Interaction between Mutual Fund Performance and Portfolio...

Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB) An Online International Research Journal (ISSN: 2306-367X)

2014 Vol: 3 Issue 4

1125 www.globalbizresearch.com

Interaction between Mutual Fund Performance and Portfolio

Turnover

Sheng-Ching Wu, Department of Finance,

Da-Yeh University, Changhu, Taiwan.

Email: [email protected] _____________________________________________________________________

Abstract

This paper examines the interaction between mutual fund performance and portfolio turnover.

Active trading could affect fund performance, but underperforming funds could also be traded

actively at the same time to perform well. Therefore, we used two-stage least squares to address

with simultaneity. The results indicate that funds with higher portfolio turnovers exhibit inferior

performance compared with funds having lower turnovers. Moreover, funds with poor

performance exhibit higher portfolio turnover. The findings support the assumptions that active

trading erodes performance, and that fund managers with poor performance attempt to trade

actively to retain employment.

____________________________________________________________________________

Keywords: Mutual funds; Portfolio turnover; Simultaneity; Two-stage least squares

JEL Classification: C30; G11; G23

http://www.globalbizresearch.com/

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2014 Vol: 3 Issue 4


1. Introduction

Previous literature has attempted to explain the performance of mutual funds, which is a

critical aspect in investor fund selection.1 Several fund characteristics have been analyzed as

potential determinants of future fund performance. These characteristics include fund size, age,

fees, and expense loads, as well as turnover ratio, flows, and returns. However, among the

potential determinants of future fund performance, more attention should be directed toward

turnover ratio. Empirical evidence on the portfolio turnover–performance relation is

inconsistent. One argument is that high turnover further erodes mutual fund performance

because of the larger level of portfolio turnover that accompanies larger expense ratios.2 Carhart

(1997) finds that expense ratios, portfolio turnover, and load fees are significantly and

negatively related to fund performance. Barber and Odean (2000) also argue that the cost and

frequency of trading, rather than portfolio selections, explain the poor investment performance

of households. However, some studies argue that the funds that spend the most on research and

trade may be uncovering underpriced stocks and thus earn rates with sufficiently high return to

offset higher charges. Grinblatt and Titman (1994) find that performance is positively related to

portfolio turnover. Wermers (2000) finds that high-turnover funds outperform index funds on

a net return basis. Ippolito (1989) indicates that portfolio turnover and management fees are

unrelated to fund performance.

Although turnover ratio affects performance, performance likewise influences turnover

ratio. Mutual fund is one of the most popular investment instruments among small-amount

investors. Shu et al. (2002) indicate that small-amount investors may focus more on short-term

performance compared with large-amount investors. To shield the revenue from management

fees, fund managers are more likely to increase portfolio turnover if they encounter poor

performance. Khorana (1996) finds a significantly larger level of portfolio turnover for the

replacement sample of mutual fund managers relative to the control sample, which is consistent

with the notion of herding and window dressing. To avoid censure from broad of directors

(investment trust) and/or unemployment, fund managers with poor performance might engage

in larger portfolio turnover activity.

Fund performance and turnover ratio simultaneously determine each other because the

threat of poor performance compels managers to trade actively, but active trading could hurt or

increase fund performance. Furthermore, the data collected for this study are on a monthly

1 These empirical regularities have been documented by a large number of studies, including Jensen

(1968), Grinblatt and Titman (1992, 1994), Ippolito (1989), Hendricks et al. (1993), Brown and

Goetzmann (1995), Gruber (1996), Carhart (1997), and Zheng (1999). 2 Cremers and Petajisto (2009) and Kacperczyk et al. (2008) also suggest the measures of fund’s manager

activity that different with portfolio turnover to explore the unobserved action of mutual funds.



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basis. A fund manager (insider) knows the performance ahead of outside investors and can

adjust the allocation of mutual funds instantly. For instance, a manager can actively adjust the

portfolio allocation on a certain day if he/she encountered a stock market plunge the previous

day. Although causality exists in the data, the monthly data collected on each disclosure date

are the interacted results. To overcome the simultaneity problem, studies that investigate the

determinants affecting fund performance usually lag the relevance variables by one period. The

use of a lagged turnover ratio is inadequate to solve the endogeneity issue because a lagged

turnover ratio may be not highly correlated with current turnover and is thus a poor proxy for

this variable. Therefore, to avoid the endogeneity raised by the fund performance and turnover

ratio, we use two-stage least squares (hereafter 2SLS) to investigate the relation between fund

performance and turnover.

