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PORTFOLIO PUMPING: AN EXAMINATION OF INVESTMENT MANAGER TRADING AND PERFORMANCE*

David R. Gallagher a

Peter Gardner a, †

Peter L. Swan a

First Draft: 1 February 2004 Current Draft: 9 May 2005

Very Preliminary – Please do not quote

a School of Banking and Finance, The University of New South Wales, Sydney, N.S.W. 2052

______________________________________________________________

ABSTRACT

Utilising a unique database of daily manager trades, we examine ‘portfolio pumping’ or ‘marking

up’ around quarter ends. We find small and momentum stocks experience significant price

inflation at quarterly intervals. This result also links to evidence that funds flow asymmetrically

to past period winners, and is greater after periods of high returns. Active and especially high

performing investment managers perpetrate this gaming behaviour in less liquid stocks. We find

the manipulation of index futures settlement prices provides a significant motive for gaming

behaviour. Increased scrutiny by the ASX reduces gaming. Attempts to reduce price

manipulation by instituting and then modifying a closing price call auction have been effective in

both limiting the occurrences and severity of gaming.

JEL Classification: G23 Keywords: Gaming behaviour, Window dressing, Portfolio pumping, Market manipulation _____________________________________________________________________ † Corresponding author. Mail: P.O. Box H58 Australia Square, Sydney, NSW 1215, Australia. Telephone: (+61 2) 9236 9153. E-mail: peterg@sirca.org.au * The authors thank Carole Comerton-Forde for helpful comments. We also thank the Australian Research Council for research funding (DP0346064), Portfolio Analytics for the manager trading data, and the Securities Industry Research Centre of Asia-Pacific (SIRCA) for the provision of the ASX SEATS data.

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1. INTRODUCTION

Institutional trading is substantial, and has grown significantly over time. This phenomenon has

resulted in significant attention concerning the institutional trading activities of participants, as

well as attempts to understand better the true motivation for trading given particular behavioural

considerations (e.g. Wermers, 1999; Sias, 2004; Brown et al. 2005). While the literature has

previously examined window dressing activities by institutions at periodic intervals, recent

evidence by Carhart et al. (2002) examines the propensity of fund managers to engage in gaming

behaviour at calendar quarter-ends. Activities undertaken by institutions to manipulate markets

around quarter-ends are widely known as either ‘portfolio pumping’, ‘marking up’, ‘marking the

close’, ‘leaning for the tape’ or ‘painting the tape’. Given the significant trading volume by

institutions around quarterly intervals, Carhart et al. (2002) provides evidence that mutual fund

managers engage in sub-optimal short-term behaviour, which occurs with managers achieving an

amplified one-day return through aggressive trading near the market close. Such activity

inevitably ‘borrows’ from future returns by temporarily inflating stock prices in which the fund

owns shares.

Market manipulation is of concern to all participants, particularly from the viewpoint that such

activity reduces confidence in the overall efficiency and integrity of the market. Despite gaming

activity (such as portfolio pumping around quarter ends) being illegal, the motivation for such

behaviour on the part of institutional traders has been linked by researchers to the convex

performance-flow relationship (Sirri and Tufano, 1998; Del Guercio and Tkac, 2002), where past

period winners receive a disproportionately higher rate of money inflows. Carhart et al. (2002)

find that high performing managers inflate quarter-end portfolio returns with last minute purchase

of stocks already held. These high performing managers experience the greatest quarter-end

reversal in returns, and stocks owned by these managers experience the greatest price inflation at

the end of the quarter. Our data confirms these findings, with a high correlation of fund price

inflation with the quarterly return (not including the last day of the quarter). Our results also

support the hypothesis that it is the top performing managers who ‘lean for the tape’.

The extent to which portfolio pumping arises is of significant interest to all market participants.

In the case of regulators, it is critically important from a legal enforcement perspective, as well as

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ensuring compliance with Australian Stock Exchange (ASX) market rules and the Corporations

Act (XXXX). Historically, the ASX has changed its closing price methodology twice as a means

of limiting such gaming behaviour. On February 10, 1997, Aitken et al. (2005) suggests that a

factor leading to the ASX changing its closing price methodology to an auction was due to the

high profile case of manipulation on the March 1996 index futures contract. In 2001, the

Australian Financial Review (McConnell 2001) reported a case of this kind of market

manipulation, with the result that the ASX and Sydney Futures Exchange again changed the

closing price auction methodology to limit this practice (ASX, 2002). Our study finds these rule

changes were not effective in reducing the amount of gaming but are valuable in reducing the

extent to which managers game stocks. Investment managers in Canada have similarly made

numerous settlements to the Ontario Securities Commission because of this making up behaviour,

as documented in Carhart et al. (2002). This behaviour presents an opportunity for investors in

active funds, who can take advantage of such seasonality in equity returns (i.e. buying the day

before the end of the month/quarter, rather than at the end of the month/quarter, which is the

common practice).

Our study extends the literature by utilising a unique database containing the daily trades of

Australian active investment managers. Accordingly, our highly granular data enables improved

precision in understanding the sources of marking up across institutional portfolios. There are

two components. We first regress a gaming proxy dummy variable against various stock and

manager characteristics, in order to understand which factors facilitate both the incentive and the

capacity for managers to game. Second, we regress the actual price movement in the final half-

hour for those quarter-end days in which our gaming proxy was equal to one. This regression

allows a measurement of the influence of various factors on the extent to which managers can

drive up prices. The empirical results confirm that marking-up behaviour is prevalent in the

Australian market, with stock prices experiencing abnormally large returns at the end of the

quarter. We find both stock and fund (i.e. NAV) prices experience abnormal increases at quarter-

end, due to last-minute purchases of stocks already owned by institutions. This activity is most

common in smaller stocks, growth stocks, stocks with past period price momentum as well as for

less liquid stocks. Interestingly, stock price increases do not quickly reverse on the first day of

the next quarter, suggesting either managers are not temporarily inflating stock prices, or that

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other factors, such as the turn-of-the-year effects (Ritter, 1988) dominate this price reversal.

Overall, our results are consistent with the findings of Carhart et al. (2002).

We also find marking up is more prevalent in small, momentum stocks with a lack of depth. It is

also more prevalent amongst high turnover, low spread stocks; however, their prices experience a

lower degree of inflation than high spread, low turnover stocks. Managers tend to hold high

turnover, low spread stocks, and thus it is not surprising that these stocks experience a higher

degree of marking up. However, potentially due to the increased liquidity of these stocks, the

level of their price inflation is lower. We also find stocks in which managers hold an overweight

position experience more gaming, particularly those managers that perform in the top five percent

of all managers. This supports the finding that managers perpetrate this gaming behaviour

(Carhart et al. 2002).

One strong motive for manager gaming arises when managers hold index futures positions

(McConnell, 2001). We find a positive relationship between manager gaming and the number of

index futures contracts managers hold. This suggests managers inflate stocks at quarter end to

inflate index futures settlement prices. However, we find no evidence managers inflate stocks in

order to take advantage of individual stock options, as these positions would only have a small

impact on the overall portfolio.

The ASX has attempted to limit this form of market manipulation, with positive results. We find

that increased scrutiny is effective in reducing both the occurrences and severity of gaming. The

introduction of a closing price call auction and the subsequent change in algorithm, both designed

to increase liquidity at the close in order to limit market manipulation of the closing price, were

effective in discouraging gaming behaviour by managers. These changes were also effective

though in limiting the degree to which managers are able to inflate prices.

The remainder of this paper is organised as follows. Section 2 contains a review of relevant

literature. Section 3 describes the data, followed by the research design in section 4. Sections 5

and 6 contain our results and conclusions, respectively.

2. Literature Review

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In this section, we examine literature concerning the turn-of-the-year effect, window dressing,

recent cases of market manipulation in Australia, and the steps of regulators to limit this

behaviour.

There is a well-developed literature documenting seasonality in equity returns, particularly at the

end of the year. Keim (1983) finds that small stocks have unusually high returns from the last

day of the year into January. Various studies propose explanations such as window dressing and

tax-loss selling (Musto (1997), Roll (1983)). Both explanations result in low demand for poor-

performing stocks at the end of the year, and high demand after the year-end. Sias and Starks

(1997) conjecture that tax-loss selling is more important than window dressing, proposing that

individual investors are more likely to sell at the end of the year for tax reasons, whereas

institutions are more likely to engage in window dressing. This study finds low institutionally

owned stocks experience a greater turn of the year effect (low return in December, high return in

January) than high institutionally owned stocks. This builds upon research by Harris (1989) who

shows the day end anomaly relates to the month and year-end effect. Ritter (1988) also proposes

the buy/sell ratio of individual investors drives the turn of the year effect. He conjectures that

individuals sell for tax reasons and then “park the proceeds”, before buying small stocks in the

New Year. Individuals are more likely to buy small stocks than institutions, causing price

pressure for these small stocks.

