Asian Equity Fund Performance :
Evidence for Japan, Singapore, Hong Kong and Malaysia
Presented by
Yao Shi(s2068869)
Supervisor
Dr. Wim Westerman
Abstract
In this paper we study the equity mutual fund performance and the relations between fund performance and fund characteristics for Asian equity funds by using evidence for Japan,
Singapore, Hong Kong and Malaysia. The sample of aggregately consists of 256 domestic invested open-end active equity funds. The results of OLS regression with Fama and French alphas as
measure of fund performance show that equity funds in Hong Kong and Japan cannot yield superior returns over the market, and the equity funds in Singapore and Malaysia can yield superior returns over the market. A determinants analysis shows that in the Asian markets studied, the relationship between expense ratio and fund performance is mixed and is heavily dependent on market
background, fund size is positively related to fund performance in all of these markets, fund age does not appear to have any explantory power on fund returns, and number of holdings has a significantly positive relation with fund performance in all of the tested markets.
Ⅰ.Introduction
This study examines the equity mutual fund performance and the relations between fund performance and fund characteristics for Asian equity mutual funds. Studying equity mutual funds has served as an excellent laboratory for numerous researchers who are interested in testing for market efficiency. Decades of studies have led to a strong consensus on the inability of mutual funds to beat the market after all relevant fees are deducted. However, most of these studies focus on the US market as a long-term data is available and investor interest is well developed (Jensen, 1969; Malkiel, 1995; Cruber, 1996), and evidence on Asian mutual funds is scare. The existing studies about mutual funds related to Asia just focus on the international mutual funds invested in Asian market (e.g. Hsieh et al., 2011). Even when there are previous studies about Asian mutual fund markets, they focus on only one specific country (e.g. Lai et al., 2010).
Therefore, the purpose of this paper is to give an overview of the largely unexploited Asian mutual fund area by examining whether equity fund can yield superior returns over the market and the relations between mutual fund performance and fund characteristics for open-ended actively managed equity mutual funds of Japan, Singapore, Hong Kong and Malaysia separately. More specifically, we consider the following fund characteristics as determinants of fund performance: fund size, fund age, expense ratio and number of holdings. By the end of 2011 there was $70.82 billion of assets under management in Asia ex-Japan equity funds, and $11.11 billion of assets under management in Japan’s equity funds. They altogether are more than assets amount of Western Europe’s equity funds, which is $59.14 billions1. This indicates that the Asian equity fund market
plays an important role in the world capital market. The four countries are chosen for three reasons. Firstly, they are the major Asian financial markets with sufficient historical data provided.
Furthermore, they have free and healthy financial markets. And the last but not least, these four markets cover both developed and emerging markets. According to the MSCI classification, Hong Kong, Singapore and Japan are classified as developed markets, and Malaysia is classified as emerging market2. The comparative analysis of the equity fund performance of these countries can
help us to further analyze the reason behind the differences existing in different markets.
1 The EPFR statistics
The research questions can be addressed as follows:
Can equity mutual fund performances yield superior returns over the market in Japan, Singapore, Hong Kong and Malaysia?
Are fund characteristics factors like expense ratio, age, size and number of stock holdings the determinants of fund performance in these markets?
The main added value of our study over the existing mutual fund literature is twofold. First, this is the first study doing a comprehensive analysis of Asian fund performance. Mutual fund performance issues like whether mutual fund returns beat the market, and what are the determinants of mutual fund performance, have been broadly researched. Also this analysis on Asian markets is making it possible to examine whether findings are in line with former studies showing the inability of equity funds to beat the market, or that there exist country-specific differences. Second, we include number of holdings as one of the fund characters to analyze the influence on the fund performance. As far as we know, only Prather et al. (2004) and Kalkhuis (2010) did this before. And the relevance of this study is given by the important role that equity funds play in for investors, as the amount of funds that are under the purview of professional management is large and increasing. What are the factors that investors look for to make choices and is it possible to rank these choices? To what extent do these factors differ between emerging and developed markets? Insight on the relations between fund performance and fund characteristics contributes in finding answers to those questions, in addition to the ones singled about above.
To accomplish our goals, we first apply a Fama and French three-factor model with country/ region-specific benckmarks to estimate the risk-adjusted fund performance of funds individually. We use Ordinary Least Square analysis (OLS) for the regressions. The analysis of performance in country/region is done through a combined analysis of panel regressions for each country/region and a statistical analysis of individual funds in each country/region. Then we apply a time-fixed effects regression model to test the relationships between returns and various fund characteristics in a cross-sectional regression setting.
investing only in the equity of home country). We think this allows us to dig deeper into the determinants of equity fund performance by eliminating unstable international factors.
In our study we find that equity funds in Hong Kong and Japan cannot yield superior returns over the market, and the equity funds in Singapore and Malaysia can yield superior returns over the market. Furthermore, we find that in Asia, the relationship between expense ratio and fund
performance is mixed and is heavily dependent on market background, fund size is positively related to fund performance in all of these markets, fund age does not appear to have any explantory power on fund returns, and number of holdings have a significantly positive relation with fund performance in all the tested markets.
II. Literature review
A. Fund performance
Starting with Jensen (1969), who documents that on the average the mutual funds provide the investors with inferior and inefficient portfolio’s, most academic studies conclude that the net performance of mutual funds (after expense) is inferior to that of a comparable passive market proxy. Investors cannot take advantage of the superior abilities of the portfolio managers by purchasing shares in their mutual funds, and after having adjusted for survivorship bias, in the aggregate, funds underperformed benchmark portfolios both after management expenses and even gross of expenses (Grinblatt et al., 1989; Malkiel, 1995; Fletcher et al., 2004). One may think the existence of professional fund managers implies the possibility for active investors to gain profits. However, a study result achieved by French (2008) shows that active investment is a zero-sum game before costs and a negative-sum game after costs. If some active investors such as fund managers have a positive Jensen’s alpha, the gain should come at the expense of other investors, who have negative alphas. Choe et al. (2008) come to one possible explanation for the
outperformance of those equity funds: money managers create profits at the expense of the uninformed small individuals, whose trades comprise a considerable volume in the stock market.
