1. JOURNAL OF FINANCIAL AND OUANTITATIVE ANALYSIS VOL. 38, NO, 4, DECEMBER 2003
COPYRIGHT 2003, SCHOOL OF BUSINESS ADMINISTRATION, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195
An Examination of the Performance of the
Trades 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 and
found they realize abnormal returns. The result is significant as it stands in contrast to
the general consensus from traditional performance studies that mutual funds do not pos-
sess superior information. Employing a unique dataset, I examine the performance of the
monthly stock holdings and trades of a sample of Australian fund managers. When stock
holdings are observable, performance measures can be constructed that are more precise
than traditional fund manager performance measures. I find the stocks held by fund man-
agers realize abnormal returns consistent with some stock selection ability across fund
managers. Examining the performance of their individual trades, I find that the stocks they
buy 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 for
U.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 active
fund manager industry does not outperform a passive benchmark.' However, in
contrast to traditional performance studies, recent studies hy Daniel, Grinblatt,
* Pinnuck, mpinnuck@unimelb.edu.au. 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 Auckland
2001 conference, and the Melboume-Monash Joint Symposium. I thank Kevin Davis, tan Ramasay,
and Geof Stapledon, Frank Russell Company, and the Australian tnvestment Managers Association
for assistance with the database employed in this study.
'tn the U.S., all the recent studies also report no evidence of superior performance. Examples
are Elton, Gruber, Das, and HIavka (1993), Malkiel (1995), Gruber (1996), and Carhart (1997)). In
Australia, early studies by Bird, Chin, and McCrae (1983) and Robson (1986) employed the traditional
Jensen measure and reported no evidence of superior performance. More recent studies by Hallahan
and Faff (1999) and Sawicki and Ong (2000) employ both the Jensen measure and the extensions to
traditional factor time-series regressions and find no evidence of selection ability in the Australian
market.
811

2. 812 Journal of Financial and Quantitative Analysis
Titman, and Wermers (DGTW) (1997), Chen, Jegadeesh, and Wermers (2000),
and Wermers (2000) take a different approach and examine the performance of
the individual stocks held in fund manager portfolios. They report results con-
sistent with fund managers having the ability to choose stocks that outperform
their 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 somewhat
controversial and has not been without criticism.
In this study, I examine the performance of both the stock holdings and
trades of Australian active equity fund managers using a unique database of their
monthly equity portfolio holdings. I contribute to the emerging literature that
examines the performance of the stock holdings of fund managers in three main
ways. 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 both
construction 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 calendar
quarter-ends. Moskowitz (2000) argues the performance attributable to quarter-
end portfolios may not be representative of the typical fund portfolio. This is
on the basis there may be a systematic difference between the characteristics of
the stock holdings in the quarter-end portfolio and the portfolio holdings in the
between quarter month-ends, due to fund reporting biases.''
Finally, in addition to examining the performance of stock holdings, I also
examine the performance of the individual trades of each fund manager. Chen,
Jegadeesh, and Wermers (2000) argue an examination of the trades as opposed
to the holdings of each fund manager is a more powerful metric to determine
the existence of superior information. Further, an examination of trades allows
one to make some simple theoretical predictions of differential performance be-
tween subgroups of trades. Assuming a valid theory, then results consistent with
predictions alleviate, to some extent, concerns regarding the robustness of the
performance benchmark employed and also provide some insights into how fund
managers 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 fund
industry briefly examine the performance of the trades of pension funds. Except for those pension
funds 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 ante
predictors of cross-sectional patterns in common stock retums in the Australian capital market are
size, book-to-market, and momentum. Second, the traditional time-series factor models report no
evidence of superior performance by the Australian fund management industry. Citations for this
Australian 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 different
from intervening monthly portfolio holdings not reported.

3. Pinnuck 813
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, I
find 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 that
there is a greater probability of fund managers possessing superior information
for large relative to small stocks. Overall, both the existence and magnitude of
the abnormal returns give support to the conclusion from DGTW (1997) that fund
managers 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 deliver
superior net returns to investors. To consider whether the benefits of any superior
information 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 results
suggest 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 as
transactions costs, management fees, and poor market timing decisions.
The remainder of the paper is set out as follows. In the next section, I discuss
the units of observation I employ. Section III sets out the performance evaluation
methodology employed. The construction of the database is discussed in Section
IV. The characteristics of the stocks are examined in Section V. Empirical find-
ings are presented in Section VI. Section VII examines the performance of the
net 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 two
distinct units of observation: i) stock holdings and ii) trades. An examination
of the performance of stock holdings measures the performance return on each
stock (' held in the fund manager's portfolio at each month-end t. The portfolio
performance of fundy at time t is then simply the value-weighted performance of
all stocks held. The weight of security i in the portfolio of the fund managery' at
time 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 fund
manager^ in stock / at time t, and A is the number of different stocks held by each
^
fund manager,^
^Where a fund manager atso holds option contracts, 1 replaced each actual option position for a
company 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 determine
the number of ordinary shares that must be bought/sold in order to have the same exposure to a small
movement in the share price as the option contracts held. For call options, the delta is computed using
the partial derivative of the Black-Scholes model modified for dividends and early exercise. For put
options, as there is no closed-form valuation solution, I numerically compute each options delta using
the numerical procedures of the Cox-Rubinstein binomial pricing model.