Musto (1997) analyses the commercial paper market and finds that institutional investors sell out

of poor-performing securities at the end of the year so that their portfolios appear more attractive

(window dressing). They also do not have to justify why they held poor-performing securities.

This study finds that window dressing is more important than tax-loss selling. However, while

empirical evidence supports both these explanations, they fail to explain why this phenomenon

begins the day before the end of the year. Carhart et al. (2002) attributes this to fund managers

inflating quarter-end portfolio prices with last-minute purchases of stocks already held. This

behaviour is most active amongst small stocks, where lower levels of liquidity facilitate this

activity. Carhart et al. (2002) also conjectures that this fund manager behaviour also explains the

day-end anomaly documented by Harris (1989), who shows this is greatest at month-end

(quarter- and year-ends were not analysed separately).

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Gallagher and Pinnuck (2005) document the seasonality in Australian active manager

performance (as well as the well-documented equity returns). However, this paper gave only

preliminary reasons for this seasonality, proposing that researchers should study this area in more

depth. In the US press, Zweig (1997) shows that on average equity funds outperform the S&P

500 by 0.53 percent on the last trading day of the year and in turn underperform by 0.37 percent

on the subsequent year’s first trading day (this effect was more pronounced for small-cap funds).

This does not match the outperformance of small-cap stocks on both of those days. He attributes

this to equity funds manipulating prices on the last day of the year in order to shift performance.

This effect is consistent with the research of Sias and Starks (1997). They find that stocks with

high institutional ownership systematically outperform low institutionally owned stocks at the

end of the year and subsequently underperform in the New Year. This result holds both for loser

and winner stocks.

Other industries display evidence of this performance shifting behaviour. Degeorge, Patel and

Zeckhauser (1999) show that directors have considerable flexibility in managing earnings and as

a result may shift earnings from one period to another so as to maintain specific levels of

performance such as positive earnings, improvement over the previous year or to beat the

market’s consensus forecast. Directors may also do this to maximise executive compensation.

This study finds significant evidence at all three thresholds, with significant discontinuities just

below those levels, showing that managers engage in short term strategies to improve the

appearance of their company, or in our case, portfolio. Elton et al. (2003) document the

existence of incentive fees for mutual funds. These incentive fees may provide motivation for

managers to shift performance forward (due to the time value of money, i.e. receiving incentives

now is preferable to receiving money in the next quarter). The occurrence of high water marks in

mutual fund incentives may also lead to the shifting of performance between periods.1

Carhart et al. (2002) demonstrates that equity fund managers who ‘lean for the tape’ perpetrate

this ‘portfolio pumping’. That is, high performing managers who wish to improve their relative

placing amongst managers, manipulate prices near the close of the market. They do this to

1 High water marks mean that incentive fees for out-performance are not paid until prior underperformance is made up.

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capture the ‘lion’s share’ of fund inflows. They utilize the findings of Sirri and Tufano (1998)

who show in the investment management business, high performing managers receive a

disproportionate amount of fund inflows, whilst low performing managers fail to record similar

outflows. They also show search costs are an important determinant of fund flows. They

propose if consumers could collect and process fund information at zero cost, then we might

expect four results: 1) low performing funds to have fund outflows, 2) a weaker flow-

performance relationship for high performing funds, 3) fund flows away from riskier funds, and

lastly 4) outflows from funds with high fees. However, due to the occurrence of significant

search costs, the majority of inflows go to the highest performing funds, whilst there are no

significant outflows from low performing funds. This forms a call option-like payoff, rewarding

managers for taking on more risk, as low performance bears no significant costs. Hence,

managers benefit more from moving from 2nd to 1st than from 21st to 20th. The evidence suggests

these high performing managers have the largest quarter end reversals. Additionally, stocks

disclosed in the portfolios of the top performing managers experience the greatest price inflation.

Del Guercio and Tkac (2002) document the differences in the flow-to-performance for mutual

and pension funds. Pension fund owners, who exhibit greater sophistication, are more likely to

withdraw assets from poor-performing funds, and do not disproportionately invest in recent high-

performing funds. Hence, pension funds have fewer incentives to artificially inflate stock prices

at quarter-end.

One of the additional data requirements requested by Carhart et al. (2002) are audit trails

showing the actual trades of these fund managers. This study is able to analyse this behaviour as

we have access to finer granularity of data. Unfortunately, the data does not specify the time of

day of the transaction, therefore the exact identity of who makes the trades that inflates portfolio

prices cannot be determined precisely.

The Australian context is interesting due to the utilisation of a closing call auction from February

10, 1997. Hillion and Suominen (2004) develop a theoretical model showing that closing call

auctions decrease the possibility of manipulation, by reducing the price volatility at the close.

Our study enables an evaluation of this proposition. Aitken et al. (2005) suggests one factor in

Australia changing its closing price methodology to an auction was due to the high profile case of

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manipulation on the March 1996 index futures contract. ASX (2001) notes that after the

increased closing price call auction volatility in December 2000, the ASX decided to ‘improve

the closing price call auction by increasing transparency and certainty’. They then proceeded to

contact market participants to remind them of their obligations to the market at future quarter

ends. McConnell (2001) reports a high profile case on the June 2001 index futures contract,

leading to more stringent regulation designed to limit gaming behaviour. ASX (2002) documents

the abolition of the closing price auction for September 28, 2001, rather choosing to close the

market at a random time between 4pm and 4:06pm. On that day, the ASX again called relevant

market participants with an incentive to manipulate prices to remind them of their

responsibilities. Following this quarter end, the ASX re-instated the closing call auction but

changed the price algorithm to limit manipulation and increase price discovery.2 As ASX (2002)

reports, this allows a high turnover of stock without an accompanying large price movement.

The aim of this change is to limit the ability for market participants to game. Our study is unique

in measuring the effectiveness of such changes in regulation, particularly the decoupling of the

SPI settlement price, and increased regulatory scrutiny.

3. Data

Previously, data relating to investment manager trades has been scarce due to the sensitivity or

proprietary nature of the information. Previous work examining gaming behaviour at the end of

the year have relied on stock and fund return data, as well as quarter-end holdings information to

determine whether managers inflate prices, and if so, which types of managers engage in these

trades. To our knowledge, this is the first study utilising actual daily trading data of investment

managers. The sample comprises 30 active equity managers, sourced from the Portfolio

Analytics Database. The period of this study is 2 January 1994 to 31 December 2001.

The Portfolio Analytics Database includes monthly portfolio holdings and daily trading data for

Australian equity funds. This data contains manager positions in individual stock options as well

as index futures. Gallagher and Looi (2005) and Brown et al. (2005) present further information

concerning the construction of the database. For this study, 38 funds comprise the sample, all of

which are benchmarked against either the S&P/ASX200 or S&P/ASX300 indexes. This database 2 For further analysis of this change in algorithm, see Comerton-Forde and Rydge (2004).

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provides a sample that is representative of the investment management industry, and includes

data from six of the largest ten managers, six from the next ten, four from those managers ranked

21-30 and 14 managers from outside the largest 30 (by funds under management as at 31

December 2001). The sample includes six boutique firms, which manage less than $A100

million each.

Assirt also provided fund unit price information for all Australian equity funds in their

comprehensive sample. We exclude those funds that did not provide daily fund price

information. The funds in our sample reported daily unit prices and period income distribution

data. Those funds that invest only in certain sectors or with specialised investment mandates

(such as ethical investments) are excluded from our study. We also exclude overseas-originated

funds that invest in local funds, as this would constitute double counting. Finally, we perform a

final check in order to ensure only active equity funds with a benchmark of the S&P/ASX200.

We calculate the correlation of daily fund returns with the ASX200 Index returns. We exclude

those funds with a correlation lower than 0.6 from our sample. After removing the necessary

funds, 205 funds remained.

We supplement our database with minute-by-minute stock bid-ask quotes and trade price data

from the ASX Stock Exchange Automated Trading System (SEATS) in order to ensure pricing

consistency. SIRCA provided this data. (EXPAND) The SEATS database contains all trade

information for stocks listed on the ASX, as well as stock specific data such as market

capitalisation, turnover, spread and liquidity.

4. Research Design

A. Stock and Fund Price Inflation

Do stock prices experience inflation at quarter-ends? We use the research design employed by

Carhart et al. (2002), regressing daily stock returns against dummy variables for the final trading

day of the quarter (year).

tittttti YBEGYENDQBEGQENDR ,43210, **** εβββββ +++++= (1)

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We also perform regressions for stocks partitioned into quintiles according to size, book-to-

market ratio, and momentum to determine if gaming behaviour was greater in stocks with

specific characteristics.

We perform a similar regression using equation (1) on fund returns to determine whether this

phenomenon also mimics stock prices. We also perform this regression by partitioning funds

according to their fund size and style.

