Journal of Asia Business Studies

Emerging market mutual fund performance: Evidence for China

Zia-ur-Rehman Rao Muhammad Zubair Tauni Amjad Iqbal
Article information:
To cite this document:
Zia-ur-Rehman Rao Muhammad Zubair Tauni Amjad Iqbal , (2017)," Emerging market mutual fund performance: Evidence
for China ", Journal of Asia Business Studies, Vol. 11 Iss 2 pp. -
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http://dx.doi.org/10.1108/JABS-10-2015-0176
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 Emerging market mutual fund performance: Evidence for China
1. Introduction :

Emerging market mutual funds have shown a dramatic growth in last two decades (Hujj & Post,
2011). Despite this enormous growth, there is a dearth of literature regarding the analysis of
performance and especially persistence in performance of emerging market mutual funds,
presumably due to limited availability of data. Most of the literature is about the mutual funds of
developed markets like US and Europe. The question is, whether the findings observed in
developed markets carry over to emerging markets. For example, do emerging market mutual
funds beat the market? Do emerging market mutual funds possess market timing ability? Is there
persistence in performance of emerging market mutual funds? Do size, expense ratio and age of
emerging market mutual funds have any relationship with fund risk adjusted performance?
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Emerging markets are perceived to be less efficient than developed markets and they give
opportunity to fund managers to earn abnormal returns (Hujj & Post, 2011). On the other hand,
there is a positive relationship between risk-adjusted return and variables like development of
capital market and strength of legal institutions (Ferreira et al., 2013 and Khorana et al., 2005).
Following these arguments, one might expect emerging market mutual funds to underperform. In
mutual fund industry, the information about performance of fund is very important as investors
base their investment decisions on the past performance of fund (Ippolito, 1992; Sirri & Tufano,
1998; Goetzmann & Peles. 1997; Gupta & Jithendranathan, 2012)
A lot of research has been done on the persistence in the performance of US funds. Many authors
document persistence in performance of US funds (e.g. Carhart 1997; Hendricks et al., 1993;
Brown & Goetzmann, 1995). Tony Chieh-Tse Hou (2012) documents performance persistence in
Taiwanese mutual fund industry. Bialkowski & Otten (2011) find the evidence of persistence in
performance of Polish equity mutual funds. There is a lack of literature on the persistence in
performance of emerging market funds especially of Chinese funds. Though some research is
available on Chinese funds but little work is done to show the comprehensive investigation of
performance, timing abilities, influence of funds characteristics on performance and persistence.
We take China for our study because of the following reasons: First, China is the biggest
emerging economy of the world and its mutual fund industry has huge growth potential. Second,
Chinese fund market offers an opportunity to study a fast growing market. During the 11 year
period of our analysis, the number of Chinese equity mutual funds (henceforth CEM funds) grew
by 759%. Thirdly, the studies on persistence in performance of Chinese equity funds are scant.
Previous studies that investigate the performance of Chinese mutual funds are either hampered
by survivorship bias or short time-series (which limits the power of time-series based tests). This
study takes the data of 520 CEM funds which is uncontaminated by survivorship bias, for the
period from January 2004 to December 2014. The fund performance is analyzed by applying
CAPM (Capital Asset Pricing Model) and Carhart four-factor model. This study also examines
market timing ability, persistence in performance and impact of fund characteristics on fund
performance.
 Our paper contributes to the current literature on emerging market mutual funds in several ways.
First, we provide a brief overview and particular insights of Chinese mutual fund market which
is an attractive market for international investors. The specific features of Chinese mutual fund
market can assist investors in understanding the financial development dynamics in emerging
markets. Second, the Chinese fund market enables us to study the impact of fast growing market
on fund performance. Third, our findings of fund performance differ from findings in US. Our
findings suggest that CEM funds give better return than the market. These results are in line with
the view that emerging markets are comparatively less efficient and give chance to fund
managers to get abnormal returns. This study also finds that CEM funds possess positive market
timing ability. Fourth, to the best of authors’ knowledge, this is the first elaborative study to
analyze persistence in performance of CEM funds. By using the methodology of Bialkowski &
Otten (2011) we do not find any evidence of persistence in CEM funds. These results about
persistence are in contrast to the findings in US, Taiwanese and Polish market (Brown &
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Goetzmann, 1995; Tony Chieh-Tse Hou, 2012; Bialkowski & Otten, 2011). One of the practical
implications of the non-existence of persistence in fund performance is that the strategy of
buying winner funds of last year and selling loser funds of last year does not look attractive in
China. Fifth, this study finds evidence of positive relationship of fund’s size and fund’s expense
ratio with fund’s performance, but negative relationship of fund’s age with its performance.

We organized our paper as follows: the next section represents the literature review. Section 3
provides the overview of Chinese mutual fund industry. Section 4 contains data description and
summary statistics. Empirical findings are discussed in Section 5. We present the funds’
performance analysis and then investigate the market timing ability of CEM funds. After that we
study the persistence in performance of CEM funds and then document the influence of fund’s
characteristics on risk adjusted performance. Finally, section 6 concludes.

2 - Literature Review:
2.1 - Risk-Adjusted Performance:
The performance of mutual funds has long been the topic of discussion. Some studies show the
underperformance and some show the outperformance of mutual funds. Risk adjusted
performance of mutual funds shows the positive relationship with the variables like strength of
legal institutions and development of capital markets (Ferraira et al., 2006; Khorana et al., 2005).
Following these arguments, the mutual funds of emerging markets are expected to underperform.
Hayat & Kraeussl (2011) find that equity funds are unable to beat the market and fund managers
are bad market timers. Otten & Bams (2002) show that average European mutual funds
performed not different from the market and on adding back management fees, most funds beat
the market. Christensen (2013) finds that mutual funds provide lower than market returns and
have negative alphas.
 On the other hand, Hujj & Post (2011) show that emerging market mutual funds give positive
alphas and provide better than market return to their investors. This outperformance of emerging
markets mutual funds is consistent with the view that as compared to developed markets,
emerging markets are less efficient and, therefore, they provide opportunities to fund managers
to get abnormal returns. Swinkels & Rzezniczak (2009) and Bialkowski & Otten (2011) find that
mutual funds in less efficient market of Poland outperform the market and give positive alphas
but those alphas are not statistically different from zero. Emerging markets mutual funds perform
comparatively better than that of developed markets and fund managers of emerging markets
have some advantage in the times of crisis (Borensztein & Gelos, 2000); Hujj & Post, 2011). On
average, they get the opportunity to rebalance their portfolios one month before crisis. Emerging
markets still give the chance to get the higher return and assist in reducing risk for the portfolios
of developed markets (Buchanan et al., 2011).
Like other emerging markets, Chinese mutual funds also perform better than the benchmark
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market (Tang et al, 2012 and Halil Kiymaz, 2015). By taking the sample of 159 Chinese equity
funds from 2003 to 2008, Li & Lin (2011) find that Chinese equity funds give higher Sharpe
ratios than their benchmark. Chinese fund managers are successful in obtaining positive alphas
on their investment portfolios. Institutional investors, like mutual funds, have information
advantages and professional management, and take timely and rational decisions to endure better
returns for their investors. In a market where institutional investors are large in number, there is a
strong competition among them and it is difficult for institutional investors like mutual funds to
beat the market. This is what happens in US where 60% of the assets in American capital market
are owned by institutional investors. Hence, mutual funds in US underperform the market. As
compared to US, China has fewer numbers of institutional investors. Even in the bull market of
2007, institutional investors own no more than 30% of assets in Chinese capital market. In such
markets, it is relatively easy for mutual funds to beat the market as there are few rivals. (Tang et
al, 2012).