To sum up, we expect the funds in developed markets underperform the market, and the funds in emerging markets outperform the market.
B. Fund characteristics and fund performance
1. The relation between fund performance and expense ratio
The efficient market theory relates to the relationship between fund performance and expense ratio. The efficient market hypothesis assumes the information to be free. If so, security prices should incorporate all available information. So the market efficiency hypothesis implies that there exist no opportunities for fund managers to earn back the expenses related to active fund management (Fama, 1970). From this perspective, higher expenses could not increase fund performance.
However, the assumption that information is freely available is doubted by Grossman (1989). Information is costly and therefore stock prices cannot fully reflect all information. This equilibrium offers informed investors profitable trading opportunities that compensate their cost. Following this perspective it is expected that before costs, active managed funds outperform passive funds (Grossman, 1989).
Empirical results of previous studies show that expense ratio is negatively related to fund performance (Golec, 1996; Gruber, 1996; Payne, 1999; Prather, et al., 2004). The empirical significantly negative estimate for the expense ratio coefficient suggests that administration
expenditures are not used to effectively support research, marketing, and managerial expertise. And Prather et al. (2004) state that the negative coefficient indicates unsatisfactory performance and reflects the fact that investors overcompensate fund managers for their poor results. Carhart (1997) finds that expense ratios appear to reduce performance a little more than one-for-one. Otten and Bams (2002) conclude that the relationship between management expenses and risk-adjusted performance is significantly negative in three out of four European countries.
2. The relation between fund performance and fund size
Mutual fund size has been one of the most studied variables in mutual fund research, and the relationship between fund size and performance still puzzles the academic world. From an
economics perspective, there are both advantages and disadvantages of large fund sizes. Larger funds are experiencing economies of scale. They are able to spread fixed expenses over large asset base and have more research sources and information channels (Glosten et al., 1988; Brennan et al., 1991; Ciccotello et al., 1996). However, large fund size would cause liquidity constraints (Chen et al., 2004). And when funds become larger, fund managers must continue to find worthwhile investment opportunities, which would dilute the effect of managerial skills (Ferreira et al., 2009).
Empirical studies give mixed results for the relationship between fund performance and fund size. The most recent study by Bialkowski and Otten (2011) uses Poland as a representative of an emerging market, concluding that the size of assets under management has a statistically
significant positive impact on fund performance. Ferreira et al. (2009) conduct a cross-country study and get the result that large funds tend to perform better, which suggests the presence of significant economies of scale in mutual fund industry worldwide. Otten and Bams (2002) study four countries in Europe and found that all countries show a significantly positive relationship between the log of fund assets and risk-adjusted performance. They suspect that there are still economies of scale available at least in the European fund market. Koh et al. (2003) study Asian hedge funds and draw the conclusion that fund size seems to be positively correlated with post-fee returns in a univariate setting and it may or may not due to the fact that larger funds have lower expenses than smaller funds. However this effect goes away in a mulivariate setting. If investors are seeking to maximize risk-adjusted returns, they should generally seek for larger funds (Payne et al., 1999).
Kalkhuis (2010) finds a negative relationship between fund performance and fund size for both a subsample of domestic funds and international funds in the Netherlands. And he suggests the reason to be the over-scale of those funds, for which size negatively impacts on fund performance because of liquidity contraints.
From the mixed results given by theoretical background and empirical results above, we could not forecast the direction of the relationship between fund performance and fund size in our study of Asian fund market, however we do expect fund size playing a role in determining fund performance.
3. The relation between fund performance and fund age
Since the relationship between fund performance and fund age has not received much attention in the academic world, the empirical results toward it and reason exploring of those results are limited. So there is no well-developed theory about this relation. Logically, fund age is
connected to fund performance, with the fund size as an intermediary. Performance of younger mutual funds may be affected by an investment learning period and younger funds also tend to be smaller than older ones (Gregory, et al. 1997). Younger mutual funds would be exposed to higher market risk while they invest in fewer titles, and the returns and ratings of them are also more vulnerable to manipulation (Bauer et al. 2002).
From the theoretical analysis mentioned above, fund age and fund performance should have a positive relationship. However, empirical results tell a different story. Only Kalkhuis (2010) indicates fund age to be positively related to fund performance, so older funds outperform younger funds.
success, but the past does not necessarily secure future performance. And Koh et al. (2003) conclude that fund age does not appear to have any explanatory power on fund return.
Even though the former studies about relationship between fund age and fund performance are few in number, and most of them get insignificant result, it is still worthwhile to pay attention to their relationship, since fund age is a key fund attribute.
4. The relation between fund performance and number of holdings
The logical relation between number of security holdings in equity funds and fund
performance is expressed through portfolio diversification. Sapp et al. (2008) finds that number of holdings provides significant incremental information for explaining fund performance controlling for both security and industry concentration. Funds with a small number of holdings apply a concentrated strategy, whereas funds with a large number of holdings apply a diversified strategy. On the one hand, the reason for funds with small number of holdings outperforming diversified funds could be that a concentrated fund does not pretend to approximate the market, so that
managers cannot easily hide a lack of skill by mimicking the market’s performance. Accordingly, it is reasonable to expect that fund managers who are confident to identify undervalued equities to gravitate toward holding a more concentrated portfolio. On the other hand, there are at least two possible arguments why funds with small number of holdings should not outperform funds with large number of holdings. First, the desire of fund managers to hold concentrated portfolios could be influenced by the agency conflict between fund companies and fund shareholders. Second, liquidity of funds plays a critical role. If the ownership of some equity becomes relatively large, the fund is unable to quickly react to new information without diminishing the performance of the fund. Furthermore, purchases and redemptions could have a relatively large price impact on those trades (Sapp et al., 2008).
