Does Overseas Experience Matter for Fund Managers … · 2019-04-26 · Abstract：Being different...

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Does Overseas Experience Matter for Fund Managers’ Performance in

China?

Ying Zhou(周莹), Allan Zebedee

(4th Floor, Administrative Building, Center for Economics, Finance and Managment Studies, Hunan

University, Changsha, Hunan, 410006)

Abstract：Being different from the common manager characteristics that other literatures focus on, this

paper examines the relationship between managers’ overseas experience and mutual fund performance

in China. Using the data we collect by hand, we find that the overseas experience is negatively and

significantly related to fund performance. And those managers tend to take less risk while fail to reach a

tradeoff between return and risk, and they trade less frequently in capital market. We also find some

results similar to other famous literatures.

Keywords: Mutual fund manager performance; overseas experience; manager characteristics

中图分类号： F830.91 文献标识码：A

1. Introduction

In recent years, with the constant improvement of the domestic capital market and

increasing investment consciousness of a broad range of investors, Chinese fund

industry develops rapidly. According to Asset Management Association of China, by the

end of 2017, the number of fund in domestic market increases from 4 in 1998 to 4841,

the total net asset reaches to 11,600 million yuan, in which the open-ended fund

increases from 2 in 2001 to 4361, and the net asset is 10,990 million yuan. It’s easy to

see that funds have gradually become one of the most important institutional investors in

the domestic stock market, and have an increasing impact on the security market. Among

those funds, the open-ended funds have been the mainstream and grown in quite fast

speed especially after 2006. So the research on open-ended funds need further updated

and improved.

As the final executor of fund investment decision and implementation, fund

managers’ behavior has a direct influence on the fund performance. Choosing fund

managers is a very important part for both fund corporations and investors, so there is

important theoretic and practical meaning about the research on the relationship between

the individual characteristics of fund managers and performance. As a matter of fact, with

the increasing number of fund managers, the group of fund managers has shown the

individual characteristics which are different from other occupation groups. Before being

fund managers, those people have different background or experience, and there is no

doubt that their former experience will affect their thinking pattern, which will affect their

final decision. Especially the overseas experience, which is quite different compare to

other managers’ domestic experience, and those experience may affect their cognitive

schema, such as knowledge of facts, events and trends, knowledge about alternatives

and knowledge or assumptions about how consequences are attaches to alternatives.

On the other hand, overseas experience may implies higher ability, because it is known

to us all that as a foreigner in another country, a person who want to find a good job


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usually need to get more recognition, and this recognition reflects ability that we cannot

observe. So the changing cognitive schema and the unobserved higher ability may affect

managers’ investment styles and performance. So does overseas experience really

matter for fund managers’ performance? This question needs data to prove.

Another phenomenon is that studying abroad has been an increasing popular choice

for many people. According to the data from Ministry of Education, the number of persons

studying abroad is more than 600 thousand in 2017, and increased by 11.74% compare

to 2016. China remains the largest source country of overseas students in the world. And

a lot of persons going abroad would like to leave there to live and work. Does this mean

the brain drain? Facing such phenomenon, should Chinese government take efforts and

policy to attract those persons to come back to make contributions to Chinese economy?

This paper may partly help to answer the question.

The rest of the paper is organized as follows. Section 2 is a review for the literatures

that have investigated the influence of manager characteristics on fund performance.

Section 3 describes the data while Section 4 provides the methodology. Section 5

contains the results and discussion, and Section 6 presents the conclusions.

2. Literature review

The research on relationship between fund manager characteristics and fund

performance starts from 90s in last century. Using annual return of fund and excess

return adjusted by market risk as performance metric, Golec(1996) investigates the

effects of managers’ characteristics on performance, such as age, tenure, years of

education and whether they have MBA degree, and he finds that those managers who

have MBA degree, longer tenure on the fund and are younger perform better. Chevalier

and Ellison(1999) finds those managers with MBA degree have an obvious investing

tendency on low book-to-market ratio growing stocks, while the older managers tend to

use performance momentum strategy so that they make some adjustment on the effect

of investing styles of managers. On the basis of simple excess return and market excess

return, they use four-factors adjusted excess return to measure the performance and

adjust the investment styles of managers, and they find that managers who graduate

from the undergraduate college perform with higher mean SAT score will achieve better

performance through all kinds of metrics. By investigating the effects of education

background on the fund performance, Gottesman and Morey(2006) find that those

managers that have attended high-quality MBA projects perform much better than those

who have not attended MBA projects or the projects are not great enough in a

significance level, and simultaneously, whether owning CFA certificate, the master degree

besides MBA or doctor degree have nothing to do with the fund performance. Li (2011)

firstly investigates the relationship between hedge fund manager characteristics and

performance and gets the similar result, that is, hedge fund managers who graduate from

higher SAT universities always achieve higher raw return and risk-adjusted return, and

also, lower exposing risk.

The domestic research on fund performance mainly focus on the fund performance

assessment (Shen and Huang, 2001; Wang, 2001) and performance persistency (Xu and

Zhao, 2006) and so on. There are also scholars focusing on the relationship between

ownership structure and performance fund (Jiang et. al, 2011). The valuable research

examining relationships between managers and their performance from angel of


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individual characteristics are not too much. By investigates the pressure and competence

faced by close-ended managers from the prospect of tenure, Yao et. al (2006) find that

the appoint and dismiss of managers are not based on the performance and there does

not exist persistency on the performance of close-ended fund. Xu and Zhao(2008) focus

on the influence of manager characteristics on performance and risk and they get the

conclusion that the younger and managers with longer tenure perform better, and the

risk-controlling consciousness need to be improved, however, they only consider 72 open

and close-ended fund during the period January 2nd, 2001 and September 30th, 2004.