2.2 - Market Timing Ability:

The overall performance of mutual funds is commonly divided into two components: fund
selection skill and market timing ability. Selection skill means that fund manager tends to
identify those stocks that are undervalued or overvalued relative to market in general. Fund
manager tends to invest in those stocks that offer high risk premium. Selectivity means
identifying rewarding securities within the asset classes. Timing ability means that fund manager
increase the market exposure to portfolio when he foresee that market is going up (bullish) and
similarly reduce the market exposure of portfolio when he think that market is going down
(bearish). Timing is about asset allocation where fund manager rebalances a portfolio among
asset classes and cash (Dhanraj Sharma, 2016). Several studies have examined market-timing
ability in the mutual fund market and the findings are not unanimous in all the studies. Jensen
(1968), Treynor & Mazuy (1966) and Henriksson & Merton (1981) study timing activity in
mutual fund performance and find that timing does not pay off. Chang & Lewellen (1984) find
negative market timing skills of fund managers. Annuar et al., (1997) show that Malaysian fund
 managers possess negative market timing abilities but show positive alphas. Islamic fund
managers, on average, are not able to time the market (Hayat & Kraeussl, 2011). Daniel et al.
(1997) fail to find evidence of market timing in mutual funds.
In contrast, Jiang et al. (2007) show that, on average, actively managed US domestic equity
funds have positive market timing ability. Bollen & Busse (2001) find the evidence of market
timing ability in mutual funds. Mutual fund managers successfully time the market in bad times
(Kacperczyk et al., 2014). M. Christesnsen (2013) applies Treynor & Mazuy (1966) model to
check the market timing ability in Danish equity mutual funds and reports that Danish equity
fund managers possess market timing abilities. In Taiwan, well performing equity funds show
negative market timing performance and worst performing funds report positive market timing
performance (Tony Chieh-Tse Hou, 2012).
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2.3 - Persistence:
Mutual funds with the higher (lower) return in the previous time period tend to provide higher
(lower) return in the following time period (Hujj & Post, 2011; Bollen & Busse, 2001; and
Mamaysky et al., 2007). Taiwanese mutual funds also exhibit performance persistence (Tony
Chieh-Tse Hou, 2012). Some studies e.g. Hendricks et al., (1993), Brown & Goetzmann (1995)
and Bialkowski & Otten (2011) have documented the persistence in performance of mutual
funds over short term time horizons. Grinbalt and Titman (1992), Elton, Gruber, Das, & Blake
(1996) and Allen & Tan (1999) find evidence of predictability in performance of mutual funds
over long term time horizons. Bialkowski & Otten (2011) find strong persistence in performance
of Polish mutual funds. Carhart (1997) however shows that the “hot hands” effect is because of
persistence in expense ratios and following persistence strategies. On the other hand Jensen
(1968) does not support the persistence as he does not find any evidence for this persistence in
performance. Sirri & Tufano (1998) also work on persistence analysis where they find large
capital inflows into last year top performers and large outflows from last year poor performers.
Bollen (2007) and Rao et al.,(2015) find evidence of cash inflow into mutual funds who have
performed well in recent time period and cash outflow from the mutual funds who showed poor
performance in recent time.

2.4 - Influence of fund characteristics on fund performance:

Numerous studies have highlighted the relationship between fund size and performance. Large
funds have high hierarchical and coordination costs which erode the performance of funds
(Williamson, 1988; Perold & Salomon, 1991; Becker & Vaughan, 2001). The organizational
structure friction and liquidity constraints are the problems faced by larger funds. Smaller funds
are in a better position to convert their portfolios into their best ideas that can lead to better risk
adjusted returns (Chen et al., 2004). In large funds, it is difficult for fund managers to convince
others to implement better strategies (Stein, 2002). Babalos et al., (2015) show the absence of
economies of scale and report that fund size affects adversely fund performance.
 On the other hand, the argument in favor of large funds is that large funds have better skills in
processing available information and have less brokerage, marketing and research costs. Lower
expenses lead to better performance of funds. Small funds face high transaction costs due to
diseconomies of scale that erode fund performance (Golec, 1996).
Management fees and expense ratios are also important funds characteristics that researchers
have analyzed. Elton et al., (1993), Malkiel (1995), Carhart (1997) and Golec (1996) show the
negative relationship of expense ratio and risk adjusted performance for US markets. Bialkowski
& Otten (2011) show negative relationship in Polish market and Wongsurawat (2011) shows
negative relationship in Thailand market. Berk & Binsbergen (2014) report that top performing
funds charge higher fees. US mutual funds have negative relationship between size of fund and
its risk-adjusted performance and European industry and Polish industry have positive
relationship between size of fund and its risk-adjusted performance (Bialkowski & Otten, 2011).
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3 - Chinese Mutual Fund Industry:

After the launch of first Chinese mutual fund in September 2001, the mutual fund industry has
witnessed a strong growth (Tang et al., 2012). With the passage of time, China allows foreign
investors also to invest in Chinese capital markets. Many foreign asset management companies
have made their investments in Chinese capital markets. The collaboration between Chinese and
foreign financial firms have increased gradually. Many new funds are launched and numerous
Chinese fund managers have come back to China after getting experience in international leading
asset management companies (He Wei et al., 2015). Another important factor for the growth of
Chinese mutual fund industry is its particular investor clientele. The habit of saving for future
uncertainties is more dominant in Chinese citizens as compared to citizens of western countries.
The rate of savings in Chinese people is 30 to 40% which is much higher than the rate in western
countries. In China, government encourages people to plan privately for retirement which leads
to growth of pension funds and other special purpose mutual funds.
Chinese open ended fund includes actively managed funds, index funds, money market funds,
bond funds, and Qualified Domestic Institutional Investor (QDII) funds. Yu & Du (2008) show
that mutual funds own 28% assets of total Chinese equity market at the end of 2007. As
compared to US and other developed markets, China has little number of mutual funds1. In 2005,
in the UK and the USA, equity funds represent more than 50% of the mutual fund industry
assets, while in China they have an average weight of 7%. (Sofia B. Ramos, 2009).Moreover, the
history of Chinese mutual fund industry is also short. In addition to this short history and small
scale, Chinese mutual fund industry differ from US and other developed markets in several ways.
First, mutual funds in US are corporate entities and a specific board of directors (or trustees)
oversees each fund (Tufano & Sevick, 1997; Gil-Bazo & Ruiz-Verdu, 2009); whereas in China

1
In US, there were 26708 funds at the end of year 2009, out of which 9713 funds were domestic equity funds and if those funds are excluded
who invest less than 50% in common shares, the remaining funds were still 9204 whereas in China, the number of actively managed equity funds
were fewer than 350 in year 2009(Jun et al., 2014).
 mutual funds are not corporate entities but they are contract funds. Chinese mutual funds are
wholly managed by their funding companies. Second, mutual fund investors in US are fund
shareholders; they invest and own shares that represent a portion of their holdings and their
interests are watched by board of directors. On the contrary, Chinese mutual fund investors are
the beneficiaries, not the shareholders of funds, and their interests are not represented properly
by the board of directors. Third, in the US mutual fund industry, the fee structure is relatively
flexible and board of directors do negotiation on management fees and those fees fluctuates
according to the competition in market and fund performance. In China the management fees of
all equity funds is almost fixed at 1.5% of total net assets under management since 2002 and
therefore, fees do not reveal directly the fund performance. Finally, in the US, there are several
different channels for mutual fund distribution including (1) the direct channel; (2) the advice
channel; (3) the retirement plan channel ; (4) the supermarket channel; and (5) the institutional
channel (Reid & Rea, 2003). However, in China, mutual funds distribution is mostly done by
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commercial banks and securities companies. Insurance firms’ role is very little in distribution
(Jun et al., 2014).
International investors seek opportunities in emerging markets and target these markets also for
the diversification of their portfolios. CEM funds have attracted many international investors in
last several years. China may continue to attract international investors in the future if following
events occur. First, Chinese economy should continue to grow. Second, the development of
Chinese capital markets must bring more transparency, higher efficiency and improved
regulations. Last, the exchange rate of Yuan (RMB) should be stable.

4 - Data:
We take data of Chinese mutual funds from the RESSET Financial Database2 for the period from
January 2004 to December 2014. We take only the equity funds because they offer the most
widely accepted benchmarks and risk-adjusted approaches, as compared to other types of funds..
Initially we take the sample of 817 funds. We take only actively managed funds and exclude
index fund, QDII funds, and conservative allocation fund. QDII funds invest only in foreign
stocks. Following other similar studies (like, Huang et al., 2007; Olivier & Tay., 2009; Jun et
al,2014)We take only those funds which have data of atleast 24 months in order to calculate risk
adjusted CAPM and Carhart model results. We also drop principal guaranteed funds, as they
heavily invest in fixed income securities/bonds and further, when we run the regression only for
principal guaranteed funds, beta for principal guaranteed funds is 0.149, which is very low for
equity fund study. After dropping funds falling in these criteria, our final sample comes to 520
mutual funds. All the data is in Chinese currency (RMB, Renminbi). We take the data from the
year 2004 as before the non-tradable shares reform in 2004, there were only few stocks in the
market for trade(Chen & Xiong, 2001 and Jiang et al., 2008) and secondly, before 2004, there

2
www.resset.cn
 were only few equity mutual funds in China(Tang et al,2012). To limit the survivorship bias, our
sample includes all those funds which become inactive or dead during the period of our study3.
Our extensive sample has following advantages: it covers a long period from 2004 to 2014 which
also includes the time of financial crisis of 2008 and our sample is free from survivorship bias as
we also include those funds which are no more alive or liquidated.