Following the logic relationship between fund performance and number of holdings,
study, the negative relation is not significant. However, Shaky and Smith (2005) study the optimal number of stock holdings in mutual fund portfolios based on performance and find the relation to be nonlinear. Specifically, as the number of stocks increases, a higher risk-adjusted net return is
expected initially. However, beyond a certain point, the risk-adjusted net returns decreases because after that point, an increase in the number of stocks held would cause marginal monitoring costs to exceed the marginal diversification benefit.
By this token, the relationship between fund performance and number of holdings would be positive at first then negative after the optimal number is attained.
C. Literature review summary
We use Table 1 to briefly summarize all the findings.
Table 1 Previous findings related to fund performance and attributes
This table gives a brief summary of previous studies and their results related to mutual fund performance and relationship between performance and fund characteristics. They are classified by directions of results in each research area.
Authors Findings
Fund
performance Cannot beat the market
Jensen (1969) On average the mutual funds provided the investors with inferior and inefficient portfolios.
Fund
performance Cannot beat the market
Grinblatt and Titman(1989) Investors cannot take advantage of the superior abilities of these portfolio managers by purchasing shares in their mutual funds. Fund
performance Cannot beat the market
Malkiel (1995) After adjustment for survivorship bias, in the aggregate, funds have underperformed benchmark portfolios.
Fund
performance Cannot beat the market
French (2008) If some active investors have positive Jensen’s alphas, the gain should come at the expense of others who have negative alphas. Fund
performance
Can beat
the market Ippolito (1989) Mutual funds, net of all fees and expenses except load charges, outperformed index funds on a risk-adjusted basis. Fund
performance
Can beat the market
Otten and Bams (2002) European mutual funds are able to add value, as indicated by their positive after cost alphas.
Fund performance
Can beat the market
Lai and Lau (2010) Mutual fund performances yield superior returns with relatively low systematic risk.
Fund performance
Can beat the market
Borensztein and Gelos
(2000) Managers of emerging market mutual funds are characterized by better market timing. Fund
performance
Can beat the market
Barber, Lee and Odean
(2009) The US evidence of fund underperformance does not carry over into emerging market funds. Relationship
Authors Findings Relationship between fund performance and expense ratio Negative (expense
ratio) Carhart (1997) Expense ratio is significantly and negatively related to performance and reduces performance a little more than one-for-one.
Relationship between fund performance and expense ratio Negative (expense ratio)
Payne, Prather and Bertin
(1999) Expense ratio significantly and negatively associated with risk-adjusted returns across all fund classifications. Relationship between fund performance and expense ratio Negative (expense ratio)
Prather, Bertin and Henker
(2004) Expense ratio is negatively related to fund performance. Relationship between fund performance and expense ratio Negative (expense ratio)
Golec (1996) The negative relationship between alpha and expense ratio indicates that administration expenditure reduces alpha.
Relationship between fund performance and expense ratio Negative (expense ratio)
Otten and Bams (2002) The relationship between management expenses and risk-adjusted performance is significantly negative in three out of four European countries. Relationship between fund performance and fund size Positive
(fund size) Ferreira, Miguel and Ramos (2009) Large funds tend to perform better suggesting the presence of significant economies of scale in mutual fund industry worldwide. Relationship between fund performance and fund size Positive (fund size)
Payne, Prather and Bertin
(1999) Investors seeking to maximize risk-adjusted returns should generally seek for larger funds. Relationship between fund performance and fund size Positive (fund size)
Otten and Bams (2002) All countries studied show a significantly positive relationship between the log of fund assets and risk-adjusted performance Relationship between fund performance and fund size Positive (fund size)
Koh, Koh and Teo (2003) Fund size is positively correlated with post-fee returns, at least in a univariate setting. Relationship between fund performance and fund size Positive (fund size)
Bialkowski and Otten
(2011) The size of assets under management has statistically significant positive impact on fund performance. Relationship between fund performance and fund size Negative
(fund size) Kalkhuis (2010) Fund size is negatively related to fund performance for both a subsample of domestic funds and international funds. Relationship between fund performance and fund size Negative (fund size)
Yan (2008) A significant inverse relation between fund size and fund performance is found. Relationship between fund performance and fund size Both
(fund size) Indro, Jiang, Hu and Lee (1999) Actively managed mutual funds have to attain a minimum fund size before they achieve returns sufficient to cover their costs for acquiring and trading on information.
Relationship between fund performance and fund age
Positive
(fund age) Kalkhuis (2010) Older funds outperform younger funds. Relationship
between fund performance
and fund age Negative (fund age) Ferreira, Miguel and Ramos (2009) Fund age is negatively related with fund performance indicating that younger funds tend to perform better. Relationship
between fund performance
and fund age Negative (fund age)
Otten and Bams (2002) Younger funds perform better than older funds while all coefficients of fund age are negative
Relationship between fund performance and fund age
No relation-ship (fund age)
Prather, Bertin and Henker
(2004) The fund age coefficient is slightly negative, but not significant. Relationship
between fund performance and fund age
No relation-ship
(fund age) Golec (1996) The fund age coefficient is negative, although statistically insignificant.
Relationship between fund performance and fund age
No relation-ship (fund age)
Koh, Koh and Teo (2003) Fund age does not appear to have any explanatory power on fund returns. Relationship between fund performance and number of holdings Positive (number of holdings)
Sapp and Yan (2008) Funds with a large number of holdings significantly outperform funds with a small number of holdings both before and after expenses. Relationship between fund performance and number
of holdings Negative(number of holdings)
Kalkhuis (2010) Significant negative relation between fund performance and holdings. Relationship between fund performance and number
of holdings Negative(number of holdings)
Bialkowski and Otten
Authors Findings and number of holdings Both (number of holdings)
Ⅲ.Methodology and Hypothesis
Following, we first estimate the risk-adjusted fund performance for each country/region, thereafter we apply OLS regression to estimate the relations between fund performance and fund characteristics to find out the determinants of mutual fund performance for each country/region.
