Master Thesis
The Performance of U.S. Based International Mutual
Funds
Can Cao
s2085208
Supervisor:
Prof. Auke Plantinga
University of Groningen
Faculty of Economics and Business
Master Finance
1. Introduction ... 1
2. Literature Review... 4
3. Mutual fund performance models ... 6
3.1 Single index model ... 6
3.2 Multi-factor model ... 8
3.3 The influence of fees on performance ... 9
4. Data ... 11
4.1 Sample of funds ... 11
4.2 Sample characteristics ... 12
4.3 Benchmarks ... 15
5. Empirical results ... 16
5.1 Unadjusted performance ... 16
5.2 All funds portfolio ... 17
5.3 Risk-adjusted performance ... 18
5.4 Multi-factor models results ... 20
5.5 The influence of fund characteristics ... 22
5.6 Robustness test ... 23
6. Conclusion ... 24
References ... 26
Appendix A: Sample Description ... 29
Funds
Can Cao
Faculty of Economics and Business, University of Groningen, Groningen, the Netherlands
Email address: c.cao@student.rug.nl
Abstract
This paper presents an overview of the U.S. based international mutual fund industry and investigates the performance of 218 open-end international mutual funds, which invested outside of the U.S. market between 2008 and 2012. Using a survivor-bias free database of international mutual funds, I apply CAPM, Fama French (1993) 3-factor model and Carhart (1997) 4-factor model in this study. In addition, the influence of funds’ expenses on risk-adjusted performance is concerned as well. The overall results suggest that the performance of international mutual funds is strongly affected by market movements while funds’ expenses do not significantly affect. Moreover, the risk-adjusted returns on the international mutual funds were not significantly different from that on the domestic benchmark. Finally, international mutual funds underperform the market, and this is generally insignificant. The benefits for the U.S. investors to hold international mutual funds appear to be limited in this study period.
Keywords: international mutual funds; performance evaluation; CAPM; 3-factor model; 4-factor model.
JEL classification: G12, G15, G20, G23
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1. Introduction
In the 1980s and 1990s, The U.S. based global and international mutual fund assets grew from $46.2 billion to $501.4 billion. This growth had attracted attention of investors who seek to diversify their portfolios into foreign markets. Indeed, mutual funds have become one of the most ideal vehicles for small investors who wanted to create diversified portfolios (Lang and Niendorf, 1992). Firstly, the mutual fund has better access to essential economic and financial data. Furthermore, the fund’s professional management group is more likely to have the expertise of data integration. Clearly, most small investors would be hard to access and interpret that data. Secondly, since the mutual fund’s typical transaction will encounter a much larger amount, economies of scale may allow it to reduce transaction costs. At the meantime, volume purchases and sales may allow it to access some wholesale markets to lower further costs. These kinds of cost savings can help to overcome high transaction costs, which is one of challenges to invest in international markets.
Table 1
The U.S. Has the World’s Largest Mutual Fund Market Total worldwide mutual fund assets
$26.8 trillion
United States 49
Europe 31
Africa and Asia/Pacific 13
Other Americas 8
Total U.S. mutual fund assets
$13.0 trillion
Domestic equity funds 33
World equity funds 12
Bond funds 26
Money market funds 21
Hybrid funds 8
Note: Components may not add to 100 percent because of rounding.
Sources: Investment Company Institute, European Fund and Asset Management Association, and other national mutual fund associations
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billion in the first quarter, which was the largest since the fourth quarter of 2006 ($17 billion) (Investment Company Institute, 2013). The number of mutual funds has also increased dramatically to more than 73,900 funds worldwide. It is obvious that this industry plays a vital role in the financial market. The U.S. mutual fund market has $13 trillion in assets under management at the end of 2012. It is the largest mutual fund market in the world (Table 1), accounting for 49 percent of the $26.8 trillion in mutual fund assets worldwide.
The first international mutual fund was introduced in U.S. in 1955. International mutual funds accounted for 9% of mutual fund industry asset in 2008. Starting with Jensen (1969), most of studies on mutual funds conclude that the net performance of mutual funds (deducted expense) is inferior to that of a comparable passive market index. With many of the most attractive investment opportunities existing outside the United States, U.S. investors cannot restrict themselves to invest only in domestic stocks. In addition, over the past decade many countries have relaxed their investment restrictions, therefore, trading in assets abroad spring up. The rapid growth of international mutual funds can be attributed to the benefits of diversification. To be more specific, diversification may smooth out non-systematic risk (Cumby and Glen, 1990). However, this kind of portfolio risk reduction is hard to achieve by a purely domestic strategy (Dimson et al., 2002). International mutual fund faces several types of risk which domestic market does not bothered, such as currency risk, trading costs, legal and regulatory risk, and political/country risk. The value of the asset closely related to the local market, and the cost of collecting information is higher in terms of monetary and time concerns. Many empirical studies show that internationally diversified portfolios generally have risk and return properties which are more desirable than those of a portfolio consisting of securities on one single country (Essayyad and Wu, 1988). Therefore, even though international mutual funds can facilitate both the functions of asset allocation and currency exposure, whether they can fulfill the diversification purpose and earn higher returns remains a matter of debate.