4. 814 Journal of Financial and Quantitative Analysis
I also examine the subsequent abnormal performance of the stocks a fund
matiager 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 likely
to represent a signal of private information than the passive decision of holding
the existing position in the stock. They suggest a fund manager may continue
to hold a stock for reasons other than future abnormal performance because of
the frictions involved in trading such as trading costs, as well as more implicit
costs such as the triggering of a capital gains tax event through a sale. As a
consequence of these frictions, the return on holdings may not reveal the true
private information possessed by fund managers. Thus, trades may provide more
powerful evidence of the information fund managers possess about future returns.
I measure Trade,/; as the change in the weight of stock i from the beginning
to 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 - 1
that are evaluated at the same end-of-month prices as weight, Wy;. The Trade
metric in equation (2) therefore measures the difference between two different
portfolios (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 place
during the month.*
I categorize these trades as either purchases or sales (where purchase stocks
are all stocks with a positive Trade measure). I then construct purchase and sale
portfolios and analyze their returns with the performance evaluation methods doc-
umented in Section III.
III. Performance Evaluation Methodology with Observable
Portfolio 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 to
the benchmark employed and not superior information, I employ two specifica-
tion checks. First, I employ a performance evaluation methodology proposed by
Grinblatt and Titman (GT) (1993) that does not require an arbitrary model of ex-
pected returns. Second, I develop some simple a priori predictions of differential
performance between different classes of stocks and trades. Results consistent
with the predictions alleviate, to some extent, concerns regarding the benchmark
employed.
*Both holdings Wy, and Wjj,— are evaluated at the same prices so that there are no spurious price
change effects, allowing me to separate trades from price momentum effects.

5. Pinnuck 815
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 from
the stock's return. Formally, the DGTW performance measure for fund manager
j 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 the
month 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 of
benchmark portfolios is constructed to represent the stock characteristics of size
and 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 adjustment
for momentum. The benchmark portfolios are constructed in a similar manner to
DGTW (1997),^
B, The GT (1993) Measure of Performance
The measure, developed by GT (1993) (hereafter the GT measure) uses the
past portfolio weights of a given mutual fund to calculate a benchmark return
for the evaluation period. The advantage of the GT measure for the abnormal
return 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 return
of the portfolio of fund manager^ for its risk in a given month t is the month f's
return 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 can
be expressed as
(5) GT, =
(=1 1=1
where /?,, is the security return on / from date ttot+l. Wu is the portfolio weight
of security / at date t. W,,,-i2 is the portfolio weight of security i at date t - 12. T
is the number of periods,
'The size and book-to-market benchmark-based portfolios are constructed as follows. Beginning
in December 1989 and each following December 31, each stock in the AGSM Price Relative File that
satisfied the data requirements, is placed into size and book-to-market portfolios. The composition of
each portfolio is determined by each December sorting of the universe of stocks into quintiles based
on each firm's market value of equity. Then, firms in each size quintile are further sorted into quartiles
based on their book-to-market ratio. This yields 20 benchmark portfolios. The average number of
firms in each portfolio is 32, The size, book-to-market, and momentum benchmark-based portfolios
are constructed by sorting firms in each of the 20 size and book-to-market portfolios into a further
three portfolios based on their preceding 12-month return calculated to the end of November, This
gives a total of 60 size, book-to-market, and momentum portfolios. The average number of firms in
each portfolio is 10,