Next, we regress stock and fund returns against dummy variables for the individual quarters

(March/June/September/December) to determine if there are individual factors that result in a

higher level of gaming behaviour by managers.

tittt

tttttti

DBEGDENDSBEGSENDJBEGJENDMBEGMENDR

,876

543210,

********

εβββββββββ

++++

+++++= (2)

B. Stock Price Inflation in the Last Half-Hour of Trading

Carhart et al. (2002) found that managers tended to inflate quarter-end portfolio prices by

engaging in trades in the last half-hour of trading.3 To test this proposition for Australia, we also

perform regressions on the excess return for the last half-hour of the quarter using equations (3)

and (4).

tittti YENDQENDR ,210, ** εβββ +++= (3)

tittttti DENDSENDJENDMENDR ,43210, **** εβββββ +++++= (4)

We also test for a return reversal in the first half-hour of trading in the new quarter (equations (5)

and (6)). A positive result for this test would lend more weight to the hypothesis that managers

inflate stock prices at quarter-end,

3 We also complete this test for last quarter-hour returns, yielding even stronger findings.

11

tittti YBEGQBEGR ,210, ** εβββ +++= (5)

tittttti DBEGSBEGJBEGMBEGR ,43210, **** εβββββ +++++= . (6)

An alternative method of showing that managers temporarily inflate prices is to determine

whether the price of stocks is systematically higher at quarter-end than at surrounding days.

Hence, for robustness we perform a regression on the ratio of the quarter-end price over the

average of the stock price in the five preceding and following days against a constant. This ratio

of stock price can also be regressed against the average manager relative weight position as well

as the relative weight of individual managers to test whether those stocks in which managers hold

overweight positions, tend to experience greater price inflation at quarter-end.

C. Stock Price Inflation by Managers

After establishing that quarter-end stock prices are inflated, we must determine who actually

performs the trades that inflate stock prices. If managers engage in systematic gaming behaviour,

then those stocks in which managers are overweight would experience higher price inflation at

the end of the quarter than those stocks in which managers are underweight. Thus, we regress the

excess returns of stocks at quarter-end (day before quarter-end return minus day after quarter-end

return) against the percentage of managers that are overweight in that stock (OM),

titititi OMRR ,,1,, * εβα ++=− + . (7)

We also perform a regression against the relative weight of individual managers (MRW) to see if

particular managers are engaging in this gaming behaviour,

timtititi MRWRR ,,,1,, * εβα ++=− + . (8)

D. Performance Methodology

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Another interesting experiment is to test which active managers are more likely to engage in

stock price inflation activity. Managers may engage in performance shifting behaviour in order

to increase their own ranking. We can test whether the highest, median or lowest performing

managers game the most. Carhart et al. (2002) find that the top-performing managers mark up

the price of their shares. In order to further test this hypothesis, we first regress a rank of the

return on the last day of the quarter against the prior quarterly return rank (see equation (9)), to

see if higher performing managers benefit most from this gaming behaviour.

tititi grRtnRankQuarterlyMRtnRank ,,, * εβα ++= (9)

We also create dummy variables for the highest, median and lowest managers in terms of

performance.

E. Daily Transactions Methodology

For our first regression using all periods, we create a dummy variable identifying stock periods

with strong evidence of gaming behaviour. This dummy variable yields a value of one when

these four conditions are satisfied, otherwise zero. Firstly, the return in the last half-hour of

trading in the quarter must be greater than zero. Secondly, this return must be greater than the

index return during this period. Thirdly, the return during the opening call option of the

following day, based upon the midpoint price at 10:10am (at the end of the call option)4 must be

negative and less than the index return.5 We do not use the midpoint price for the last half hour

return, as managers have no incentive for manipulating the midpoint price, as the prices by which

they calculate their end of quarter portfolio values are based upon the last traded price. However,

to determine whether the price has retreated at the beginning of the following quarter, investors

do not need to trade the stock, to move its price back to its pre-inflation value.

4 We used the price based upon the midpoint of the spread, as in some less liquid stocks, there might not be a trade executed during the opening call auction. For robustness, we also calculate the return during the first 15 and 30 minutes, yielding similar results. 5 In Appendices E-G, we re-calculate our findings based upon our criteria for our gaming proxy changing. We establish four options, where the gaming proxy is equal to one when the stock return must be outside the one percent intervals (i.e., greater than one percent for the last half hour return and less than negative one percent for the first half hour of the following quarter) and separately for the two percent intervals. Our third and fourth alternate proxies receive a value of one when the excess return (above the index) is outside the one and two percent intervals, respectively.

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In our second regression, our dependent variable is the magnitude of this stock price change in

the final half-hour of the quarter.6 We complete this regression only for observations where our

gaming proxy receives a positive value. By regressing only observations we identify as

providing evidence of gaming, we can measure the effectiveness (measured in terms of price

movement) of manager gaming behaviour. The rationale is that if managers decide to game a

certain stock, we can observe that gaming activity to be more effective the greater the price

movement.

We use various stock characteristics in our regression, namely stock size, book-to-market ratio

and momentum (measured in quintiles, 1=small, growth and low prior six month return, 5=large,

value and high prior six month return).7 We also include three variables, to proxy for liquidity,

including average turnover8, an order flow variable (developed by SMARTS)9 and relative bid-

ask spread10. Next, we measure the total trades of our managers divided by the market

capitalisation of the stock, to examine whether managers are using small or large trades to

perpetrate gaming. We also measure the manager rank, based upon the Assirt database, to

determine whether performance affects a manager’s likelihood of gaming.

We also create variables, such as manager relative weight11 for both a single manager and an

average manager position (based on a portfolio equally weighting the portfolios of our

6 For robustness, we also test the last 15-minute return, yielding even stronger findings. 7 Following the methodology of Jegadeesh and Titman (1993, 2001), who provide U.S. evidence of excess returns by investing in stocks with a positive excess return, supported in the Australian context by Hurn and Pavlov (2003) and Demir et al. (2004). 8 Reported results use the average stock turnover over the prior one month. For robustness, we also calculate the average turnover over the last three and six months, yielding consistent (unreported) results. 9 This variable represents the cost of moving the price up or down by 5 percent at any instant in time.

otherwise 0

midpoint above %5ordermidpoint if priceMidpoint -midpoint above 5% Price

PriceOrder -midpoint above 5% Price or

midpointordermidpoint below 5% if midpoint below 5% Price - priceMidpoint

midpoint below 5% Price - PriceOrder htingOrder Weig where

hting)Order WeigQuantity)(er Price)(Ord(Order

=

<<=

<<=

∑

SMARTS stands for Securities Markets Automated Research, Trading and Surveillance. 10

Price TradeMidpoint) - Price (Trade*2*

TradesDaily of SumTrade SpreadAsk -Bid Relative =

11 We use three variables for manager relative weight: mgr weight – index weight, mgr weight/index weight, or a dummy variable equal to 1 if a manager is overweight in a certain stock.

14

managers). We use these variables to confirm our sample of managers is gaming stocks in which

they are overweight (if this variable was negative, it would undermine our assumptions that

managers are indeed engaging in gaming behaviour). Carhart et al. (2002) suggests the top

performing managers engage in portfolio pumping, thus we also calculate these variables only for

those managers that are in the top 5/10 percent of Assirt manager quarterly return performance.

Managers may artificially inflate stocks at quarter-end to manipulate the settlement prices of

individual stock options, or index futures. Hence, we create variables equal to the exposure

arising from long positions in individual stock options and the number of long index futures

contracts.

On February 10, 1997, the ASX introduced a call auction on the closing price. On September 28,

2001, the ASX introduced a new algorithm for the closing call option to try to limit this gaming

practice. To try to gauge the effect of these changes, we include two dummy variables equal to

one after February 10, 1997 and after September 28, 2001 respectively.

5. Results

In this section, we show equity prices have an abnormal return on the last day of the quarter.

However, there is no evidence of a subsequent reversal on the first day of the following quarter.

We also show fund prices have large abnormal returns around the June quarter-end, and under-

perform the following day.

A. Stock Price Inflation

Firstly, we find that there is evidence of systematic inflation of stock prices at the end of each

quarter/year. We regress stock returns using the following equation:

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1)

We also regress stock returns using dummy variables for each quarter, as this allows us to

differentiate between various factors, such as calendar or tax-year-ends.

15

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

Table I displays evidence of systematic stock price inflation at quarter/year end. This inflation is

greatest in the June and September quarters, with an out-performance on those days equal to 126

percent computed annually. However, when we analyse the returns of the first day of the quarter,

there is no similar underperformance. This is inconsistent with the findings of Carhart et al.

(2002), showing that the outperformance found on the last day of the quarter is cancelled out by a

similar underformance of the first day of the following quarter. This is consistent with theories of

tax-loss selling, window-dressing and the parking-the-proceeds hypothesis of Ritter (1988).

These theories predict investors sell losers at the end of the year and subsequently purchase small

stocks at the start of the year.