There are several papers focusing on the effects of experience of managers on their

performance. Porter and Thrifts (1998) firstly regard experience as the focus of research.

But the researchers do not find evidence for persistence among those managers with

rankings of annual fund manager performance, also no evidence that performance over

the first five years can be used to predict the next five years’ performance. Porter and

Thrifts (2012) find even less encouraging results with respect to the performance of

long-serving managers. Based on the previews studies, Clare (2017) focuses on 357

managers that have had tenure in excess of ten years, however, little evidence shows the

performance persists from one year to the next.

Even though there are a lot of paper focusing on the effects of manager

characteristics and even their experience on fund performance, few papers mainly put

their attention on the overseas experience. However, fund is hard to get in, while fund

prefer those people who have worked abroad, do those people have higher ability and

achieve higher significant performance? This paper is to answer this question.

3. Data

3.1 Fund selection criteria

This paper examines the performance of open-ended stock fund managers from Jan

2014 to Dec 2016, in order to better reflect the stock-picking ability of manager, passive

investments such as index funds are excluded, and for the reason that the tenure of

Chinese fund managers are short, we choose the managers that have been managing

the fund more than one year. Using the classification of database CSMAR, 1028 records

of funds exist during 2014-2016, after dropping the funds that do not satisfy the standards

above, 284 records of managers left.

For every manager, this paper record their specific managing period which includes

starting date and ending date, if a manager starts managing this fund before Jan 2014,

then his starting date is Jan 2014, if the manager stop managing this fund after Dec 2016,

then his ending date is Dec 2016.

3.2 Data source

The managers’ individual characteristics are collected by hand from CV disclosed on

the website, the raw return of funds, risk-free rates and market return come from CSMAR,

the fundamental data and financial data are obtained from Wind, other data such as the

momentum factors in four-factors model, excess return and investing style coefficients of

funds are obtained through calculation and regression, the fee ratio and turnover ratio are

calculated through financial data.

3.3 Variables definition

(1) Individual characteristics of managers


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The variables contained in the paper include managers’ gender, age, security tenure,

fund industry experience, researcher, tenure, master, doctor, MBA, work abroad, whether

they have studied abroad and whether they have overseas experience.

Where, for the variables gender, female = 0, male = 1. For age, we use the age of the

fund managers as of the year of observation, for those managers whose age are not

disclosed, we refer it according to the mangers’ education background and resume, the

undergraduate graduation is 23 years old, master's graduation is 26 years old and

doctor’s graduation is 30 year’s old. The security tenure is how long they have been

working in security industry by the start of their own testing period, the unit is year. The

fund industry experience is how long they have been working in fund industry by the start

of their own testing period, the unit is year. For the variable researcher, if they have been

industry researcher or other similar occupation, we record it as 1, otherwise 0. The

variable tenure is how long they have been managing this fund before the testing period,

if this fund is funded during the testing period, then this variable is 0, and the unit is year,

but specified to month. For the variables master and doctor, if the manager’s highest

education is doctor, then doctor = 1, master = 1, if the manager’s highest education is

master, then doctor = 0, master = 1, if the manager’s highest education is bachelor,

doctor = 0, master = 0. For whether they have MBA degree, yes = 1, no = 0. For whether

they have worked abroad, whether they have worked abroad, yes = 1, no = 0. For

whether they have overseas experience, if this manager have worked abroad or studied

abroad, then we note it as 1, otherwise 0.

(2) Fund characteristics

The fund characteristics in this paper refer to fund size, fund age, fund fee ratio and

fund turnover ratio. The fund size is the natural logarithm of the average net assets of the

fund in the testing period. The average net asset is the average value of the beginning

and end of the period. The fund age is the existing time since the foundation of the fund.

The fee ratio is the total expenses divided by average net asset in the same period, this

definition is a little bit different from the method of Chevalier and Ellison (1999) which

used the management expenses to represent total expenses, because pricing freely for

management fee is not allowed in China and the management expenses is 1.5% of the

managing assets, so the influence of fee on fund performance is actually that of the fund

size. The turnover ratio = (cost of buying stocks + income of selling stocks) / (2*average

net assets) during the testing period.

3.4 A data collecting example

For the reason that the data of this paper are collected by hand, so I would like to

introduce how I collect every record of the manager step by step.

Taking the fund whose code is 000979 as an example. Firstly, we search this fund

according to its code on the Morningstar website, and we can find two managers who

managed this fund between 2014 and 2016, that are, Tianling Xie managed this fund

from May 2015 to May 2016 and Wuke Bao managed this fund from June 2016 to

December 2016, so their managing time are 1 year and 7 months respectively. So only

the manager whose name is Tianling Xie satisfies our standard for the reason that we

require the manager have managed this fund at least 1 year and only one manager

manage this fund in this period. And we record the manager’s managing period, which is

used to calculate their performance metrics. Using the information disclosed on the


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website and the annual report of this fund in 2015, we can get Tianling Xie’s individual

information such as gender, education background and whether she has been the

researcher. The variable tenure is how long the manager has been managing this fund

before the testing period. In this example, her starting date is just in the testing period so

that the tenure is zero. The security tenure in manager’s resume part in the fund’s annual

report is 14, this number records how long this manager has been working in security

and fund industry by 2015, so we can calculate this manager began to work in 2001, that

is when she graduate from university (In the paper, we assume that manager begin to

work after graduation and there is no gap.). Then we can infer her birth year, that is 1976,

which equals to 2001 minus 25, for the reason that most managers haven’t disclosed

their birth year and we assume that those manager get their bachelor degree at the age

of 22, master degree at 25 and doctor degree at 30. So Tianling Xie’s age is 39 by the

year she starts to manage this fund, that is starting year minus birth year (2015-1976 =

39). Then we find that this manager has worked in the place besides mainland, so we

regard her as having worked abroad. And she got the master degree in Taiwan University,

so the variable studying abroad is 1.

