Financial Markets Grow Dramatically

Published Date: 02 Nov 2017

Track: Financial Management

Name: Qiqiang Shi (s1199447)

Supervisor: Xiaohong Huang

H.C. (Henry) van BeusichemOA Operational Audit omslag A4v

1 Introduction

1.1 Background

As financial markets grow dramatically, more and more investment vehicles come out, such as mutual funds and hedge funds. Both individual and institutional investors widely apply professional money managers to supervise asset effectively and efficiently. Nowadays, tremendous amounts of money are plowed into mutual funds in mature markets as well as in emerging markets; due to provide diversification profit opportunities.

The mutual fund is an investment vehicle, which is a large of capital composited from many investors. The purpose of mutual funds is to provide benefit, like diversification, low costs and professional management, through investing in equity, bond, money market instruments and similar assets. Based on the investment style, the mutual fund can be categorized differently, such as an aggressive growth-, growth-, growth and income-fund. An aggressive growth fund attempts to reach the highest capital gains but no income from dividends. Then, a growth fund is mainly above-average growth in earnings. The commonly acceptable and ever-popular fund its growth and income, which is moderate with risk, and also have income generating from dividends or interest payments.

The finding from Lhabitant & Learned (2002) indicated that the mutual fund has an ability to invest in more portfolios and then reduce the portfolio's volatility. Therefore, mutual funds that a pool assets from investors, may gain more benefit from plowing into diversified portfolios compared with a single investor. This is one of the main reasons that a mutual fund would attract more investors, and grow dramatically.

From a mature market perspective, such as the U.S., the data were collected by the Investment Company Institute (2011). They indicated that mutual fund is playing an essential role in investing in alternative for the US's individual and institutional investors. The assets of all the U.S. mutual fund were more than $11.8 trillion, and even approximately ninety million investors possessed mutual funds. Indeed, the mutual fund has played a more important investment vehicle compared with others, the survey showed that sixty-four percentages investors hold more than half of their financial assets as the mutual fund.

Turning to emerging markets, such as China, Indonesia, Malaysia, the mutual fund has been growing dramatically. Huij & Post (2011) documented that the equity mutual fund in emerging market had performed better than in developed market. For instance, Carhart (1997) demonstrated that the top- and bottom-decile of US fund had 2.64% and -5.4%, respectively. Huij & Post (2011) documented that these rates are relatively double in emerging markets compared with the US fund, indicating 4.29% and -2.8%, separately. As China’s Mutual fund introduced in March, 1998, the securities investment funds grow significantly. Until the end of 2007, in the China's mutual funds market the total net asset of different types of asset categories reached 3.3 trillion Yuan. Meanwhile, one survey was conducted by Galaxy Securities, France-Presse (2008). Their result indicated that 12,284 (83%) out of 14,800 answerers preferred mutual fund as a first investment vehicle for financial asset management. Moreover, the total asset value of securities investment funds was more than RMB 2.52 trillion at the end of 2010, which was decrease compared with 2007. But the total asset value of securities investment funds was more than 10% of A Shares Market [1] .

In China mutual fund market, most mutual funds are equity funds, like the U.S., From the end of 2010, equity funds had half (330) of total mutual fund, while the number of bond funds, money market funds, hybrid funds and qualified domestic institutional investors (QDII) [2] had 163, 95, 45, and 23, respectively. See figure 1.1 [3] demonstrates the detailed proportion of the different mutual fund types at the end of 2010. This figure is collected from the China Securities Regulatory Commission (CSRC) website. Equity fund has already become one of the most important investment vehicles for investors. Therefore, it is meaningful to study on the investment preference of the Chinese equity fund. In this thesis, I mainly concentrate on the equity mutual fund.

Data source: China Securities Regulatory Commission (CSRC)

The mutual fund can also be categorized, besides the classification above section mentioned, as open-end or closed-end mutual fund. Commonly, the majority of mutual funds are open-end fund, which is defined that it issues or buys back shares continuously when investors wish to. For closed-end funds, it is a publicly traded investment firm with a fixed number of shares at the point of initial public offering time. In China, the closed-end mutual funds clearly define the duration which is more than 5 years, issued fund cannot be redeemed within this period. Moreover, the amount of both mutual funds had boosted to 1019 funds in which 94.6% were open-end mutual funds in the Chinese equity mutual funds at the end of 2011. Meanwhile, the percentage rate of open-end equity funds was 93.5% in total equity funds. Furthermore, the majority of equity funds invest in domestic or few foreign companies listed in Chinese stock markets.

Managerial style in China, see figure 1.2 [4] below, the majority of equity mutual funds was actively-managed. The fund managers attempt to outperform the market benchmark by picking and choosing investment strategies. Index fund as passively-managed, on the other hand, that manager wants to copy the performance of a specified index. The evidence from a recent period in China market by Wang (2012), passively-managed funds had better performance than actively-managed funds during bull market periods (2006, 2007 and 2009), while actively-managed fund showed an advantage for risk averse due to diversification. She also revealed that actively-managed as a better choice for long-term investment. Furthermore, from the figure 1.2, we can clearly see that the passively-managed fund increased after 2008, even dramatically boosted since 2009. Because investors have concerns that the index funds as bringing better performance compared with actively-managed funds in recent years. Even so, the actively-managed funds still occupied a large portion. Therefore, based on above, we will choose the open-end equity funds which exclude the index fund as our research sample in this thesis due to the managerial style difference.

Data source: Hexun database website

1.2 Problem discussion

As mutual fund growth enormously, how to improve mutual funds’ performance or strength the persistence of mutual fund’s performance have become critical issues. Based on a large number of academic investigated, Peterson et al. (2001) indicated that discussion of mutual fund performance can be classified three category perspectives. The first category is the persistence of mutual fund performance. Secondly, this area is documented the fund managers’ ability and skills whether can improve mutual fund performance. The last category analyzes the fund’s characteristics whether can predict, and describe mutual fund performance.