A.Performance measure
The most famous model to estimate portfolio performance is the capital asset pricing model (CAPM) applied by Jensen (1968). The measure of excess performance is expressed by α, the so-called Jensen’s alpha, as below:
Rt - RFR= αi + βi *(Rm - RFR) + εt
Where Rt - RFR = excess return of the portfolio t; Rm - RFR = excess return of the market
portfolio; αi = Jensen’s alpha, the performance measure; βi = beta coefficient of the portfolio t.
The CAPM assumes that investment behaviour could be approximated by only market index. However, the wide diversity of investment styles ranging from growth to value and large cap to small cap makes it preferable to use a multi-factor model. Then Fama and French(1992) extend the CAPM by including a size and a book-to-market risk factor, in addition to the market risk factor. Through research, they found that value stocks outperform growth stocks and small cap stocks tend to outperform large-cap stocks. By including these two additional factors, the Fama and French three-factor model is adjusted for the outperformance tendency. By doing so, it fits better than the CAPM with the cross-sectional variation of fund returns, resulting in more efficient performance estimates. The model is given by equation 1.
Ri,t - Rf,t = αi + β1,i *(Rm,t - Rf,t) + β2,i * SMBt + β3,i * HMLt + εi,t (1)
Where Ri,t - Rf,t = excess return of fund i for month t; Rm,t - Rf,t = excess return of the market
portfolio for month t; SMBt = the difference in return between a small capitalization portfolio and
large capitalization portfolio for month t; HMLt = the difference in return between a portfolio of
We estimate the risk-adjusted fund performance with the Fama and French three-factor model, with country/region-specific benchmarks. In the model, alpha is a measure of performance on a risk-adjusted basis. A positive alpha means the fund has outperformed its benchmark index. Corresponsingly, a negative alpha would indicate an underperformance of fund compared with market. We include a dummy variable to ease the influence of financial crisis. The dummy is 1 for every month from July 2008 to December 2009 and 0 for other months.
The alpha for each country/region will be estimated in two ways. Firstly, we will apply OLS regression to get the alphas of every funds using time-series data and then weighing average those alphas of funds in each country/region to get the country/region-specific alpha. We will also estimate country/region-specific alpha’s by applying panel regression, because panel data (data containing both time-series and cross-sectional data) increase the degree of freedom, thus the power of the test.
Combining this model with the discussion in the literature review regarding the mutual fund performance, we form the first pair of hypotheses as:
H0: The fund performance measured by alpha (α) for Hong Kong, Singapore and Japan are
negative, but for Malaysia it is positive.
B.Determinants analysis
This analysis examines if the mutual funds performances of these countries can be attributed to their basic characteristics containing expense ratio, fund size, fund age and the number of
holdings. In this study, the fund size is measured by median market capital of fund in a time period, the fund age is expressed in years and the number of holdings is measured as the number of stocks held in a portfolio. In order to analyse the impact of funds’ characteristics on their performance, we form the time-fixed regression model given by equation 2. We run the model with Fama and French three-factor alphas estimated with equation 1 (Table A in the appendix reports the result of the test on the regression with Fama and French alphas). Ottens and Bams (2002) and Kalkhuis (2010) used the similar model in their studies to find out the determinants of fund performance.
Where αi = risk-adjusted performance of mutual fund i measured as the Fama and French
alpha; Expense-ratioi = expense ratio for fund i; Lg(Size) i = denary logarithm of median market
capital for fund i; Lg(Age)i = denary logarithm of fund age in years for fund i; Lg(Holdings)i =
denary logarithm of number of holdings for fund i.
Based on the empirical results listed in the literature review, we form the following four hypotheses for each market.
H1,0: Expense ratio is not related to fund performance(c1,i =0);
H1,a: Expense ratio is negatively related to fund performance(c1,i ≠ 0).
H2,0: Fund performance is not related to fund size(c2,i =0);
H2,a: Fund performance is related to fund size (c2,i ≠ 0).
H3,0: Fund performance is not related to fund age (c3,i =0);
H3,a: Fund performance is related to fund age (c3,i ≠ 0).
H4,0: Fund performance is not related to number of holdings(c4,i =0);
Ⅳ.Data description
In this part, we firstly describe the characters of sample selected. Thereafter we introduce the data collected to estimate the fund performance. At last we describe and discuss the data collected to estimate the determinants of fund performance.
A. Sample description
We use data of open-end active equity funds for estimating the fund performance of each country and the relation between fund characteristics and fund performance. Table 2 provides an overview of the basical description of sample data for each country.
Table 2 Summary description of sample data for each country
This table gives a simple summary of sample collection for each country. In the last row of table, HSI means Hang Seng Indexes, STI means Straits Times Index and KLSE means Kuala Lumpur Stock Exchange. HSI, STI, KLSE and Nikkei are main stock exchange markets for Hong Kong, Singapore, Malaysia and Japan respectively.
Hong Kong Singapore Malaysia Japan Sample period 2004.1-2011.9 2004.1-2011.9 2004.1-2011.9 1992.1-2011.9
Original data frequency for
fund returns Quarterly Quarterly Quarterly Monthly Number of funds observed 42 29 86 99
Data frequency for other
variables Monthly Monthly Monthly Monthly Target market of funds HSI STI KLSE Nikkei225
B. Data collected for estimating fund performance
We estimate the risk-adjusted fund performance for each country/region with monthly total fund returns acquired from Morningstar3. And the total fund returns assume reinvestment of
dividends, therefore we avoid possible biases as a concequence of fund’s dividend policy.
For the Fama and French three-factor model, we collect data for HML and SMB in the Kenneth R. French Data Library4. An overview of these two factors are presented in Table B of the
appendix. And the data for the rest market varibles are collected from Yahoo Finance and local websites. Detailed descriptions of market variables data sources for each country/region are presented in Table 3.