--
Insert table 1 here
--
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Table 1 shows the summary statistics of equity mutual funds in Chinese market from 2004 to
2014. The number of funds has grown from 68 in 2004 to 516 in 2014 which is equivalent to the
compounded annual growth rate of 20.2 %4. The number of mutual fund companies has
increased from 33 in 2004 to 70 in 2014. Total net assets (TNA) of equity mutual funds in our
sample have increased from RMB 139 bn at the start of 2004 to RMB 1090 bn at the end of
2014, with the peak of RMB 2550 bn in 2007. Equity fund assets have shown the highest growth
in the years before the financial crisis of 2008. In year 2007, TNA show the annual growth of
531% but after 2009, there is a negative growth. Average TNA also showed the same trend.
The cross-sectional average raw return after dividend adjustment varies considerably over the
sample period, ranging from -49.79% in 2008 to 120% in 2007, which shows the compounded
annual growth rate of 15.82% during the eleven year test period. The cross-sectional standard
deviation of raw returns varies significantly from 12.3% in 2004 to 37.03% in 2007. The high
annual return of funds and high volatility in those returns depict the high volatility in Chinese
stock market. To provide the general overview of the volatile Chinese stock market, we show
market capitalization weighted annual return and annualized standard deviation of daily stock
market returns in the last two columns of table 1. The stock market return varies substantially
from -64.28% in 2008 to 131.96% in 2007. The Chinese stock market is highly volatile in years
2007 and 2008. The annualized standard deviation of daily stock market returns is 35.82% in
2007 and 46.68% in 2008.

3
Survivor ship bias is a serious problem in empirical asset pricing research. The exclusion of dead or liquidated funds leads to upward bias of
asset returns. Those funds which are near to death, shows inferior returns otherwise they continue to survive. Their exclusion shows upward bias
returns of assets (Brown & Goetzmann, 1995).
4
Wahal & Wang (2011) show the compounded annual growth rate of 16% in US from 1980 to 2008. Jun et al., 2014 show the compounded
annual growth of 52.4% in China from 2004 to 2009.
 --
Insert table 2 here
--

Table 2 shows the number of fund companies and their average total net assets. The average fund
family total net assets here are composed of only those funds which are present in our sample.
The number of mutual fund companies ranges from 33 in 2004 to 70 in 2014. The total net assets
of fund family range from RMB 2.98 bn in 2005 to RMB 45.54 bn in 2007, indicating 2007 as a
bull market. Like fund size, the distribution of total assets of fund families also shows a humped
shape.
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5 – Discussion of Results
5.1 Performance Analysis:
We study the risk and return characteristics of equity mutual funds and analyze the performances
of mutual funds by using the CAPM model and Carhart four-factor model. This model tells that
whether funds are able to beat the market or not. Following is the CAPM regression equation, we
run for monthly returns of 520 mutual funds for the period from January 2004 to December
2014:

 −
 =  +
 
 −
 +  --------------------------------- (1)

Where
 is the raw return of fund i in month t,
 is the risk free rate (i.e. the one-month
interest rate for deposits in China) and
 is the market return in month t (i.e. market
capitalization weighted monthly rate of return on China A-share stock market composite index
after dividend distribution adjustment).  (alpha) is the coefficient which shows the
outperformance of fund i from the market in month t and
 (beta) is the systematic risk and
sensitivity of fund i’s return with respect to the market return.  is an error term.
As compared to CAPM, Fama-French 3 factor model and Carhart four-factor model are
considered to provide a better explanation of fund performance. In Carhart four-factor model,
three additional risk factors are included which are size (SMB), book to market ratio (HML), and
momentum strategy (PR12m). In finance literature, this has become the standard model to
analyze the mutual fund performance. Following is the Carhart four-factor model which we use
to analyze Chinese equity fund performance:

 −
 =  +
 
 −
 +
 SMB +
 HML +
 PR12m +  ------ (2)

Where  is the Carhart’s alpha for fund i;
 is the raw return of fund i in month t,
 is the
risk free rate (i.e. the one-month interest rate for deposits in China) and
 is the benchmark
market return in month t; SMB is the difference in return between a portfolio of small cap stocks
 and a portfolio of large cap stocks in month t; HML is the difference in return between a
portfolio having high book to market stocks and a portfolio of low book to market stocks in
month t; PR12m is the return difference between a portfolio of past 1-year winner stocks and a
portfolio of past 1-year losers in month time t;  is an error term.
Three factors: SMB, HML and PR12m are taken from RESSET database. To get the SMB factor,
all stocks are sorted according to last year’s size. Top 30% comprises the big portfolio and
bottom 30% makes small portfolio. The market capitalization weighted return difference
between small and big portfolio provides the SMB factor. In the same way, to calculate HML
factor, we rank all stocks according to last year’s book to market value. Top 30% is assigned the
portfolio with high book to market value and bottom 30% comprises the portfolio with low book
to market value. Their market capitalization weighted return difference provides the HML factor.
For PR12m factor, all funds are ranked on their past 12 month return. The market capitalization
weighted return difference between the top 30% and bottom 30% market capitalization provides
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the PR12m factor. All returns are adjusted for corporate activities as stock splits, dividends and
mergers.

--
Insert table 3 here
--

Summary statistics on factor portfolios are presented in Table 3, which show that Carhart four-
factor model can explain sizeable variation in returns. First, there is comparatively high variance
of the SMB, HML and PR12m zero investment portfolios and their correlations are low with
each other and the market proxies. This indicates that Carhart model can describe considerable
time series variation. Second, as factors SMB, HML and PR12m show high mean returns, it
suggests that these three factors could account for much cross-sectional variation in the mean
return on stock portfolios. Moreover, low values of correlations in correlation matrix infer that
multicolinearity does not greatly influence the estimated four-factor model loadings.
--

Insert table 4 here

--

In table 4, Panel A shows the CAPM regression results for the period from 2004 to 2014. The
CAPM alpha is positive in most of the time period under study. Chinese equity mutual funds
outperform the market as alphas in second column are positive except in the years 2009, 2011
 and 2014. Overall, Chinese equity mutual funds are able to beat the market and gives the mean
alpha of 0.0021. Findings of this study are in contrast to findings evidenced in US mutual fund
market, where equity mutual funds are not able to beat the benchmark market (Grinblatt &
Titman, 1989; Sirri & Tufano, 1998 and Zheng, 1999). The overall beta is 0.7408 which shows
that these Chinese equity funds are less risky than the market. Our findings about beta are similar
to Hayat & Kraeussl (2011) where they also show that equity funds are less risky than the market
and have beta less than 1.
Regression results of Carhart four-factor model are presented in Panel B of table 4. CEM funds
show positive Carhart alpha in whole sample period except years 2008, 2010, 2011 and 2014.
Overall Carhart alpha is positive and significant which implies that CEM funds outperformed the
market. The overall beta is 0.76 which shows that CEM funds are less risky than the market.
Overall SMB factor is negative which indicates that CEM funds prefer large cap stocks. Year
2008 which is the year right after the financial crisis of 2007, shows interesting results.
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Throughout the entire sample time period, SMB factor is negative but shows positive sign only
in year 2008 and moreover, PR12m factor is insignificant only in year 2008. Positive SMB factor
implies that after the financial crisis of 2007, investors invest their capital in small cap stocks
which, they think, are not influenced much by global financial crisis. Overall R2 of Carhart four-
factor model is 0.75 which is greater than the R2 of CAPM (i.e.0.718). It indicates that Carhart
four-factor model explain fund behavior in a better way.
To sum up, using data from emerging economy, China, we corroborate previous studies on
mutual funds. Similar to the findings of Bialkowski & Otten, (2011), Tang et al (2012) and Halil
Kiymaz (2015) we find that equity mutual funds outperform the market. Chinese fund managers
are successful in giving better than market return to their investors. Results of this study are in
line with the view that emerging markets are comparatively less efficient and thus provide
opportunities to fund managers to earn abnormal return. For robustness, we check whether our
initial findings are biased by the insignificant coefficients or small funds. For this purpose, we
follow Hayat & Kraeussl (2011) and run the regression by using the market capitalization of the
funds as weights. We weight the alphas and betas of table 4 by the market capitalization of
funds. Table 5 shows the results of this robustness check, where we weight the equity mutual
funds based on market capitalization (Total net assets) as of December 2014. Table 5 shows that
size weighted regressions give the similar alpha and beta coefficients as we found in table 4.
Overall, Chinese funds outperform the market. In sum, we conclude that the results of this
robustness test confirm our initial findings. Chinese equity mutual funds, most of the time, have
beaten the market and they are less risky than the market as beta in all cases is less than 1.