In the last two decades, most authors found evidences from developed markets that past mutual fund performance can forecast the future fund return in the short-term, such as Goetzmann & Ibbotson (1994), Elton, Gruber, & Blake (1996), and Benos & Jochec (2011) or long-term horizons, e.g. Grinblatt (1992), and Brown & Goetzmann (1995). Indeed, their findings indicated that the persistence of mutual fund largely appeared in the worse-performance, but still can see in the best-performing. This means an investor can buy a fund with the best-performed in the past instead of the worst-performed that will generate the abnormal return in the future. Notably, the winning chase strategies contributed to the persistence of fund performance. However, Carhart (1997) revealed that the fund performance cannot exist for long-term period. As studies in persistence in emerging markets, He & Hao (2008), Lai & Lau (2010) showed that the best-performed mutual fund appears persistence as well as worst-performed fund in short-term horizon.

Turning to the managerial skill or ability, the evidence from Du, Huang, & Blanchfield (2009), Berk & Green (2004), and Baks, Metrick, & Wachter (2001) indicated that skilled or experienced managers cannot get outperformance. Indeed, Prather, Bertin, & Henker (2004) documented that fund performance became worse due to manager transferring their efforts to different funds, when fund manager had more experienced or mastered in the U.S. mutual fund industry. In emerging market aspects, there are holding similar opinion compared with developed market. Evidence from China mutual fund industry, Zeng, Zha, & Gong (2006) revealed that the manager tenure was negatively significant related to fund performance.

For fund-specific characteristic, examining the expense ratio was common explanatory variable, which was studied in the majority of literatures. The results held almost the same in developed markets e.g. sees Otten & Bams (2002), Carhart (1997), Haslem, Baker, & Smith (2008), Prather, Bertin, & Henker (2004) or in emerging markets e.g. sees Gottesman & Morey (2007), Babalos, Kostakis, & Philippas (2009). Their finding indicated that mutual fund with low expense ratio had outperformance compared to one with a high expense ratio. Furthermore, other fund-specific factors, which were commonly tested in previous literatures, included turnover, age, and size explanatory variables. The turnover ratio was a controversy factor in both developed and emerging markets. Carhart (1997) found that the turnover was negatively related to mutual fund performance in the U.S. market, while Tang, Wang, & Xu (2012) indicated the turnover ratio was positively related to fund performance in China markets.

From above, the difference and similar findings between developed- and emerging- markets are presented. There are a series of conjectures for explaining those differences in emerging markets compared with developed markets. Firstly, majorities of investors are lower finance education background or inexperienced in emerging markets compared with developed markets. Thereafter, the individual investors may really concentrate on the fund manager’ speech instead of investigation by themselves. However, the professional qualities of fund manager have irregularity in emerging markets. Some fund managers are likely to low integrity, prudence and diligence, even manipulate or mislead the market trading in order to get higher compensation. Secondly, fund's investment is less liquid and efficient assets that are difficult to trade in emerging markets compared with developed market. Last but not least, the Chinese mutual fund industry exist oligopolistic company due to the small size of the mutual fund market. Therefore, the informational asymmetry will provide more opportunities for some fund manager to find the abnormal return. More specifically, manager of a mutual fund has an information advantage or has a sophisticated ability can achieve excess returns. Hence, fund managers have more opportunities to find abnormal returns due to a less efficient market compared with developed market, and then are able to beat the market. In this thesis, we explore the impact of fund-specific characteristics and managerial attributes influencing on the open-end equity mutual fund performance with actively-managed in order to get new insight and understanding the Chinese equity mutual fund.

1.3 Problem definition and purpose

Despite there are several literatures discussed the mutual funds’ performance in emerging markets, but it is still a remarkable lack of understanding and information about the mutual fund industry, especially mutual funds in China, amongst fund specific characteristics and fund performance relationship. Some previous paper typically applied several fund-specific characteristics to predict mutual fund performance, like the expense ratio, age, size, and turnover ratio e.g. see Białkowski & Otten (2011), and Babalos, Kostakis, & Philippas (2009). For managerial attribution, the investigation of managerial variables in emerging market did show limited. Zeng, Zha, & Gong (2006) applied Jensen’s Alpha determined abnormal return to see the impact of managerial attribute in Chinese markets. This evidence cannot hold strong robustness due to applied single performance measurement. Based on the above, this thesis explored not only more than one different measures to determine the mutual fund performance, but also to investigate extra managerial factors such as managerial education and structure to examine the impact on fund performance.

The purpose of this thesis is trying to find the most relative predictive factors in which are reliable signed and reliable significant influencing on the China’s open-end equity mutual fund performance during the beginning of 2006 to the end of 2010. In order to fully explanatory and understanding, we examine the relationship under the overall sample period as well as the different market conditions (bull and bear markets) to see whether the results are consistent. Based on this, we would like to see, initially, how China equity mutual fund performance is. After that, we examine the fund-specific variables and managerial attributes influencing on fund performance. Therefore, the two main research questions can be formulated following:

In terms of investment returns, how have open-end equity fund with actively-managed in China performed?

How do fund-specific characteristics and managerial attributes influence the performance (return) of China’s equity mutual fund?

Before we analyze the equity fund performance, the fund return should be defined. Mutual fund investors, usually, receive two kinds of return: dividends and capital gains. We used Net Asset Values (NAVs) of equity funds as raw return. The monthly raw returns of the funds are inclusive of dividends which are paid out by common shares. Fund raw return is calculated net of fees. The market return is formed by the weight three major indexes in Chinese markets (40% Shanghai Composite Index + 20% Shanghai Government Bond + 40% Shenzhen Component Index). And then risk-free rate is proxied by the one-year fixed bank deposit rate.

In order to determinate the open-end equity funds’ performance, research literatures reveal various standpoints with respect to the use of risk-adjusted returns. Until now, there are six benchmarks to measure fund performance: Average Abnormal Return, Sharpe Ratio, Treynor Ratio, Jensen’s Alpha, Fama-French 3-factor Alpha and Carhart’s 4-factor Alpha. The most common performance measure is the average abnormal return, which measures the return in excess of market benchmark or risk-free rate. For above mentioned two ratios, the greater a mutual fund’s Sharpe or Treynor ratio, the better its risk-adjusted performance has been. In addition, Alpha is highly concerned method by recent researchers for measuring mutual fund performance, such as the Jensen’s Alpha, Fama-French’s 3-factor Alpha and Carhart’s 4-factor Alpha, which are based on the Capital Asset Pricing Model (CAPM). Alpha can be calculated based on different duration, like 1-, 3-, 5-year and even more periods. Based on the above, the positive value of Alpha can imply that the fund has outperformance. The more detail of those definitions and equation, see appendix A

The present study examines the open-end equity fund which excludes the index fund in China market by applying the two models: market adjusted model and Jensen’s Alpha. The combined dataset amounts to a total of 456 annual observations for 157 open-end equity funds. The duration of our sample includes bull- (2006, 2007, and 2009) and bear-(2008, and 2010) markets, which was claimed by Cao, Huang, & Zhang (2011). The determinant of market periods is based on the stock market performance. The results of this paper will extend the existed literatures by adding more value of fund-specific factors and managerial attributes influencing on mutual fund performance. Those findings also can support investors to understand and then diversify their capital investment in the China mutual fund industry.