6. 816 Journal of Financial and Quantitative Analysis
Under the null hypothesis of no superior information, the changes in weights
from the prior period are uncorrelated with current returns. In this case, the
measure converges to zero. Under the alternate hypothesis that a fund manager
is informed, the measure converges to the average eovarianee between R „ and
{Wi, - Wi,,-x2). Expression (5) will be positive for informed investors and zero
for uninformed.
C. Performance Predictions for Different Classes of Stocks and Trades
In this section, I develop some simple a priori predictions of differential
performance 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 measure
should 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 in
the literature. First, it has been observed fund managers are in general long only
investors (i.e., they only hold assets in non-negative amounts). ^ It has been shown
analytically by Saar (2001) and argued by Chan and Lakonishok (1993) and Keim
and Madhavan (1995) that being a long only investor creates a situation in which
it is optimal for fund managers to predominately engage in searching for stocks
whose 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 stocks
whose price they expect to go down. However, as fund managers can only sell
stocks they already hold, they have a limited number of alternatives. Thus, they
may have to sell stocks for which they simply expect the price to go nowhere. As
a consequence, buy trades are more likely to be motivated by information and sell
trades to be motivated by portfolio rebalancing.'"
The second reason for buys being more informative than sells is that analysts
are a source of information for fund managers. It has been argued by McNichols
and 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 of
losing 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 of
unlimited losses if the stock price goes up and the charters of many mutual funds explicitly restrict the
usage 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 to
buy.
'"tt is important to note that this argument does not suggest that fund managers never possess
private information with respect to bad news. The argument simply suggests it is more likely that the
typical 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 with
institutional investors possessing good but not bad news. Chen, Jegadeesh, and Wermers (2000) have
provided evidence consistent with the aggregated buys but not sells realizing abnormal returns. Chan
and Lakonishok (1993) in an examination of intraday price impact of institutional block trades found
that buys but not sells have a permanent price impact. They interpret this as being consistent with

7. Pinnuck 817
Standard models of informed trade (i.e., Kyle (1985)) show that, ceteris
paribus, there is a positive relationship between trade size and abnormal retums.
I therefore examine the differential performance among trades of different size. It
should, however, be recognized that the relationship between trade size and abnor-
mal retums is significantly more complex tban that presented. Standard models
of informed trade show the relationship also depends on stock liquidity, infor-
mation precision, and risk aversion. Therefore, the evidence with respect to the
performance of different sized trades is descriptive only and does not represent an
examination 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, to
minimize the risk of underperformance of the market index, they will hold large
firms 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 price
are greater. This discussion suggests, due to the differential incentives for infor-
mation search, fund managers possess more precise information for large than for
small firms.
IV. Data
A. Construction of Database
My data consists of monthly observations on the equity portfolio holdings
of 35 Australian active equity fund managers from January 1990 to December
1997. All the portfolios are fund products where the objective is to outperform the
market. The portfolios have 24-72 months of data. The monthly equity holdings
data over the period were obtained from two sources. First, data was sourced from
a collaborative project between the University of Melboume and the Australian
Investment Managers' Association (AIMA). Secondly, portfolio holding data was
obtained from Frank Russell Company, which maintains a database of portfolio
holdings 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% of
the 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 equity
holdings of the Australian funds' population. Therefore, the sample, notwith-
standing what may appear to be a small number of funds relative to a typical
U.S. study, can be taken as representative of tbe Australian funds management
industry.'^
traders having good but not bad news. At a market level Hong, Lirti, and Stein (1999) provide evidence
that bad news is incorporated into prices more slowly than good news. They conjecture that this is
consistent 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. Survivorship
bias is therefore likely to affect the results in this paper. The potential impact of survivorship bias is
discussed in Section VI.

8. 818 Journal of Financial and Quantitative Analysis
TABLE 1
Sample and Population of Equity Fund Managers in Australia
Sample as
Population Sample % of Population
Aggregate Aggregate Aggregate
No. of TNA No. of TNA No. of TNA
Year Funds ($Mill) Funds (SMill) Funds(%) (%)
1990 22 760 14 507 63 67
1991 23 1,258 15 898 65 71
1992 24 1,394 17 1002 71 71
1993 28 2,350 19 1873 68 79
1994 37 2,598 32 2154 86 82
1995 40 3,053 35 2745 87 89
1996 43 4,435 35 3853 81 86
1997 48 4,401 28 2904 58 66
Table 1 sfiows the number of active equity funds in both the sample and the Australian population from 1990 to 1997 as of
January 31 each year. The population is active Austraiian equity fund managers. The table aiso shows the dollar amount
of total net assets (TNA) in $AUS million.
V. Stock Characteristics of Aggregate Mutual Fund
Holdings
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 similar
to Chan, Chen, and Lakonishok (2002), by examining some key investment style
characteristics of the stocks the sampled fund managers prefer to hold. First, I
examine whether the fund manager prefers to hold large or small stocks where
size 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 also
examine the characteristics of the fund manager's stock holding with respect to
prior stock returns (12-month return ending one month prior to holding), volatil-
ity (standard deviation of monthly returns over the 36-month interval ending three
months prior to holding date), and liquidity (annual trading volume in the firm's
stock in the year immediately preceding holding date, divided by the average total
number of shares outstanding for the year).
At the end of each financial year, all available domestic stocks listed on the
Australian Stock Exchange (recorded in the Australian Graduate School of Man-
agement (AGSM) price relative file) are ranked in ascending order by the relevant
characteristic (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 the
holdings of each fund manager y at 30 June each year to compute the weighted
average of the percentile rankings over all stocks in the portfolio at that point in
time. The weight of a stock is the proportion of the portfolio's value invested
in the stock. This metric is then averaged across time for fund manager j and
then averaged across all fund managers in the sample to provide the reported re-
sults. As explained by Chan, Chen, and Lakonishok (2002), the characteristic
rank 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 across
all 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