--

Insert table 5 here

--
 5.2 - Market timing ability:
Treynor & Mazuy (1966) and Henriksson & Merton (1981) models are considered as the
standard models to evaluate the timing abilities of fund managers. Several studies (like Das &
Rao, 2015; He Wei et al., 2015; Christensen, 2012; Chen et al., 2010) have used TM (Treynor &
Mazuy, 1966) and HM (Henriksson & Merton, 1981) models to examine timing abilities in
mutual funds. These two models are based on non-linear regressions of actual fund return against
benchmark market return. In this section, timing abilities of fund managers are evaluated by
applying TM and HM models. By this approach, we also test whether our previous conclusions
drawn about outperformance (alpha) and systematic risk (beta) still hold when allowing for
varying systematic risk. Treynor & Mazuy added a quadratic term to the CAPM given by Jensen
(1968) for the evaluation of timing abilities. They argued that fund manager will invest more in
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market portfolio when the market is going up (bullish) and will invest less in market portfolio
when the market is going down (bearish). Therefore, the portfolio return will exhibit a convex
relationship with the market return. According to the multivariate regression model of Treynor &
Mazuy (1966), we run the following regression for each equity fund:

 −
 =  +
 
 −
 +  
 −
 ² +  ---------------- (3)

Where
 −
 is the excess return of fund i at time t over risk free rate (i.e. one-month interest
rate for deposits in China) and
 −
 is the excess return of market portfolio over risk free
rate.  ,
 and  are coefficients representing outperformance, systematic risk and market
timing ability, respectively and  is an error term. If  is positive and significantly different
from zero, it means that there is fund selection skill and when  is positive, we identify fund
timing ability of fund manager.
Henriksson & Merton (1981) propounded an alternate model to study timing abilities of fund
managers. According to HM model, when fund managers foresee that market return is higher
than risk free rate (
 >
 ), they will increase the market exposure of portfolio. Increasing the
market exposure of portfolio means adjusting the portfolio to a higher target beta. When fund
managers anticipate that market return is lower than the risk free rate (
 <
 ), they will
decrease the market exposure of portfolio. The regression model of HM is as follows:

 −
 =  +
 
 −
 +  [$ 
 −
 ] +  ---------------- (4)

 −
 ,
 −
 , .  ,

 and  are already defined. $ is a dummy variable and it is 1 if
market return is more than the risk free rate and zero otherwise.

--

Insert table 6 here

 --

Table 6 shows the regression results of TM and HM models for 520 equity mutual funds for the
period from 2004 to 2014. The overall results show that equity funds give better return than the
market. In table 5, the average overall alpha is 0.0018 in TM model and 0.0017 in HM model
which shows that funds outperform the market. Average overall beta is 0.7413 in TM model and
0.7341 in HM model which shows that Chinese equity funds are less risky than the market. The
results of alpha and beta are significant. Gamma coefficient which indicates the fund timing
ability also shows significant results. In TM model, the overall gamma coefficient is 0.0479 and
in HM model, it is 0.0134. Average overall gamma is positive and significant in both TM and
HM models, which shows that Chinese equity fund managers possess the market timing ability.
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Chinese fund managers forecast the future market movements correctly and they are successful
in getting the benefit from the market premium. Our findings are in line with the studies of Jiang
et al., (2007) and Bollen & Busse (2001) where they give evidence of timing abilities in US
mutual funds. Our findings are also in conformity with the findings of Tony Chieh-Tse Hou
(2012) in Taiwanese market, where he also report the positive market timing ability in mutual
funds.
 Figure 1
50 40
Number of Equity Funds
20 30
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10
0

-4 -2 0 2 4
Gamma t-stat bucket

Figure 1: Distribution of gamma t-statistics. This graph is the distribution of t-statistics of market timing ability
coefficient, gamma which is calculated by Treynor & Mazuy (1966) model as presented in Table 5. It shows the
range of t-statistics of gamma coefficient for all 520 equity funds for the period from 2004 to 2014.

Figure 1 is the distribution of t-statistics of gamma coefficient which is calculated by Treynor &
Mazuy (1966) model. It clearly indicates the skew to the right (positively skewed), which implies
that many Chinese equity fund managers have successful market timing abilities. Skewness here
is 0.49418. In fact, the positive skew shows that the fund managers attempt to beat the market
positively affects their returns. They overweight exposure to the market when the market gives
positive return and vice versa when market gives negative return. These findings are in contrast
to Annuar et al., (1997) and Hayat & Kraeussl (2011) who find negative market timing abilities
for equity funds.
5.3 - Persistence
To find persistence in case of CEM funds, we follow the procedure adopted by Bialkowski &
Otten (2011) and rank the funds according to their monthly returns. We rank the funds after
watching their performance over last 12 months, then divide into three groups: I (high), II
(middle), and III (low). The top 1/3 funds makes the portfolio I and low 1/3 funds makes the
portfolio III. The rest of the 1/3 funds goes into portfolio II. After dividing into three equally
weighted portfolios, we see their performance over next 12 months and then we rebalance the
 groups again according to their performance. This process is continued throughout all the sample
period until we find the time series of monthly returns on all 3 portfolios.

--
Insert table 7 here
--
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Table 7 shows the results of persistence analysis of CEM funds for the period from 2004 to 2014.
Interestingly, no significant persistence is observed in CEM funds. Moreover, we find the spread
in mean returns of high and low is -0.05%. The negative sign here shows that poor performers of
last year performed better than top performer of last year. But this result of spread is highly
insignificant and we may say that that there is no persistence in performance of CEM funds. We
find significant difference in volatilities of high and low portfolio, which implies that as
compared to portfolio I (high), portfolio III (low) expose investors to higher level of risk
measured in terms of standard deviation of monthly returns. Our preliminary findings showed no
significant persistence in performance of CEM funds. For further verification we use the Carhart
four-factor model and compare both high and low portfolios. This approach provides the
opportunity to analyze persistence in risk adjusted performance after controlling for factors as
excess market return, size, book to market value and momentum strategy. We find that top
(worst) performers of last year fail to continue to give better (worst) returns in the following
year. The Carhart alpha of portfolio I (high) is even lower than the Carhart alpha of portfolio III
(low) and the difference is -0.0009 which is significant at 10%. The negative sign shows that top
performers of last year instead of giving better returns in the following year, give lower returns
than that of worst performers of last year. In Chinese equity funds market, the well performing
funds of last year do not continue to perform well in the following year and similarly worst
performing funds of last year do not continue to perform badly in the following year.
Several studies find persistence in performance of mutual funds (e.g. Hujj & Post, 2011, Tony
Chieh-Tse Hou, 2012, Grinbalt & Titman, 1992), Elton, Gruber, Das, & Blake, 1996 and Allen
& Tan, 1999 and Bialkowski & Otten, 2011). On the contrary, we find no performance
persistence in case of Chinese equity funds. In China, the strategy of buying last year’s top
performing mutual funds and selling last year’s worst performing mutual funds does not look
attractive. The reason of not finding persistence in China may be that, worst funds change their
investment strategy and also change the fund manager in urge to get high return for their investor
and to attract new cash inflow from potential investor and as a result they perform well in the
following time period. One of the reasons for non-existence of performance persistence in
 winner funds is diminishing investment opportunities of well-performing funds. Fund manager
can take the benefit of investment opportunity if only the little amount of money is involved
because if the large sum of money gets involved, the market effect will be sizeable and that
investment opportunity could be arbitraged away. Another explanation is that superior
performance leads to increase in management fee. When management fee is increased, fund
managers find it less rewarding to show better performance. Moreover, the reputation of fund
manager also gets better after exhibiting superior performance and fund managers tend to encash
their improved reputation and move to some more lucrative job like in hedge fund (Berk &
Green, 2004).
As our findings are different from other findings in other countries, further research is needed, in
this area, to more elaborate this non-existence of persistence in Chinese equity funds market and
to find the reasons for this non-existence of persistence. Our findings are somehow matched with
the findings of Bollen & Busse (2004) where they find persistence over short time horizons but
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do not find persistence over long time periods. According to them, persistence exists only if
mutual funds are evaluated several times a year. As we evaluated funds after 12 months, we also
do not find any persistence in Chinese equity funds.
After doing analysis of persistence, we further our analysis to see whether good performers of
last year (in portfolio I) have good market timing ability in the following year or the worst
performing funds of last year (in portfolio III) have that timing ability in the following year. This
further analysis also serves the purpose of robustness check for the above findings of no
significant persistence in CEM funds. For this analysis, we adopt the Treynor & Mazuy (1966)
model and run the following regression equation like we applied in above section of market
timing ability:

 −
 =  +
 
 −
 +  
 −
 ² +  ---------------- (3)

Table 8 shows results of regression equation and reports the market timing ability of high and
low performing funds. Alpha, beta, and gamma show the outperformance, systematic risk and
market timing ability respectively. Similar to findings of Tony Chieh-Tse Hou, (2012) in Taiwan
market, we find that funds with previous high performance (portfolio I) have significant negative
market timing ability whereas funds with previous low performance (portfolio III) possess
significant positive market timing ability. These findings in high and low performing portfolios
substantiate our previous results of no significant persistence in CEM funds. Positive market
timing ability of portfolio III shows that worst performing funds of previous year try to come out
of the list of worst performing funds whereas negative market timing ability of portfolio I
indicates that well performing funds of last year fail to time the market in following year. These
findings indicate that return chasing behavior exists in Chinese equity fund market. CEM funds
show poor market timing ability in good performing funds. This suggests that return chasing
attitude is a costly exercise even in good performing funds. Overall, these findings indicate that
Chinese equity funds are more sensitive to bad news and simultaneously they are also involved
 in performance-chasing behavior when having good news in the market. However, further
research is required to investigate these points more and also to see whether these findings can be
generalized beyond the Chinese mutual fund market.