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little evidence about whether international mutual funds could benefits from foreign markets, especially it exists the market frictions such as barriers to information flows, costs of information transmission, cultural and other differences. Recently, some small investors keen to seek domestic investment opportunities instead of international investment options. Because the domestic markets that not only are larger, but also offer a variety of investment options. Even though the global market faces less restriction, a large amount of investors continue to invest their money mostly in the domestic market, rather than in a diversified world market portfolio. This phenomenon is being called the “home-bias” (French and Poterba, 1991). Therefore, an interesting question whether a foreign investor, such as a U.S. investor, is expected to benefit from investing in the international mutual funds rises.
The purpose of this paper is to examine whether U.S. investors could benefit their rate of return by purchasing international mutual funds. The study explores the issue of possible potential benefits for U.S. investors through measuring the performance of international mutual funds, incorporated in the U.S. Current literature focuses on international mutual funds is scarce, but Cumby and Glen (1990) provide some directions for the further research. They examined the performance of a sample of fifteen U.S. based international mutual funds by applying the Jensen measure and weighting measure by Grinblatt and Titman (1989). They found no evidence of outperformance between 1982 and 1988. I examine the performance of international mutual fund differs across regions from the perspective of a U.S. investor. By absorbing and integrating the previous research, this study mainly discusses the performance of U.S. based international mutual funds from 2008 to 2012. In brief, this paper attempts to answers two questions about international mutual funds:
1. Whether the international mutual funds earn higher returns than the benchmark returns in terms of risk.
2. Which international mutual funds characteristics significantly affect the performance.
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and compare it with the performance of benchmarks. Thirdly, I discuss the management fee for international mutual funds, to examine whether these fees actually affect the fund’s performance.
The rest of the paper is organized as follows. Section 2 provides a literature review. Section 3 is a description of the methods used in the performance evaluation of the sample of international mutual funds. Section 4 describes the sample of international funds and the benchmarks used are presented. In section 5, I provide the empirical findings. Finally, section 6 concludes the paper.
2. Literature Review
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Mutual fund performance is usually treated as an attractive topic for investors and researchers, because the performance is the most significant factor to shape investors’ confidence in the fund industry. There is an abundant literature dealing with the issue of mutual fund performance. Pioneers made an important contribution to the seminal works in respect of risk and return, notably, Markowitz (1952), Sharpe (1964, 1966), Treynor (1965) and Jensen (1968, 1969), developed the standard methods to measure risk adjusted mutual fund returns, based on the mean-variance framework. Starting with Jensen (1969), most of the studies conclude that the net performance of mutual fund (net of expenses) shows inferior performance compared to benchmarks. However, some contradictory options arose during the late 80s and early 90s. Grinblatt and Titman (1989) found mutual funds did manage enough private information to offset the expenses they made, hence these mutual funds presents outperformance. Malkiel (1995) and Gruber (1996) argued that most of the studies are subject to survivorship bias, when they adjust of this effect, the mutual fund on average underperform the market proxy.
In 1990, Cumby and Glen extended the literature by including international mutual funds. They examined the performance of 15 U.S. based internationally diversified mutual funds between 1982 and 1988 and compared their performance to the Morgan Stanley Index for the U.S. and the Morgan Stanley World Index. Both Jensen’s alpha and Grinblatt and Titman (1989) methodologies were employed. They find no evidence for superior fund performance, but there was some evidences showing the funds outperforming the U.S. domestic index.
Eun et al. (1991) reported similar results. They used Standard and Poor’s 500 Index, the Morgan Stanley Capital International World Index, and a self-constructed index of U.S. multinational firms as benchmarks. They find the majority of international mutual funds outperformed the U.S. market during 1977-1986, but most have failed to outperform the world index.
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Standard and Poor’s 500 Index, the Morgan Stanley Europe, Australia and Far East Index (EAFE) and the World Index. They used the Jensen, Sharpe, and Treynor’s measures to evaluate the performance. They found that international funds generally underperformed the U.S. market and the international market.
Engstrom (2003) examines European-based international mutual funds that invest in Asia and Europe and concludes that international funds underperform. He suggests that local investors can exploit the information advantage when investing locally and hence perform better.
3. Mutual fund performance models
I use two models of performance measurement: the Capital Asset Pricing Model (CAPM), which is developed from Sharpe (1964), and 4-factor model introduced by Carhart (1997). This section briefly describes these two models and evaluates international mutual fund performance with relative Index. For comparative purposes, this section also presents performance estimated from the Fama and French (1993) 3-factor model.