(INSERT TABLE I)

When we partition stocks according to size, we find stronger evidence of stock price inflation in

small stocks (Quintiles 1 & 2), but relatively little in stock quintiles 3 & 4. We also find

significant evidence of gaming in the largest stocks, perhaps due to the larger sample size in this

group of more liquid stocks, however the magnitude of gaming is much lower than for small

stocks. Smaller stocks are generally easier to manipulate due to their relative size and liquidity.

Those stocks may also be not as widely held by rival funds; consequently, managers would gain

the maximum relative benefit by inflating these stocks. This evidence is consistent with the

findings of Carhart et al. (2002).

(INSERT TABLE II)

When we analyse gaming in relation to stock performance in Table III, we find that the highest

momentum stocks tend to be gamed the most. This supports the results of Carhart et al. (2002)

who find that it is the highest performing managers who game. Hence, those high performing

managers will tend to hold the stocks with the highest performance, which would be most

beneficial for those managers to game.

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Another interesting finding from this table is that there seems to be a significantly positive return

on July 1 for stocks with a low previous quarter return. This could be because managers have

engaged in tax-loss selling before the end of the previous financial year (to June) and then re-

purchase those stocks in the new financial year.

(INSERT TABLE III)

Carhart et al. (2002) found that managers tended to mark up prices by trading in the last half-hour

of the day, hence we also performed a regression of the last half-hour returns against just dummy

variables for the end of period variables (as the last half-hour on the first day of the quarter will

not be important).

(INSERT TABLE IV)

The data suggests that gaming is again highest in the June and September quarters. From these

tables, we can see that stock prices are increasing significantly in the final half-hour of the

quarter.12

We also test the returns for the first half-hour of the new quarter in Table V. These results

provide weak evidence that the excess returns in the last half-hour of the previous quarter reverse

in the first half-hour of the following quarter. These results may be weaker due to other factors at

work, which push up stock prices at the beginning of the period, such as the purchase of small

stocks, or the re-purchase of stocks sold at the end of the previous period for window dressing or

tax reasons.

(INSERT TABLE V)

We also regress the return for the following two weeks against the last half-hour return, yielding

a significantly negative correlation, indicating inflated stocks at the end of the quarter under-

12 For robustness we also tested the return of the final quarter-hour, yielding even stronger results.

17

perform in the next two weeks. Indeed, 60 percent of the return experienced in each quarter’s last

half-hour is subsequently lost in the next two weeks,

titi hourRtnHalfweekRtnFirst ,, *5926.00058.02 −−= .

(6.65) (-9.87)

For robustness, we also complete another test for the inflation of stock price at the end of the

period. We divide the stock price at the end of the quarter by the average of the five preceding

following days, and we find that this ratio is significantly greater than one. This supports our

earlier results suggesting that participants inflate stock prices at quarter-end,

(3.61) 0027.0)1( ,, titiRatio ε+=−

.

B. Fund Price Inflation

When we analyse fund returns, we find strong evidence that managers inflate fund returns at the

end of the June quarter (Table VI). This could be due to tax reasons or also because manager

bonuses calculated based upon performance to the end of the financial year (June 30). When we

study the excess fund returns above the index, we find fund returns out-perform index returns at

each quarter-end and subsequently under-perform on the first days of April and July. This

provides significant evidence that fund managers are attaining abnormal out-performance at

quarter-end.

In Appendix A-D, we observe gaming is slightly more prevalent in small funds over large funds,

and substantially greater in growth funds over value funds.

(INSERT TABLE VI)

C. Does Gaming relate to Performance?

Carhart et al. (2002) found that the top-performing managers mark up the price of shares. We

find similar results by regressing a rank of the return on the last day of the quarter against the

18

prior quarterly return rank (see equation (9)), to see if higher performing managers benefit most

from this gaming behaviour. The better performing managers tend to experience a higher out-

performance on the last day of the quarter. This suggests that those managers who benefit most

would be engaging in such gaming behaviour.

)11.0(R (9.83) (16.22)

*33.050.292

,,,

=

++= tititi grRtnRankQuarterlyMRtnRank ε (9)

D. Is there an increase in the level of trading at quarter-end?

If additional incentives exist, encouraging managers to trade, then we would expect the level of

trading to increase on the final day of the quarter. We complete a regression of net trades (buys –

sells) and gross trades (buys + sells) against the quarter-end and year-end dummy variables.

There is clearly an increase in the level of trades at the end of the June quarter, more than

doubling the average trade volume, and a particular increase in the level of purchases. This is

consistent with our earlier findings of portfolio pumping at the end of June,

(1.67) (-1.84) (1.85) (0.83) (6.41) *1.27*9.29*1.30*5.135.6 ,, tittttti DENDmSENDmJENDmMENDmmNetTrades ε++−++=

(0.15) (1.74) (2.52) (0.67) (31.34) *2.4*0.49*9.70*9.181.55 ,, tittttti DENDmSENDmJENDmMENDmmsGrossTrade ε+++++=

E. Portfolio Pumping over time

During our sample period, there were two instances when the regulators identified portfolio

pumping. In each case, a change in the rules followed as a means of closing down such

behaviour. Aitken et al. (2005) suggests that after a high profile case of gaming on the March

1996 index futures contract, the ASX instituted a closing price call option on February 10, 1997.

Similarly, McConnell (2001) reports that after the ASX prosecuted managers for market

manipulation on the June 2001 index futures contract, the ASX changed the way it calculates the

closing call auction algorithm.

19

Using our gaming proxy, we measure the percentage of stocks that display evidence of marking

up. Measured quarterly, this measure is quite volatile, due to seasonal factors affecting gaming

(such as the quarterly index return, with a correlation of 0.33, discussed later). Thus, we also

measure the annual average, measured by calendar and financial year. We plot this in Figure 1,

and find gaming increased significantly, leading up to the March 1996 contract. After this high

profile case of gaming, the percentage of stocks investors game decreases substantially. It is

interesting to note that this drop in gaming occurs after the ASX presumably increases their

scrutiny on gaming, and not after the change in legislation. Similarly, gaming once again

increases from this time, only dropping during the 2001 year, once again in the year leading up to

the change in legislation. This suggests increases in scrutiny are effective in limiting manager

gaming. In the next section, we show the impact of the introduction of the closing call auction

and the subsequent change in algorithm on gaming behaviour.

(INSERT FIGURE 1)

F. Daily Trades Regression

First we develop a gaming proxy, equal to one if the last half-hour return of the quarter is both

greater than zero and greater than the index return, as well as the first half-hour return of the

following quarter being both less than zero and the index return, otherwise zero. We regress this

proxy against various stock and manager characteristics to determine the factors that influence

manager gaming behaviour. In Table VII, we find gaming is consistently more prevalent

amongst small, momentum (high prior six month return) stocks with a lack of order depth.

Gaming is also greater in high turnover, low spread stocks. Small stocks with a lack of order

depth are easier for managers to manipulate, as there are fewer traders active in those stocks to

take the opposite position in order to profit from this price moving activity. However, it is not

intuitively obvious why gaming is higher in high turnover, low spread stocks. Falkenstein (1996)

(for US managers) and Pinnuck (2004) (for Australian managers) show investment managers are

more likely to hold stocks with high liquidity, so it is unsurprising that these stocks would be

gamed more. Intuitively, stocks with a previous high turnover and low spread, but with a current

lack of depth would be ideal for managers to game. Later, we will show to what extent managers

can move the price in these stocks. Carhart et al. (2002) shows high performing funds are more

20

likely to game, which in general would hold more momentum stocks. Gaming is more likely in

those periods when the index return is greatest (prior_6mth_index_rtn). This is consistent with

the findings of Fortune (1998) who shows fund inflows are greatest after high stock market

returns and Sirri and Tufano (1998) who show that the top performing managers receive the

lion’s share of these inflows.13 Consequently, managers will wish to outperform in those periods

with high index return, resulting in more gaming during those periods.

Table VII also shows stocks in which managers are overweight are more likely to experience

pumping activity. Managers that are in the top 5 percent of returns for all managers also tend to

overweight those stocks experiencing an inflation at quarter-end, further supporting the findings

of Carhart et al. (2002), that it is the top performing managers who are the major beneficiaries

and thus most likely to be perpetrating this gaming behaviour. While regression (4) shows that

the manager trade dummy (equal to one if our managers purchased a particular stock on the last

day of the quarter) is positive, this variable becomes insignificant when we include liquidity

variables. However, when we create a manager trade dummy equal to one when managers trade

in stocks that have a lower liquidity than the average, this variable is positive and significant,

even in the presence of liquidity variables (Regression (6)).14 Consequently, we can conclude

that our database contains both non-gaming and gaming trades on the final day of the quarter.

Managers are more likely to make those gaming trades in less liquid stocks.