--
Insert table 8 here
--

5.4 - The influence of fund characteristics on risk adjusted performance:

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Under this heading, we analyze whether the performance of equity funds can be attributed to
some basic characteristics of mutual funds such as net value of assets under management and age
of funds. We also study whether funds with higher expense ratios, also provide higher returns to
their investors. In order to investigate the influence of these characteristics on fund’s
performance, the following regression equation is used:

 = & + & '()*+_-./01 + & 2*34567 + & 2*3687 +  ------------ (3)

where  is the CAPM alpha (outperformance) in time t ; '()*+_-./01 is expense ratio of
fund i in time t ; 2*34567 is the natural logarithm of total net assets of fund i at time t ;
2*3687 is the natural logarithm of age of fund i in years at time t; and  is the error term.
Table 9 shows that there is a positive relationship between risk-adjusted performance of fund and
fund size. It means that larger funds give higher return to their investors. Our findings about the
size of fund are in contrast to the studies of Williamson (1988), Perold & Salomon (1991),
Becker & Vaughan (2001), and Babalos et al., (2015) where they report that fund size adversely
impacts fund performance. We find that the risk-adjusted performance of equity funds is
positively correlated with the natural logarithm of assets of fund. This can be explained by the
economies of scale as their cost and expenses might reduce because of the bulk amount. Larger
funds have superior information and better expertise to process information which help them to
earn abnormal returns. Larger funds work more efficiently than smaller funds because of better
and more efficient allocation of available resources and get substantial discount on trading
commissions as bulk amount characterizes their transactions (Indro et al., 1999). Bikker et al.,
(2009) show that bigger funds make more risky investments as compared to smaller funds which
implies that big funds have more chance to get higher return.
Our findings show that there is a positive relationship between risk adjusted performance and
fund’s expense ratio. Funds which charge higher fees, give better returns to their investors. CEM
 funds give superior returns to their investors but charge higher fee for that higher return. Berk &
Binsbergen (2014) also reports that better funds charge higher fees. Our findings are in contrast
to studies of Elton et al., (1993), Malkiel (1995) and Carhart (1997) where they show the
negative relationship of expense ratio and risk adjusted performance for US markets. Bialkowski
& Otten (2011) show negative relationship in Polish market and Wongsurawat (2011) shows
negative relationship in Thailand market whereas we find positive relationship between expense
ratio and risk adjusted performance of fund in Chinese equity fund market. The coefficient of age
of fund is positive which shows that older funds give higher returns to their investors and
younger funds give lower returns. However, the results about size and age of funds are not
statistically significant.
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--

Insert table 9 here

--

Table 10 shows the correlation matrix of four variables used in regression equation (3). It shows
positive relationship of risk-adjusted performance with asset size, age and expense ratio of fund.
Like Wongsurawat (2011), this study finds that larger funds charge higher fees to their investors.
This study also finds that older funds charge lower fees to their investors and older funds have
larger size.

--

Insert a 10 here

--

6 – Conclusions and Implications:

 This study investigates the Chinese mutual fund industry as it offers a good opportunity to study
emerging market mutual funds. The CAPM and Carhart four-factor model are used for
performance analysis of CEM funds and it is found that equity funds beat the market. These
findings are in line with the view that emerging markets are less efficient than developed markets
and they provide a chance to fund managers to get abnormal returns. We examine the market
timing ability in equity funds by using Treynor & Mazuy (1966) and Henriksson & Merton
(1981) models and find that overall CEM fund managers possess positive market timing abilities.
However, market timing performance is negative and significant in well performing funds of last
year but positive and significant in worst performing funds of last year. These findings indicate
that return chasing behavior exists in Chinese mutual fund industry. Overall, these results
indicate that investors are more sensitive to continued bad news and simultaneously exhibit
performance-chasing behavior when there is good news in the market. However, more research
is required to explore this point and observe whether these findings could be generalized beyond
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the Chinese mutual fund market.

To investigate persistence in CEM funds, we rank funds according to their past 12-months
returns, divide them into three groups and analyze their performance in the following year (as
done by Bialkowski and Otten, 2011). We do not find any significant return spread between the
top and bottom one-third group. It means that well performing (worst) funds of last year do not
continue to perform well (worst) in the following year. These findings are in contrast to the
findings of Bialkowski & Otten (2011) where they find significant persistence in performance of
Polish equity mutual funds.
Fund performance is affected by many characteristics of mutual funds; among them fund size,
expense ratio and age of fund are considered to be important issues in mutual fund industry. In
this paper, we examine the influence of these characteristics on fund’s performance and find that
fund size, age and expense ratio have positive relationship with the fund’s performance.
This study offers implications for individual investors, fund managers and policy makers as well.
The strategy of buying well performing funds of last year and selling poor performing funds of
last year does not look very attractive in China. It implies that past performance of fund has not
any predictive power in Chinese mutual fund industry. This study helps investors to understand
the Chinese managed funds industry. Such an understanding is also helpful for fund managers
who want to inform investors about the performance of funds and also useful for asset
management companies to use performance information in marketing strategies. Investors are
better off if they invest in equity funds instead of index funds as results illustrate that equity
funds outperform the market. The findings indicate that poor investor timing is significantly
associated with previous well-performing funds, and reveals that performance-chasing behavior
can be a costly endeavor. This raises an important implication for policymakers in China.
Regulators must strive to promote financial market liberalization and augment transparency and
information disclosure policies of the mutual fund industry to reduce the level of information
asymmetry.
Limitations and Future Research:
 To analyze persistence, we divide our sample funds into only three groups as done by
Bialkowski & Otten (2011) and we do not find any persistence in Chinese equity funds. As our
results are different from findings in other countries, more research is required to investigate
further this point. Future research on Chinese equity funds can be done by dividing funds into
eight groups (octiles) as done by Hendricks et al., (1993) or ten groups (deciles) as done by
Carhart (1997). Moreover, we investigate performance persistence annually and further research
can be done by analyzing persistence quarterly and monthly.

References:
1. Allen, D. E., & Tan, M. L. (1999). A Test of the Persistence in the Performance of UK
Managed Funds. Journal of Business Finance & Accounting, 26(5‐6), 559-593.
Downloaded by Fudan University At 05:25 17 March 2017 (PT)