1.4 Thesis structure

The remaining part of this thesis is organized as follows. Section 2 shows recent decade studies on mutual fund performance in developed markets as well as in emerging markets. In this section, we can know general information about mutual fund performance from persistence, fund manager skill or ability, and fund-specific characteristic. And then the hypotheses are built at the end of this section. Section 3 and 4, the methodology and data collection is discussed. Further, section 5 is analyzing the results of an empirical study, where section 5.1 presents fund performance based on the two different measurement models. Then, section 5.2 runs the regression equation to analyze the relationship and answer the hypotheses. The robustness tests are discussed in section 6. The conclusion of this thesis is allocated in Section 7. The last part is coming up with the limitation of this study and suggestion for further research.

2 Literature review and hypotheses

2.1 Literature review

Nowadays, a majority of investigation have concentrated on mutual fund returns rather than on stock returns under the modern asset pricing framework. A large number of academic research have been studied the mutual fund performance in the developed markets during the last two decades, like the US funds, while for emerging market several studies existed in recent years. But it still has some limitations compared with developed markets. In this section, the literatures of developed and emerging markets will be reviewed. After that, comparing two different markets will be summarized, and then coming up part is testable hypotheses.

2.1.1 Evidence from developed markets

2.1.1.1 Mutual fund performance

From the developed market perspective, a large amount of studies of mutual fund have come out since 1990’s in the United States. Several most relatively studies are gathered, which were concentrated on the U.S. market. For instance, Elton, Gruber, & Blake (1996) applied risk–adjusted return, which is based on the Alpha from a four-index CAPM model, to determine the common stock fund performance between 1977 and 1993. And they applied similar measurement horizons period 1- and 3-year with previous studies e.g. Goetzmann & Ibbotson (1994), Brown & Goetzmann (1995). The average return of mutual funds had negatively performed during this period. After that, used ranking of different Alpha (1-year selection Alpha, and 3-year selection Alpha) examined the relationship between past performance and future performance (1-and 3-period). Their result showed that the past performance can provide information about future performance based on the 1- and 3- year period future fund performance evaluation. Further, samples of total return were higher related under Alpha with 1-year period compared with 3-year period.

Another more recent related study, Huij & Verbeek (2007) investigated more than 6400 U.S. equity mutual funds during the period 1984-2003. Then, they applied both ordinary least square (OLS) regression and Bayesian analysis [5] to calculate the Carhart (1997) Alphas. Their result obviously indicated that former equity mutual funds’ performance can forecast the future fund performance in 1- and 3-year period, when funds were ranked the Carhart’s Alpha that were calculated based on Bayes approach. Meanwhile, the best-perform fund showed higher abnormal return under 1-year period compared under the 3-year period. When funds were ranked on the Carhart’s Alpha based on standard OLS, the result indicated that the short-run persistence existed but significantly lower than that of Bayes approach. Based above, those two studies showed the same result that past fund performance can predict future performance, especially within the one-year period.

2.1.1.2 Managerial skill and ability

In addition, investigation in fund manager, Golec (1996) documented that the younger managers (less than 46 years old) with longer tenure (more than 7 years) who held MBA degrees had better risk-adjusted return during 1988 to 1990 in the U.S. market. Furthermore, Gottesman & Morey (2006) examined the relationship between fund manager education and mutual fund performance in the U.S. fund industry during the 2000 to 2003 period. The manager education was measured by the mean of the GMAT score and the ranking of MBA programs. The results showed that the fund manager with a diploma from top 30 MBA programs which were ranked by Business Week got better performance compared with fund manager without MBA degree or not graduation from top 30 MBA programs.

Karagiannidis (2010) examined the relationship between management structure (single- or multiple-manager) and open-end mutual fund performance (growth-and income-oriented funds) in the U.S. during 1997-2004. He used ordinary least squares regression to see the Bull- (1997-2000) and Bear- (2001-2004) market periods. The results indicated that the growth-fund with managed by team-manager had lower mutual fund performance than by single-manager using the risk adjusted returns (Carhart’s Alpha) as fund performance during the bear market period. Moreover, they indicated more specifically for team-manager in which separated mixed-team funds (multiple managers and multiple advisors) and pure-team funds (multiple managers but only one investment advisor). Their result indicated that the mixed - team brought the underperformance in bear market for growth-, income-oriented fund when the Carhart’s Alpha was adapted as a measure of fund performance.

2.1.1.3 Fund-specific characteristics

With respect to the fund-specific characteristics, some concept of explanatory variables will be introduced in order to easily understand, see appendix B. Droms & Walker (1996) first major researched on the impact of fund characteristics influencing on risk-adjusted performance of equity fund using a pooled cross-section and time series analysis during 1971 to 1990. Their sample size was formed 151 the U.S. equity mutual funds with the impact of minimized survivor bias. Their results revealed that the equity mutual fund performance was not related to fund size, turnover ratio, and load fee. Notably, the fund’s expense ratio was positively significant related to risk-adjusted performance.

Otten & Bams (2002) investigated the European mutual fund performance in the five most important mutual fund countries (France, Germany, Italy, Netherlands, and UK) during 1992-1998. Initially, they applied the Carhart’s 4-factor model and the Jensen’s Alpha to measure the mutual fund performance. Afterwards, the outcomes showed that fund size in all four countries was positively significant with risk adjustment fund performance (Carhart 4-factor Alpha), while the expense ratio and age were negatively related to performance in Germany and UK.