This paper aims to investigate the determinants of mutual fund performance and portfolio

turnover under the condition of possible endogeneity. After controlling for endogeneity, we

find that funds with higher portfolio turnover outperform those with lower turnover. This result

has a similar implication as that of Grinblatt and Titman (1994) and Wermers (2000) in that

mutual fund managers who actively trade stocks add value. By contrast, fund managers with

poor performance are more likely to actively trade. This result also has a similar implication as

that of Khorana (1996) in that a relation exists between the fund manager replacement and

performance.

This study makes several contributions to the literature on mutual funds. First, we examine

the interaction between fund performance and portfolio turnover and obtain a consistent

estimation of the independent variables. The investigation enriches our understanding on the

decision making process of fund managers in terms of investing in mutual funds and whether

actively trading could add value. Second, we use domestic equity funds in Taiwan as the

research sample. As mentioned by Bialkowski and Otten (2011), the vast majority of these

studies about mutual fund however focused on the US mutual fund industry which in terms of

assets under management and holding of the domestic equity market is far ahead of the rest of

the world. The composition of investors in the equity mutual fund market in Taiwan is different

from the US equity mutual fund market in which institutional investors have a significantly

larger share. This result can help us better understand non-US funds, especially for countries

with more small-amount investors.

The remainder of this paper is organized as follows: Section 2 describes the empirical

results in the literature and develops determinants that affect fund performance and portfolio

turnover. Section 3 describes the data used and discusses the problems associated with

simultaneity. The empirical results are presented in Section 4, whereas Section 5 concludes.



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2. Determinants of Fund Performance and Portfolio Turnover

To investigate the determinants of domestic equity mutual fund performance and portfolio

turnover ratio, we present the related variables cited in the literature.

2.1 Fund characteristics that affecting fund performance

2.1.1 Fund size

The effect of fund size on performance can run in both directions. Therefore, the literature

on the relationship between fund returns and fund size shows mixed evidence. Larger funds can

spread fixed expenses over a larger asset base and have more resources for research. In addition,

large funds can negotiate lower transaction costs because they have larger positions and trading

volumes.

By contrast, Chen et al. (2004) find that fund returns decline with lagged fund size because

of liquidity constraints. Larger mutual fund managers must trade larger volumes of stock, and

this action causes an increase in stock price. However, small funds can more easily purchase

and sell securities without altering stock price (see also Pollet and Wilson, 2008; Yan, 2008).

2.1.2 Fund family size

Chen et al. (2004) find that fund family size has a positive and statistically significant effect

on performance. The source of the advantage is twofold. First, economies of scale and scope

exist within some fund families because expenses such as research and administrative fees can

be shared among funds. Second, large fund families also benefit from economies of scale from

trading commissions and lending fees. In addition, Khorana and Servaes (1999) find that large

families and families that have more previous experience in opening funds are more likely to

open new funds because the cost of generating a new fund is lower for large companies, which

can benefit from economies of scale and scope. Nanda et al. (2004) suggest that fund families with higher

variation in investment strategies across funds are more likely to generate star performance.

2.1.3 Age

The effect of age on performance can run in both directions. One might argue that younger

mutual funds will quickly achieve better performance to survive. Nevertheless, newer funds

usually face higher costs and lack the experience to operate during the newborn period.

Moreover, newer mutual fund returns and ratings are also more vulnerable to manipulation

because of their small size.

2.1.4 Expenses

Empirical evidence on the fee–performance relation is inconsistent. Gruber (1996)

indicates that expenses are not higher for top-performing funds nor do expenses increase more

rapidly in the future for top performing funds. Similarly, Carhart (1997) shows that the expense

ratios, transaction costs, and load fees directly and negatively affect fund performance. Gil–

Bazo and Ruiz–Verdu (2009) find a negative relation between fees and before-fee performance,



2014 Vol: 3 Issue 4


whereas others find no relationship between fees and performance (Chen et al., 2004).