9. Pinnuck 819
provide the fund manager stock preferences with a basis of comparison, I use as a
benchmark the All Ordinaries Accumulation Index, which I assume to represent
the average weights of the hypothetical average investor. '^ The portfolio average
characteristic 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 results
are 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 0.70
Ali 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-haif
years ago and ending six months ago, and the most recent past one-year stock return. The Ail Ordinaries Accumulation
Index 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 stocks
are ranked by the reievant characteristic and assigned a score from zero (iowest) to one (highest). The portfolio average
for the index is the capitalization-weighted average of these rank scores across aii stocks in the index; the average for
the fund portfoiio is the weigfited average across stocks in the aggregated portfolio of ail funds, with weights giveh by
the vaiue of the fund's hoidings of the stock. Based on its portfoiio characteristic, a fund is assigned to one of 10 groups
determined by the decile breakpoints of ail domestic stocks in the index.
Table 2 shows fund managers have a strong preference for large stocks. The
average size rank for the portfolio of stocks held is 0.95. This rank average for
the fund managers is similar to the index rank average of 0.96, suggesting that
fund managers tend to concentrate their portfolio in the same large-sized stocks
as 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 more
concentrated toward growth than value stocks compared to the All Ordinaries Ac-
cumulation Index (average rank 0.40). The average momentum rank is 0.6, which
is 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 managers
tend to hold more liquid rather than less liquid stocks. Finally, the volatility rank
of 0.2 suggests fund managers prefer less risky stocks. In summary, the basic
finding 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 attributes
examined, similar to the All Ordinaries Accumulation Index. This is consistent
with 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 by
Chan, Chen, and Lakonishok (2002).
VI. Performance Evaluation: Results
This section discusses the results of each of the two performance evaluation
methods 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.

10. 820 Journal of Financial and Quantitative Analysis
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, I
calculate the benchmark-adjusted performance on an equally weighted portfolio
of funds, existing at a point in time, for each of the t months in the database, I then
compare 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 percentage
return per month,
I present performance measures for the portfolio of holdings and trades of the
fund 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 from
month+1 through month+6. As an example for portfolio holdings at March 31
the performance estimates for month+1 represents the abnormal return on the
stocks in the month of April, The performance estimate for month+2 represents
the 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 is
that 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 have
superior information that is revealed to the market within one month, the month+1
measure provides the most power. However, if information is incorporated into
market prices more slowly, then month+3, +4, +5, or month+6 may have more
power,
A. Performance Evaluation Results of Holdings
Table 3 presents performance results using the DGTW (1997) measure for
an equally weighted portfolio of fund managers. Performance results after ad-
justment for the benchmark return from size and book-to-market portfolios are
hereafter referred to as DGTW alpha (1), Performance results after adjustment
for the benchmark return from size, book-to-market, and momentum portfolios
are hereafter referred to as DGTW alpha (2), The DGTW alpha (1) results show
the 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 positive
selectivity 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 with
fund managers benefiting from momentum in retums.
Table 3 also presents performance results using the GT (1993) measure for
an equally weighted portfolio of fund managers. The results for the entire sample
show that the average GT performance is significantly positive in each of the three
months (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 standard
errors it is possible they may be misspecified due to inter-temporal dependence between the residuals
from this time-series. This concern is however alleviated to some extent as there was no evidence of
correlation between the residuals at monthly lags of one through six.