In addition, Haslem, Baker, & Smith (2008) examined the impact of characteristics of 1,779 actively-managed retail equity mutual funds on performance from January 2004 to December 2006 in the U.S.. They applied Sharpe ratios, Jensen’s alphas, Morningstar ratings [6] , annualized return, Cumulative returns and Russell Index-adjusted returns [7] as performance measurement for mutual funds. Their results documented that expense ratio and turnover ratio were negatively significant related with Sharpe ratio, and Jensen’s Alpha. Further, fund size was positively significant related to all measurements of fund performance.

2.1.2 Evidence from emerging markets

2.1.2.1 Mutual fund performance

From the emerging market perspective, He & Hao (2008) examined the persistence of open-end mutual funds in China from 2004 to 2006, and applied Bernoulli trial scan statistics [8] to investigate the persistence of mutual fund performance. They introduced a value as a performance benchmark to decide the fund "success" (win) or "failure" (lose). The outcome showed that the best-performed and worst-performed equity-, money market-, and hybrid-fund existed a persistence during this sample period. Further, Cao, Huang, & Zhang (2011) applied the quarterly data of the overall 250 China open-end fund during the period of 2004-2010. And then they adjusted the raw returns with a market benchmark adjusted model and one-factor CAPM model (Jensen’s Alpha). Based on their results, the overall sample funds underperformed under one-factor CAPM models during this period, while those funds outperformed under the market benchmark adjusted model. In addition, they divided their sample period into two different periods (bear and bull) to exam the fund persistence. Their finding documented that the persistence of fund performance differed under different market periods. More specifically, the persistence of fund performances showed weaker in the bull market compared within the bear market. Hence, investors in bull or bear market can purchase the fund with best performance instead of the worst performance.

In addition, Białkowski & Otten (2011) discussed the fund persistence in Poland. They investigated the persistence of 140 mutual fund performance on bond and mixed mutual funds from January 2000 to January 2008. They applied the Carhart 4-factor model to test performance in the Polish mutual fund industry over a period of time. The outcomes of this investigation showed that the performance of Polish mutual funds was under their relative benchmark value, and also domestic funds had better performance compared with international funds. Their results, lastly, indicated that applied the strategy which purchased last year’s best-performed fund and sold last year’s worst-performed fund, can generate to better performance.

2.1.2.2 Managerial skills and ability

Zeng, Zha, & Gong (2006) used closed-end fund as sample between 2001-2004 in China market via applying Ordinal Least Squares regression to examine the managerial attributes influencing on fund performance. And then applied Jensen’s Alpha measures abnormal return. Their results documented that fund manager tenure was negatively significant related to fund performance.

In addition, Liu (2009) studied both the 147 open-end and 30 closed-end mutual funds during the 2001-2008 period in China mutual fund industry, and then applied Logistic regression model [9] to predict managerial attribution on influencing mutual fund performance. His outcome revealed that single- and team-manager was no significant related to fund performance. Meanwhile, a fund manager with both science and engineering and economics background can generate better fund performance.

2.1.2.3 Fund-specific characteristics

BiaÅ‚kowski & Otten (2011) addressed the fund size issue; their result revealed that the size of assets under management is positively significant related to mutual fund performance. More detail study of fund size in emerging markets, for example, Tang, Wang, & Xu (2012) examined that the relationship between mutual fund performance and fund size from January 2004 to June 2010 in the Chinese mutual fund market. They used 136 open-end equity mutual funds and the Fama and MacBeth regression [10] . And then their results showed that the relationship between size and performance of mutual funds was an inverted U-shape curve. When fund’s size goes up, more specifically, the impact of the economy of scale can bring the better fund performance. Further, when the fund’s size is too large, the impact of liquidity constraint can cause the worse fund performance. The scale of economy and liquidity constraint factors can determine the fund size how to influence on the fund performance. Therefore, the inverted U-shape can be explained by the economy of scale and liquidity constraint.

Another frequently discussed fund characteristic is the relationship and impact of the expense ratio on mutual fund performance. To disclose the relationship, Babalos, Kostakis, & Philippas (2009) extended research for investigating the relation between expense ratio and open-end equity fund performance from 2000 to 2006 in the Greek mutual fund industry. As used Jensen’s alpha and Carhart 4-factor alpha method, they found that the expense ratio was negatively significant related to both risk-adjusted fund performance, which means, a high expense ratio played a vital role to bring about low equity fund performance. Meanwhile, they also documented the impact of fund age and size on mutual fund performance. The results showed that the fund age was positively significant related to fund performance, while the fund size was negatively significant related.

2.2 Comparing developed and emerging mutual fund markets

For fund performance, in both two markets, Brown & Goetzmann (1995), Goetzmann & Ibbotson (1994), Huij & Verbeek (2007), He & Hao (2008) and Huij & Post (2011) found that past mutual fund performance can predict the future return during a short-term period. This phenomenon is contributed by the common investment strategies, such as buying past-winning and selling past-losing stocks. In addition, Grinblatt & Titman (1992), and Elton, Gruber, & Blake (1996) documented that the persistence of mutual fund performance can show in long-term period. However, the contrast evidence is claimed by Carhart (1997), demonstrated that the persistence cannot exist in long-term horizons, only appear during 1-2 years.

Turning to managerial skills, from both economic markets, they held similar findings for skilled- or unskilled-manager that there were no differences impact on fund performance, claimed by Baks, Metrick, & Wachter (2001), Berk & Green (2004), and Du, Huang, & Blanchfield (2009), even worse performance in both markets e.g. see Bertin, & Henker (2004), and Zeng, Zha, & Gong (2006). This is due to hold many assets, and helms several funds at the same time. Specifically, when a fund manager helms several funds in the meantime, their energy and ability will be separated into different funds so as to handle those funds difficultly and unequally, and thus erode fund performance. In addition, for managerial structure, several authors revealed that there was no obvious distinction on mutual fund performance when compared the performance of single- to team-manager in both markets, presented by Prather & Middleton (2006), Liu (2009), Karagiannidis (2010), and Bryant & Liu (2011). A manager with the financial and economic background can perform better in China, claimed by Liu (2009). However, Gottesman & Morey (2006) documented that only fund manager with MBA degree who can bring better fund performance, graduated from top 30 MBA programs in the U.S. market. The possible reason of this difference is because of the gap between manager’s education and regulation. Compared with emerging markets, fund manager had higher education as well as fund industry had well-regulated in developed markets. Therefore, only a small percentage of manager who had the best knowledge or educational background can achieve better performance.