4. Methodology

In this paper, we use five metrics to measure the performance of the fund managed

by managers, they are mean monthly excess return, Sharpe ratio (1966), single-index

alpha, Fama-French (1993) three-index alpha and Carhart (1997) four-index alpha, and

we suppose that they can reflect the characteristics of managers and fund only. The

mean monthly excess return, four-index alpha and conditional alpha are used by

Gottesman and Morey (2006), where the former two metrics are commonly used in

literature and the conditional alpha takes the historical public information into

consideration and use a dynamic strategy to match the time-varying risk exposure of

return.

The mean monthly excess return is mean monthly return minus mean risk-free rate.

The Sharpe ratio is calculated as:

Where t is the standard deviation of return during the sample period for fund i. When

evaluating the performance of fund, Sharpe ratio includes the investment risk and

considers both excess return and total risk. So it is a metric which is overall. The higher

the sharpe ratio is, the better the fund performance.

The single-index alpha is defined as:

where it ftR R is the excess total return (net of one-year bank deposit rate for regular

savings) for fund i during time period t, RMRF is the value-weighted average return of all

stocks traded in Shanghai and Shenzhen in excess of risk-free rate.

To estimate the three-index alpha, we use the following regression to make estimation:

sharpe = t f

t

t

R R

1it ft i i t itR R RMRF


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where SMB is the value-weighted average return difference of small and large cap firms.

HML is the value-weighted average return difference of high and low book-to-ratio firms.

On the basis of three-index alpha, we can estimate through the following regression

by adding a factor:

UMD is the difference in return between the high-return and low-return stocks, the

momentum effect which is reflected by momentum factors can be as long as either one

year or one month, in this paper, we choose one year as the time gap, which is the same

as Carhart (1997). These four factors take the market information and some abnormal

return phenomenon into consideration, so we can regard it as, after excluding the effect

of market and some factors that have been proved to be related to stock performance,

then can be regarded as to be associated with the characteristics of managers and

mutual fund.

For all five performance metrics (mean monthly excess return, sharpe ratio, single

index alpha, 3-index alpha and 4-index alpha), we perform estimation using excess

return. It is calculated from accumulative net asset values (NAV) and is calculated as

, , 1 , 1( ) /p t p t p tNAV NAV NAV , where ,p tNAV and , 1p tNAV are the accumulative net

asset values of portfolio p at time t and t-1 respectively and can be obtained from

CSMAR database. As the basic unit of purchase and redemption, the data NAV is easy to

be obtained, and can be used to measure the historical return of the fund, for the reason

that NAV in each period contains the cash dividends.

5. Results

5.1 Summary statistics

Table 1 presents summary statistics for the data we use in the study. The table

reports that the approximately 9.5 percent of the managers have worked abroad and 12.7

percent of managers have studied abroad, which is larger than the former one, this

shows that not all the person who have studied abroad will stay abroad to work, maybe

they cannot or they do not want. Then the percentage for those who have overseas

experience is 13.7, which does not equal to the percentage of studying abroad, this

shows that a small part of managers that graduated from domestic university while

choose to work abroad, which is quite difficult and may require their excellent

background such as ability.

Table 1 Descriptive statistics

Variable Obs Mean Std.Dev. Min Max

Work abroad 284 0.095 0.294 0 1

Study abroad 284 0.127 0.333 0 1

Overseas experience 284 0.137 0.345 0 1

Age/10 284 3.669 0.461 2.900 5.800

Gender 284 0.884 0.321 0 1

Security tenure 284 9.687 4.344 3 34

1 2 3it ft i i t i t i t itR R RMRF SMB HML

1 2 3 4it ft i i t i t i t i t itR R RMRF SMB HML UMD


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Researcher 284 0.782 0.414 0 1

Other experience 284 0.088 0.284 0 1

Tenure 284 0.714 1.466 0 9.170

Master 284 0.979 0.144 0 1

Doctor 284 0.169 0.375 0 1

MBA 284 0.084 0.279 0 1

Fund age 284 1.987 3.038 0 14

Turnover ratio 284 3.584 3.314 0.048 19.36

Fund size 284 20.14 1.516 15.51 24.02

Expense*100 284 2.706 1.355 0.465 10.63

Beta 284 0.769 0.307 -0.051 1.395

Table 2 Descriptive statistics for the performance metrics

Variable Obs Mean Std.Dev. Min Max

Rp*100 284 0.638 1.265 -2.646 5.540

Sharpe*100 284 10.57 37.32 -31.08 471.0

a1*100 284 0.269 0.779 -2.211 3.138

a3*100 284 0.550 0.921 -2.892 4.680

a4*100 284 0.444 0.887 -3.719 3.610

Table 3 presents the correlations between the variables. The results indicate a

correlation coefficient of 0.74 between working abroad and studying abroad. This

indicates that a large part of fund managers who have studied abroad usually will choose

to work abroad, but not all the persons will do so. This result is important as the relatively

high degree of correlation implies more potential multi-collinearity so that these two

variables cannot be put in the same regressions.

Table 3 also shows some interesting results regarding the overseas experience. First,

the overseas experience is not the only chip that those managers hold to get in fund

industry as the correlation between overseas experience and security tenure is 0.20,

besides the overseas experiences which is used to show their high ability, they also

should own practical experience. Secondly, even though 78.2 percent of the managers

had researcher experience, there is an inverse relation between overseas experience

and researcher experience. More specifically, the correlation between researcher and

overseas experience is -0.24. It seems that we can regard the overseas experience as a

substitute for the researcher experience.

The correlation coefficients between the main variables are all smaller than 0.7

besides overseas experience, working abroad and studying abroad the three variables,

this indicates that there does not exist multi-collinearity to a certain degree.