2.1.5 Flows

Gruber (1996) and Zheng (1999) find that funds experiencing net inflows perform

significantly better than funds that experience outflows. This phenomenon is referred to as the

smart money effect. If the smart money effect is true, investors can detect skilled managers and

direct their money to these managers. Therefore, fund flows should have a positive correlation

with future returns.

2.1.6 Past performance

Performance persistence of mutual funds over the short horizon is well documented in the

literature (Hendricks, Patel, and Zeckhauser, 1993; Grinblatt and Titman, 1994; Brown and

Goetzmann, 1995; Carhart, 1997). This performance persistence seems to be stronger among

the most poorly performing funds.

2.2 Fund characteristics that affecting fund portfolio turnover

2.2.1 Transaction cost

When fund managers adjust the portfolio allocation, they incur a transaction cost. Chen et

al. (2000) find that the general increase in turnover over time is likely (at least in part) to be

related to the general decline in trading costs over time.

2.2.2 Past performance

Given the presence of the fund manager replacement–performance relation, poor

performers can attempt to prevent dismissal by engaging in larger portfolio turnover activity.

Khorana (1996) finds a significantly larger level of portfolio turnover for departing mutual fund

managers relative to nonreplaced fund managers. We expect that fund managers with poor

performance will adjust the portfolio allocation to avoid pressure from investors and fund

families, particularly for managers that have experienced longer underperformance. Therefore,

we use current and prior performance as the regressors.

2.2.3 Fund manager characteristics

Investor characteristics affect the investment style, including overconfidence from gender

(Barber and Odean, 2001) and home-bias from familiar (Huberman, 2001). Likewise, fund

manager characteristics also could affect the portfolio allocation, including high turnover. We

use the calendar-year cumulative turnover ratio as a proxy for manager trading style.

2.2.4 Stock market condition

Fund portfolio turnover usually increases in a bull market and decreases in a bear market.

Therefore, we use the Taiwan weighted stock index return as a proxy for the stock market

condition.

3. Data and Methodology

In this section, we first describe the sample and then discuss the methods for the



2014 Vol: 3 Issue 4


simultaneous equation model.

3.1 Sample description

We analyze all 170 open-end equity funds that invested equities in Taiwan from 37 fund

families from 2003 to 2012. The market value of all 170 open-end equity funds is NT$ 280

billion comprising 15.18% of all the funds in December 2012.3 The total number of investors

is 748,960, comprising 742,553 (99.14%) general natural investors and 6,407 (0.86%)

institutional investors.

The monthly data on domestic equity mutual funds came from the CMoney database, which

covers numerous fund characteristics, such as total net asset, fund family, fund age, monthly

and yearly returns, fees, turnover ratio, and other details. We also use treasury bills as the risk-

free asset and the Taiwan weighted stock index return as the market portfolio.

Table 1: Descriptive statistics for entire database of 128 open-end mutual funds

This table presents the descriptive statistics for 170 equity funds in Taiwan between the years 2003

and 2012. Total net assets are expressed in billions of New Taiwan dollars. Monthly (Yearly) Return is

defined as the change rate of net value during a month (year). General (Institutional) Investor is defined

as general natural (institutional) investors. Jensen’s Alpha and Beta are calculated for each fund with 12

monthly return data using Capital Asset Pricing Model with treasury bills as the risk-free asset and the

Taiwan weighted stock index return as the market portfolio. Sharpe Index and Treynor Index are the

average monthly excess return divided by the standard deviation of monthly fund returns over the

preceding year and by Beta, respectively. Management fee is the percentage of assets paid as

management fees during a month. Expense ratio is the percentage of fund assets spent on operating

expenses during a month. Buy (Sell) turnover ratio is the percentage of total assets bought (sold) during

a month. Subscription (Redemption) is the dollar amount that investor subscribe (redeem) from a fund

during a month. Age is the average age of mutual funds measured from fund inception to the year before

they were selected.