McConnell (2001) and others document the link between the expiry of index futures contracts

and gaming by investment managers. Managers can artificially inflate the closing index price,

thereby increasing the value of these futures contracts. Managers may also inflate individual

stock prices in order to make stock options more profitable. In Regression (7) we introduce

variables equal to the number of stock options and index futures expiring at the end of that

quarter. We find that individual stock options held by managers do not affect the level of

portfolio pumping. However, the number of index futures held by the managers in our sample

positively affects the likelihood of portfolio pumping.

13 Pension funds experience a less convex flow-to-performance relationship (Del Guercio and Tkac (2002)). However, this leads to consistent manager incentives to out-perform during periods with high index return. 14 If we create a manager trade dummy equal to one when managers trade in stocks with a prior 1mth spread above the average, we yield even more significant results.

21

(INSERT TABLE VII)

Our second regression (Table VIII) measures the level of price movement in the last half-hour for

stocks where our gaming proxy is equal to one. We regress the price movement in the last half-

hour of trading against the same characteristics as those used in Table VII, finding that the prices

of those low turnover, high spread stocks are inflated to a greater extent (in terms of price

movement) than higher turnover, low spread stocks. Managers have less incentive to inflate the

prices of these stocks, as they tend to hold less of them. However, when they do choose to inflate

these stocks, their price movement is substantial.

We also find that after the introduction of the call auction on the closing price, the level of price

movement decreased, though not statistically significant at conventional levels. Similarly, after

the change in algorithm of the call auction, the level of price movement again decreased. We can

therefore conclude that the closing price call auction and subsequent change in algorithm were

effective changes in limiting the degree to which managers can inflate prices. This is likely due

to the increased liquidity as a result of the introduction of the closing price auction. The change

in algorithm also reduces the potential price change because of the closing price auction. These

changes, however, were ineffective in limiting the number of occurrences of manager gaming

(although increased scrutiny limits manager gaming for a significant period).

We also found that the number of index futures held by the managers in our sample positively

affects the severity of portfolio pumping behaviour. This suggests that the inflation of index

prices to manipulate the index futures settlement price is one significant motive behind gaming

behaviour. Regression (2) also displays a strong correlation between stock price inflation and

stock price deflations in the first half-hour of the following quarter. This provides strong

evidence that stock price inflation in the final half-hour leads to a decrease, which is similar in

magnitude to the first half-hour of the subsequent quarter.

(INSERT TABLE VIII)

We also test the validity of our gaming proxy in Appendices E-G. We re-calculate our gaming

proxy, changing the criteria for our dummy variable to equal one. For our new proxies, the

22

definition becomes even stricter, where the stock return in the last half hour of the quarter is

greater than one (two) percent and similarly the return in the first half hour of the following

quarter is less than negative one (two) percent. For our third and fourth proxies we measure these

new percentage requirements for excess returns above the index. These results are generally

consistent with our previous findings, although they show that the government interventions were

effective in limiting gaming.

G. Cost of Portfolio Pumping to Investors

Portfolio pumping behaviour is detrimental to markets because of its manipulation of prices away

from their ‘fair’ value. This dilutes the informational quality of prices as true indicators of the

value of a stock, and discourages less informed market participants from trading in equities.

However, there is also a cost of this behaviour on fund investors, whose manager’s engage in this

behaviour. The first cost is on new investors who buy units in the fund at the close of trading at

quarter-end. They buy at inflated fund prices, which do not reflect the true value of the assets of

the fund. Their cost is the degree of inflation, which we can measure as the level of price

deflation at the beginning of the new quarter. In Table VI, we can observe the average reduction

in prices below the index price is equal to 0.29% per quarter (average of the four beta values, b2,

b4, b6 and b8 in Panel D). This is a 7.45% reduction, compounded annually. This is the average

loss for all managers; however, the loss would be greater for investors in the individual managers

who perpetrate this portfolio pumping. Considering the findings of Sirri and Tufano (1998), that

these high performing funds receive a disproportionate level of fund inflows, we expect this

average loss to be even greater.

Taking advantage of our daily transactions data, we calculate the reduction in midpoint prices for

those stocks receiving a gaming proxy dummy variable value of one. By multiplying these

values by the average portfolio weight of our 38 funds, we are able to ascertain the average

portfolio reduction that is caused by gaming behaviour. The average reduction is equal to 0.15%

for our funds.

23

The second cost of portfolio pumping is the cost to ongoing investors of executing the actual

portfolio pumping trade, including transaction costs as well as the premium paid above the fair

value of the stock, in order to mark up the price. We calculate this using the following formula:

1%)1-Pricen Transactio

daynext on open after hour half PriceMidpoint (*

FundsizeTradeTradeEach ofCost +=

where one percent (the final term) is added to approximate for transaction costs.

We calculate the average cost of each portfolio pumping trade to be equal to 0.001374%

reduction in return to the portfolio. This value displays the ease with which managers can

manipulate prices at a low cost to existing investors. Due to the size of fund portfolios with

respect to daily trading volume, managers only need to execute relatively small trades to inflate

stock prices.

6. Conclusion

This study examines ‘portfolio pumping’ by active investment managers at quarter-ends using a

unique database of daily institutional trades. Evidence of gaming behaviour by investment

managers represents a form of market manipulation, which is illegal under the Corporations Act,

and is contrary to ASX Market Rules, which attempt to provide an environment that promotes

market integrity and efficiency. Our results reveal there are significant abnormal stock returns on

the final business day of the quarter-end. This abnormal return is greatest in small stocks, which

are easier to manipulate, and which provide a greater excess return to the index for investors with

an overweight portfolio position. This excess return occurs in the final half-hour of trading,

consistent with the findings of Carhart et al. (2002). Fund returns are similarly inflated at

quarter-end, particularly the June quarter-end (tax-year-end). Our data suggests fund price

inflation is more prevalent in small growth managers.

We also study which funds perpetrate gaming behaviour, and find high performing funds

experience the greatest price inflation. These managers would be more likely to hold stocks that

have out-performed in the pervious six months, which also experience greater price inflation than

under-performing stocks. Higher performing funds have the greatest incentive to game,

according to the convexity of the performance-flow documented by Sirri and Tufano (1998) and

Del Guercio and Tkac (2002). Fund inflows are larger after periods of high return (Fortune,

24

1998), therefore it is not surprising that we find gaming behaviour is elevated at the end of

quarters of high return. We also find managers inflate quarter-end prices to boost the futures

settlement price. New investors in high performing funds experience the greatest cost from this

gaming behaviour as they invest at a peak in the market. These investors would benefit

substantially by delaying their entry to the fund until the day after the end of the quarter.

However, portfolio pumping does not substantially effect the returns of existing investors, as

gaming trades are relatively small.

This study also finds that stocks in which managers overweight their portfolios experience greater

price inflation, and this provides some evidence of the types of managers that may need a higher

degree of surveillance. During times of increased scrutiny by the ASX, managers did reduce their

gaming activities. Other attempts by the ASX to reduce price manipulation, such as instituting a

closing price call auction, and then changing the algorithm on this auction, were effective in

limiting the both number of occurrences as well as the severity of gaming.

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28

Table I We regress the daily stock return for all stocks during the period January 1994 to June 2002, using the following equations:

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1) where QEND (QBEG) and YEND (YBEG) are dummy variables equal to one for the last (first) day of each quarter and year respectively.

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

where MEND (MBEG), JEND (JBEG), SEND (SBEG) and DEND (DBEG) are dummy variables equal to one for the last (first) trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b2 b3 b4

Panel A: Daily Stock Returns, Quarterly and Yearly0.05 0.38 -0.02 0.37 -0.02

(6.87) (5.35) (-0.30) (2.87) (-0.15)c0 c1 c2 c3 c4 c5 c6 c7 c8

Panel B: Daily Stock Returns, Individual Quarters0.05 -0.03 -0.10 0.45 0.02 0.47 0.06 0.37 -0.02

(6.87) (-0.23) (-0.80) (3.49) (0.13) (3.65) (0.44) (2.87) (-0.15)

29

Table II We regress the daily stock return for stocks divided into quintiles based on market capitalisation, during the period January 1994 to June 2002, using the following equations.

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1) where QEND (QBEG) and YEND (YBEG) are dummy variables equal to one for the last (first) day of each quarter and year respectively.