Table 3 Descriptions for market variables data sources in Fama and French model
Hong Kong Singapore Malaysia Japan
Return on market
(Rm) Monthly return of HSI Monthly return of STI Monthly return of KLSE Monthly return of NIKKEI 225 Risk-free-rate
(Rf) 7-days yield for HKMA's Exchange Fund Bills End of period average buying Rates of government securities dealers
91-day T-bill yield plus country spread yield
Rates for 1 year Japanese
Government Bond (JGB)
This table presents the descriptions for market varibles data descriptions in the Fama and French three-factor model for each country/region. In this table, HSI means Hang Seng Indexes, STI means Straits Times Index and KLSE means Kuala Lumpur Stock Exchange. HSI, STI, KLSE and Nikkei are main stock exchange market indices for these countries respectively. All the monthly returns are month end closing prices, adjusted for dividends and splits5. The measure of the risk-free-rate is country-specific. The Hong Kong
3 For Hong Kong, Singapore and Malaysia, the original data are quarterly fund returns. However, as all of the other variables are monthly data, we use the monthly geometric average of quarterly fund return data as a measure of monthly fund returns. This could also increase the degree of freedom to increase the quality of estimation.
4http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 5 Monthly return of HSI:
http://finance.yahoo.com/q/hp?s=%5EHSI&a=00&b=1&c=2004&d=09&e=27&f=2011&g=m Monthly return of STI:
http://sg.finance.yahoo.com/q/hp?s=%5ESTI&a=00&b=01&c=2004&d=09&e=28&f=2011&g=m
Monthly return of KLSE:
http://finance.yahoo.com/q/hp?s=%5EKLSE&a=00&b=01&c=2004&d=09&e=30&f=2011&g=m
Government does not issue government bonds. Here we use the yearly end of period fugure of 7-days yield of HKMA (Hong Kong Monetary Authority)'s Exchange Fund Bills as risk-free-rate of Hong Kong6. The
risk-free-rate of Singapore is always measured by the End of Period Average Buying Rates of Governmentt Securities Dealers (SGS). Here we use the 3-Month T-Bill Yield of SGS7. Since Malaysia neither has
government issued bonds, nor does it have alternative local rates, the risk-free-rate of Malaysia is derived from the risk-free-rate of US. We measure the risk-free-rate of Malaysia by adding a country spread yield8 to
a 91-day T-bill yield9. We divide all these annual yields by 12 to get the monthly rate.
The detailed data of return on market(Rm) for each country/region is given in Table C in the appendix.
C. Data collected for estimating determinants of fund performance
The description of data collected for estimating determinants of fund performance is structured as follows. Firstly we give the definition and measure of fund characteristics in Table 4, which provides an overview of the fund characteristics that we consider in this study as
determinants of equity fund performance. Then we report the descriptive statistics of the fund characteristics for each country in Table 5. The results of the correlation test on interrelationships between independent variables are reported in Table 6.
6http://www.hkma.gov.hk/eng/key-functions/exchange-fund/history.shtml 7https://secure.sgs.gov.sg/fdanet/BenchmarkPricesAndYields.aspx
Table 4 Definitions of fund characteristics used as independent variables
Fund Attributes Definition
Expense ratio A measure of what it costs an investment company to operate a mutual fund. It is determined through dividing a fund’s operating expenses by the average dollar value of its assets under management.
Fund size Fund size is expressed by median market cap. Median market cap is the midpoint of market capitalization (market price multiplied by the number of shares outstanding) of the stocks in a portfolio.
Fund age Number of years since the establishment of the fund. It is measured in years, and we approximate months to fractional parts.
Number of holdings Number of stocks held in an equity fund.
This table gives the definitions of the fund attributes that we consider in this study as determinants of fund performance: expense ratio10, fund size11, fund age and number of holdings.
We obtained almost all of the data from Morningstar. They are directly provided on the funds’ webpages and are measured recently. However, we need to point out three matters. First, data for fund size measured by median market cap are expressed in local currency, so we translated them to US dollar when collecting them12. Second, for the missing data points, we use the data of a
comparative company as substitutes. Third, since Japanese funds do not provide data for their number of holdings, we can only study expense ratio, fund size and fund age as determinants for Japanese fund performance. In Table D of the appendix, we report the collected fund characteristics data for each market. The descriptive statistics of the fund characteristics for each country are reported in Table 5.
10 http://www.investopedia.com/terms/e/expenseratio.asp#axzz1fqFmraD9 11http://financial-dictionary.thefreedictionary.com/Median+Market+Cap
Table 5 Descriptive statistics of fund characteristics
Hong Kong Hong Kong Hong Kong Hong Kong
TER (%) Age (year) Size ($,million) Holdings
Mean 1.56 8.74 274.73 63.48 Median 1.65 8.25 143.26 56.50 Maximum 3.02 24.60 1059.93 127.00 Minimum 0.1 3.00 5.35 31.00 Std.Dev. 0.71 4.52 349.85 24.94 Observations 42 42 42 42 Singapore Singapore Singapore Singapore
TER (%) Age (year) Size ($,million) Holdings
Mean 1.75 13.10 9314.85 31.07 Median 1.74 10.50 10123.50 30.00 Maximum 4.65 33.50 17270.44 53.00 Minimum 0.09 2.00 834.10 15.00 Std.Dev. 0.98 8.63 4116.55 7.50 Observations 29 29 28 27 Malaysia Malaysia Malaysia Malaysia
TER (%) Age (year) Size ($,million) Holdings
Mean 1.85 13.15 3671.31 33.01 Median 1.65 11.25 3260.29 32.50 Maximum 7.45 45.00 10560.82 56.00 Minimum 0.80 2.00 155.79 11.00 Std.Dev. 0.91 9.68 2705.95 9.20 Observations 86 86 80 82 Japan Japan Japan Japan
TER (%) Age (year) Size ($,million) Holdings
Mean 0.75 14.44 36568.36 -Median 0.65 13.00 3078.00 -Maximum 1.63 25.50 677149.00 -Minimum 0.09 5.00 17.00 -Std.Dev. 0.37 5.36 108436.40 -Observations 99 99 99
In the remaining study we use the denary logarithm for fund size, age and number of holdings. The reasons to use logarithms are given as follows. First, through rescaling the data, logarithms result in a more constant variance. Second, logarithms can make a non-linear
relationship between variables more linear. The results of the correlation test on interrelationships between independent variables are reported in Table 6.