Statistics N Mean Medium Standard

Deviation Maximum Minimum

Total Net Asset (billion NT$) 18589 1.83 1.13 2.07 22.52 0.02

General Investor (thousand) 18204 5.63 3.61 6.18 53.43 0.00

Institutional Investor 18204 40.25 39.00 19.45 258.00 0.00

Monthly Return (%) 18553 0.78 0.80 7.11 31.81 -27.80

Yearly Return (%) 18053 8.77 7.18 29.13 121.50 -64.84

Jensen's Alpha 18038 0.16 0.09 1.06 5.73 -4.13

Beta 18045 0.96 0.95 0.23 2.02 -0.68

Sharpe Index 18036 0.10 0.11 0.34 1.67 -1.50

Treynor Index 18046 0.49 0.71 5.06 41.87 -520.92

Management Fee (%) 18589 0.13 0.13 0.03 1.01 -1.41

Expense Ratio (%) 18589 0.14 0.15 0.03 1.10 -1.62

Total Fee (%) 18589 0.29 0.26 0.13 3.93 -1.51

Buy Turnover Ratio (%) 18589 26.43 21.51 21.39 279.01 0.00

Sell Turnover Ratio (%) 18589 22.34 18.06 23.63 276.61 -736.44

Subscription (million) 18589 101.86 29.14 272.83 8022.72 0.00

Redemption (million) 18589 103.00 43.86 176.16 3130.30 0.00

Age (year) 18589 9.44 9.36 4.90 27.17 0.01

3 The exchange rate is approximately NT$ 30/ US$ 1.



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Table 1 reports the descriptive statistics of fund characteristics. Total net assets are

expressed in NT$ billions. The mean (median) fund size in terms of net assets under

management is NT$ 1.83 (NT$ 1.13) billion. General (institutional) investor is defined as a

general natural (institutional) investor. The mean (median) general natural investors of a fund

is 5.63 (3.61) thousand with mean (median) institutional investors of 40 (39). Monthly (yearly)

return is defined as the change rate of net value during a month (year). The mean (median)

monthly return is 0.78% (0.8%), and the mean (median) yearly return is 8.77% (7.18%).

Jensen’s Alpha and Beta are calculated for each fund with 12 monthly return data sets using

the capital asset pricing model with treasury bills as the risk-free asset and the Taiwan weighted

stock index return as the market portfolio. Sharpe and Treynor Indices are the average monthly

excess return divided by the standard deviation of monthly fund returns over the preceding year

and by the beta, respectively.

Expense ratio is the percentage of fund assets spent on operating expenses during a month.

Expense ratio consists of management fees, recordkeeping, custodial services, taxes, legal

expenses, as well as accounting and auditing fees. The average (medium) monthly expense is

0.14% (0.15%). Management fee is the percentage of assets paid as management fees during a

month. Management fees account for 93% of expense ratio on average. Total fee is defined as

expense ratio plus direct transaction cost.

Buy (sell) turnover ratio is the percentage of total assets bought (sold) during a month. The

mean (median) buy turnover ratio of a fund is 26.43% (21.51%) with a mean (median) buy

turnover ratio of 22.34% (18.06%). Age is the average age of mutual funds measured from fund

inception to the year before such funds are selected. The average fund age is approximately

9.44 years.



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Table 2: Correlation Coefficients

This table presents the Pairwise Pearson correlation coefficients for fund characteristic variable from 2003 to 2012. Pairs of independent variables with significant

correlation coefficients (0.05 significant levels) are expressed in boldface.