11. Pinnuck 821
TABLE 3
Performance Estimates for Fund Managers' Stock Holdings (in % return per montfi)
Event Time
fvlonth 0 fvlonth4.1 Month-^2 Month+3 Month+4 Month+5 Month+6
GT performance measure 0.69 0.20 0.20 0.16 0.08 0.11 0.34
(8.2)*** (3.11)*" (3.08)*** (2.24)** (1.20) (1.43) (1.57)
DGTW alpha (1) 0.60 0.24 0.18 0.12 0.08 0.00 0.11
(3.1)*" (1.87)- (1.28) (0.94) (0.70) (1.01)
DGTW alpha (2) 0.51 0.16 0.11 0.07 0.01 0.00 0.00
(7.07)-" (2.25)** (1.05) (0.79) (0.09) (0.01) (-0.35)
Table 3 reports three performance measures for the equally weighted time-series portfolio of funds in the sampie. The
GT performance measure is caicuiated by subtracting the time (return of the portfolio heid at month ( — 13 from the
time (return of the portfoiio heid at ( - 1. To compute the DGTW aipha (1) and DGTW aipha (2) benchmark-adjusted
return for a given stock during a given month, the buy-and-hoid return on a value-weighted portfolio of stocks having the
same size, book-to-market value of equity characteristics (and momentum for DGTW aipha (2)) as the stock is subtracted
from the stock's buy-and-hold return during the month. Each fund manager's DGTW aipha (1) and (2) measure, for a
given month, is then computed as the portfolio-weighted benchmark-adjusted return of the individuai stocks in the funds
portfoiio (normalizing so that the weights of all stocks add to one). The performance estimates for each performance
measure for event months from Months 1 through MonthH.6 for portfolios with weights based on the fvlonth-i-O hoidings of
that 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 fund
managers. The weights for each calendar month were hased on the value of the
assets under management as of January 1 each year. In results not reported, all
three performance metrics, GT, DGTW alpha (1), and DGTW alpha (2), are pos-
itive and statistically significant in the first month after the holding measurement
date, 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 fund
managers. I focus the discussion on the implications of the DGTW alpha (2) re-
sults for the performance ofthe fund manager. '^ The buy stocks have statistically
significant 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% is
larger than the comparable DGTW performance result for holdings in month+1
of 0.16%. This is consistent with fund managers holding stocks beyond the time
horizon for which they provide positive abnormal returns. The reason for this,
as suggested by Chen, Jegadeesh, and Wermers (2000), may be to avoid high
transaction 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) except
tfiey are of slightly greater magnitude.
"in the portfolio formation month, denoted month 0, there is no evidence of the trades realizing
positive abnormal returns attributable to superior information. More specifically, the returns on the
sell trades are greater than those on the buy trades. The reason for this can probably be attributed to
a 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, in
the month of the trade, if fund managers sell stocks that performed poorly in the prior month, these
stocks would outperform the stocks bought by an economically significant magnitude.

12. 822 Journal of Financial and Quantitative Analysis
TABLE 4
Performance Estimates for Fund Manager Trades (in % return per month)
Event Month
Month 0 Month+1 Month+2 Month+3 Month+4 Month+5 Month+6
Panel A. Gross Returns
Buys (Trades > 0) 145 1.26 1.13 1,25 1,08 1.14 0,91
(3.5)"- (3.0)"- (3.04)"' (2,9)*** (2,65)*** (2.83)*** (2.7)*"
Sells (Trades < 0) 2.30 0.78 0.92 0,97 0,99 1,00 0.92
(4.3)"- (2.42)" (2,20)" (2,42)** (2,40)" (2,55)** (2,10)"
Buys less Sells -0.85 0.48 0.21 0,28 0,09 0,14 -0,01
(2.17)" (1.68) (2.34)" (1.42) (0,51) (0,79) (0.99)
Panel B. DGTW alpha (1)
Buys (Trades > 0) 0.66 0.45 0.36 0,25 0,06 0,13 0,00
(3.6)"- (3.29)"- (2,9)"* (1,70) (0,53) (0,91) (0,06)
Sells (Trades < 0) 1.38 0.13 0,00 0,00 0,02 0,03 0,17
(4.47)"- (0.96) (0,13) (0.10) (0,19) (0,27) (1.49)
Buys less Sells -0.72 0.32 0,36 0,25 0,04 0.01 -0.17
(2.57)" (1.50) (2,10)" (1.10) (0,23) (0.56) (1.08)
Panel a DGTW alpha (2)
Buys (Trades > 0) 0.62 0.36 0,32 0,22 0,00 0.04 -0.03
(4.28)"' (2.63)"- (2.67)"* (1.55) (0,41) (0,33) (0,24)
Sells (Trades < 0) 0.90 0.07 -0,02 -0,14 0,02 0,03 0,07
(7.59)'" (0.54) (0,12) (0.93) (0,13) (0,22) (0.60)
Buys less Sells -0.28 0.29 0,33 0.33 -0,02 0,01 -0,10
-(1.72) (1.74) (2,71)"* (2,06)** (0,45) (0,11) (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 positive
trade 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 weighted
time-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 of
stocks having the same size, book-to-market value of equity, and momentum for DGTW (2) characteristics as the stock
is subtracted from the stock's buy-and-hold return during the month. Each fund manager's DGTW measure, for a given
month, is then computed as the portfolio-weighted behchmark-adjusted return of the ihdividuai stock trades in the funds
portfolio (normalizing so that the weights of aii stocks add to one), Ali returns for event months from Month+1 through
Month+6 for trades with weights based on the Month+0 trade size of that stock by the fund manager are reported. The
returns are computed as the equaily weighted time-series portfolio of fund trades in the sample, ^statistics based on the
time-series standard deviation are in parentheses, *** . **, and * indicate significance at the 1%, 5%. and 10% two-taii
levels, respectively.
abnormal retums is consistent with the average sell trade not revealing superior
information about poorly performing stocks. Note that the insignificant ^-statistic
with 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 apparent
from 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 managers
classified by trade size.'^ Across all buys and sells trade size categories, fund
managers only eam statistically significant superior performance in tbe buy large
and medium trade size category. Relative to the large trades, the medium size buy
trades have smaller abnormal retums and larger standard errors. As the trade size
increases, 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-market
but not momentum characteristics, are similar in all respects to the results reported in Table 5, except
they are of a slightly greater magnitude.