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Table 3 Correlation coefficient

work

a~d

study

~d

overs

e~e

gend

er

securi~

e

resea

r~r

other

~e tenure master doctor mba

fund

age

Turnov

er ratio

fund

s~e

expen

se beta Age

work

abroad 1

study

abroad 0.74 1

overseas

experience 0.81 0.96 1

gender -0.03 -0.03 -0.02 1

security

tenure 0.23 0.21 0.20 0.00 1

researcher -0.24 -0.21 -0.24 0.02 -0.21 1

other

experience -0.06 -0.08 -0.09 0.04 -0.01 0.01 1

Tenure 0.12 0.07 0.08 -0.04 0.24 -0.07 -0.02 1

Master -0.04 0.06 -0.01 -0.05 -0.17 0.04 -0.04 0.00 1

Doctor 0.05 0.05 0.04 0.11 0.05 -0.06 -0.04 -0.06 0.07 1

Mba 0.12 0.07 0.10 0.07 0.17 -0.05 0.08 0.00 0.04 -0.10 1

fund age -0.08 -0.04 -0.04 -0.05 0.08 0.16 0.05 0.05 0.01 -0.01 0.08 1

Turnover

ratio -0.11 -0.12 -0.14 0.06 -0.06 0.15 -0.03 0.03 -0.01 0.08 -0.03 0.03 1

fund size -0.09 0.01 -0.03 0.00 0.12 0.13 -0.03 -0.05 -0.03 -0.12 0.02 0.06 -0.18 1

Expense 0.00 -0.05 -0.05 -0.02 -0.02 0.14 0.02 0.04 -0.01 0.07 -0.03 0.08 0.80 -0.33 1

Beta -0.20 -0.21 -0.23 0.11 -0.13 0.04 0.06 -0.16 -0.04 -0.10 -0.06 -0.37 -0.07 0.11 -0.11 1

Age 0.24 0.21 0.19 0.06 0.89 -0.19 0.16 0.18 -0.10 0.33 0.14 0.08 -0.05 0.09 0.01 -0.10 1


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5.2 Fund performance and manager characteristics

The results of our tests of fund performance on manager and fund characteristics are

presented in Tables 4-6. In each of these three tables, we estimate fund performance over

2014-2016 using excess returns. We use five performance measures: simple excess

(mean monthly) returns, the Sharpe ratio, the single-index alpha, the 3-index alpha and the

4-index alpha.

Table 4 presents the result of regression using overseas experience as the

independent variable. The results show that there exists a negative and significant

relationship between overseas experience and manger’s performance when we use

Sharpe ratio, single-index alpha and three-index alpha as the performance metrics. This

result is different from that of Zhao et al (2010), which concluded that the foreign

experience will not affect the fund’s performance. The explanation may be that Zhao

directly use the return of fund as the performance metric while this measure does not

exclude the influence of the market so that the result may be mixed, and we can also see

that our result is the same with Zhao when we use the excess return as metric. The reason

why overseas experience will reduce the manager’s performance may be that those

managers usually have longer security tenure which has been proved in section 5.2, while

managers with longer security tenure tend to have worse performance, which can also

been seen in fourth line of table 4 that there is an inverse relationship between security

tenure and manager performance, even though the result is not significant. The inverse

relationship is consistent with the findings of Porter and Thrifts (2012).

In Table 5 and Table 6, we replicate the regressions presented in Table 4 but we

replace the overseas experience dummy with the work abroad dummy and study abroad

dummy respectively to see which explains the managers’ performance more specifically.

Then we can see that the results of these two are similar with overseas experience as

independent variable, even though the coefficient of working abroad is not so significant.

5.3 Risk and manager characteristics

This part gives evidence that why managers with overseas experience underperform

others. One potential explanation for this is cross-sectional differences in manager behavior,

a subject we begin to explore in this section. Table 4 presents the results of the regressions

of fund characteristics on manager characteristics.

5.3.1 Total risk

We use the standard deviation of the return of each mutual fund manager to represent

total risk. The sample size for each manager ranges from 12 months to 36 months, which is

not the same for every manager. The total risk contains systematic risk and unsystematic

risk.

The first column of Table 4 shows the relationship between total risk of fund and

manager characteristics and we can find managers with overseas experience are more

likely to manage funds of lower risk.

5.3.2 Beta

However, the return of a portfolio should be positively related to its systematic risk

rather than the total risk because the unsystematic risk can be diversified through investing

on the large variety of assets. So we next focus on the relationship between beta which


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measures the systematic risk and manager characteristics.

We calculate a beta for each mutual fund in our sample by regressing the fund’s

monthly return in that testing period minus the risk-free rate on the monthly return of the

market minus the risk-free rate. The testing period ranges from 12 months to 36 months,

which is not the same for every manager, this horizon may give us fewer data points for the

estimation that one might want, but this can avoid longer horizon because of the possibility

of a fund’s riskiness changing over time.

In the second column of Table 4, the coefficient estimates from a regression of fund’s

beta on the manager characteristics described previously are listed. The results show an

inverse relationship between beta and overseas experience. The reason may be that those

managers are not so familiar with the domestic capital market and those stocks that they

managed, so that they tend to adopt more conservative investing strategies to avoid

making the net asset loss. As expected, given that managers with overseas experience do

not appear to take on more systematic risk, their performance remains lower and

significant.