Jensen's

Alpha

Monthly

Return

Lag

Monthly

Return

Buy

Turnover

Ratio

Sell

Turnover

Ratio

Yearly buy

Turnover

Total Net

Asset Beta

Expense

Ratio Age

Family

Market

Share

Net Flow

Index

Monthly

Return

Jensen's Alpha 1.00

Monthly Return 0.18 1.00

Lag Monthly Return 0.17 0.06 1.00

Buy Turnover Ratio -0.05 0.07 0.07 1.00

Sell Turnover Ratio -0.11 0.02 0.05 0.80 1.00

Yearly buy Turnover -0.06 -0.02 -0.05 0.58 0.48 1.00

Total Net Asset 0.07 0.01 0.02 -0.22 -0.20 -0.18 1.00

Beta 0.15 -0.06 -0.03 0.02 0.01 0.04 0.02 1.00

Expense Ratio -0.06 -0.17 -0.01 0.00 0.04 0.04 -0.07 0.01 1.00

Age -0.08 -0.04 -0.04 -0.10 -0.07 -0.08 0.12 0.10 -0.16 1.00

Family Market Share 0.08 0.01 0.02 -0.16 -0.12 -0.14 0.46 0.01 -0.06 0.12 1.00

Net Flow 0.11 0.07 0.07 0.14 -0.01 0.05 0.12 -0.02 -0.13 0.00 0.02 1.00

Index Monthly Return 0.08 0.88 0.08 0.09 0.06 -0.03 0.00 -0.06 -0.15 -0.03 0.01 0.02 1.00



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Considering the potential for interrelationships among these independent variables, we

analyze their correlations and present this information in Table 2. Multicollinearity does not

appear to be a severe issue as most correlation coefficients are below 0.30. The highly positive

correlation (0.8) between buy and sell turnover ratios indicates that fund managers with high

buy turnover ratio are more likely to sell the stocks of fund. The main reason for the highly

positive correlation is the institutional regulation that equity fund usually must retain at least

70% of assets in equity.

3.2 Simultaneous equations models

Equations (1) and (2) present a generic two-equation model (these equations are called the

structural equations):

Performanceit = α1Turnoverit + β1′ 𝐗1 + ε1 (1)

Turnoverit = α2Performanceit + β2′ 𝐗2 + ε2. (2)

Performanceit is the performance of fund i at time t. Turnoverit is the turnover ratio of

fund i at time t. 𝐗1 and 𝐗2 are the vectors of independent variables relative to performance and

turnover ratio of fund, respectively. Performanceit and Turnoverit simultaneously determine

each other. Changes in Turnoverit will result in modifications in Performanceit via (1).

However, the resulting changes in (1) will immediately lead to changes in Turnoverit via (2).

Ordinary least squares (OLS) cannot be used to estimate these models because the relationship

specified by the equations violates the OLS assumption of zero covariance between the

disturbance term and the independent variables. Therefore, we use 2SLS to correct the

endogeneity.

2SLS is a two-stage process. In the first stage, the endogenous variable is regressed on all

the exogenous variables, and the predicted value of this regression is obtained.

Performanceit = Π1′ 𝐗 + ω1 (3)

Turnoverit = Π2′ 𝐗 + ω2, (4)

where X is the union of the exogenous variables in Equations (1) and (2). Equations (3) and

(4) are called reduced-form equations, which express the endogenous variables solely in terms

of the exogenous variables. From (5) and (6), the predicted values are obtained and uncorrelated

with error terms ε1 and ε2.

Performanceit = Π1′𝐗 (5)

Turnoverit = Π2′𝐗. (6)

In the second stage, the model-estimated values from stage one replace the original

endogenous variables in Equations (1) and (2) We then compute an OLS model for the response

of interest in Equations (7) and (8). The resulting coefficient estimates will be consistent.



2014 Vol: 3 Issue 4


Performanceit = α1Turnoverit + β

1′ 𝐗1 + ε1 (7)

Turnoverit = α2Performanceit + β

2′ 𝐗2 + ε2. (8)

4. Empirical result

4.1 Correlations between mutual fund performance and portfolio turnover as revealed by

sorting

To examine the relation between fund turnover and performance, we first rank funds at the

end of a given month based on their turnover levels in the contemporaneous months in Table

3. We use contemporaneous year turnover rather than prior year for ranking because the data

are on a monthly basis.4 Fund managers can access any inside information at once and react

immediately if any change is observed in the performance and turnover.

Table 3: Univariate comparisons for fund-specific characteristics across high, medium, and low

turnover ratio

At the end of each month during the period beginning January, 2003 and ending December, 2012,

we sort funds into three subgroup based on their turnover level of the month. Panel A presents the

performance measures and fees of three subgroup sorted by monthly buy turnover, while Panel B presents

the performance measures and fees of three subgroup sorted by monthly sell turnover. The median value

of each variable is reported in the brackets. ***, **, and * indicate significance at the 1%, 5%, and 10%

level, respectively.