As taken fund characteristics into consideration, there are several popular factors that are usually tested by previous economists. As follows, Table 2.1 summarizes the implications and empirical evidences from matured and emerging markets on the relationship between each of the above determinants. One fund characteristic, expense ratio, is documented by several economists. The relationship between expense ratio and mutual fund performance had reliable singed. The interesting thing is that the impact of the expense ratio on mutual fund performance showed the similar opinion in both economic markets. For expense ratio, the majority of authors documented, such as Carhart (1997), Prather, Bertin, & Henker (2004) and Babalos, Kostakis, & Philippas (2009), that the expense ratio was negatively related to fund performance.

For turnover ratio, the results can be a variance in different economies. Carhart (1997), Edelen , Evans , & Kadlec (2007), Haslem , Baker , & Smith (2008) documented that turnover ratio was negatively related to performance in developed markets, while Tang, Wang, & Xu (2012) held the opposite opinion that was positively related in China emerging market. From those contrast findings, it implies that China mutual fund industry may exist asymmetric information that can generate better performance, and then cover transaction and tax costs. Further, Benos & Jochec (2011), Otten & Bams (2002), Haslem and Baker , & Smith (2008) revealed that fund size was positive significantly related to fund performance in both markets.

In addition to the fund age explanatory variable, Babalos, Kostakis, & Philippas (2009) claimed that fund age was positively significant related to mutual fund performance in emerging markets, but Otten & Bams (2002) documented that it was negatively in developed markets. This impact of fund age on mutual fund performance contributes to companies experience, life cycle and managers’ skills. In emerging markets, an elder mutual fund company seems to be more valuable compared with young companies. However, the mutual fund industry has existed a long period in developed markets. The fund performance decreases as age progress, and attribute this to the increasing complexity of the fund operation or size.

Table 2.1 Summary of the pervious empirical findings

Summary of the empirical evidences on fund-specific characteristics and managerial attributes influence on mutual fund performance from both matured and emerging markets. The signs are based on literature reviews. It can clearly show the similarities and different findings from both markets. The sample and examined period also includes.

Determinants

Sign

Empirical evidence from matured markets

Examined period

Sample

Empirical evidence from emerging markets

Examined

period

Sample

Fund age

+

(Babalos, Kostakis, & Philippas, 2009)

2000-2006

75 Greek funds

-

(Otten & Bams, 2002)

1992-1998

507 E.U. funds

Fund size

+

(Otten & Bams, 2002)

(Haslem , Baker , & Smith , 2008)

1992-1998

2004-2006

507 E.U. funds

1779 U.S. funds

(Białkowski & Otten, 2011)

(Lai & Lau, 2010)

2000-2008

1990-2005

140 Poland funds

331 Malaysia funds

-

(Chen J. , Hong, Huang, & Kubik, 2004)

(Bhojraj, Cho, & Yehuda, 2010)

1962-1999

1992-2008

741 U.S. funds

U.S. funds

(Babalos, Kostakis, & Philippas, 2009)

2000-2006

75 Greek funds

Expense ratio

+

Droms & Walker (1996)

1971-1990

151 U.S. funds

-

(Carhart, 1997)

(Otten & Bams, 2002)

(Prather, Bertin, & Henker, 2004)

(Haslem, Baker, & Smith, 2008)

1962-1993

1992-1998

1996-2000

2004-2006

1892 U.S. funds

507 E.U. funds

over 5000 distinct funds

1779 U.S. funds

(Babalos, Kostakis, & Philippas, 2009)

2000-2006

75 Greek funds

Turnover ratio

+

(Kacperczyk, Sialm, & Zheng, 2005)

1984-1999

1771 U.S. funds

(Tang, Wang, & Xu, 2012)

2004-2010

136 China funds

-

(Carhart, 1997)

(Edelen , Evans , & Kadlec, 2007)

(Haslem , Baker , & Smith , 2008)

1962-1993

1995-2005

2004-2006

1892 U.S. funds

1706 U.S. funds

1779 U.S. funds

Fund manager education

+

(Golec, 1996)

(Gottesman & Morey, 2006)

1988-1990

2000-2003

530 U.S. funds

518 U.S. funds

(Liu, 2009)

2001-2008

147 China funds

-

Fund managerial structure (team or single)

+

(Han, Noe, & Rebello, 2008)

1993-2002

2171 U.S. funds

-

(Karagiannidis, 2010)

1997-2004

2031 U.S. funds

2.3 Hypotheses development

It has been documented that some different fund characteristic variables can significantly influence the fund performance. Following, the most common variables, which have an influence on mutual fund performance, have been studied. These include fund size, fund age, expense ratio, turnover ratio, management structure, and managerial education. And with each examination of variable, a hypothesis has been made regarding to each variable for the ease of further analysis. Before introducing the fund characteristic variables, the open-end equity fund performance should be determined.

Equity fund performance

Jin & Wu (2007), and Tang, Wang, & Xu (2012) documented that the average Jensen’s alphas in CAPM model and the market benchmark-adjusted return are indicated positive in Chinese market from 2004 to 2006 and 2004 to the first half of 2010, respectively. Indeed, Lai (2010) indicated that the average of Jensen’s Alpha, Fama-French’s Alpha and Carhart’s Alpha were positive during the 2002-2009 period in Chinese mutual fund industry. This indicated that the majority of equity fund had outperformance in China. Therefore, we predict the equity mutual fund performance in the Chinese market as follows:

Hypothesis 1: on average, Chinese open-end equity funds have outperformance

Bull- and Bear- market period

The vast majority of fund performance studies on equity mutual funds have not concentrated on their behavior under different market periods. One of studies in China, Jin & Wu (2007) documented that open-end equity mutual fund on average are able to beat to the market under two market conditions, as indicated by their positive return after risk-adjusted. Therefore, we predict following hypothesis:

Hypothesis 2: on average, the performance of Chinese open-end equity mutual funds is outperformance under bear- as well as under bull-market period after risk adjusted.

Fund size

As a big size of the mutual fund, it can keep finding investment opportunities. Indeed, there are several advantages in a large size of mutual funds compared with small one from theoretical aspects. On the one hand, funds can collect more opportunities to get the abnormal return, because a fund has large asset can invest more stocks or bond to diversify their unsystematic risk. On the other hand, economy of scale can explain that the larger the fund size leads to the better the fund performance. For instance, the large size fund has less brokerage commissions and marketing research costs than a small fund size. From empirical study, (Białkowski & Otten, 2011), (Lai & Lau, 2010), (Zeng, Zha, & Gong, 2006) found a positive relationship between fund size and performance of the equity funds.