13. Pinnuck 823
mal returns increase. This is consistent with a central premise from the standard
models of informed trade that the position acquired in an information-motivated
trade 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)
Eveht Mohth
Month 0 Month+1 Month+2 Month+3 Month+4 Mohth+5 Mohth+6
Sells
Smali trades -0.33 -0.17 0.33 -0.21 0.23 0.65 -0.24
(1.44) (0.75) (0.88) (0.73) (0.74) (1.35) (1.29)
Medium trades 0.22 0.00 -0.54 -0.12 -0.27 -0.28 0.12
(0.94) (0.00) (0.90) (0.49) (1.20) (1.16) (0.67)
Large trades 1.45 0.08 -0.06 -0.04 0.00 0.00 0.14
(3.48)*** (0.56) (0.51) (0.24) (0.03) (0.06) (0.72)
Buys
Smali trades -0.50 -0.08 0.32 0.38 0.04 0.02 0.28
(2.58)"* (0.34) (1.05) (1.48) (0.18) (0.07) (1.02)
Medium trades -0.09 0.25 0.15 0.15 -0.26 0.19 0.08
(0.61) (2.88)*" (0.69) (1.03) (1.65) (0.78) (0.60)
Large trades 0.81 0.38 0.37 0.24 0.00 -0.01 -0.06
(4.91)*" (2.54)** (2.89)*** (1.48) (0.06) (0.08) (0.43)
Large buys less large sells
Return -0.64 0.30 0.43 0.28 0.00 -0.01 -0.20
(1.48) (1.62) (2.71)*" (1.23) (0.06) (0.01) (0.81)
Table 5 calculates the Trade as the change in holdings at the end of each calender month for eaoh fund manager for
each stock. All stocks traded for each fund manager are classified into buys and sells (where buy stocks are all stocks
with 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 weighted
time-series portfolio of fund buy and seil trades in the sample is presented. To compute the DGTW benchmark-adjusted
return for a given stook trade during a given month, the buy-and-hoid return on a vaiue-weighted portfoiio of stocks having
the same size, book-to-market vaiue of equity and momentum characteristics as the stock is subtracted from the stock's
buy-and-hoid return during the month. Each fund manager's DGTW measure, for a given month is then computed as the
portfolio-weighted benchmark-adjusted returh of the individual stock trades in the funds portfolio (normalizing so that the
weights of ail stocks add to one). The DGTW performance estimates for event months from Month+1 through to Mohth+6
for portfolios with weights based the Month+0 trade size of that 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-tail levels, respectively.
Finally, I test for differential information between large and small firms. All
listed stocks are classified into deciles based on the market capitalization at the
end of December each year. Stocks in the top decile are classified as large and
stocks 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 the
magnitude of abnormal returns realized by the buy trades in the month subsequent
to the trade. One potential explanation for this result is that fund managers may
systematically choose stocks outside the top decile that have similar information
environments to my proxy for large stocks (being those stocks in the top decile).
Taken together the performance results for the holdings and trades can be
interpreted as being consistent with fund managers possessing superior informa-
tion. However, the performance results presented in Tables 3, 4, and 5 are based
on the arithmetic mean of individual monthly abnonnal rates of return. This is
consistent with prior fund performance research and is appropriate for an investor
with a one-month time horizon. For an investor with a longer time horizon, for
example six months, the geometric mean abnormal return over this interval would

14. 824 Journal of Financial and Quantitative Analysis
be more appropriate. I therefore calculate the compounded abnormal return over
both a six- and 12-month investment horizon. I calculate this for an investor who
purchases 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 purchases
and holds the next month's stock holdings, and so on. I calculate the compounded
abnormal return on this strategy executed for periods of both six months and 12
months.'^ The compounded abnormal return performance is calculated for the
fund 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 that
the average compounded abnormal return over a six- and 12-month investment
horizon 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 considered
significant 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 realize
positive abnormal returns over investment horizons of six and 12 months. The
evidence, however, is not strong and should be treated with some caution.
C. Limitations
The significance and magnitude of the abnormal return performance results
over a one-month horizon provides out-of-sample evidence supporting the recent
findings of DGTW (1997) and Wermers (2000). However, the results should be
treated with some caution for a number of reasons. First, because as documented
above, the evidence of superior information over longer horizons is not as strong.
Second, it is possible the abnormal returns are a consequence of price pressure
from the trades rather than fundamental information. This concern is alleviated to
some extent by the distinct pattern of the abnormal returns realized. A price pres-
sure hypothesis would suggest both buy and sell trades should realize abnormal
returns. In this study, only buys realize abnormal returns consistent with an infor-
mation hypothesis. In addition, a price pressure hypothesis would suggest some
return reversal in the future as prices revert to their fundamental levels. No such
return 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 reason
for caution is that the 1990-1997 time period examined is relatively short. It is
therefore possible the results are time period specific and do not fairly represent a
longer historical record.
'^Formally I calculate a compounded abnormal return for fund manager y by compounding across
r 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.

