocks in each trade size portfoiio are vaiue weighted. The DGTW performance measure for the equaiiy weightedtime-series portfolio of fund buy and seil trades in the sample is presented. To compute the DGTW benchmark-adjustedreturn for a given stook trade during a given month, the buy-and-hoid return on a vaiue-weighted portfoiio of stocks havingthe same size, book-to-market vaiue of equity and momentum characteristics as the stock is subtracted from the stock'sbuy-and-hoid return during the month. Each fund manager's DGTW measure, for a given month is then computed as theportfolio-weighted benchmark-adjusted returh of the individual stock trades in the funds portfolio (normalizing so that theweights of ail stocks add to one). The DGTW performance estimates for event months from Month+1 through to Mohth+6for portfolios with weights based the Month+0 trade size of that stock by the fund manager are reported, (-statistics basedon the time-series standard deviation are in parentheses. *** , **, and • indicate significance at the 1%, 5%, and 10%two-tail levels, respectively.

Finally, I test for differential information between large and small firms. Alllisted stocks are classified into deciles based on the market capitalization at theend of December each year. Stocks in the top decile are classified as large andstocks in the other nine deciles are classified as small. The results, not reported,show that there is no significant difference between small and large stocks in themagnitude of abnormal returns realized by the buy trades in the month subsequentto the trade. One potential explanation for this result is that fund managers maysystematically choose stocks outside the top decile that have similar informationenvironments to my proxy for large stocks (being those stocks in the top decile).

Taken together the performance results for the holdings and trades can beinterpreted as being consistent with fund managers possessing superior informa-tion. However, the performance results presented in Tables 3, 4, and 5 are basedon the arithmetic mean of individual monthly abnonnal rates of return. This isconsistent with prior fund performance research and is appropriate for an investorwith a one-month time horizon. For an investor with a longer time horizon, forexample six months, the geometric mean abnormal return over this interval would


be more appropriate. I therefore calculate the compounded abnormal return overboth a six- and 12-month investment horizon. I calculate this for an investor whopurchases the fund manager's stocks holdings (or alternatively the stocks traded)at the end of each month and holds them for one month only and then purchasesand holds the next month's stock holdings, and so on. I calculate the compoundedabnormal return on this strategy executed for periods of both six months and 12months.'^ The compounded abnormal return performance is calculated for thefund manager stock holdings, the fund manager buys, and the fund manager sells.For brevity, I only report results employing DGTW alpha (2) as the benchmark. "

The results are reported in Table 6. The results for stock holdings show thatthe average compounded abnormal return over a six- and 12-month investmenthorizon are, respectively, 1.25% and 2.74%, which are both marginally statisti-cally significant at the 10% level (two-tail). The results for buy trades over six-and 12-month horizons are also positive and significant at similar marginal lev-els (the results for buy trades over a 12-month horizon can only be consideredsignificant at the 10% level (one-tail)). Taken together the results suggest an in-vestor who buys the fund's stock holdings at the end of each month would realizepositive abnormal returns over investment horizons of six and 12 months. Theevidence, however, is not strong and should be treated with some caution.

C. Limitations

The significance and magnitude of the abnormal return performance resultsover a one-month horizon provides out-of-sample evidence supporting the recentfindings of DGTW (1997) and Wermers (2000). However, the results should betreated with some caution for a number of reasons. First, because as documentedabove, the evidence of superior information over longer horizons is not as strong.Second, it is possible the abnormal returns are a consequence of price pressurefrom the trades rather than fundamental information. This concern is alleviated tosome extent by the distinct pattern of the abnormal returns realized. A price pres-sure hypothesis would suggest both buy and sell trades should realize abnormalreturns. In this study, only buys realize abnormal returns consistent with an infor-mation hypothesis. In addition, a price pressure hypothesis would suggest somereturn reversal in the future as prices revert to their fundamental levels. No suchreturn reversal is detected over the six months following the trade. Nevertheless,notwithstanding these observations, a price pressure hypothesis cannot with cer-tainty be eliminated as an explanation for the abnormal returns. The third reasonfor caution is that the 1990-1997 time period examined is relatively short. It istherefore possible the results are time period specific and do not fairly represent alonger historical record.