3.1 Single index model
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Jensen’s alpha. Jensen (1968) was the first who systematically tested the
performance of mutual funds. He also was the first one to examine whether mutual funds ‘beat the market’. According to Jensen (1968), equilibrium average return is the return of the portfolio by the market with respect to systematic risk (volatility) of the portfolio. Jensen’s alpha measure is computed using a regression equation which allows for a nonzero intercept. The intercept of following model 𝛼𝑖 gives the Jensen alpha, which is typically interpreted as a measure of out- or under-performance relative to a market index. If the alpha is positive, the portfolio has performed better and if alpha is negative, it means the performance does not beat the benchmark.
𝑅𝑖𝑡 − 𝑅𝑓𝑡 = 𝛼𝑖+ 𝛽𝑖(𝑅𝑚𝑡− 𝑅𝑓𝑡) + 𝜀𝑖𝑡 (1)
Where:
𝑅𝑖𝑡 and 𝑅𝑓𝑡 denote the unadjusted total return on fund i and the return on a risk-free proxy (one month T-bill) in month t, 𝑅𝑚𝑡 the return on the relevant equity benchmark in month t. 𝛽𝑖 measure the exposure, it is a measure of the fund’s systematic risk. 𝜀𝑖𝑡 is an error term.
Sharpe measure. The return-based style analysis introduced by Sharpe (1992),
is a good technique to analysis mutual funds’ performance. The most original papers are by Sharpe; he discussed how to choose the asset class indexes (Sharpe, 1964) and presented the basic methodological aspects of style analysis (Sharpe, 1992). He shows that the asset returns can be attributed to the returns of investment style on both value and size. The Sharpe ratio is estimated by the following:
𝑆ℎ𝑎𝑟𝑝𝑒 =𝑅̅𝑖− 𝑅𝑓
𝜎𝑖 (2) Where:
𝑅̅𝑖 is the average monthly return on fund i, 𝑅𝑓 is the return of risk-free interest rate and 𝜎𝑖 is the standard deviation of monthly return on fund i.
Treynor measure. The Treynor (1965) measure assumes an investor holds a
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systematic risk. Therefore, the appropriate risk measure becomes the mutual fund’s beta. The Treynor ratio is defined as:
𝑇𝑟𝑒𝑦𝑛𝑜𝑟 =𝑅̅𝑖 − 𝑅𝑓
𝛽𝑖 (3)
Where:
𝑅̅𝑖 is the monthly return on fund I and 𝛽𝑖 is the systematic risk of the fund i. A large
value of the portfolio over the market indicates a better performance of the fund. The fund has superior performance when the Treynor ratio exceeds the market risk premium over the same period.
3.2 Multi-factor model
The rationale for using a multi-factor asset pricing model is presented in the literature on the cross-sectional variation of stock returns (e.g., Fama and French (1993, 1996)). The results of these studies question the adequacy of a single index model to explain mutual fund performance (Otten and Bams, 2002). Therefore, the Fama and French (1993) 3-factor model has been considered to give a better explanation of fund performance. Besides containing a value-weighted market proxy, this model includes two additional risk factors size (SMB) and book-to-market ratio (HML). Fama and French (1993, 1996) found that the 3-factor model explains much more of the variation in average returns for different portfolios than the CAPM. These factors formed on size; book-to-market ratio thus has some explanatory power.
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I measure the performance of international mutual funds through the use of benchmark-adjusted returns, which is the monthly net return on the fund in excess of that of its analyst assigned benchmark over the study period.
The Fama and French 3-factor model is:
𝑅𝑖𝑡− 𝑅𝑓𝑡 = 𝛼𝑖 + 𝛽0𝑖(𝑅𝑚𝑡− 𝑅𝑓𝑡) + 𝛽1𝑖𝑆𝑀𝐵𝑡+ 𝛽2𝑖𝐻𝑀𝐿𝑡+ 𝜀𝑡𝑖 (4)
The Carhart 4-factor model is:
𝑅𝑖𝑡− 𝑅𝑓𝑡 = 𝛼𝑖 + 𝛽0𝑖𝑅𝑀𝑅𝐹𝑡+ 𝛽1𝑖𝑆𝑀𝐵𝑡+ 𝛽2𝑖𝐻𝑀𝐿𝑡+ 𝛽3𝑖𝑃𝑅12𝑚𝑡+ 𝜀𝑡𝑖 (5)
where, Rit is the monthly return in fund i from January 2008 to December 2012. Rft is
the 1 month U.S. Treasury bill in month t. RMRFt is the monthly excess return
between the market return (S&P 500 Index return) and the risk-free rate in month t.
SMBt is the difference in return between a small cap portfolio and large cap portfolio.