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

where MEND (MBEG), JEND (JBEG), SEND (SBEG) and DEND (DBEG) are dummy variables equal to one for the last (first) trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b2 b3 b4

Panel A: Results partitioned by size, Quarterly and YearlyQ1 (small) -0.07 0.47 -0.08 0.50 0.10

(-3.53) (2.60) (-0.45) (1.60) (0.31)Q2 -0.02 0.32 0.02 0.52 -0.29

(-1.30) (2.73) (0.17) (2.57) (-1.46)Q3 0.02 0.32 -0.21 0.32 0.12

(2.14) (3.67) (-2.39) (2.08) (0.82)Q4 0.04 0.04 0.16 0.09 -0.14

(5.05) (0.52) (2.00) (0.63) (-1.03)Q5 (large) 0.05 0.19 0.10 0.17 -0.13

(8.99) (3.40) (1.81) (1.73) (-1.36)c0 c1 c2 c3 c4 c5 c6 c7 c8

Panel B: Results partitioned by size, Individual QuartersQ1 (small) -0.07 0.26 -0.04 0.56 0.02 0.54 -0.19 0.50 0.10

(-3.53) (0.79) (-0.12) (1.77) (0.06) (1.89) (-0.68) (1.60) (0.31)Q2 -0.02 -0.30 -0.22 0.91 0.26 0.34 0.01 0.52 -0.29

(-1.30) (-1.45) (-1.05) (4.45) (1.27) (1.71) (0.06) (2.57) (-1.46)Q3 0.02 0.02 -0.34 0.44 -0.26 0.51 -0.01 0.32 0.12

(2.14) (0.17) (-2.31) (2.94) (-1.73) (3.32) (-0.09) (2.08) (0.82)Q4 0.04 -0.04 0.12 0.08 0.23 0.08 0.13 0.09 -0.14

(5.05) (-0.28) (0.90) (0.61) (1.65) (0.56) (0.93) (0.63) (-1.03)Q5 (large) 0.05 -0.03 0.05 0.26 0.08 0.34 0.17 0.17 -0.13

(8.99) (-0.35) (0.56) (2.75) (0.85) (3.53) (1.75) (1.73) (-1.36)

30

Table III We regress the daily stock return for stocks divided into quintiles based on prior six-month return, during the period January 1994 to June 2002, using the following equations.

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1) where QEND (QBEG) and YEND (YBEG) are dummy variables equal to one for the last (first) day of each quarter and year respectively.

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

where MEND (MBEG), JEND (JBEG), SEND (SBEG) and DEND (DBEG) are dummy variables equal to one for the last (first) trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b2 b3 b4

Panel A: Results partitioned by momentum, Quarterly and Yearly-0.05 0.39 0.62 0.55 0.19

(-4.35) (3.54) (5.55) (2.93) (1.03)Q2 0.00 0.33 -0.13 0.42 0.05

(0.47) (4.07) (-1.56) (2.96) (0.32)Q3 0.06 -0.08 0.06 0.02 -0.19

(5.12) (-0.79) (0.55) (0.12) (-1.06)Q4 0.04 -0.04 0.12 0.06 -0.06

(5.95) (-0.58) (1.73) (0.52) (-0.53)0.08 0.57 -0.20 0.46 -0.09

(8.23) (6.04) (-2.07) (2.76) (-0.53)c0 c1 c2 c3 c4 c5 c6 c7 c8

Panel B: Results partitioned by momentum, Individual Quarters-0.05 0.08 0.63 0.84 0.82 0.26 0.41 0.55 0.19

(-4.35) (0.43) (3.28) (4.35) (4.24) (1.40) (2.17) (2.93) (1.02)Q2 0.00 0.02 -0.09 0.38 -0.30 0.59 0.00 0.42 0.04

(0.47) (0.14) (-0.65) (2.73) (-2.11) (4.19) (0.03) (2.96) (0.31)Q3 0.06 -0.16 0.01 -0.15 0.20 0.07 -0.04 0.02 -0.19

(5.12) (-0.93) (0.04) (-0.80) (1.11) (0.37) (-0.19) (0.12) (-1.06)Q4 0.04 -0.17 0.12 -0.05 0.16 0.11 0.07 0.06 -0.06

(5.95) (-1.46) (1.01) (-0.47) (1.40) (0.92) (0.59) (0.52) (-0.52)0.08 -0.13 -0.37 0.94 -0.30 0.85 0.07 0.46 -0.09

(8.24) (-0.79) (-2.24) (5.82) (-1.86) (5.33) (0.42) (2.76) (-0.54)

Q1 (low prior 6mth return)

Q5 (high prior 6mth return)

Q1 (low prior 6mth return)

Q5 (high prior 6mth return)

31

Table IV We regress the stock return during the last half-hour of each day for all stocks during the period January 1994 to June 2002, using the following equations:

tittti YENDbQENDbbR ,310, ** ε+++= (3) where QEND (YEND) are dummy variables equal to one for the last day of each quarter (year). tittttti DENDcSENDcJENDcMENDccR ,75310, **** ε+++++= (4) where MEND, JEND, SEND and DEND are dummy variables equal to one for the last trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b3Panel A: Last Half-hour Stock Returns, Quarterly and Yearly

0.13 0.16 -0.10(16.22) (2.29) (-0.79)

c0 c1 c3 c5 c7Panel B: Last Half-hour Stock Returns, Individual Quarters

0.13 -0.08 0.25 0.32 -0.10(16.22) (-0.66) (2.00) (2.49) (-0.79)

32

Table V We regress the stock return during the first half-hour of each day for all stocks during the period January 1994 to June 2002, using the following equations:

tittti YBEGbQBEGbbR ,420, ** ε+++= (5) where QBEG (YBEG) are dummy variables equal to one for the last day of each quarter (year). tittttti DBEGcSBEGcJBEGcMBEGccR ,86420, **** ε+++++= (6) where MBEG, JBEG, SBEG and DBEG are dummy variables equal to one for the last trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b2 b4

Panel A: First Half-hour Stock Returns, Quarterly and Yearly0.40 -0.11 -0.31

(41.61) (-1.22) (-1.98)c0 c2 c4 c6 c8

Panel B: First Half-hour Stock Returns, Individual Quarters0.40 -0.10 -0.17 -0.06 -0.31

(41.61) (-0.66) (-1.11) (-0.36) (-1.98)

33

Table VI We regress the daily fund return for all funds in the ASSIRT database during the period January 1994 to June 2002, using the following equations:

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1) where QEND (QBEG) and YEND (YBEG) are dummy variables equal to one for the last (first) day of each quarter and year respectively.

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

where MEND (MBEG), JEND (JBEG), SEND (SBEG) and DEND (DBEG) are dummy variables equal to one for the last (first) trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b2 b3 b4

Panel A: Daily Fund Returns, Quarterly and Yearly0.01 0.32 -0.09 0.45 0.19

(0.52) (3.83) (-1.11) (3.07) (1.31)Panel B: Daily Fund minus Index Returns, Quarterly and Yearly

0.00 0.21 -0.41 0.23 0.02(-0.14) (2.58) (-4.91) (1.56) (0.16)

c0 c1 c2 c3 c4 c5 c6 c7 c8

Panel C: Daily Fund Returns, Individual Quarters0.01 -0.27 0.08 0.72 -0.59 0.49 0.28 0.44 0.19

(0.53) (-1.90) (0.55) (5.23) (-4.27) (3.32) (1.92) (3.01) (1.31)Panel D: Daily Fund minus Index Returns, Individual Quarters

0.00 0.30 -0.31 0.13 -0.85 0.22 -0.01 0.22 0.02(-0.14) (2.10) (-2.19) (0.94) (-6.14) (1.52) (-0.04) (1.50) (0.16)

34

Table VII We complete a logistic15 regression on our gaming proxy dummy variable, equal to one when the following conditions are satisfied. Firstly, the stock return in the last half-hour of the final trading day of the quarter is both greater than zero, and the index return during this period. Secondly, the return (measured by the change in midpoints, intended to alleviate a lack of liquidity) in the first half-hour of the following day, is both less than zero and the index return. We regress this variable against various stock and manager characteristics for the largest 300 stocks during the period July 1994 to June 2002. Regression No. (1) (2) (3) (4) (5) (6) (7)Constant -0.9881*** -0.9672*** -2.1333*** -1.9727*** -2.1075*** -0.9386*** -2.1074***Size Quintile -0.126*** -0.1199*** -0.0577*** -0.0167 -0.0149 -0.1289*** -0.0165Book-to-market Quintile -0.0217 -0.0215 -0.0396 -0.036 -0.0405 -0.022 -0.0383Momentum Quintile 0.065*** 0.0645*** 0.0678*** 0.0672*** 0.0662*** 0.0635*** 0.0666***Index Return (6mth) 4.0645*** 4.0135*** 4.0478*** 3.7656*** 3.9258*** 3.7846*** 4.0434***Turnover Quintile 0.3546*** 0.3658*** 0.2893***Liquidity (3:30pm) Quintile -0.1354*** -0.1373*** -0.0489*** -0.0362**Spread (1mth) Quintile -0.1535***Individual Stock Options 0.0000Index Futures 0.0001***Manager Trade Dummy -0.1302 0.0387 0.2126*** 0.1644* 0.1511*Illiquid Manager Trade Dummy 0.5088***Average Manager Weight / Index Weight 0.037** 0.0398*** 0.042*** 0.049*** 0.034** 0.049***Relative Weight of mgrs in Assirt Top 5% 0.1426 0.0802 0.2374*** 0.2899*** 0.12 0.2995***After Closing Auction Algorithm Change 0.2579*** 0.2668*** 0.2678*** 0.1836* 0.1121 0.1436After Introduction of Closing Auction -0.0617 -0.0414 -0.0611 -0.0305 0.0071 -0.069Broker 1 Dummy -0.1401Broker 2 Dummy 0.2917Broker 3 Dummy 0.5022Broker 4 Dummy -0.8069*Broker 5 Dummy 0.3267Broker 6 Dummy 0.0429Broker 7 Dummy -0.8218Broker 8 Dummy -0.6516Broker 9 Dummy 0.1672Broker 10 Dummy 1.0656***