Table 6 Correlation matrix of independent variables for determinants analysis
Hong Kong Hong Kong Hong Kong Hong Kong
Expense ratio Lg (Age) Lg (Holdings) Lg (Size) Expense ratio Lg (Age) Lg (Holdings) Lg (Size) 1.00 0.20 1.00 0.25 0.22 1.00 -0.01 -0.18 -0.56 1.00 Singapore Singapore Singapore Singapore
Expense ratio Lg (Age) Lg (Holdings) Lg (Size) Expense ratio Lg (Age) Lg (Holdings) Lg (Size) 1.00 -0.14 1.00 0.05 -0.10 1.00 -0.72 0.29 0.13 1.00 Malaysia Malaysia Malaysia Malaysia
Expense ratio Lg (Age) Lg (Holdings) Lg (Size) Expense ratio Lg (Age) Lg (Holdings) Lg (Size) 1.00 -0.22 1.00 -0.14 -0.12 1.00 -0.10 -0.10 0.24 1.00 Japan Japan Japan
Expense ratio Lg (Age) Lg (Size) Expense ratio Lg (Age) Lg (Size) 1.00 0.43 1.00 -0.75 -0.30 1.00
This table shows the correlation coefficients of independent variables for determinants analysis. The variables age, number of holdings and fund size are measured as the denary logarithm values. Lg (Age), Lg (Holdings) and Lg (Size) stand for the logarithm of age, holdings and size respectively.
Results on Singapore and Malaysia confirm this relationship. However, as to Hong Kong we get a strong negative out of line relationship. For other pairs of relationships we get mixed results between these four countries/regions.
Ⅳ. Empirical results
This section reports the regression analysis results using Eviews. We firstly report the results of the performance analysis, then we report the results of the determinants analysis.
A. Performance analysis results
In this section we the main results from the performance analysis with the OLS regression of the Fama and French three-factor model. We estimate equation 1 for each fund individually.
Because this only provides an individual view of each fund, we also form equally weighted portfolios containing all domestic invested equity funds within the market for each country to provide an aggregate picture of the equity fund performance of each market. In short we get an average alpha from all the alphas of all the equity funds in each market and regard it as the
estimation of risk-adjusted fund performance for each market. The main empirical OLS regression results of the Fama and French three-factor model indicating fund performance are reported in Table 7. And the alphas of every fund in each market are reported in Table A in the appendix.
Table 7 Performance analysis regression results
Alpha
(Panel regression) (Weighing average)Alpha R
2adj Number of
‘+’, the percentage of significantly negative alphas under ‘-’ and the percentage of alphas that are not significantly different from 0 under ‘0’. The significant level is 10% for all of the alphas.
From this table we can see that 90% of the equity funds in Hong Kong have significantly negative alphas, and the average alpha regarded as the estimation of risk-adjusted fund
performance for Hong Kong is -0.02, indicating that the fund performance after adjusting for risks and expenses could not win over the market in Hong Kong. This result is in line with most theories and empirical results of former studies (e.g. Jensen, 1969; Malkiel, 1995; French, 2008). Also, we can see that in Japan most of the funds have negative alphas13 , and the average alpha is slightly
negative. However, in Japan, only 1% of the equity funds show significant results. Also the The results of Hong Kong and Japan support the former observation that investors cannot take advantage of the superior abilities of the portfolio managers by purchasing shares in their equity funds, and active investment is a negative-sum game after costs. Even if there are positive alphas, the gain should come at the expense of other investors who have negative alphas.
On the other hand, 38% of the equity funds in Singapore have significantly positive alphas, and the average alpha regarded as the estimation of risk-adjusted fund performance is also positive. In Malaysia, the average alpha is slightly higher than zero (0.001), and there are more equity funds with a significantly positive alpha (8%) than the ones with a significantly a negative alpha (2%). This result is line with Lai and Lau (2010), who study the mutual fund performance in Malaysia and conclude that mutual fund performances yield superior returns with relatively lower systematic risk. So for Singapore and Malaysia, the result of a positive alpha is in line with some recent findings (e.g. Otten et al., 2002; Barber et al., 2009). Also, the overall evidence of underperformance observed from developed market such as US does not carry over into the emerging fund market such as Singapore and Malaysia.
The alphas under panel regressions show consistent results with market weighing average
alphas and all of them are significant at 1% level.
In summary, equity funds in Hong Kong and Japan underperform the market, and the equity funds in Singapore and Malaysia outperform the market. So H0 stating that the equity funds
performances in Hong Kong, Singapore and Japan cannot beat the market is not fully verified because Singapore got an opposite result.
B. Determinants analysis results
In this section we report the main results from the determinants analysis with the time-fixed effects model, whereby we use the alphas from equation 1 as dependent variables. The empirical results of the time-fixed effects model as specified by equation 2 are reported in Table 8. It gives the estimated coefficients after using the White’s heteroscedasticity corrected standard errors. The detail results of regressions for each country/region are reported in Table G in the appendix.
Table 8 Determinants analysis regression results
Coefficient Expense ratio Lg (Size) Lg (Age) Lg (Holdings) R2adjusted
Hong Kong Singapore Malaysia Japan 0.175 ***0.050 0.042 *0.118 0.263 ***6.647 ***0.201 0.002 **0.213 0.510 *-0.519 0.014 -0.022 ***0.107 0.198 -1.357 0.002 0.013 - 0.044
This table reports the coefficients of four fund characteristics for each country/region by regressing equation 2. And the coefficients are estimated after using the White’s heteroscedasticity. The sample consists of 256 observations as a whole, and Hong Kong, Singapore, Malaysia and Japan have 42, 29, 86 and 99
observations respectively. Significance levels are marked besides coefficients. * indicates significance at 10% level, ** indicates significance at 5% level and *** indicates significance at 1% level.