In terms of other manager characteristics that influence beta, both Golec and Chevalier

and Ellison find that age is positively related to beta. Chevalier and Ellison (1999) attempt

to explain this behavior by concluding that younger managers hold less risk in an effort “to

minimize the probability of job loss”. And the result in this research is consistent with their

conclusion. As for the variable age, Gao (2014) find that female fund managers tend to take

less market risk for their own gender characteristics, and their results confirms to the meta

analysis of Byrnes (1999), that is, women have higher levels of risk aversion than men. And

our result is the same with their findings. Another manager characteristics found to be

related to beta in the literature is manager tenure. Both Chevalier and Ellison and Golec

find that manager tenure is negatively related to the beta of the fund, although Golec’s

results are not significant at standard levels. We also find very significant negative

relationship between manager tenure, security tenure and beta. Hence, the longer the

manager has worked for the fund, the lower the beta.

Besides manager age, gender and tenure, the education variables are also found to be

related or unrelated with beta in different literatures. Both Golec and Chevalier and Ellison

find that funds with MBA managers have significantly higher betas. While Morey and

Gottesman (2006) find no significant relationship between beta and the quantity of

education (MBA, Ph. D and other graduate degree). However, in this research, there is a

very significant negative relationship between beta and doctor degree, while no relation

between beta and master or MBA. One possible explanation for doctors tend to take less

risk is that well-educated managers might take on less risky positions in the market, while

for the MBA degree, it is a common sense that the quality of MBA education in China is not

as high as America.

Table 4 Regressions of fund characteristics on manager characteristics

Independent variables Dependent variables

Total risk Beta Fund size expense Turnover ratio

Overseas experience -0.1083* -0.1941*** -0.1189 -0.0775 -1.0668*


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(0.0555) (0.0533) (0.2702) (0.2466) (0.5970)

Gender 0.1779*** 0.1067* -0.0115 -0.1488 0.4970

(0.0575) (0.0552) (0.2799) (0.2555) (0.6183)

Age 0.2132 0.2441* 1.1100* 0.0400 -0.6305

(0.1361) (0.1305) (0.6620) (0.6043) (1.4625)

Tenure -0.0411*** -0.0279** -0.0901 0.0572 0.1396

(0.0129) (0.0124) (0.0627) (0.0572) (0.1384)

Master -0.2277* -0.0978 -0.1116 -0.2539 -0.5460

(0.1311) (0.1257) (0.6378) (0.5821) (1.4090)

Doctor -0.1877*** -0.1791*** -0.9206*** 0.3017 1.0539

(0.0688) (0.0660) (0.3349) (0.3057) (0.7399)

MBA -0.0378 -0.0562 -0.0402 -0.0342 0.0142

(0.0677) (0.0649) (0.3293) (0.3006) (0.7275)

Security tenure -0.0214 -0.0266** -0.0416 -0.0061 0.0267

(0.0136) (0.0131) (0.0663) (0.0605) (0.1464)

Researcher 0.0100 -0.0337 0.5207** 0.4584** 0.9855**

(0.0460) (0.0441) (0.2236) (0.2041) (0.4939)

Other experience -0.0418 -0.0399 -0.5290 0.0939 -0.2475

(0.0765) (0.0733) (0.3720) (0.3395) (0.8218)

Constant 0.4170 0.2445 16.4687*** 2.5541 4.8529

(0.3749) (0.3596) (1.8239) (1.6648) (4.0294)

Observations 284 284 284 284 284

R-squared 0.124 0.118 0.071 0.031 0.050

5.4 Sharpe ratio, Treynor ratio and manager characteristics

We have found that one of the reasons why managers with overseas experience

perform worse than others is that they tend to take less systematic risk in managing fund,

but can they realize a balance between risk and return in making investment strategies?

Table presents the result of regression using Sharpe ratio and Treynor ratio as the

dependent variables. We calculate Sharpe ratio through mean monthly excess return

divided by standard deviation, and Treynor ratio through mean monthly excess return

divided by beta, which correspond to the total risk and systematic risk in above part

respectively. The first two columns of table are the regression using only the manager

characteristics, and the other two columns add the fund characteristics as control variables

as well.

However, the results show that no matter whether we control the fund characteristics,

we find little evidence that there is a significant relationship between overseas experience

and Sharpe ratio, Treynor ratio. That is to say, managers with overseas experience are not

able to maintain a tradeoff between return and total risk, systematic risk, so that they

cannot get equivalent return even though they prefer lower risk portfolio.


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Table 5 performance regression using Sharpe and Treynor ratio as independent variables

Independent variables Dependent variables

Sharpe ratio Treynor ratio Sharpe ratio Treynor ratio

Overseas experience -8.7829 -3.6061 -8.4262 -2.5631

(6.7096) (10.1679) (6.8276) (10.3493)

Age -3.8189 4.2399 -2.6176 3.8102

(14.0209) (21.2477) (14.2140) (21.5457)

Gender -15.9804** 7.5062 -16.2091** 7.5107

(7.0006) (10.6089) (7.1123) (10.7809)

Security tenure 0.4954 0.3315 0.4759 0.2671

(1.4299) (2.1670) (1.4410) (2.1843)

Tenure 1.7237 -1.0682 1.5500 -1.1132

(1.5674) (2.3753) (1.5849) (2.4024)

Researcher -1.8007 3.0977 -1.4985 0.7764

(5.5924) (8.4749) (5.8589) (8.8809)

Master 7.6533 3.7373 7.7765 4.0119

(15.8481) (24.0167) (15.9403) (24.1624)

Doctor 9.4976 17.5875 8.0818 17.6860

(7.7321) (11.7174) (7.9044) (11.9815)

MBA -2.0411 -1.7590 -2.2015 -2.1245

(8.2111) (12.4434) (8.2807) (12.5519)

Fund age 0.0616 0.5792

(0.7569) (1.1473)

Fund size -1.3367 1.3350

(1.6436) (2.4913)

Expense -0.2481 0.2475

(2.9846) (4.5240)

Turnover ratio 0.4367 0.7235

(1.1735) (1.7787)

Constant 26.3596 -30.3513 48.2188 -58.0054

(40.5986) (61.5243) (51.0249) (77.3437)

Observations 284 284 284 284

R-squared 0.037 0.024 0.041 0.028

5.5 The relationship between other fund characteristics and manager

characteristics

5.5.1 Fund size


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Chevalier and Ellison examine how fund manager characteristics are related to fund

size, as measured by net assets under management. They find that manager tenure and

fund size are positively and significantly related while manager age and fund size are

negatively and significantly related. Conversely, in our results we find little relationship

between fund size and tenure and positive relationship between fund size and age. The

reason may be that older person will have more confidence on themselves to manage a

larger fund well.