Panel A: rank by turnover ratio

Statistics High Buy

Turnover

Medium Buy

Turnover

Low Buy

Turnover p-value

Monthly Return (%) 0.680 0.834 0.812 0.44

[0.725] [0.830] [0.875] [0.45]

Yearly Return (%) 8.148 8.832 9.292 0.10

[6.080] [7.120] [8.260] [0.00] ***

Jensen's Alpha 0.104 0.161 0.209 0.00 ***

[0.037] [0.080] [0.148] [0.00] ***

Expense Ratio (%) 0.144 0.143 0.143 0.05 *

[0.145] [0.145] [0.145] [0.07]

Total Fee (%) 0.392 0.270 0.204 0.00 ***

[0.367] [0.261] [0.196] [0.00] ***

Sell Turnover Ratio (%) 41.193 19.552 7.147 0.00 ***

[37.670] [18.665] [6.705] [0.00] ***

N 5941 6462 6186

Panel B: rank by sell turnover ratio

Statistics High Sell

Turnover

Medium Sell

Turnover

Low Sell

Turnover p-value

Monthly Return (%) 0.608 0.810 0.907 0.06

[0.640] [0.850] [0.890] [0.09]

Yearly Return (%) 7.206 8.576 10.502 0.00 ***

[5.530] [6.920] [9.250] [0.00] ***

Jensen's Alpha 0.043 0.142 0.290 0.00 ***

[-0.007] [0.069] [0.195] [0.00] ***

Expense Ratio (%) 0.145 0.143 0.142 0.00 ***

[0.145] [0.145] [0.145] [0.49]

Total Fee (%) 0.395 0.270 0.201 0.00 ***

[0.368] [0.259] [0.193] [0.00] ***

4 We also rank funds on their turnover level of the prior calendar month, but the results remain

unchanged.



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Buy Turnover Ratio (%) 45.603 23.144 11.431 0.00 ***

[40.950] [21.245] [9.590] [0.00] ***

N 5943 6462 6184

After ranking the monthly buy turnover ratio, the most actively traded thirds of funds are

labeled “high buy turnover,” whereas the least actively traded is labeled “low buy turnover.”

Turnover levels are updated each month in the CMoney files. Thus, we reconstitute turnover

thirds of funds once a month. Panel A of Table 3 reports the performance and fee measures by

ranking the monthly buy turnover ratio of the funds. All performance measures, except for

monthly return, have a negative relationship with buy turnover ratio. The high buy turnover

funds have the poorest performance, whereas the low buy turnover funds have the best

performance. These results support the arguments of Carhart (1997) that high portfolio turnover

hurts the fund performance.

In addition, the high buy turnover funds have the highest expense, whereas the low buy

turnover funds have the lowest expense, and the differences are significant except for the

median of these three groups, which are not significantly different. Considering that total fees

are equal to expense ratio plus transaction costs, funds with high buy turnover have highest

total fees, whereas funds with low buy turnover have lowest total fees. The highly positive

correlation between buy and sell turnover reported in Table 2 reveals that high buy turnover

funds have a high sell turnover ratio. Panel B of Table 3 reports the performance and fee

measures of funds ranked by monthly sell turnover ratio. The results are similar to Panel A of

Table 3.

Table 4: Univariate comparisons for fund-specific characteristics across high, medium, and low

performance

At the end of each month during the period beginning January, 2003 and ending December, 2012,

we sort funds into three subgroup based on their performance of the month. Panel A presents the turnover

ratios and fees of three subgroup sorted by Jensen’s Alpha, while Panel B presents the turnover ratios

and fees of three subgroup sorted by monthly return. The median value of each variable is reported in

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

Panel A: rank by Jensen's Alpha

Statistics High Jensen's

Alpha Medium Jensen's

Alpha

Low Jensen's

Alpha p-value


[21.090] [19.620] [23.625] [0.00] ***


[16.310] [16.870] [21.370] [0.00] ***

Expense Ratio (%) 0.142 0.143 0.145 0.00 ***

[0.144] [0.145] [0.146] [0.00] ***

Total Fee (%) 0.277 0.275 0.307 0.00 ***

[0.252] [0.250] [0.274] [0.00] ***

N 5741 6273 6024



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Panel B: rank by monthly return