The table reveals that the relationship between fund performance and expense ratio is mixed. A positive relationship appears in Hong Kong and Singapore and the positive relationship is significant for Singapore at 1% level. This is in line with the theory of Grossman (1989). It
between expense ratio and fund performance. This indicates that the relationship between fund performance and expense ratio is heavily dependent on market background, even though all former empirical studies about this relationship give a negative result.
The table shows that fund performance is positively related to fund size in all of the four markets, and this positive relationship is significant at the 1% level in Hong Kong and Singapore. This result is in consistent with the majority of former empirical studies stating a positive
relationship between fund performance and fund size (e.g. Ferreira et al., 2009; Payne et al., 1999; Otten et al., 2002), which indicates that even though large fund size would cause liquidity
constraints diluted managerial skills, the advantage brought up by economies of scale could win over them. There exists another possibility that in Asian markets, fund size is not large enough to reach the optimal size beyond which marginal returns become negative because the expenses related to size management can not be covered (Indro et al. 1999).
For the relationship between fund performance and fund age we could not get a significant result from any one of these markets, which is consistent with most of the former empirical studies (e.g. Prather, 2004; Golec, 1996). However, in contrast to the slightly negative relationship between fund performance and fund age found in those empirical results stating insignificance, our study shows a slightly positive relationship between them in three out of four markets. This indicates that the logic of connecting fund age to fund performance with fund size as an intermediary is
reasonable and younger funds would expose to higher market risk while they invest in fewer titles, which would lead to lower return. Even though fund age is connected to fund performance with the fund size as an intermediary, fund age does not appear to have any explantory power on fund returns, however.
Table 8 also reports a significant positive relationship between fund performance and
number of holdings for all of these markets. This positive result is consistent with the finding of
At last, as shown from the adjusted R-squared, the regression model explains 26.3% of the variation in risk-adjusted returns of Hong Kong, 51% of the variation in risk-adjusted returns of Singapore, 19.8% of the variation in risk-adjusted returns of Malaysia and 4.4% of the variation in risk-adjusted returns of Japan. This indicates that the variables included in the model are properly justified, at least for Hong Kong, Singapore and Malaysia. Also the relatively low adjusted R-squared in Japan may be the reason of the collective coefficients insignificance in Japan. A high adjusted squared indicates that all of the significant causes have been found. If the adjusted R-square is low, there should be more factors acting on the data14. Ramasamy et al. (2003) has
indentified factors that are cannot be measured such as qualification of fund managers, experence of fund managers, investment style of fund managers, and attributes of mutual funds. So the existance of factors that cannot be measured could be the explanation of relative low adjusted R-squared in these four markets. The long history and rapid changes in Japan’s stock market may bring more unmeasurable factors, which could lead to the low adjusted R-squared in Japan.
C. Comparative analysis and summary
In this part e summarize the overall results of hypotheses listed in Methodology part for each market in Table 9. Then we conduct a comparative analysis between the results of developed markets in Asia, represented by Hong Kong, Singapore and Japan, and the results of emerging market, represented by Malaysia. In the comparative analysis, we just pay attention to the derections of coefficients.
Table 9 Summary of hypothesis tests
Hong Kong Singapore Malaysia Japan Fund performance (H0) Plus-minus Fund performance (H0) Significance level Performance and Expense ratio (H1) Relationship Performance and Expense ratio (H1) Significance level Performance and Fund size (H2) Relationship Performance and Fund size (H2) Significance level Performance and Fund age
(H3)
Relationship Performance
and Fund age
(H3) Significance level Performance and Number of Holdings (H4) Relationship Performance and Number of Holdings (H4) Significance level - + + -Significant at
1% Significant at 1% Significant at 1% Significant at 1%
+ + -
-can not reject H1,0
reject H1,0 at
1% level reject H10% level1,0 at can not reject H1,0
+ + + +
reject H2,0 at
1% level reject H1% level2,0 at can not reject H2,0
can not reject H2,0
+ + - +
can not reject H3,0
can not reject H3,0
can not reject H3,0
can not reject H3,0
+ + +
reject H4,0 at
10% level reject H5% level4,0 at reject H1% level4,0 at
This table provides an overall summary of the results towards the hypotheses. For the results of H0 , ‘+’
indicates absolutely positive and ‘-’ indicates absolutely negative. For the results of other hypotheses, ‘+’ indicates a positive relationship and ‘-’ indicates a negative relationship. And the column ‘Significance level’ indicates whether or not can we reject the hypothesis and in what level.
returns still cannot cover the expenses. However, equity funds of Singapore significantly over-perform the market. So the strong consensus achieved by former studies focus on developed capital markets showing that in developed capital market, performance of equity funds cannot yield
superior returns over the markets cannot be adopted by all the developed markets in the world. In Asia, we have found Singapore being an exception.
The outperformance of equity funds in Malaysia draws our attention to the situation of emerging markets. The volatility of markets, the extent of regulations and the size of government involvement are a few factors which distinguish equity funds in emerging markets from their counterparts in more established markets. In Malaysia, the government plays an important role to stimulate the capital market development. The key factors driving the outperformance would partly due to the stable politics as well as the inflow of worldwide liquidity into the region. Emerging market are assumed to be less efficient than well developed market, reflecting in the greater likelihood of asymmetric information in the market. The asymmetric information in emerging markets, both at the descriptive and analytical level, makes the role of financial advisors critical in the market. The equity fund performance can yield superior returns over the market in Malaysia may due to the better market timing of fund managers (Borenszten et al., 2000) and more
information grasped by them. So the return gained from superior abilities of fund managers could cover their expenses.
Ⅴ. Conclusions
This paper investigates equity fund performance and the relationship between fund performance and fund characteristics in Asia, taking Hong Kong, Singapore and Japan as
representatives of Asian developed capital markets and taking Malaysia as a representative of Asian emerging capital markets. More specifically, we consider expense ratio, fund size, fund age and number of holdings as determinants of equity fund performance. The sample of this study aggregately consists of 256 domestic invested open-end active equity funds. Data about fund returns and market returns are monthly data collected over the period January 2004 to September 2011 for Hong Kong, Singapore and Malaysia, and over the period January 1992 to September 2011 for Japan. Data about fund characteristics are collected at the beginning of October 2011. We
employ the Fama and French three-factor model with country/region-specific benchmarks to estimate fund performance. We employ the time-fixed effects model to estimate the relations between fund performance and fund characteristics.