Hypothesis 3: fund size is positively related to the mutual fund performance

Fund age

Fund age could play an essential role in the determinant of fund performance. Theoretically, younger funds may need high costs during their initial establishment period, such as doing the advertisement to attract new investors. This will cause younger fund has worse performance compared with a fund with a longer existence in a fund market. In addition, as increase experiences and social network, a mutual fund performs better. Furthermore, Babalos, Kostakis, & Philippas (2009) indicated that risk-adjusted returns are significantly positively related to the fund age, verifying that managers’ and firms’ experience tends to be valuable for increasing the fund performance. Therefore, we would like to assume that the fund age has impacted on Chinese fund performance. The following hypothesis is setting up:

Hypothesis 4: fund age is positively related on fund performance.

Expense ratio

The expense ratio indicates that the proportion of the fund’s assets related to the expense of operating a mutual fund. The expense fee includes the investment advisory fee, the administrative expenses, 12b-1 distribution fees (market costs), and other operating costs. Golec (1996), Carhart (1997), Dellva & Olson (1998), Prather, Bertin, & Henker (2004) indicated that a negative relationship between fund’s performance and expense ratios, which means, the investor pay a high expense fee to bring a low fund's performance. This issue can be explained by several reasons. One of the main reasons is a rational explanation. Berk & Green (2004) explained that manager’s rewards were related to their managed assets. The fund manager was likely to get higher compensations via holding more inflows fund assets. When a mutual fund performs better, the inflows money to their fund increase naturally. Indeed, the amount of total asset increase more than expense, and then lead to a low expense ratio. Therefore, we predict following hypothesis:

Hypothesis 5: fund expense ratio is negatively related to fund performance.

Turnover ratio

The turnover ratio is a measure of the number of times a fund manager trades during a given time period. For theoretical basis, a fund manager trades frequently that is likely to have more success compared with infrequently trade when investigation of open-end fund. The frequent trade, however, also exists some problems, such as taxes and transaction costs, and therefore reduces the return. In the recent empirical studies, Wermers (2000), Moskowitz (2000), Kacperczyk, Sialm, & Zheng (2005), and Tang, Wang, & Xu (2012) documented that the turnover ratio was positively significant related to fund performance. Based on theoretical and empirical study, the high turnover ratio leads to high fund performance. All in all, we predict the following hypothesis:

Hypothesis 6: fund turnover ratio is positively related to fund performance.

Management structure

Management structure can be set as a single-manager or team-manager. For theory aspect, Team management can bring a variant style and fair judgment, and even enlarge their professional skills and knowledge to deal with a broader range of information. Even more, team-manager with various backgrounds, cultures to cope different situations, and enhance fund performance, claimed by Bikhchandani, Hirshleifer, & Welch (1998). But with team management, free rider may occur. In addition, team management can prolong the decision making process since it will take longer time to reach an agreement within a team than with single management. Overall, we predict following hypothesis:

Hypothesis 7: In fund’s management structure, the fund with team-manager has positively related to mutual fund performance.

Managerial education

Fund manager’s education has been concerned a lot on mutual fund performance. With high level of education, the fund manager has better professional skill, knowledge and ability, and psychological quality to make an efficient and effective decision. Golec (1996) documented that the high level education of fund manager had better performance than those with low level educations, especially manager hold MBAs comparing with those without MBAs. Fund manager with holding MBAs degree has a sufficient knowledge background of management and financial areas. Therefore, we predict this hypothesis:

Hypothesis 8: fund manager with an MBA degree is positively related to fund performance.

3 Research method

3.1 Fund performance models

Before introducing the methodology, the way to measure fund performance is a critical issue in determinants the impact of fund characteristics and management attributes on mutual fund performance. Based on the previous literature review, the methods of measuring mutual fund performance are various. The main methods include: Market Benchmark Adjusted Model, the Single- and Multi-factor Risk Adjusted Model.

Mutual fund performance is measured usually by the intercept which calculate in a regression, also known as a fund’s Alpha. From the prior most mutual fund investigation, a majority of researchers applied the CAPM based single index model to determinate the mutual fund performance, such as Lai & Lau (2010), Białkowski & Otten (2011) and Tang, Wang, & Xu (2012). The intercept of Alpha, also known as the Jensen’s Alpha, widely interpret the measurement of a mutual fund over- or under-performance. However, only using the return of the market to describe the returns of the mutual fund is not sufficient compared with the multi-factor model. Therefore, a multi-factor CAPM was commonly applied in recent literature due to the diversity of investment style in a mutual fund. One better explanation of the fund performance model was introduced by Fama & French (1993), which is Fama and French 3-factor model. This model added two additional factors (SMB, and HML) into a single index CAMP model. Those two factors described the excess return difference between small cap and big cap, and excess return from a high book-to-market minus low, respectively. In the recent study, Carhart (1997) adjusted the Fama-French 3-factor model by extended one more factor that was a momentum anomaly. This factor measured the excess return between past winners and past losers. From several empirical studies examined e.g. (Otten & Bams, 2004), (Babalos, Kostakis, & Philippas, 2009), and (Lai & Lau, 2010), Carhart 4-factor model has become more effective and standard method to explain mutual fund performance.

For the investigation of mutual fund performance, there are several measurement horizons, such as 1-, 3- and 5-year horizons. The majority of literatures applied three-year previous data to estimate the Betas, and calculate abnormal mutual fund performance in developed markets, such as Elton, Gruber, & Blake (1996), Carhart (1997), and Kosowski, Timmermann, Wermers, & White (2006). Due to a short span of time on the mutual funds market, however, one year measurement horizon is one of the major methods to determinate the Betas, and calculates abnormal mutual fund performance, claimed by Prather, Bertin, & Henker (2004), Lai & Lau (2010), and Białkowski & Otten (2011). When applying a longer historical fund return, measurement of a fund’s performance can lead to more accurate inferences. Nevertheless, some disadvantages exist, especially in emerging market, when using of longer measurement horizons. One of the main disadvantages is a small sample size. For instance, applying the historical of three-year return, there are only 18 equity mutual funds data that exist at the end of 2006 over an extended period in China mutual fund industry, and this may cause to other statistical biases. Another main disadvantage is that fund performance will change over time, especially under different market time periods. On the other hand, applying higher frequency data (such as daily-, weekly- returns) can lead to more accurate estimation of mutual fund performance, but it's limited. Thereafter, it is necessary to consider more accurate ways to estimate mutual fund performance based on the short span time of Chinese mutual funds market. We adopt the method that is mentioned by Huij & Verbeek (2007), the calculation of betas are conduced by using pervious at least 12- to at the most 36- monthly return data. As follows, the figure 3.1 indicates the methods to calculate the annual Alpha for the observation year from 2006 to 2010, respectively.