Statistics High Monthly

Return

Medium

Monthly Return

Low Monthly

Return p-value


[20.740] [21.000] [22.980] [0.00] ***


[16.760] [18.020] [19.720] [0.00] ***

Expense Ratio (%) 0.142 0.143 0.146 0.00 ***

[0.143] [0.145] [0.147] [0.00] ***

Total Fee (%) 0.277 0.282 0.303 0.00 ***

[0.250] [0.257] [0.273] [0.00] ***

N 5933 6449 6171

Furthermore, Table 4 sorts the funds based on the performance measures and lists the mean

and median value of turnover and fees. We use Jensen’s Alpha and monthly return as separate

performance measures. In Panel A of Table 4, turnover ratio is increasing as the Jensen’s Alpha

is decreasing, and the difference is significant regardless of buy or sell turnover ratio. The

results support the findings of Khorana (1996) that a fund manager with poor performance can

attempt to prevent dismissal by engaging in larger portfolio turnover activity. The expense ratio

decreases with Jensen’s Alpha, and expense ratio among the various thirds ranges from 0.142%

to 0.145%. Total fees for funds with high and low Jensen’s Alphas are 0.277% and 0.307,

respectively. This result supports the arguments of Gruber (1996), Carhart (1997), and Gil–

Bazo and Ruiz–Verdu (2009). Panel B of Table 4 reports the turnover and fee measures by

ranking funds by monthly return, and the results remain unchanged.

After introducing the relationships in the context of a simple sorting procedure, the

preliminary results indicate that funds with higher turnover and fees could decrease

performance, similar to the finding by Carhart (1997). Funds that belong to the

underperforming group are more likely to adjust actively the portfolio allocation, which is

similar to the argument of Khorana (1996). These preliminary results also show that an

interaction occurred between fund performance and portfolio allocation.

4.2 2SLS

Table 5 uses 2SLS to solve endogeneity and to investigate the interrelation between the fund

performance and portfolio turnover. All the regressions include time-fixed effects (year

dummies) to account for cross-sectional dependence.

Table 5: 2SLS regression result

This table reports the coefficient estimates for the structural equations from two-stage least squares

model. Panel A presents the structural equations of Jensen's Alpha and Monthly Buy Turnover and Panel

B presents the structural equations of Jensen's Alpha and Monthly Sell Turnover. Controls are included

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



2014 Vol: 3 Issue 4


Panel A: Structural Equation (Jensen's Alpha and Monthly Buy Turnover)

Jensen's Alpha Monthly Buy Turnover

coefficient t-value coefficient t-value

Constant 0.231 3.860 *** 14.416 11.730 ***

Predicted Buy Turnover -0.002 -2.750 ***

Predicted Jensen's Alpha -15.069 -5.130 ***

Fund family market share 1.250 6.070 ***

Yearly Buy Turnover (%) 0.068 72.360 ***

Index return (%) 0.372 16.150 ***

Net flow (million NT$) 0.001 13.710 0.021 12.330 ***

Lag Monthly Return 0.016 16.240 *** 0.547 10.650 ***

Total Net Asset (billion NT$) 0.027 6.950 *** -0.846 -6.540 ***

Beta (%) 0.177 5.110 *** 4.057 4.830 ***

Expense Ratio (%) -1.420 -5.470 *** -18.761 -3.170 ***

Age (year) -0.019 -11.720 *** -0.317 -5.250 ***

Year Dummies Included Included

N 17912 17920

Adjusted R2 0.282 0.398

Panel B: Structural Equation (Jensen's Alpha and Monthly Sell Turnover)