We also find a very strong inverse relationship between fund size and doctor degree

while positive and significant relationship between fund size and researcher experience.

These two results can be explained by practical experience. A manager who has been the

industry researcher knows more about the industry even the specific company so they tend

to have more confidence to manage larger fund.

5.5.2 Expenses

In the previous literature, Morey and Gottesman find funds with managers who hold

MBAs tend to have high expenses, while we do not get similar result, the reason may be

that the calculating methods for expense ratio are not the same.

5.5.3 Turnover ratio

Our results show that the two factors influence turnover. Specifically, we find that the

overseas experience reduce fund turnover ratio. That is, managers with overseas

experience buy and sell stocks not so frequently as other managers. While those managers

who have been researcher have higher turnover ratio, this confirms to our common sense

for the reason that they know the stocks and industry they are in well, so they can react

very quickly to those information. However, we do not find other manager characteristics to

be related with turnover ratio, which differs from Morey and Gottesman who find that

security tenure and lack of MBA reduce fund turnover.


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Table 5 Performance regressions using overseas experience dummy only

Independent

variables Dependent variables

Risk

premium Sharpe ratio Single-index Three-index Four-index

Risk


Overseas experience -0.4489** -8.7829 -0.2937** -0.4300*** -0.1804 -0.4948** -8.4262 -0.3087** -0.4407*** -0.1923

Age -0.3664 -3.8189 0.0993 0.3411 0.1828 -0.3298 -2.6176 0.0751 0.3306 0.1562

Gender 0.1588 -15.9804** -0.0019 0.1588 0.1731 0.1521 -16.2091** -0.0137 0.1377 0.1505

Security tenure 0.0372 0.4954 -0.0234 -0.0515 -0.0365 0.0412 0.4759 -0.0201 -0.0480 -0.0332

Tenure 0.1641*** 1.7237 -0.0815** -0.0941** -0.0701* 0.1633*** 1.5500 -0.0761** -0.0895** -0.0621*

Researcher -0.4421** -1.8007 -0.2464** -0.2682** -0.2845** -0.3243* -1.4985 -0.2243* -0.2365* -0.2585*

Master 0.5557 7.6533 0.5651* 0.6882* 0.9414** 0.5595 7.7765 0.5930* 0.7237* 0.9744***

Doctor 0.1773 9.4976 0.0225 -0.2373 -0.1304 0.1442 8.0818 0.0443 -0.2295 -0.0980

MBA -0.0779 -2.0411 -0.1516 -0.2415 -0.1240 -0.0481 -2.2015 -0.1065 -0.1877 -0.0665

Fund age -0.0429* 0.0616 -0.0484*** -0.0595*** -0.0616***

Fund size -0.0796 -1.3367 0.0410 0.0348 0.0621*

Expense 0.0232 -0.2481 0.0687 0.0720 0.0632

Turnover ratio -0.0282 0.4367 -0.0093 -0.0019 -0.0036

Constant 1.2046 26.3596 -0.1198 -0.6199 -0.6173 2.6776 48.2188 -0.9875 0.144 0.126

Observations 284 284 284 284 284 284 284 284 284 284

R-squared 0.072 0.037 0.082 0.101 0.076 0.093 0.041 0.121 0.144 0.126


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Table 6 Performance regressions using working abroad dummy only

Independent


Risk

premium

Sharpe

ratio

Single-inde

x

Three-inde

x Four-index

Risk

premium

Sharpe

ratio

Single-inde

x

Three-inde

x Four-index

Work abroad -0.3425 -7.6010 -0.2124 -0.3458* -0.1618 -0.4467* -7.7887 -0.2507 -0.3915** -0.1975

Age -0.3453 -3.2885 0.1116 0.3636 0.1945 -0.2908 -1.9335 0.0962 0.3646 0.1740

Gender 0.1547 -16.0969** -0.0042 0.1542 0.1705 0.1422 -16.3833** -0.0192 0.1290 0.1460

Security tenure 0.0340 0.4320 -0.0255 -0.0546 -0.0378 0.0376 0.4135 -0.0225 -0.0513 -0.0346

Tenure 0.1658*** 1.7690 -0.0806** -0.0923** -0.0691* 0.1656*** 1.5907 -0.0751** -0.0876** -0.0609*

Researcher -0.4137** -1.3665 -0.2263** -0.2434* -0.2763** -0.2994 -1.0948 -0.2056* -0.2136 -0.2515*

Master 0.5324 7.1658 0.5502* 0.6652* 0.9312** 0.5325 7.3121 0.5770* 0.6999* 0.9632***

Doctor 0.1697 9.3402 0.0176 -0.2448 -0.1337 0.1286 7.8163 0.0348 -0.2433 -0.1042

MBA -0.0853 -2.1045 -0.1574 -0.2470 -0.1248 -0.0491 -2.2011 -0.1098 -0.1892 -0.0646

Fund age -0.0444* 0.0347 -0.0491*** -0.0608*** -0.0624***

Fund size -0.0850 -1.4306 0.0379 0.0301 0.0597

Expense 0.0207 -0.2826 0.0659 0.0695 0.0633

Turnover ratio -0.0254 0.4805 -0.0069 0.0008 -0.0031

Constant 1.1338 24.7850 -0.1636 -0.6914 -0.6508 2.6698 48.0854 -0.9923 -1.4415 -1.8878