2. Annuar, M.N., Shamsher, M. and Ngu, M.H. (1997), “Selectivity and timing: evidence
from the performance of Malaysian unit trusts”, Pertanika Journal of Social Science and
Humanities, Vol. 5(1), 45‐57.
3. Babalos, V., Mamatzakis, E. C., & Matousek, R. (2015). The performance of US equity
mutual funds. Journal of Banking & Finance, 52, 217-229.
4. Beckers, S. E., & Vaughan, G. (2001). Small is beautiful. The Journal of Portfolio
Management, 27(4), 9-17.
5. Berk, J. B., Green, R. C., & Naik, V. (2004). Valuation and return dynamics of new
ventures. Review of Financial Studies, 17(1), 1-35.
6. Berk, J. B., & Van Binsbergen, J. H. (2015). Measuring skill in the mutual fund
industry. Journal of Financial Economics, 118(1), 1-20.
7. Białkowski, J., & Otten, R. (2011). Emerging market mutual fund performance: Evidence
for Poland. The North American Journal of Economics and Finance,22(2), 118-130.
8. Bikker, J. A., & De Dreu, J. (2009). Operating costs of pension funds: the impact of
scale, governance, and plan design. Journal of Pension Economics and Finance, 8(01),
63-89.
9. Bollen, N. P. (2007). Mutual fund attributes and investor behavior. Journal of Financial
and Quantitative Analysis, 42(03), 683-708.
10. Bollen, N. P., & Busse, J. A. (2001). On the timing ability of mutual fund managers. The
Journal of Finance, 56(3), 1075-1094.
11. Borensztein, E. R. & Gelos, R. G. (2000). A panic-prone pack? The behavior of emerging
market mutual funds, IMF working paper, WP/00/198.
URL:http://www.imf.org/external/pubs/cat/longres.aspx?sk=3909
12. Brown, S. J., & Goetzmann, W. N. (1995). Performance persistence. Journal of finance,
679-698.
 13. Buchanan, B. G., English, P. C., & Gordon, R. (2011). Emerging market benefits,
investability and the rule of law. Emerging markets review, 12(1), 47-60.
14. Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of
finance, 52(1), 57-82.
15. Chang, E. C., & Lewellen, W. G. (1984). Market timing and mutual fund investment
performance. Journal of Business,57(1) 57-72.
16. Chen, J., Hong, H., Huang, M., & Kubik, J. D. (2004). Does fund size erode mutual fund
performance? The role of liquidity and organization. American Economic Review, 94(5),
1276-1302.
17. Chen, Y., Ferson, W., & Peters, H. (2010). Measuring the timing ability and performance
of bond mutual funds. Journal of Financial Economics, 98(1), 72-89.
Downloaded by Fudan University At 05:25 17 March 2017 (PT)

18. Chen,Z.., & Xiong, P. (2001). Discounts on illiquid stocks: Evidence from China. Yale
ICF Working Paper, 00-56.
URL:http://faculty.som.yale.edu/zhiwuchen/EmergingMarkets/ChenXiong.pdf
19. Chieh-Tse Hou, T. (2012). Return persistence and investment timing decisions in
Taiwanese domestic equity mutual funds. Managerial Finance, 38(9), 873-891.
20. Christensen, M. (2013). Danish mutual fund performance. Applied Economics
Letters, 20(8), 818-820.
21. Daniel, K., Grinblatt, M., Titman, S., & Wermers, R. (1997). Measuring mutual fund
performance with characteristic-based benchmarks. Journal of Finance, 1035-1058.
22. Elton, E.J., Gruber, M.J., Das, S. and Blake, C. (1996), “The persistence of risk-adjusted
mutual fund performance”, Journal of Business, Vol. 69, pp. 133-57.
23. Elton, E. J., Gruber, M. J., Das, S., & Hlavka, M. (1993). Efficiency with costly
information: A reinterpretation of evidence from managed portfolios. Review of
Financial studies, 6(1), 1-22.
24. Ferreira, M.A., Keswani, A., Miguel, A.F. and Ramos, S.B. (2013). The Determinants of
Mutual Fund Performance: A Cross Country Study. Review of Finance. 17 (2), 483-525.
25. GIL‐BAZO, J. A. V. I. E. R., & RUIZ‐VERDÚ, P. A. B. L. O. (2009). The relation
between price and performance in the mutual fund industry. The Journal of
Finance, 64(5), 2153-2183.
26. Goetzmann, W. N., & Peles, N. (1997). Cognitive dissonance and mutual fund
investors. Journal of financial Research, 20(2), 145-158.
27. 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.
 28. Grinblatt, M., & Titman, S. (1992). The persistence of mutual fund performance.The
Journal of Finance, 47(5), 1977-1984.
29. Gupta, R., & Jithendranathan, T. (2012). Fund flows and past performance in Australian
managed funds. Accounting Research Journal, 25(2), 131-157.
30. Hayat, R., & Kraeussl, R. (2011). Risk and return characteristics of Islamic equity
funds. Emerging Markets Review, 12(2), 189-203.
31. Hendricks, D., Patel, J., & Zeckhauser, R. (1993). Hot hands in mutual funds: Short-run
persistence of relative performance, 1974-1988. Journal of finance, 93-130.
32. Henriksson, R. D., & Merton, R. C. (1981). On market timing and investment
performance. II. Statistical procedures for evaluating forecasting skills, Journal of
business, 54(4), 513-533.
Downloaded by Fudan University At 05:25 17 March 2017 (PT)

33. Huang, J., Wei, K. D., & Yan, H. (2007). Participation costs and the sensitivity of fund
flows to past performance. The Journal of Finance, 62(3), 1273-1311.
34. Huij, J., & Post, T. (2011). On the performance of emerging market equity mutual
funds. Emerging Markets Review, 12(3), 238-249.
35. Ippolito, Richard A., 1992. Consumer reaction to measures of poor quality: evidence
from the mutual fund industry. J. Law Econ. 35, 45–70.
36. Jensen, M. C. (1968). The performance of mutual funds in the period 1945–1964. The
Journal of finance, 23(2), 389-416.
37. Jiang, G. J., Yao, T., & Yu, T. (2007). Do mutual funds time the market? Evidence from
portfolio holdings. Journal of Financial Economics, 86(3), 724-758.
38. Jiang, B. B., Laurenceson, J., & Tang, K. K. (2008). Share reform and the performance of
China's listed companies. China Economic Review, 19(3), 489-501.
39. Jun, X., Li, M., & Shi, J. (2014). Volatile market condition and investor clientele effects
on mutual fund flow performance relationship. Pacific-Basin Finance
Journal, 29,September 2014, 310-334.
40. Kacperczyk, M., NIEUWERBURGH, S. V., & Veldkamp, L. (2014). Time‐Varying
Fund Manager Skill. The Journal of Finance, 69(4), 1455-1484.
41. Khorana, A., Servaes, H.,&Tufano, P., 2005. Explaining the size of the mutual fund
industry around the world. Journal of Financial Economics 78(1), 145-185.
42. Kiymaz, H. (2015). A performance evaluation of Chinese mutual funds.International
Journal of Emerging Markets, 10(4).
43. Li, N., & Lin, C. Y. (2011). Understanding emerging market equity mutual funds: The
case of China. Financial Services Review, 20(1), 1-19.
 44. Malkiel, B. G. (1995). Returns from investing in equity mutual funds 1971 to 1991. The
Journal of finance, 50(2), 549-572.
45. Mamaysky, H., Spiegel, M., & Zhang, H. (2007). Improved forecasting of mutual fund
alphas and betas. Review of Finance, 11(3), 359-400.
46. Perold, A. F., & Salomon Jr, R. S. (1991). The right amount of assets under
management. Financial Analysts Journal, 47(3), 31-39.
47. Ramos, S. B. (2009). The size and structure of the world mutual fund industry.European
Financial Management, 15(1), 145-180.
48. Sharpe, W. F., 1964. Capital asset prices: A theory of market equilibrium under
conditions of risk*. The journal of finance, 19(3), 425-442.
49. Sirri, E. R., & Tufano, P. (1998). Costly search and mutual fund flows. Journal of
Downloaded by Fudan University At 05:25 17 March 2017 (PT)

finance,53(5) 1589-1622.
50. Stein, J., 2002. Information production and capital allocation: decentralized versus
hierarchical firms. Journal of Finance 57(5),1891–1921.
51. Swinkels, L., & Rzezniczak, P. (2009). Performance evaluation of Polish mutual fund
managers. International Journal of Emerging Markets, 4(1), 26-42.
52. Tang, K., Wang, W., & Xu, R. (2012). Size and performance of Chinese mutual funds:
The role of economy of scale and liquidity. Pacific-Basin Finance Journal, 20(2), 228-
246.
53. Treynor, J., & Mazuy, K. (1966). Can mutual funds outguess the market.Harvard
business review, 44(4), 131-136.
54. Tufano, P., & Sevick, M. (1997). Board structure and fee-setting in the US mutual fund
industry. Journal of Financial Economics, 46(3), 321-355.
55. Wahal, S., & Wang, A. Y. (2011). Competition among mutual funds. Journal of Financial
Economics, 99(1), 40-59.
56. Williamson, O. E. (1988). Corporate finance and corporate governance. The journal of
finance, 43(3), 567-591.
57. Wongsurawat, W. (2011). Management fees and total expenses of mutual funds in
Thailand. Journal of the Asia Pacific Economy, 16(1), 15-28.
58. Yu, L., Du, Y., 2008. Stocks selectivity and market timing ability of Chinese open-end
funds. Security Markets 342, 93–96 (In Chinese).
59. Rao, Z.U.R., Tauni, M. Z., & Iqbal, A. (2015). Comparison between Islamic and General
Equity Funds of Pakistan: Difference in Their Performances and Fund Flow
Volatility. Emerging Economy Studies, 1(2), 211-226.
 60. He, W., Cao, B., & Baker, H. K. (2015). The performance and market timing ability of
Chinese mutual funds. Financial Services Review, 24(3), 289.
61. Indro, D.C., Jiang, C.X., Hu, M.Y., Lee, W.Y., 1999. Mutual fund performance: does
fund size matter? Financial Analysts Journal 3, 74–87.
62. Praveen K. Das S. P. Uma Rao, (2015), Market timing and selectivity performance of
socially responsible funds, Social Responsibility Journal, 11 (2), 258 – 269.
63. Sharma, D. (2016). An Empirical Analysis of Market Timing Performance of Indian
Asset Management Companies under Unconditional Model. International Journal of
Finance and Accounting, 5(1), 1-12.
64. Zheng, L. (1999). Is money smart? A study of mutual fund investors' fund selection
ability. The Journal of Finance, 54(3), 901-933
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I) The authors thank Professor Chi Guo Hua at Dongbei University of Finance and Economics, China and
anonymous referees for their insightful comments and valuable suggestions. All the errors are own.
II) This paper is a part of doctoral dissertation of 1st author.
 Emerging market mutual fund performance: Evidence for China
Table 1
Summary Statistics on mutual funds and stock market volatility. This table reports the descriptive statistics for actively managed
Chinese equity mutual funds at the end of each year for the period from January 2004 to December 2014.Column 5 and 6 present
the annual growth in TNA (Total Net Assets) and Average TNA. Annual return is the annual raw return after dividend
adjustment at the end of each year. The standard deviation of fund return is the annualized standard deviation of the sample funds
at the end of each year based on monthly returns after dividend adjustments for the past 12 months. To present the general view
about the volatility in Chinese stock markets, we show the annual stock market return and the annualized standard deviation of
daily stock market return in China in last two columns.