Jensen's Alpha Monthly Sell Turnover

coefficient t-value coefficient t-value

Constant 0.270 4.570 *** 7.408 4.940 ***

Predicted Sell Turnover -0.005 -9.270 ***

Predicted Jensen's Alpha -7.042 -1.960 **

Fund family market share 1.175 5.750 ***

Yearly Sell Turnover (%) 0.077 39.470 ***

Index return (%) 0.219 8.060 ***

Net flow (million NT$) 0.001 13.670 0.003 1.560

Lag Monthly Return 0.017 16.810 *** 0.375 6.100 ***

Total Net Asset (billion NT$) 0.020 5.090 *** -0.893 -6.180 ***

Beta (%) 0.204 5.900 *** 3.903 3.760 ***

Expense Ratio (%) -1.319 -5.090 *** 2.124 0.310

Age (year) -0.019 -11.810 *** -0.184 -2.530 **

Year Dummies Included Included

N 17912 17920

Adjusted R2 0.285 0.318

A negative relation is found between monthly buy turnover ratio and performance of mutual

funds in Column (1), which is consistent with the evidence in Carhart (1997). Funds with higher

turnover ratio are more likely to underperform the market expectation. We find that funds that

belong to a large fund family seem to perform better than that those that belong to a small fund

family. The positive effect of family size on performance is pervasive for funds in Taiwan and



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is consistent with the findings of Chen et al. (2004). We find that funds that receive more new

money subsequently perform better than those that receive less new money. Flows of the

previous period have a positive and statistically significant coefficient, indicating a smart

money effect. The evidence supports the idea that investors are able to detect skilled fund

managers in Taiwan.

The short-run past performance persistence documented in the literature is also evident in

Taiwan funds. Column (1) of Table 5 shows that fund size (total net asset) is positively related

to fund performance at 0.027 with a t-statistic of 6.95. Therefore, larger funds seem to have

better risk-adjusted performance than smaller funds. The negative relation between the expense

ratio and net-of-fees performance is similar to the findings of Gruber (1996), Carhart (1997),

as well as Gil–Bazo and Ruiz–Verdu (2009). The evidence supports the idea that more fees

might erode fund performance. Older funds underperform younger funds, and the difference is

significant.

The negative and significant coefficient of predicted Jensen’s Alpha in monthly buy

turnover ratio equation shows that funds with poor performance are more likely to adjust the

portfolio allocation actively. Funds with high yearly turnover ratio usually have high monthly

turnover, which indicates that fund managers might have a tendency to trade more.

Furthermore, funds that receive more new money buy stocks more actively than those that

receive less new money. We also find that funds during bull market or with good past

performance are more likely buy stocks. Funds that have lower transaction costs or younger

funds buy stocks more actively than those with high transaction costs or older funds.

The result might be the same for the monthly sell turnover because fund managers must

buy some equities once they sell part of equities in the fund portfolio. The results in Panel B of

Table 5 support a negative relation between monthly sell turnover ratio and mutual fund

performance. Fund performance has a negative and statistically significant effect (the

coefficient is -7.04 with a t-statistic of -1.96) on the monthly sell turnover of the fund. This

relation between new flow and monthly sell turnover, however, is statistically insignificant

because funds have more cash inflow, but selling stock to retain the level of cash in portfolio is

unnecessary. No consistent evidence of a statistically significant negative relation between fees

and monthly sell turnover exists.

5. Conclusion

This study investigates the interaction between mutual fund performance and turnover ratio

using a sample of open-end actively managed equity funds in Taiwan from 2003 to 2012. The

literature has indicated that increasing portfolio turnover could influence fund performance,

whether from the perspective of transaction cost eroding the fund performance or of active trade



2014 Vol: 3 Issue 4


adding value. Meanwhile, poor performance further increases portfolio turnover, thus giving

rise to simultaneity problems occur. Structural equations are specified to ensure an exactly

identified performance and turnover equation, and the 2SLS procedure was implemented to

overcome simultaneity problems.

The results show that mutual funds with higher turnover and expenses do not earn rates of

return sufficiently high to offset the higher charges. These findings are consistent with the

notion that active trading hurts mutual fund performance (Carhart, 1997). We also find that

underperforming funds are more likely to have higher portfolio turnover ratios and higher

expenses in relation to a sample of well-performing funds. The results support the findings of

Khorana (1996) that fund managers engage in larger portfolio turnover activity to prevent the

termination of their employment.

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