Figure 3.1: An outline of the estimation betas and alpha processes

Using estimation betas to calculate monthly alpha, and then annually alpha

T+12

Privous-12 to 36- monthly data to estimate the betas

T

Where: T is a month; the annual alpha for a period T to T+12, its indicted observation year.

In the sake of easy understanding, For example, if a fund has a monthly return available on 05-31-2005, we can conduct 20-monthly observations to estimate beta on January of 2007. We assume that the Betas are constant over following one year period. This means Beta is used to compute monthly Alpha (between t and t+12). And then calculate this month’s Jensen’s Alpha that is in a unit of % as monthly fund performance on January of 2007 using monthly- raw return, -risk free rate and –market return. Afterwards, we collect each month Jensen’s Alpha for 2007 whole year period. Consequently, the annually Alpha is compounded by 12-monthly Alpha as a measure of fund performance.

To measure mutual fund performance, in order to investigate whether the different ways mutual fund performance will influence the result, we will use the following two different models: Market Benchmark Adjusted Model and single CAPM model. Those two models are as follows: (2) and (3). In this thesis, the equity mutual fund performance is calculated by using monthly raw return (1), which includes the dividend. This formula is defined by Guo’ Tai’ An Database [11] .

Market benchmark adjusted return:

(2)

Where:: is t period mutual fund I’s market adjusts return; : is t period market return (in this thesis, firstly, we use = 40% * Shanghai Composite Index + 20% * Shanghai Government Bond Index + 40% * Shenzhen Component Index), there are several articles mentioned see e.g. Zeng, Zha, & Gong (2006), Jin & Wu (2007). Moreover, after June 2004, when the new law of the People’s Republic of China on equity mutual funds was carried out, the item of equity mutual fund has been canceled that invest at least 20% of its assets in the government bonds. Until now, a majority of equity mutual funds do invest approximately 10% of their total assets on bond fund. Additionally, the Shanghai Government Bond index reflects to Chinese bond market changes as a whole. Therefore, we adopt a new way of calculating market benchmark return ( = 45% * Shanghai Composite Index + 45% * Shenzhen Component Index + 10% * Shanghai Government Bond Index) to adjust equity mutual fund returns, and do the robustness check whether the equity fund performance is consistent with the same results.

Jensen’s Alpha:

Jensen’s Alpha is based on the Capital Asset Pricing Model (CAPM). This is a way to determine the abnormal return that we can know the returns of actively-managed funds are under- or over market returns. When Jensen’s alpha is positive, the fund has beaten market, vice versa. The portfolio beta with using previous at least 12- to at the most 36- monthly return data and running the ordinal-least square regression of the following equation (3) can be estimated. It describes the volatility of the portfolio with respect to the market. And then get the monthly abnormal return (Jensen’s Alpha):

(3)

Where : is t period mutual fund I’s return; the risk free rate (the one-year bank deposit rate); : is t period market return (in this thesis, we use the above mentioned market index); : is Jensen’s alpha, the performance measure; :is beta as systematic risk of the its portfolio in Chinese market.

Finally, we take the monthly return of one-year bank fixed deposit rate as the proxy risk free rate. With respect to market benchmark returns, we use two measures of market benchmark returns. First benchmark is calculated by 40% *Shanghai Composite Index + 20% * Shanghai Government Bond Index + 40% * Shenzhen Component Index. Second benchmark is composed by 45% * Shanghai Composite Index + 45% * Shenzhen Component Index + 10% * Shanghai Government Bond Index. The aim of adopting a second benchmark is to do the robustness check. Two ways of performance measurement are based on the monthly data. Consequently, the annual fund performance is compounded by 12-monthly returns.

3.2 Regression analysis

In this thesis, I would like to investigate fund-specific characteristics and managerial attribute influencing on equity fund performance. Several previous literatures e.g. Prather, Bertin, & Henker (2004), and Karagiannidis (2010) were applied an Ordinary-Least-Square (OLS) regression, which is an essential methodology applied for financial problems to examine a causal relationship such as fund-size/ -age on fund performance. Indeed, Moutinho & Hutcheson (2011) indicated that this regression can be applied to single- or multiple variables, and also easily check the model assumptions, like constant variance and the impact of outliers. Moreover, for investigation of causal relationship, most researchers conduct pooled cross-sectional study by combining time series data. It can be assumed that individual or multivariate factors could be predicted the mutual fund performance during a period of time. There are several advantages. One of the main advantages is to increase the sample size by having repeated observations. Another main advantage is that pooled models can examine the effects of change over time, and even test multiple and complex hypotheses. As we discussed above, the primary investigative method I follow in this thesis is pooled cross-sectional via Ordinary-Least-Squares regression model.

Obviously, mutual fund performance is the dependent variable. And then fund characteristics and managerial attributes are independent variables. In this thesis, the main research methodology is applied multi-regression model, which can provide more information to explain the relationship between dependent variable and independent variables. After that, R-square will be applied to check the goodness of fit between predictor variables and mutual fund performance. Initially, we will apply following regression to examine the overall sample period. And then investigate deeper into the differences in bull- and bear-market periods, in order to find which factor is the most relevant influencing on mutual fund performance under different market periods. Below is referring, we consider the multiple-regression (5) for each of the two performance metrics. This is our main research method in this thesis.