Number of Annual Avg total Std. Dev of

Total Net Annual Std. Dev of
Number mutual Growth Net fund return Stock Market
Year Assets(TN return of Market return
of funds fund in TNA Assets (annualized), Return, %
A),billions funds, % (annualized), %
companies ,% ,billions %
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2004 68 33 139 187.78 2.04 1.31 12.3 -14.99 20.88

2005 94 45 134 -3.6 1.43 3.44 14.42 -6.76 22.58

2006 150 50 404 201.49 2.75 117.87 23.96 107.62 23.59

2007 215 56 2550 531.19 11.9 120.56 37.03 131.96 35.82

2008 261 59 1130 -55.69 4.34 -49.79 28.46 -64.28 46.68

2009 321 60 1740 53.98 5.43 65.58 28.31 89.79 30.67

2010 380 60 1620 -6.9 4.28 4.18 19.13 -7.47 23.33

2011 451 64 1240 -23.46 2.76 -23.72 16.28 -23.27 19.58

2012 514 70 1230 -0.81 2.39 5.01 19.81 4.83 19.02

2013 519 70 1190 -3.25 2.3 15.87 19.33 3.51 18.97

2014 516 70 1090 -8.4 2.12 24.34 16.84 49.14 17.31

 Table 2
This table shows the number of mutual fund companies and Average total net assets of mutual fund companies, in
billions. The average fund family Total net assets here are composed of only those funds which are present in our
sample.

Number of mutual Avg Fund Family Net

Year
fund companies Assets(billions)

2004 33 4.21

2005 45 2.98

2006 50 8.08

2007 56 45.54
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2008 59 19.15

2009 60 29

2010 60 27

2011 64 19.38

2012 70 17.57

2013 70 17

2014 70 15.57

Table 3_______________________________________________________________________________________
Summary statistics of performance measurement model from January 2004 to December 2014. RMRF is
the excess market return from risk free rate. SMB and HML are factor-mimicking portfolios for size and
book to market equities. PR12m is a factor-mimicking portfolio for 12 months momentum. Correlation
matrix is presented for 4 factors of Carhart model.

Correlation matrix
Excess
Factor monthly t-stat for
Portfolio return Std. Dev. mean=0 RMRF SMB HML PR12m

RMRF 0.75 7.97 18.73 1.00

SMB 1.13 4.44 5i0.60 0.04 1.00

HML 0.25 3.46 14.27 0.21 -0.38 1.00

PR12m 0.52 5.00 20.78 0.15 -0.10 0.03 1.00

 Table 4_______________________________________________________________________________
Analysis of CEM funds’ performance. The table reports the regression results of 520 Chinese equity mutual funds
for the period from January 2004 to December 2014 by using CAPM regression equation and Carhart four-factor
model. To proxy the market return, we use return on China A-share stock market composite index. Column 2 and 3
shows the CAPM alpha (α) and beta (β) coefficients respectively. Column 5 reports the Carhart alpha. RMRF is the
excess market return from risk free rate. SMB is the return difference between small cap portfolio and large cap
portfolio. HML is the difference in return between high book to market value portfolio and low book to market
portfolio. PR12m is the difference of return between portfolio having past 1-year winners and a portfolio of past 1-
year losers. Column 4 and 10 reports the R2 of CAPM and Carhart model respectively. ***, **, * indicate the
significance at 1% , 5% and 10% level respectively.
Panel A Panel B
CAPM Carhart Four-Factor model
Year Alpha Beta R² Alpha RMRF SMB HML PR12m R²

2004 0.0079*** 0.5648*** 0.7098 0.0098*** 0.7025*** -0.5506*** 0.0507 -0.1787*** 0.7869
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2005 0.0041*** 0.489*** 0.5169 0.0025*** 0.5751*** -0.6476*** -0.8672*** 0.0787*** 0.8239

2006 0.0114*** 0.7193*** 0.7185 0.0128*** 0.6447*** -0.0788*** -0.4694*** 0.1682*** 0.7284

2007 0.0144*** 0.7679*** 0.5224 0.0159*** 0.7995*** -0.0720*** -0.1892*** 0.0633* 0.5371

2008 0.0006 0.6741*** 0.8084 -0.0028*** 0.6631*** 0.1680*** -0.1657*** 0.0349 0.8265

2009 -0.0042*** 0.8289*** 0.8793 0.0057*** 0.7887*** -0.1637*** -0.1585*** 0.2152*** 0.8884

2010 0.0065*** 0.6691*** 0.7007 -0.0071*** 0.7271*** -0.0631*** -0.6713*** 0.0677*** 0.7640

2011 -0.002*** 0.8803*** 0.7288 -0.0013*** 0.8295*** -0.2138*** -0.4578*** 0.1023*** 0.8093

2012 0.0001 0.7853*** 0.7815 0.0028*** 0.7565*** -0.0902*** -0.4982*** -0.0462*** 0.8165

2013 0.0087*** 0.7842*** 0.6757 0.0074*** 0.8234*** -0.2141*** -0.6330*** 0.0522*** 0.7474

2014 -0.0032*** 0.5638*** 0.2844 -0.0023*** 0.8641*** -0.0894*** -0.5146*** 0.1793*** 0.4105

Overall 0.0021*** 0.7408*** 0.7184 0.0025*** 0.7614*** -0.0191*** -0.3355*** 0.1100*** 0.7500

Table 5_______________________________________________________________________________________
Performance analysis with weighted coefficients. This table reports the regression results of CAPM regression
equation and Carhart four-factor model for the period from January 2004 to December 2014. We weight the alpha
and beta coefficients by using the market capitalization in RMB as of December 2014. RMRF is the excess market
return from risk free rate. SMB is the return difference between small cap portfolio and large cap portfolio. HML is
 the difference in return between high book to market value portfolio and low book to market portfolio. PR12m is the
difference of return between portfolio having past 1-year winners and a portfolio of past 1-year losers. Column 4 and
10 reports the R2 of CAPM and Carhart model respectively. ***, **, * indicate the significance at 1%, 5% and 10%
level respectively.
Size weighted
Panel A Panel B
CAPM Carhart four-Factor model
Year Alpha Beta R² Alpha RMRF SMB HML PR12m R²
2004 0.0078*** 0.579*** 0.7428 0.0096*** 0.7200*** -0.5539*** 0.1115* -0.2078*** 0.812

2005 0.0041*** 0.5002*** 0.5241 0.0023*** 0.5875*** -0.6377*** -0.7832*** 0.0754*** 0.8298

2006 0.0117*** 0.7443*** 0.7325 0.0130*** 0.6762*** -0.0824*** -0.5243*** 0.1526*** 0.7421
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2007 0.0164*** 0.7686*** 0.6081 0.0178*** 0.8062*** -0.0623*** -0.2409*** 0.0576* 0.6285

2008 0.0001 0.6961*** 0.8514 -0.0029*** 0.6872*** 0.1712*** -0.2168*** 0.0393** 0.8729

2009 -0.0039*** 0.8672*** 0.9003 0.0074*** 0.8331*** -0.2252*** -0.2136*** 0.2129*** 0.9103