'^Formally I calculate a compounded abnormal return for fund manager y by compounding acrossr months as i'ollows,

T r N - | r r j v

BHAR.v = n ' + H '*''•.'-1 '(=1L i=i

where all variables are as previously defined, r takes on values of either six or 12 months."The compounded abnormal returns employing DGTW alpha (1) as the benchmark were

marginally larger.

Pinnuck 825

TABLE 6

Compounded Performance Estimates over Six and 12 Months (in % return per period)

Panel A.

6-month period

12-month period

Panel B.

6-month period

12-month period

6-month period

12-month period

Holdings

1.25%(2.01)-

2.74%(1.94)-

Small

-0.33%(-0.54)

-0.56%(-0.77)

Small

-0.98%(-1.13)

-1.36%(-1.49)

Buys

2.10%(1.89)*

3.12%(1.62)

Trade Size Buys

Medium

1.30%(2.63)**

2.92%(2.28)*

Trade Size Selis

Medium

0.48%(1.07)

1.54%(1.46)

Seils

0.30%(0.26)

0.42%(0.65)

Large

2.26%(1.79)

4.24%(2.01)*

Large

0.30%(0.24)

0.67%(0.28)

Table 6 reports the DGTW alpha (2) performanoe measure compounded over six- and 12-month horizons. The measure iscomputed as the compounded abnormai return reaiized by an investor who purchases the fund manager's stocks holdings(or alternativeiy the stocks traded) at the end of each month and holds them for one month only and then buys and holdsthe next months stock hoidings, and so on. The compounded abnormal return on this strategy is caicuiated for periods ofboth six and 12 months, (-statistics based on the time-series standard deviation are in parentheses. ***, **, and * indicatesignificance at the 1%, 5%, and 10% two-taii levels, respectively.

The final reason for caution is the sample only includes surviving funds. Sur-vivorship bias is therefore likely to affect the reported results, Carhart, Carpenter,Lynch, and Musto (2002) provide a comprehensive study of survivorship issuesin the context of mutual fund research. They find a strong positive relation be-tween survivor bias and sample time length. In studies where the time period isrelatively short, they find survivorship bias, although small, is still likely to existto some extent. More specifically, for five-year samples, a time period roughlyequivalent to my study, they measure bias in the monthly abnormal return as ap-proximately 3,1 basis points per month. On this basis, the reported results in thisstudy overstate by roughly three basis points the average performance of a typicalfund. While the magnitude of this bias does not preclude a conclusion that fundmanagers appear to possess superior information, it does indicate the true level ofthe performance of an average fund is likely to be lower than that reported.

VII. Net Returns

The last section presented evidence consistent with fund managers being in-formed. However, as Wermers (2000) has shown, this does not imply they deliversuperior net retums to their unit holders. To consider this, I follow Wermers(2000) and examine whether the net return delivered to fund unit holders is inexcess of the retums to an appropriate benchmark portfolio.

The data on net retums is sourced from a database maintained by Morn-ingstar, which contains monthly data on net retums of surviving and non-surviving


Australian retail equity funds. The funds from the stock holding database werematched to those in the Momingstar database by fund name. This resulted in afinal sample of 31 funds for which I had net returns. To estimate the performanceof fund managers from their net return time-series, I use tbe intercept from tbeCarhart (1997) four-factor regression measure of performance. '^^ The model isestimated as

(6) Rj^, - Rf^, = aj + 41RMRF, -i- Pj^SMB, + ^_,-3HML, + /

where Rj, is the return on fundy in month t; /?/,, is the risk-free return in month t(30-day Treasury bill yield), RMRF, is tbe montb t value-weigbted market return(as proxied for by tbe All Ordinaries Accumulation Index), ^' and SMB,, HML,,and PRl are the month t returns to zero-investment, factor-mimicking portfoliosdesigned to capture size, book-to-market, and momentum effects, respectively.The SMB, and HML, portfolios are constructed in a manner similar to Fama andFrench (1993) and tbe momentum portfolio is constructed in a manner similar toCarhart (1997).22