(5)

Where: : is the measure of fund performance; = constant; : is the denary logarithm of the fund total net assets at the end of year t; : is the denary logarithm of the fund age at the end of year t; : expense ratio for the fund i at the end of year t (examining in annual data);: is turnover ratio for the fund I during t period; : is a dummy variable (0 = single manager, and 1 = team-manager); : is a dummy variable (0 = without MBAs, and 1 = holding MBAs) and : is a random error term. Considering the potential for interrelationships among these independent variables, we analyze their multicollinearity. Based on the above model, our six hypotheses can be tested. Hypothesis 3, 4, 6, 7 and 8 predicts,, , and to be positive, respectively. Hypothesis 5 predicts to be negative.

4 Data collection and statistics description

4.1 Sample selection and data source

Since I mainly concentrated on the China’s open-end equity mutual fund with actively-managed, the dataset of this thesis is based on primary and secondary data such as database, annual report and website. Firstly, like most authors of investigating the mutual fund performance, they applied the database to retrieve the data. From the database, it is a time-effective and –efficient way to get the necessary data, because it contains lots information that a researcher need, such as fund monthly return. Second, reviewing the website also plays a critical role, it can provide additional information that the database cannot clearly define, like managerial attritions. Last but not least, the functions of the annual report provide some robustness’ information to complete our sample data.

In this thesis, all available Chinese open-end equity mutual funds monthly return, risk-free rate and total fund assets were collected from Guo’Tai’Jun’An (GTA) database, which also is named China Securities Market & Accounting Research (CSMAR). GTA is a leading global provider of China financial market data, China industries and economic data. The market return was composited by hand-collection from website (finance.sina.com.cn). For other explanatory variable, fund age, expense ratio, turnover rate, management structure and fund managerial education have been hand-calculated based on GTA database and website (Chinafund.cn).

We obtained the all Chinese open-end equity mutual funds from CSMAR mutual fund data in Guo’Tai’Jun’An (GTA) database during January 2006 - December 2010. Due to study Chinese open-end equity fund, some fund invests outside of the Chinese market that will be excluded. Further, as managerial style difference, concerned with activity-managed fund, the passively-managed fund also is called as an index fund (Exchange-Traded Funds) that will be excluded from our sample. We will eliminate funds with missing important data, like raw monthly return. When investigating the mutual fund, the survivorship bias is a well-indicated issue in many previous studies. Many empirical studies showed that survivorship bias can influence the result in average performance measures, because the sample only includes survivor mutual funds but not dead mutual funds during the observation period. Brown et al. (1992) indicated that the average performance is overstated because of losing the poor or bad fund in the database. From the database, we cannot find dead fund. Therefore, the survivorship bias may exist in our investigation.

According to the above criteria stated, the sample is gathered. The following refers to the thing that summarizes the retrieve:

345 open-end equity mutual funds in Chinese market until the end of 2010;

325 open-end equity funds left, after excluding funds (QDII) with invested in foreign stocks;

249 equity mutual funds left, after excluding fund with passively-managed ( Exchange-Traded Funds);

The duration of equity mutual funds less than continuous operation two years should be excluded from the sample because of the way of calculation CAPM alpha.

Equity mutual funds will be eliminated, when important data, like a raw monthly return is missing.

Therefore, our sample has 157 equity mutual funds left. The combined dataset amounts have a total of 456 annual observations for 157 equity funds during the period of 2006-2010. All the data are from CSMAR Mutual Fund [12] Database. And then those data are analyzed with MS Excel and IBM SPSS software.

After retrieving the database based on above mentioned criteria. As follows, Table 4.1 presents some characteristics of our sample during the period 2006-2010. It is worth noting that the total assets of equity fund under management showed a very large variation, dramatically rising from 1755.95 million Yuan in 2006 to 12538.01 million Yuan at the end of 2007, and then significantly falling to 5367.81 million Yuan at the end of 2008. The raw returns of open-end equity funds were in line with the market returns. One interesting observation we found that one average of market returns achieved a higher performance compared with the open-end mutual fund during 2009, which figures indicated 71.40% and 65.80%, respectively. This indicated that on average actively-managed fund cannot beat to market. Therefore, the index funds increased dramatically after 2009.

Table 4.1 our sample of Chinese open-end equity mutual fund with actively-managed from 2006-2010 periods

Year

No. funds

Av. Fund size (total assets) Mil. Yuan

Number of investment companies

ER returns

MB returns

2006

31

1755.94

57

118.88%

95.86%

2007

52

12538.01

58

120.40%

95.83%

2008

92

5367.81

60

-50.23%

-54.92%

2009

124

8402.48

60

65.80%

71.40%

2010

157

6081.13

62

5.21%

-8.42%

Notes: this table presents our sample of Chinese open-end equity funds with actively-managed during the period 1st, Jan. 2006-31st, Dec.2010. Fund size (total asset in Million Yuan) they helmed. ER returns represent the mean of the fund’s raw return for each year, while the MB returns stand for the return of the market returns (40% * Shanghai composite index+ 20% * Shanghai Government Bond index + 40% * Shenzhen component index)

4.2 Variable definitions and descriptive statistics

The raw fund return and dependent- variable, according to the literature review, which are defined, and then calculated in the following ways.

Raw return reflects a fund’s performance for the holding period including dividends. It directly collected from Guo’Tai’Jun’An (GTA) database.

Fund size: total assets for a fund at the end of time t.

Fund age simply expresses the number of years that a fund has been founded.

The expense ratio is calculated total fund expenses as a percentage of average fund net assets. Specifically, total fund expenses include manager fee, exchange fee, interest pay and other fee.

Turnover ratio is a measure of trading activity. There are two ways to calculate turnover ratio. Firstly, turnover ratio_1 is calculated as the minimum of annual purchases or sales stocks are divided by the average annual amount of fund wealth. Secondly, for robustness test, turnover ratio_2 calculated as the sum of annual purchases and sales stocks are divided by the average annual amount of fund wealth.

Management structure: it represents simply the number of managers under a fund. If two or more managers under a fund helm more than half years during a whole year period, we assume that management structure is a team-manager, vise verse. Therefore, single manager=0, team-manager=1.

Managerial education: it indicates that manager with MBAs degree or not. In team-manager, if one of manager with MBAs degree helm more than half years during a whole year period, we assume that fund manager holds an MBA degree, vise verse. Therefore, manager(s) under a fund without MBAs=0, manager(s) under a fund with MBAs=1.

Following table 4.2 presents a summary of the descriptive statistics for fund characteristics and managerial attributes as the employed variables. In order to control the extrem