Results of the performance study show that Hong Kong and Japan deliver negative aggregate alphas while Malaysia and Singapore deliver positive aggregate alphas, where only the Hong Kong fund market gets a significant result. This indicates that most of the developed capital markets in Asia adopt the strong consensus that equity fund performance cannot beat the market, but we have Singapore as an exception. Furthermore, we can indicate that emerging markets in Asia have the abitlity to yield superior returns over the market.
And the study on relations between fund performance and fund characteristics shows several different results. More specifically, we find that expense ratio is positively related to fund
and fund age, with Malaysia as an exception. However, none of the relationships is significant. This indicates that in Asian markets, fund age does not appear to have explantory power on fund returns. At last, we find a significant positive relationship between fund performance and the number of holdings in all of the tested markets. So we have strong evidence to believe that in Asia, funds with large number of holdings can benefit from portfolio diversification. Beside the results we get from the determinants analysis on Asian markets, we also identify the differences between determinants analysis results of developed markets (Hong Kong, Singapore and Japan) and determinants analysis results of emerging market (Malaysia). And we infer that these differences result from
unmeasurable factors distinguishing emerging markets from developed markets.
These results implicate that when investors are considering about investing in Asian market, they can regard large size and large number of holdings as positive signals when picking funds. One may consider that funds with longer history can perform better, however we do not find evidence on that. And if they want to pay attention to the influence of expense ratio, they should take market background into consideration. Furthermore, the market performance evidence does not mean that one cannot earn by investing in developed markets or can absolutely earn by investing in
developing markets, it is a reminder that one should collect unmeasurable market information when making decisions.
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Appendix
Table A Alphas in Fama and French three-factor model for each equity fund
This table presents the alphas of every equity funds organized by country/region. Alpha is measured by the constant variable in Fama and French three-factor model. t and p indicate the t-value and p-value of the regression respectively. And R2adjusted is the adjusted R-squared of the regression. For every fund we do
regressions using monthly data from January 2004 to September 2011 for Hong Kong, Singapore and Malaysia, and from January 1992 to September 2011 for Japan. We mark the fund by * besides the fund number if the alpha is significant at the 10% level. ** indicates significance at the 5% level, and *** indicates significance at the 1% level.
Fund
Hong Kong Hong Kong Hong Kong
Hong Kong SingaporeSingaporeSingaporeSingapore
Alpha t p R2adjusted Fund Alpha t p R2adjusted
Fund
Hong Kong Hong Kong Hong Kong
Hong Kong SingaporeSingaporeSingaporeSingapore
Alpha t p R2adjusted Fund Alpha t p R2adjusted
**39 **40 41 42 -0.021 -2.439 0.017 0.791 -0.023 -2.541 0.013 0.762 -0.010 -1.162 0.249 0.802 -0.009 -1.030 0.306 0.802
company alpha JapanJapanJapanJapant p R2adjusted companycompany alpha MalaysiaMalaysiaMalaysiaMalaysiat p R2adjusted
Table B An overview of Fama and French factors HML and SMB
This table gives an overview of the monthly data of two Fama and French factors (HML and SMB) for each country/region from Jan. 2004 to Sep. 2011 (Actually the time period for Japan is from 1992 to 2011, here we just present the data from 2004 to 2011). HML is the difference in return between a portfolio of high book-to-market value stocks and a portfolio of low book-to-market value stocks, and SMB is the difference in return between a small capitalization portfolio and large capitalization portfolio.
Hong Kong
Table C Market return data
This table presents the collected data of return on market indices(Rm). For Hong Kong, Rm is the monthly return of HSI. For Singapore, Rm is the monthly return of STI. For Malaysia, Rm is the monthly return of KLSE and for Japan, Rm is the monthly return of NIKKEI 225. For Japan, we only present the Rm from January 2004 to September 2011 for unitarity.
Hong Kong Singapore Malaysia Japan
Table D Fund characteristics data for each market
This table presents the collected data of fund characteristics for each market studied. In the table, TER means expense ratio, Age is fund age expressed in years and Size is fund size expressed with $ in million.
Hong Kong Hong Kong Hong Kong
Hong Kong SingaporeSingaporeSingaporeSingapore
TER Age Size Holdings TER Age Size Holdings
Malaysia Malaysia Malaysia
Malaysia JapanJapanJapan
TER Age Size Holdings TER Age Size
Table E Empirical results of determinants analysis
This table reports the coefficients of four fund characteristics for each country/region by regressing equation 2. And the coefficients are estimated after using the White’s heteroscedasticity test.
Variable
Hong Kong Hong Kong Hong Kong Hong Kong
Coefficient Std. error t-value Prob. Expense ratio Lg (Size) Lg (Age) Lg (Holdings) R-squared Adjusted R-squared Observations 0.175 1.426 0.123 0.903 0.050 0.013 3.780 0.001 0.042 0.046 0.901 0.374 0.118 0.068 1.752 0.088 0.335 0.263 42 Variable Singapore Singapore Singapore Singapore
Coefficient Std. error t-value Prob. Expense ratio Lg (Size) Lg (Age) Lg (Holdings) R-squared Adjusted R-squared Observations 6.647 1.442 4.611 0.000 0.201 0.026 7.752 0.000 0.002 0.036 0.054 0.958 0.213 0.089 2.379 0.026 0.580 0.510 29 Variable Malaysia Malaysia Malaysia Malaysia
Coefficient Std. error t-value Prob. Expense ratio Lg (Size) Lg (Age) Lg (Holdings) R-squared Adjusted R-squared Observations -0.519 0.275 -1.885 0.063 0.014 0.010 1.347 0.182 -0.022 0.018 -1.208 0.231 0.107 0.030 3.567 0.001 0.236 0.198 86 Variable Japan Japan Japan Japan