No. of Observations 8296 8296 8296 8296 8296 8296 8296R-squared 3.22% 3.5% 1.95% 0.87% 1.18% 2.69% 1.24%Likelihood -3375 -3363 -3427 -3472 -3459 -3396 -3456Lratio 266.1 289.2 161.7 71.7 97.7 222.8 102.8McFadden Pseudo R-squared 3.79% 4.12% 2.30% 1.02% 1.39% 3.18% 1.47%

***, **, and * indicate significance at the 1%, 5% and 10% level, respectively

15 We also perform Probit and Tobit regressions yielding similar unreported results.

35

Table VIII We complete an ordinary least squares (OLS) regression on all stock periods where our gaming proxy equals one, that is, when the following conditions are satisfied. Firstly, the stock return in the last half-hour of the final trading day of the quarter is both greater than zero, and the index return during this period. Secondly, the return (measured by the change in midpoints, intended to alleviate a lack of liquidity) in the first half-hour of the following day, is both less than zero and the index return. We regress the actual return in the last half-hour of the quarter against various stock and manager characteristics for the largest 300 stocks during the period July 1994 to June 2002.

Regression No. (1) (2)Constant 0.0271*** 0.0104**Size Quintile -0.0013 -0.0006Book-to-market Quintile -0.001 -0.001Momentum Quintile -0.0016** -0.0013**Index Return (6mth) -0.0151 -0.006Turnover Quintile -0.0048*** -0.0031***Liquidity (3:30pm) Quintile -0.0007 0.0007Spread (1mth) Quintile 0.0027*** 0.0011Individual Stock Options 0.0004 -0.0014Index Futures 0.0000*** 0.0000***Manager Trade Dummy -0.0073 0.0002Average Manager Weight / Index Weight -0.0006 0.0002Relative Weight of mgrs in Assirt Top 5% 0.004 0.0025After Closing Auction Algorithm Change -0.0126*** -0.0053*After Introduction of Closing Auction -0.0052** -0.0055***First half hour return -0.9099***

No. of Observations 1245 1245R-squared 9.33% 29.21%

***, **, and * indicate significance at the 1%, 5% and 10% level, respectively

36

Figure 1 This figure calculates the percentage of the largest 300 stocks gamed according to our gaming proxy. Our gaming proxy is equal to one when the following conditions are satisfied, otherwise zero. Firstly, the return in the last half-hour of trading in the quarter must be greater than both zero and the index return during this period. Secondly, the return during the opening call option of the following day, based upon the midpoint price at 10:10am (at the end of the call option) must be negative and less than the index return. We calculate this measure for each quarter and average over each calendar (financial) year in order to smooth out the intra-yearly variability.

Gaming Percentage (By Quarter)

-20%

-10%

0%

10%

20%

30%

40%

Sep-94

Dec-94

Mar-95

Jun-9

5

Sep-95

Dec-95

Mar-96

Jun-9

6

Sep-96

Dec-96

Mar-97

Jun-9

7

Sep-97

Dec-97

Mar-98

Jun-9

8

Sep-98

Dec-98

Mar-99

Jun-9

9

Sep-99

Dec-99

Mar-00

Jun-0

0

Sep-00

Dec-00

Mar-01

Jun-0

1

Sep-01

Dec-01

Mar-02

Jun-0

2Perc

enta

ge o

f Sto

cks

Gam

ed, R

etur

n

Gaming Percentage (by quarter)Calendar Year Gaming AverageFinancial Year Gaming AveragePrior Quarterly Return

The correlation of the gaming percentage with the prior quarterly index return is 0.33

37

Appendix A - Large Funds

We regress the daily fund return for the largest 50% (calculated quarterly) of funds in the ASSIRT database during the period January 1994 to June 2002, using the following equations:

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1) where QEND (QBEG) and YEND (YBEG) are dummy variables equal to one for the last (first) day of each quarter and year respectively.

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

where MEND (MBEG), JEND (JBEG), SEND (SBEG) and DEND (DBEG) are dummy variables equal to one for the last (first) trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b2 b3 b4

Panel A: Daily Large Fund Returns, Quarterly and Yearly0.05 0.28 0.01 0.41 0.08

(7.07) (7.25) (0.21) (6.52) (1.31)Panel B: Daily Large Fund minus Index Returns, Quarterly and Yearly

0.00 0.22 -0.36 0.22 0.05(0.35) (5.04) (-8.37) (3.18) (0.73)

c0 c1 c2 c3 c4 c5 c6 c7 c8

Panel C: Daily Large Fund Returns, Individual Quarters0.05 -0.37 0.21 0.78 -0.33 0.36 0.14 0.41 0.09

(7.09) (-5.33) (3.09) (11.89) (-4.95) (5.69) (2.20) (6.51) (1.38)Panel D: Daily Large Fund minus Index Returns, Individual Quarters

0.00 0.27 -0.41 0.24 -0.64 0.15 -0.07 0.22 0.05(0.35) (3.53) (-5.24) (3.22) (-8.65) (2.19) (-0.95) (3.13) (0.73)

38

Appendix B - Small Funds

We regress the daily fund return for the smallest 50% (calculated quarterly) of funds in the ASSIRT database during the period January 1994 to June 2002, using the following equations:

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1) where QEND (QBEG) and YEND (YBEG) are dummy variables equal to one for the last (first) day of each quarter and year respectively.

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

where MEND (MBEG), JEND (JBEG), SEND (SBEG) and DEND (DBEG) are dummy variables equal to one for the last (first) trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b2 b3 b4

Panel A: Daily Small Fund Returns, Quarterly and Yearly0.03 0.37 -0.08 0.41 0.20

(4.36) (9.02) (-2.06) (6.18) (2.97)Panel B: Daily Small Fund minus Index Returns, Quarterly and Yearly

-0.01 0.26 -0.47 0.25 0.06(-1.07) (6.05) (-11.01) (3.58) (0.90)

c0 c1 c2 c3 c4 c5 c6 c7 c8

Panel C: Daily Small Fund Returns, Individual Quarters0.03 -0.62 0.34 1.04 -1.05 0.60 0.41 0.39 0.19

(4.46) (-8.57) (4.76) (14.96) (-15.05) (9.04) (6.25) (5.96) (2.83)Panel D: Daily Small Fund minus Index Returns, Individual Quarters

-0.01 0.13 -0.31 0.34 -1.24 0.30 0.08 0.23 0.06(-1.06) (1.71) (-4.02) (4.62) (-16.83) (4.35) (1.20) (3.29) (0.88)

39

Appendix C - Growth Funds

We regress the daily fund return for all growth or growth-at-a-reasonable-price (GARP) funds in the ASSIRT database during the period January 1994 to June 2002, using the following equations:

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1) where QEND (QBEG) and YEND (YBEG) are dummy variables equal to one for the last (first) day of each quarter and year respectively.

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

where MEND (MBEG), JEND (JBEG), SEND (SBEG) and DEND (DBEG) are dummy variables equal to one for the last (first) trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b2 b3 b4

Panel A: Daily Growth Fund Returns, Quarterly and Yearly0.02 0.28 -0.08 0.37 0.14

(0.71) (1.51) (-0.45) (1.12) (0.43)Panel B: Daily Growth Fund minus Index Returns, Quarterly and Yearly

0.00 0.19 -0.34 0.16 -0.07(0.06) (1.02) (-1.81) (0.49) (-0.22)

c0 c1 c2 c3 c4 c5 c6 c7 c8

Panel C: Daily Growth Fund Returns, Individual Quarters0.02 -0.31 0.16 0.68 -0.57 0.43 0.22 0.36 0.14

(0.71) (-0.95) (0.49) (2.21) (-1.84) (1.30) (0.67) (1.11) (0.44)Panel D: Daily Growth Fund minus Index Returns, Individual Quarters

0.00 0.30 -0.11 0.09 -0.81 0.19 -0.03 0.16 -0.07(0.05) (0.93) (-0.34) (0.30) (-2.62) (0.58) (-0.08) (0.48) (-0.22)

40

Appendix D - Value Funds

We regress the daily fund return for all value-orientated funds in the ASSIRT database during the period January 1994 to June 2002, using the following equations:

tittttti YBEGbYENDbQBEGbQENDbbR ,43210, **** ε+++++= (1) where QEND (QBEG) and YEND (YBEG) are dummy variables equal to one for the last (first) day of each quarter and year respectively.