15. Pinnuck 825
TABLE 6
Compounded Performance Estimates over Six and 12 Months (in % return per period)
Panel A.
Holdings Buys Seils
6-month period 1.25% 2.10% 0.30%
(2.01)- (1.89)* (0.26)
12-month period 2.74% 3.12% 0.42%
(1.94)- (1.62) (0.65)
Panel B.
Trade Size Buys
Small Medium Large
6-month period -0.33% 1.30% 2.26%
(-0.54) (2.63)** (1.79)
12-month period -0.56% 2.92% 4.24%
(-0.77) (2.28)* (2.01)*
Trade Size Selis
Small Medium Large
6-month period -0.98% 0.48% 0.30%
(-1.13) (1.07) (0.24)
12-month period -1.36% 1.54% 0.67%
(-1.49) (1.46) (0.28)
Table 6 reports the DGTW alpha (2) performanoe measure compounded over six- and 12-month horizons. The measure is
computed 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 holds
the next months stock hoidings, and so on. The compounded abnormal return on this strategy is caicuiated for periods of
both six and 12 months, (-statistics based on the time-series standard deviation are in parentheses. ***, **, and * indicate
significance 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 issues
in 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 is
relatively short, they find survivorship bias, although small, is still likely to exist
to some extent. More specifically, for five-year samples, a time period roughly
equivalent 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 this
study overstate by roughly three basis points the average performance of a typical
fund. While the magnitude of this bias does not preclude a conclusion that fund
managers appear to possess superior information, it does indicate the true level of
the 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 deliver
superior 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 in
excess 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

16. 826 Journal of Financial and Quantitative Analysis
Australian retail equity funds. The funds from the stock holding database were
matched to those in the Momingstar database by fund name. This resulted in a
final sample of 31 funds for which I had net returns. To estimate the performance
of fund managers from their net return time-series, I use tbe intercept from tbe
Carhart (1997) four-factor regression measure of performance. '^^ The model is
estimated 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 portfolios
designed to capture size, book-to-market, and momentum effects, respectively.
The SMB, and HML, portfolios are constructed in a manner similar to Fama and
French (1993) and tbe momentum portfolio is constructed in a manner similar to
Carhart (1997).22
The results are summarized in the second column of Table 7. The estimated
alpha is —0.007%, witb a f-statistic of 0.65, so it is not significandy different from
zero. 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 and
Ong (2000) for a sample of funds drawn from tbe same population.
TABLE 7
Net Fund Performance (in % per month)
Period No. Net Carhart RMRF SMB HML PR1Yr
1990-1997 31 0.0007 0.92 0.73 -0.08 0.29
(0.56) {12.34)*" (4.56)*" (-1.54) (1.21)
The dependent variable in these regressions are the net returns that wouid aoorue to unit tioiders. The four independent
variabies 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 in
returns between high and low book-to market stocks (HML), and iv) with the difference in returns between stocks having
high 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 Section
VI, I provide evidence tbat fund managers bold and trade in stocks that outperform
their characteristic benchmarks. The difference between tbe average performance
of the fund stock holdings and that of fund net returns is similar to the difference
reported by Wermers (2000). Wermers attributes tbis difference for U.S. mutual
funds 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 expected
return as that estimated by the DGTW characteristic-matching performance benchmarks. While the
DGTW benchmarks do not directly control for the market, they do so implicitly as the benchmarks
will 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.