2010 0.0051*** 0.7119*** 0.7455 -0.0070*** 0.7638*** -0.0882*** -0.6258*** 0.0645*** 0.7929

2011 -0.0017*** 0.8801*** 0.7466 -0.0005 0.8541*** -0.2282*** -0.4005*** 0.0825*** 0.8072

2012 0.0005 0.8018*** 0.8047 0.0030*** 0.7844*** -0.1397*** -0.4469*** -0.0555*** 0.8303

2013 0.0077*** 0.7616*** 0.7001 0.0084*** 0.8109*** -0.2422*** -0.4988*** 0.0224** 0.7468

2014 -0.0046*** 0.5874*** 0.3436 -0.0023*** 0.8358*** -0.1270*** -0.4673*** 0.1529*** 0.4359

Overall 0.0028*** 0.7622*** 0.7777 0.0034*** 0.7768*** -0.0381*** -0.2796*** 0.0911*** 0.7963

Table 6_______________________________________________________________________________________
Market timing ability of equity fund managers. This table reports the regression results of estimating the fund timing
ability of CEM fund managers for the period from 2004 to 2014. We apply Treynor & Mazuy model (1966) and
Henriksson & Merton model (1981) to measure market timing ability of fund managers. Alpha, beta and gamma
coefficients indicate the outperformance, systematic risk and market timing ability respectively. These coefficients
are equally weighted averages. Column 5 & 9 shows the R2 and column 10 reports the number of observations.
Standard errors are in parenthesis. ***, **, * indicate the significance at 1%, 5% and 10% level respectively.
TM HM
 Alpha Beta Gamma Alpha Beta Gamma
Year R² R² Observations

  
  

2004 0.0086*** 0.5559*** -0.2783 0.7102 0.0063*** 0.5372*** 0.0731 0.7104 608

(0.0011) (0.0178) (0.3117) (0.0017) (0.0294) (0.0676)

*** *** *** *** ***
2005 0.0125 0.5051 -2.2544 0.5557 0.0129 0.6758 -0.3614*** 0.5421 1000

(0.0013) (0.0145) (0.2415) (0.0015) (0.0291) (0.0487)

2006 0.0077*** 1.0052*** -1.4732*** 0.7453 0.0175*** 1.1195*** -0.4484*** 0.7273 1483

(0.0013) (0.0255) (0.1182) (0.0016) (0.0593) (0.0651)

2007 0.0169*** 0.7794*** -0.2208 0.5231 0.0187*** 0.8208*** -0.0894 0.5228 2310

(0.0023) (0.0166) (0.1232) (0.0035) (0.0396) (0.0617)

2008 0.0002 0.6871*** 0.0769 0.8085 0.0022* 0.6848*** -0.0614** 0.8087 2830
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(0.0009) (0.013) (0.0673) (0.0012) (0.0083) (0.0319)

2009 -0.0007 0.8141*** -0.2217*** 0.8802 0.0010 0.8708*** -0.1009*** 0.8803 3545

(0.0009) (0.0059) (0.0419) (0.0011) (0.0091) (0.0181)

*** *** *** *** ***
2010 0.0143 0.7047 -1.5942 0.7185 0.0166 0.8591 -0.3517*** 0.7162 4303

(0.0007) (0.0068) (0.0966) (0.0008) (0.0140) (0.0230)

2011 -0.002*** 0.8806*** 0.0074 0.7288 -0.0057*** 0.8168*** 0.2134*** 0.7309 5061

(0.0005) (0.011) (0.1719) (0.0007) (0.0126) (0.0340)

2012 -0.0019*** 0.7554*** 0.5015*** 0.7833 -0.0026*** 0.7165*** 0.1071*** 0.7826 5882

(0.0005) (0.0069) (0.0729) (0.0006) (0.0138) (0.0198)

2013 0.0043*** 0.8418*** 1.2594*** 0.684 0.0013* 0.6674*** 0.3006*** 0.6819 6224

(0.0005) (0.0082) (0.0985) (0.0008) (0.0126) (0.0271)

2014 -0.0021*** 0.9089*** -3.8565*** 0.3089 0.0027*** 1.1068*** -0.6274*** 0.2937 6201

(0.0006) (0.0258) (0.2605) (0.0009) (0.0612) (0.0695)

*** *** *** *** ***
Overall 0.0018 0.7413 0.0479 0.7185 0.0017 0.7341 0.0134* 0.7184 39447

(0.0002) (0.0023) (0.0177) (0.0003) (0.0043) (0.0073)

Table 7______________________________________________________________________________________
This table reports the persistence in CEM funds. We divide the funds into three equally weighted portfolios based on
past 12 months performance data. Funds with the highest past returns (previous 12 months) make portfolio I (high)
and funds with lowest past returns make Portfolio III (low). Panel A shows the basic statistics of excess return for all
3 portfolios. Column 5 shows the spread between statistics for portfolio I (high performance) and portfolio III (low
performance). Panel B shows the performance analysis for all three portfolios after controlling for risk factors like
excess market return, size, book to market ratio and momentum strategy. RMRF is the difference between market
return and risk free rate. SMB is the return difference between portfolio of small cap stocks and portfolio of large
 cap stocks. HMB is the difference of return between portfolio of high book to market value and portfolio of low
book to market value. PR12m is the difference in return between portfolio of past 1-year winners and portfolio of
past 1-year losers. R2 is presented in the last row. ***, **, * indicate the significance at 1%, 5% and 10% level
respectively.
Portfolio

I II III Spread

(high) (middle) (low) (high-low)

Panel A

Mean 0.0072 0.0075 0.0077 -0.0005

STD 0.0689 0.0675 0.0715 -0.0026***

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Q5 -0.1035 -0.1012 -0.1013 -0.0022

Q95 0.1227 0.1174 0.1233 -0.0006

Panel B

alpha 0.0017*** 0.0022*** 0.0026*** -0.0009*

RMRF 0.7713*** 0.7689*** 0.7845*** -0.0133**

SMB 0.0115 -0.0274*** -0.0323*** 0.0437***

HML -0.4061*** -0.3313*** -0.2888*** -0.1173***

PR12m 0.1275*** 0.1091*** 0.0847*** 0.0428***

R2 0.7683 0.7827 0.7235 0.0448

Table 8
This table reports the fund timing abilities of both well-performing and worst performing funds of last year, in the
following year. We divide the funds into three equally weighted portfolios based on past 12-months performance
data. Funds with the highest past returns (previous 12 months) make portfolio I (high) and funds with lowest past
returns make Portfolio III (low). Column 2, 3 &4 shows the alpha, beta and gamma respectively. *** indicate the
significance at 1% level. ** indicate the significance at 5% level.
 Alpha Beta Gamma R² Observations

Portfolio I 0.0021*** 0.7422*** -0.0661** 0.7176 11970

(High)

Portfolio III 0.0016*** 0.7641*** 0.0743** 0.7025 11951

(Low)

Difference 0.0005 -0.0218 -0.1405***

(High-Low)
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Table 9_______________________________________________________________________________________
Influence of fund characteristics on funds’ risk-adjusted performance. This table reports the results of following
regression equation:
 =  + 
 _ +   +   +  where
 is the Carhart
alpha (outperformance) ;  _ is expense ratio of fund i. Expense ratio is taken as management expenses
divided by average total net assets (TNA). For robustness, we take the total expenses instead of only management
expense to calculate expense ratio and run the regression but the results are qualitatively the same. The R2 value is
greater when we use management expense only in expense ratio.  is the natural logarithm of total fund
net assets and  is the natural logarithm of age of fund in years. Standard errors are presented in
parentheses. All values are taken annually. . *** indicate the significance at 1% level.
Variable Coefficient

constant -0.00326

(0.00356)

Expense_ratio 0.18640***

(0.06247)

Ln(TNA) 0.00005

(0.00018)

Ln(Age) 0.00012

(0.00045)

Table 10
Correlation Matrix. This table shows the correlation matrix of four variables: where
 is the CAPM alpha
(outperformance);  _ is expense ratio of fund i. Expense ratio as management expenses divided by
average total net assets (TNA).  is the natural logarithm of total fund net assets and  is the
natural logarithm of age of fund in years.
α! LnTNA! LnAge! Expense_ratio!
 α! 1

LnTNA! 0.0145 1

LnAge! 0.0045 0.4469 1

Expense_ratio! 0.1257 0.0418 -0.0362 1

I) The authors thank Professor Chi Guo Hua at Dongbei University of Finance and Economics, China and
anonymous referees for their insightful comments and valuable suggestions. All the errors are own.
II) This paper is a part of doctoral dissertation of 1st author.
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