tittt

tttttti

DBEGcDENDcSBEGcSENDcJBEGcJENDcMBEGcMENDccR

,876

543210,

********

ε++++

+++++= (2)

where MEND (MBEG), JEND (JBEG), SEND (SBEG) and DEND (DBEG) are dummy variables equal to one for the last (first) trading day in March, June, September and December respectively. All beta estimates are in percentages. T-statistics are in parenthesis.

b0 b1 b2 b3 b4

Panel A: Daily Value Fund Returns, Quarterly and Yearly-0.01 0.34 0.07 0.48 -0.20

(-1.53) (6.59) (1.39) (4.91) (-2.10)Panel B: Daily Value Fund minus Index Returns, Quarterly and Yearly

-0.01 0.20 -0.35 0.25 -0.21(-0.74) (3.99) (-7.05) (2.69) (-2.23)

c0 c1 c2 c3 c4 c5 c6 c7 c8

Panel C: Daily Value Fund Returns, Individual Quarters-0.01 -0.33 0.49 0.90 -0.67 0.39 0.48 0.47 -0.20

(-1.53) (-3.76) (5.50) (10.67) (-7.95) (4.37) (5.36) (4.86) (-2.10)Panel D: Daily Value Fund minus Index Returns, Individual Quarters

-0.01 0.24 0.05 0.24 -1.09 0.11 0.07 0.25 -0.21(-0.74) (2.83) (0.59) (2.88) (-13.17) (1.26) (0.75) (2.61) (-2.25)

41

Appendix E – Gaming Proxy Changes We complete a logistic16 regression on our gaming proxy dummy variable, using different criteria for when our dummy variable is equal to one. In the first (second) three columns, our variable is equal to one when the stock return in the last half-hour of the final trading day of the quarter is both greater than one (two) percent, and the index return during this period. Also, the return (measured by the change in midpoints, intended to alleviate a lack of liquidity) in the first half-hour of the following day needs to be both less than negative one (two) percent and the index return. In columns 7 to 9 (10 to 12), our variable is equal to one when the stock return in the last half-hour of the final trading day of the quarter is both greater than zero, and the index return plus one (two) percent during this period. Also, the return (measured by the change in midpoints, intended to alleviate a lack of liquidity) in the first half-hour of the following day needs to be both less than zero and the index return minus one (two) percent. We regress these variables against various stock and manager characteristics for the largest 300 stocks during the period July 1994 to June 2002.

Returns > 1% Returns > 2% Excess Returns > 1% Excess Returns > 2%

Regression No. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)Constant -2.0464*** -2.3182*** -2.5458*** -3.2382*** -2.6159*** -3.0739*** -1.8532*** -2.0796*** -2.1838*** -2.9377*** -2.4903*** -2.7718***Size Quintile -0.2448*** -0.2249*** -0.1986*** -0.3099*** -0.3237*** -0.3067*** -0.2317*** -0.2157*** -0.2042*** -0.3582*** -0.3584*** -0.3785***Book-to-market Quintile 0.0059 0.0024 0.0065 -0.0963 -0.083 -0.0779 -0.0892*** -0.0894*** -0.0913*** -0.1488*** -0.1384** -0.1371**Momentum Quintile 0.0469 0.0453 0.0531* 0.0175 0.0105 0.0299 0.0495 0.047 0.0532* 0.0437 0.0366 0.0483Index Return (6mth) 3.3329*** 3.2691*** 3.2152*** 2.1288* 1.9423 2.1675* 1.3665* 1.3242* 1.1799 0.5743 0.4602 0.6172Turnover Quintile 0.1432*** -0.0854 0.1164*** -0.0623Liquidity (3:30pm) Quintile -0.1389*** -0.1259*** -0.1127*** -0.085**Spread (1mth) Quintile -0.0218 0.0742 -0.027 0.0699Index Futures -0.0001 0.0001 0.0001 0.0002 0.0002*** 0.0003*** 0.0001 0.0002**Manager Trade Dummy -0.0173 -0.5667* -0.1144 -0.5822*Illiquid Manager Trade Dummy 0.1498 0.3841 -0.0904 0.2796 0.5499** 0.7968*** 0.3548 0.5737Average Manager Weight / Index Weight -0.0177 -0.0145 -0.0219 -0.0213 -0.0229 -0.0159 -0.0351 -0.0287Relative Weight of mgrs in Assirt Top 5% 0.4597*** 0.4997*** 0.9974*** 1.0214*** 0.1917 0.2509 0.1566 0.2211After Closing Auction Algorithm Change -0.3934* -0.5745*** -1.6579*** -1.9021*** -0.7478*** -0.8656*** -1.3302*** -1.4961***After Introduction of Closing Auction -0.2586*** -0.221** -0.4363*** -0.3639** -0.2826*** -0.2421** -0.254 -0.1941

No. of Observations 8296 8296 8296 8296 8296 8296 8296 8296 8296 8296 8296 8296R-squared 1.50% 1.12% 0.80% 1.19% 1.08% 0.50% 1.53% 1.24% 0.76% 1.08% 1.06% 0.74%Likelihood -1930 -1946 -1959 -835 -839 -862 -1881 -1893 -1912 -835 -836 -849Lratio 123.3 92.3 65.7 96.3 88.0 41.5 125.8 102.3 62.9 87.8 85.8 60.5McFadden Pseudo R-squared 3.09% 2.32% 1.65% 5.46% 4.98% 2.35% 3.24% 2.63% 1.62% 4.99% 4.88% 3.44%

***, **, and * indicate significance at the 1%, 5% and 10% level, respectively

16 We also perform Probit and Tobit regressions yielding similar unreported results.

42

Appendix F – Gaming Proxy Changes We complete an ordinary least squares (OLS) regression on our gaming proxy dummy variable, using different criteria for when our dummy variable is equal to one. In the first (second) column, our variable is equal to one when the stock return in the last half-hour of the final trading day of the quarter is both greater than one (two) percent, and the index return during this period. Also, the return (measured by the change in midpoints, intended to alleviate a lack of liquidity) in the first half-hour of the following day needs to be both less than negative one (two) percent and the index return. In the third (fourth) column, our variable is equal to one when the stock return in the last half-hour of the final trading day of the quarter is both greater than zero, and the index return plus one (two) percent during this period. Also, the return (measured by the change in midpoints, intended to alleviate a lack of liquidity) in the first half-hour of the following day needs to be both less than zero and the index return minus one (two) percent. We regress the actual return in the last half-hour of the quarter against various stock and manager characteristics for the largest 300 stocks during the period July 1994 to June 2002.

Returns > 1% Returns > 2%Excess

Returns > 1%Excess

Returns > 2%Regression No. (1) (2) (3) (4)Constant 0.0399*** 0.047 0.0369*** 0.0464Size Quintile -0.0017 -0.0008 -0.0027 -0.0041Book-to-market Quintile -0.0025 -0.0044 -0.0014 -0.0038Momentum Quintile -0.0033** -0.0085** -0.003* -0.0088**Index Return (6mth) -0.0388 -0.1225 -0.0021 -0.0696Turnover Quintile -0.0078*** -0.0173*** -0.0068*** -0.0173***Liquidity (3:30pm) Quintile 0.0017 0.0078* 0.0003 0.006Spread (1mth) Quintile 0.0023 -0.0003 0.0023 -0.0011Index Futures 0.0001*** 0.0001*** 0.0001* 0.0001***Manager Trade Dummy -0.014 -0.0273 -0.0114 -0.0352Average Manager Weight / Index Weight -0.0008 -0.0013 -0.0012 -0.0024Relative Weight of mgrs in Assirt Top 5% 0.0034 0.0003 0.0057 0.0087After Closing Auction Algorithm Change -0.0239*** -0.0644 -0.0165 -0.0498After Introduction of Closing Auction -0.0017 0.0137 -0.0028 0.0097

No. of Observations 538 184 520 183R-squared 8.33% 15.06% 6.25% 12.38%

***, **, and * indicate significance at the 1%, 5% and 10% level, respectively

43

Appendix G – Gaming Proxy Changes Over Time

0%

4%

8%

12%

16%

20%

Sep-94

Dec-94

Mar-95

Jun-9

5Sep

-95Dec

-95Mar-

96Ju

n-96

Sep-96

Dec-96

Mar-97

Jun-9

7Sep

-97Dec

-97Mar-

98Ju

n-98

Sep-98

Dec-98

Mar-99

Jun-9

9Sep

-99Dec

-99Mar-

00Ju

n-00

Sep-00

Dec-00

Mar-01

Jun-0

1Sep

-01Dec

-01Mar-

02Ju

n-02

Perc

enta

ge o

f Sto

cks

Gam

ed, R

etur

n

Increase >2% over index, decrease<2% under indexIncrease >2%, decrease<-2%Increase >1% over index, decrease<1% under indexIncrease >1%, decrease<1%Yearly Average, >2% ReturnYearly Average, >1% ReturnYearly Average, >2% Excess ReturnYearly Average, >1% Excess Return