17. Pinnuck 827
picking program, ii) fund expenses incurred and fees charged for managing the
portfolio, 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 equally
applicable 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 fund
managers from 1990 to 1997. I find the stocks they hold realize economically
significant abnormal retums in the month following the holding date. This result
is consistent with fund managers possessing some stock selection ability.
As a more powerful examination of the private information possessed by
fund managers, I also examine the performance of their individual trades. I find
that 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 find
no 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 study
are 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 be
sample specific. Third, an altemate explanation for the abnormal retums of price
pressure cannot with certainty be eliminated as a possibility. Finally, survivorship
bias 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 for
the 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.
References
Atiase, R. "Predisclosure Information, Firm Capitalization, and Security Price Behavior around Earn-
ings Announcements." Journal of Accounting Research. 23 (1985), 21-36.
Bhushan, R. "Collection of Information about Publicly Traded Firms; Theory and Evidence." Journal
of Accounting and Economics. 11 (1989) 183-208.
Bird, R.; H. Chin; and M. McCrae. "The Performance of Australian Superannuation Funds." Aus-
tralian Journal of Management. 8 (1983), 49-69.
Carhart, M. "On Persistence in Mutual Fund Performance." Journal of Finance, 52 (1997), 57-82.
Carhart, M.; J. Carpenter; A. Lynch; and D. Musto. "Mutual Fund Survivorship." Review of Financial
Studies. 15 (2002), 1439-1463.
Chan, L. K., and J. Lakonishok. "Institutional Trades and Intraday Stock Price Behavior." Journal of
Financial Economics. 33 (1993), 173-199.
Chan L.; H. Chen; and J. Lakonishok. "On Mutual Fund Investment Styles." Review of Financial
Studies, 15 (2002), 1407-1437.
^'Unfortunately, I do not have data that allows a precise analysis on the fund's transaction costs,
fund expenses, and cash holdings. Whether these abnormal returns were lost due to higher manage-
ment fees, overly large transaction costs or through operational inefficiencies is a direction left for
future research as the data becomes available.

18. 828 Journal of Financial and Quantitative Analysis
Chen, H.; N. Jegadeesh; and R Wermers. "The Value of Active Fund Management: An Examination of
the Stockholdings and Trades of Fund Managers." Journal of Financial and Quantitative Analysis,
35 (2000), 343-368.
Daniel, K.; M. Grinblatt; S. Titman; and R. Wermers. "Measuring Mutual Fund Performance with
Characteristic-Based Benchmarks." Journal of Finance, 52 (1997), 1035-1058.
Elton, E.; M. Gruber; S. Das; and M. Hlavka."Efficiency with Costly Information: A Reinterpretation
of Evidence from Managed Portfolios." Review of Financial Studies, 6 (t993), t-22.
Fama, E., and K. French. "The Cross-Section of Expected Stock Returns." Journal of Finance, 47
(1993), 427-465.
Gaunt, C , and P. Gray. "Short-Term Autocorrelation in Australian Equities." Working Paper, Univ. of
Queensland (2001).
Grinblatt, M., and S. Titman. "Performance Measurement without Benchmarks: An Examination of
Mutual Fund Returns." Journal of Business, 66 (1993), 47-68.
Gruber, M."Another Puzzle: The Growth in Actively Managed Mutual Funds." Journal of Finance,
5t (1996), 783-810.
Hailahan, T., and R. Faff. "An Examination of Australian Equity Trusts for Selectivity and Market
Timing Performance." Journal of Multinational Financial Management, 9 (1999), 387^02.
Hong, H.; T. Lim.; and J. Stein. "Bad News Travels Stowly: Size, Analysts Coverage, and the Prof-
itability of Momentum Strategies." Journal of Finance, 55 (2000), 265-295.
Jensen, M. C. "The Performance of Mutual Funds in the Period 1945-1964." Journal of Finance 23
(1968), 389^16.
Keim, D. B., and A. Madhavan. "Anatomy of the Trading Process: Empirical Evidence on the Behav-
ior of Institutional Trades." Journal of Financial Economics, 37 (1995), 371-398.
Kyle, A. "Continuous Auctions and Insider Trading." Econometrica, 53 (1985), 1315-1335.
Kothari, S. P., and J. Warner. "Evaluating Mutual Fund Performance." Journal of Finance, 56 (2001),
1985-2012.
Lakonishok, J.; A. Shleifer; and R. Vishny. "The Structure and the Performance of the Money Man-
agement Industry." Brookings Papers on Economic Activity (1992) 339-379.
Malkiel, B. "Returns from Investing in Equity Mutual Funds 1971-1991." Journal of Finance, 50
(1995), 549-572.
McNichols, M., and P. C. O'Brien. "Self-Selection and Analysts Coverage." Journal of Accounting
Research, 35 (1997), 167-199.
Moskowitz, T. "Discussion of Wermers 2000." Journal of Finance, 55 (2000), 1695-1703.
Robson, G. "The Investment Performance of Unit Trusts and Mutual Funds in Australia for the Period
1969 to 1978." Accounting & Finance, 26 (1986), 55-79.
Sawieki, J., and F. Ong. "Evaluating Managed Fund Performance Using Conditional Measures: Aus-
tralian Evidence." Pacific-Basin Finance Journal, 8 (2000), 505-528.
Saar, G. "Price Impact Asymmetry of Block Trades: An Institutional Trading Explanation." Review
of Financial Studies, 14(2001), 1153-1181.
Wermers, R. "Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent,
Style, Transaction Costs and Expenses." Journal of Finance, 55 (2000), 1655-1695.