The results are summarized in the second column of Table 7. The estimatedalpha is —0.007%, witb a f-statistic of 0.65, so it is not significandy different fromzero. Tbis finding is consistent with the generally insignificant net return perfor-mance measures reported for U.S. mutual funds by Carbart (1997) and Wermers(2000). It is also consistent witb Australian evidence reported by Sawicki andOng (2000) for a sample of funds drawn from tbe same population.

Period

1990-1997

No.

31

Net Fund

Net Carhart

0.0007(0.56)

TABLE 7

Performance (in % per month)

RMRF

0.92{12.34)*"

SMB

0.73(4.56)*"

HML

-0.08(-1.54)

PR1Yr

0.29(1.21)

The dependent variable in these regressions are the net returns that wouid aoorue to unit tioiders. The four independentvariabies are the time-series ot monthly returns assooiated with i) with a value-weighted market proxy portfolio minus T-bills(RMRF), ii) the difference in returns between small and large market stocks (SMB), iii) with the difference between inreturns between high and low book-to market stocks (HML), and iv) with the difference in returns between stocks havinghigh and low prior-year return (PR1YR). (-statistics based on the time-series standard deviation are in parentheses. ***,**, and * indicate significance at the 1 %, 5%, and 10% two-tail levels, respectively.

Tbe results suggest that on a net return level fund managers do not outper-form the bencbmark and do not deliver superior returns to unit bolders. In SectionVI, I provide evidence tbat fund managers bold and trade in stocks that outperformtheir characteristic benchmarks. The difference between tbe average performanceof the fund stock holdings and that of fund net returns is similar to the differencereported by Wermers (2000). Wermers attributes tbis difference for U.S. mutualfunds to i) trade-related costs of implementing tbe manager's style and/or stock

r-factor model introduced by Carhart (1997) is used as it approximates the same expectedreturn as that estimated by the DGTW characteristic-matching performance benchmarks. While theDGTW benchmarks do not directly control for the market, they do so implicitly as the benchmarkswill vary over time in accordance with the market.

^'This index represents the value-weighted return for all stocks listed on the Australian Stock Ex-change.

^ as to the construction of the portfolios are available on request.

Pinnuck 827

picking program, ii) fund expenses incurred and fees charged for managing theportfolio, and iii) the poor performance of the non-stock holdings of the funds'cash and bonds during the period. These explanations would appear to be equallyapplicable to Australian funds.• ^

VIII. Conclusion

This paper directly investigates whether fund managers possess superior in-formation in relation to equity stock selection. I approach this through an exam-ination of the performance of the stock holdings and trades of Australian fundmanagers from 1990 to 1997. I find the stocks they hold realize economicallysignificant abnormal retums in the month following the holding date. This resultis consistent with fund managers possessing some stock selection ability.

As a more powerful examination of the private information possessed byfund managers, I also examine the performance of their individual trades. I findthat the stocks they buy realize abnormal retums and the precision of the infor-mation is greater for large buy relative to small buy trades. For sell trades, I findno evidence of abnormal returns, which suggests that fund managers do not pos-sess superior information in regard to bad news. The reported results in this studyare subject to some caveats and accordingly should be treated with some caution.First, given the limited time period, the results may be time period specific. Sec-ond, there is a small number of funds and as a consequence the results may besample specific. Third, an altemate explanation for the abnormal retums of pricepressure cannot with certainty be eliminated as a possibility. Finally, survivorshipbias is likely to have had some impact on the reported abnormal retums. Nev-ertheless, subject to these caveats, the study provides out-of-sample support forthe recent findings of U.S. studies that the stocks held by mutual funds at calen-der quarter-ends realize abnormal retums. This may alleviate concems the U.S.results are simply a spurious result due to fund quarterly reporting biases.

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