Observations 284 284 284 284 284 284 284 284 284 284

R-squared 0.064 0.034 0.072 0.088 0.074 0.086 0.038 0.112 0.133 0.125


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Table 7 Performance regressions using studying abroad dummy only

Independent


Risk

premium

Sharpe

ratio

Single-inde

x

Three-inde

x Four-index

Risk

premium

Sharpe

ratio

Single-inde

x

Three-inde

x Four-index

Study abroad -0.4470* -9.1884 -0.2741* -0.4199** -0.1882 -0.4756** -8.6303 -0.2941** -0.4355*** -0.2112

Age -0.3752 -3.9763 0.0929 0.3324 0.1796 -0.3387 -2.7658 0.0695 0.3228 0.1529

Gender 0.1558 -16.0513** -0.0034 0.1562 0.1717 0.1469 -16.3063** -0.0169 0.1329 0.1481

Security tenure 0.0394 0.5430 -0.0222 -0.0495 -0.0356 0.0432 0.5165 -0.0189 -0.0461 -0.0321

Tenure 0.1624*** 1.6906 -0.0827** -0.0959** -0.0708* 0.1615*** 1.5225 -0.0772** -0.0910** -0.0627*

Researcher -0.4284** -1.5929 -0.2350** -0.2539* -0.2801** -0.3099 -1.3199 -0.2149* -0.2251* -0.2562*

Master 0.6345 9.2845 0.6129* 0.7619** 0.9748*** 0.6430 9.3078 0.6446** 0.8005** 1.0122***

Doctor 0.1824 9.6073 0.0255 -0.2326 -0.1281 0.1501 8.2037 0.0479 -0.2238 -0.0947

MBA -0.0967 -2.3905 -0.1647 -0.2599 -0.1312 -0.0691 -2.5351 -0.1198 -0.2059 -0.0735

Fund age -0.0430* 0.0582 -0.0484*** -0.0596*** -0.0618***

Fund size -0.0759 -1.2705 0.0432 0.0381 0.0637*

Expense 0.0179 -0.3203 0.0653 0.0677 0.0620

Turnover ratio -0.0251 0.4789 -0.0073 0.0007 -0.0030

Constant 1.1274 24.7720 -0.1670 -0.6923 -0.6498 2.5274 45.4986 -1.0805 -1.5721 -1.9511*

Observations 284 284 284 284 284 284 284 284 284 284

R-squared 0.071 0.037 0.079 0.099 0.076 0.091 0.041 0.119 0.142 0.127


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Table 8 Regressions with control variables

Independent Dependent variables

variables Risk


Risk


Overseas experience -0.4207* -8.7525 -0.2948** -0.4211*** -0.1814 -0.4086* -8.6563 -0.2993** -0.4289*** -0.1883

Age -0.0620 0.6976 -0.1145 -0.1446 -0.1518 -0.7975 -5.1726 0.1592 0.3286 0.2676

Gender 0.1405 -16.1516** 0.0061 0.1741 0.1855 0.1639 -15.9643** -0.0027 0.1590 0.1721

Tenure 0.1718*** 1.7888 -0.0846*** -0.0996*** -0.0748** 0.1633*** 1.7210 -0.0814** -0.0941** -0.0700*

Researcher -0.4560** -1.9371 -0.2400** -0.2557* -0.2746** -0.4368** -1.7840 -0.2472** -0.2680** -0.2855**

Other experience 0.2331 -0.3427 0.0194 0.1560 0.0371 0.4550 1.4291 -0.0632 0.0132 -0.0894

Master 0.4935 6.7197 0.6093* 0.7888** 1.0106*** 0.6460 7.9366 0.5526* 0.6908* 0.9237**

Doctor 0.0858 8.0033 0.0933 -0.0724 -0.0192 0.3351 9.9933 0.0006 -0.2328 -0.1614

MBA -0.0852 -1.9424 -0.1564 -0.2586 -0.1320 -0.1103 -2.1430 -0.1471 -0.2425 -0.1176

Security tenure 0.0782 0.6241 -0.0291 -0.0503 -0.0446

Constant 0.5224 15.9841 0.3715 0.5036 0.1523 2.2230 29.5581 -0.2613 -0.5904 -0.8175

Observations 284 284 284 284 284 284 284 284 284 284

R-squared 0.073 0.036 0.080 0.096 0.072 0.079 0.037 0.082 0.101 0.077


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6. Discussion

The result presented above indicate that the overseas experience, no matter studying

abroad or working abroad, have a negative effect on the managers performance. The

question we now try to answer is why this might be the case.

One explanation for the results is that those overseas managers’ longer tenure will

negatively cause their worse performance. In table 3, we can easily see that there exists

a negative relationship between overseas experience and security tenure, which means

that those fund managers who have longer overseas experience have longer tenure on

the whole. Indeed we can show that the average security tenure of managers who have

overseas experience is 11.82 while that of mangers who do not have overseas

experience is 9.34. According to Chevalier and Ellison (1999), there is an inverse

relationship between security tenure and performance. However, table 8 shows that this

explanation does not work at least in this sample. The first to fourth column of the table

are the regressions of manager performance on their characteristics besides security

tenure, and the last four columns are the regression which add security tenure. And the

results show that before and after the security is added into the regression, the coefficient

of overseas experience does almost does not change and the significance are the same.

This indicates that the security tenure is not the reason that causing the bad performance

of those overseas managers. To be noticed is that the coefficients of security tenure are

not significant no matter under which performance metric.

Another possible explanation is that those managers know less about the domestic

stock market than those non-overseas managers, that is, information disadvantage.

According to the data, a large part of the overseas managers have worked long in other

areas before they go back to China or come to China, this is why those managers on

average have longer tenure, that is to say, they usually have rich experience about that

capital market. However, this does not means that they can get used to or have a better

understanding about the Chinese capital market. On the other hand, this may show that

fund managing is an industry which requires abundant information about the market.

Although though the security tenure will not add more for the manager’s performance, so

knowing more about the market will not help you perform better, but knowing less does

make you have a worse performance.

7. Conclusion

This paper examines the relationship between overseas experience and mutual fund

performance. We examine if the overseas experience, more specifically, working abroad

or studying abroad affects performance after controlling for expenses, turnover ratio, fund

size, beta, age, gender, education and tenure. We identify several notable results.

First, we find that the overseas experience is negatively and significant related to

beta over the testing period. Hence, managers with overseas experience tend to take

less risk in the capital market. In addition, those persons have lower turnover ratio.

Second, there is an inverse relationship between overseas experience and fund

performance. Even when we change to use another variable such as working abroad and

studying abroad, this relationship remains the same. That is to say, managers with

overseas experience perform worse than other managers. This is not consistent with our

common sense. The explanation is that those managers with overseas experience are


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not as great as we imagine and their experiences do not make sense in the domestic

capital market.

Third, like Chevalier and Ellison, we find some similar results, such as the positive

relationship between beta and age, gender, security tenure.

The results are informative for investors, future managers and fund companies. The

results from this paper indicate that investors, in their relentless search for funds with

superior performance, should not consider funds with managers with overseas

experience. It seems that their ability has not been shown adequately in the stock market.

for students or individuals interested in pursuing graduate education in order to obtain

fund management knowledge, the results suggest that going overseas to study or work is

not worthwhile. A fund also should not take the overseas experience as a standard to hire

managers.

References:

[1] Aron, A. Gottesman, & Matthew, R., Morey (2006), Manager education and mutual fund

performance. Journal of Empirical Finance 13(2), 145-182.

[2] Byrnes J P, Miller D C, Schafer W D (1999), Gender Differences in Risk Taking: A Meta-Analysis,

Psychological Bulletin, 125(3): 367-383.

[3] Chevalier, J., Ellison, G. (1999), Are Some Mutual Fund Managers Better Than Others?

Cross-sectional Patterns in Behavior and Performance. The Journal of Finance, 54(3), 875-899.

[4] Cong Wang (2001), Analysis of Performance Evaluation Model of Securities Investment Fund,

Economical Studies, 9, 31-38.

[5] Fama, E., and K. French (1993), Common Risk Factors in the Returns on Stocks and Bonds,

Journal of Financial Economics, 33, 3-53.

[6] He Gao, Wenwen Li, Feng Gao (2014), Fund Managers' Risk Preference, Investment Style and

Fund Performance: A Perspective Based on the Personal Characteristics of Gender, Review of

Investment Studies, 363(33), 82-96.

[7] Gary, E., Porter; Jack, W., Thrifts (1998), Performance Persistence of Experienced Mutual Fund

Managers, Financial Services Review 7(1), 57-68.

[8] Gary, E., Porter; Jack, W., Thrifts (2012), The Best Mutual Fund Managers: Testing the Impact of

Experience Using a Survivorship-bias Free Dataset, Journal of Applied Finance, 1, 1-13.

[9] Golec, J., H. (1996), The effects of mutual fund managers' characteristics on their portfolio

performance, risk and fees. Financial Services Review, 5(2), 133-147.

[10] Mark, M., Carhart (1997), On persistence in mutual fund performance, Journal of Finance 52(1),

57–82.

[11] Qiong Xu, Xu Zhao (2006), An Empirical Study on the Performance Sustainability of Closed-end

Funds, Financial studies, 5, 92-96.


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[12] Ping Jiang, Peng Tian, Cheung Yan-Leung (2011), Ownership Structure and Fund Performance of

Fund Management Companies, Financial Studies, 6, 123-135.

[13] Weitao Shen, Xingluan Huang (2001), Empirical Research and Evaluation on the Performance of

China's Securities Investment Funds, Economical Studies, 9, 22-30.

[14] William, F., Sharpe(1966), Mutual Fund Performance, Journal of Business 39(6), 119-138.

[15] Xiujuan Zhao, Shouyang Wang (2010), To What Extent Does Fund Manager Affect Fund

Performance? An Empirical Test of Performance and Personal Characteristics, MANAGEMENT

REVIEW, 22(1), 3-12.

[16] Zhengchun Yao, Shufang Deng, Zhiwen Li (2006), Competitive pressure of closed-end fund

managers, Financial studies, 9, 81-92.

[17] Zhizhong Huang, Juan Zhang, Jianqiu Wang, YongJin Zhuang (2012), Research on the Influencing

Factors of Fund Performance: Based on the Characteristics of Fund Managers, China Academic

Journal Electronic Publishing House.

海归经验对基金经理绩效的影响

周莹，Allan Zebedee

（湖南大学经济管理研究中心行政楼 4 楼，湖南长沙，410000）

摘要：本文从不同于其他文献所关注的公募基金经理的特征出发，考察了基金经理的海归经验与我国公募

基金绩效的关系。我们利用手动收集的数据，我们发现海归经验与基金业绩存在显著的负相关关系。而这

些管理者往往在投资回报和风险之间无法达成平衡的同时，承担较少的风险，而且他们在资本市场的交易

频率也较低。我们也发现了一些与其他著名文献相似的结果。

关键词：公募基金经理绩效，海外经历，经理个人特征

中图分类号： F830.91 文献标识码：A

Does Overseas Experience Matter for Fund Managers … · 2019-04-26 · Abstract：Being different...

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