HMLt is the difference in return between a portfolio of high book-to-market stocks
and a portfolio of low book-to-market stocks. Intercept 𝛼𝑖 in the time-series,
regression is zero for all funds i. PR12Mt is the one year momentum in stock returns.
In Equation (4), Intercept 𝛼𝑖 in the time-series, regression is zero for all funds i. In Equation (5), the market model assumes 𝛽1𝑖 = 𝛽2𝑖 = 𝛽3𝑖 = 0 and market-adjusted returns further assume that 𝛽0𝑖 = 1.
In this section, I will analyze the multi-factor models in three steps. Firstly, I run regressions that only use the excess market return, Rm-Rf, to explain excess returns.
Secondly, apply only SMB and HML as independent variables. Thirdly, run the regression (4), which is, using all independent variables Rm-Rf, SMB and HML to
show the impact on the excess returns.
3.3 The influence of fees on performance
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Historically, managing portfolios of mid- or small-cap, international, or sector stocks is generally acknowledged to be more expensive than managing portfolios of U.S. large-cap stocks (Investment Company Institute Fact Book, 2013). The fees and expenses paid by investors take various forms, some charges are deducted from the fund’s value are transparency to the investors, for others, the amount is disclosed yet they are deducted out of the investors’ sight. However, the total monetary costs paid by investors include the front-end and deferred loads, operating expenses, account fees and trading costs. The expense ratio consists of management fees, auditor fees and independent director fees.
In general, mutual fund managers claim that expenses do not reduce performance, because investors are paying for the quality of the manager’s information (Otten and Bams, 2002). Therefore, if the management expenses are high, investors would expect returns to increase as well, and vice versa. A well-functioning mutual fund market, the mutual fund fees should be positively correlated with expected before-fee risk-adjusted returns. In addition, in the absence of market frictions, all funds should earn zero expected after-fee risk-adjusted returns in equilibrium, because otherwise there would be excess demand (supply) for funds with positive (negative) expected after-fee risk returns (Berk and Green, 2004). The relation between mutual fund returns and expenses (including management fees) provides a test of the value of active management.
Mutual fund expenses can be seen as the price that individual investors pay to managers to invest their money. Expenses vary considerably around the world. Khorana et al. (2009) find that large funds and families charge lower fees, but funds distributed in more countries charge higher fees. In addition, they find that fees are negatively related to investor protection. To investigate the influence of fees on performance, estimated is:
𝛼𝑖 = 𝛽0 + 𝛽1(𝐿𝑁 𝑇𝑁𝐴𝑡) + 𝛽2(𝐸𝑥𝑝𝑒𝑛𝑠𝑒 𝑟𝑎𝑡𝑖𝑜𝑡) + 𝛽3(𝑀𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡 𝑓𝑒𝑒𝑡)
+ 𝛽4(𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟 𝑟𝑎𝑡𝑖𝑜𝑡) + 𝜀𝑖𝑡 (6) 𝛼𝑖 is Carhart’s alpha for fund i. LN TNAt is natural logarithm of total net assets for
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4. Data
4.1 Sample of funds
To study the performance of U.S. international mutual funds, I construct a database from the CRSP Survivor Bias free Database. The CRSP Database covers monthly return, monthly total net asset, expense ratio and turnover ratios of U.S. open-end mutual fund. Open-end funds tend to hold a large amount of securities within their given asset class such as stocks, bonds and cash equivalents. Holding a large number of individual securities in one investment vehicle is a significant plus for investors in funds because of diversification benefits (Markowitz, 1952). International mutual funds are defined as those funds that are incorporated in the U.S. and solely invested in the equity market outside of U.S. during the period from January 2008 to December 2012. Several criteria are: all funds are open-end funds; Asset class is equity; Lipper objective is international fund (IF); open to invests; consistent with the definition of international funds, all sample funds must be “pure” international funds, which mean these funds do not invest in their own country, otherwise they should be called “global” funds. Furthermore, the final sample exclude of institutional funds, index or enhanced index funds, socially conscious funds and life cycle funds. All returns are in US dollar.
Since many mutual fund families offer multiple versions of shares from the same funds with different fee structures, this study uses only the A class funds for analysis to avoid the double counting. In addition, all funds used in this paper were first offered before 2008 and are actively traded through 2012 in order to have enough time-series data to evaluate return and risk. It also had to have published data on net asset value, as well as dividend and capital gains distribution.
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excluding non-surviving funds comparing the mean returns of surviving mutual funds leads to a substantial overestimation of an average return 0.31% per year.
Table 2
Entries and Exists of International mutual funds
This table shows the number of international funds at the end of each year for the period of 2008-2012. It also presents the number of international funds that enter and exit each year.
2008 2009 2010 2011 2012 Entry 36 15 39 38 29 Exit 1 22 17 18 32 Year End 342 335 357 377 374 Survived* 269 283 314 344 374 Attrition rate* 21% 16% 12% 9% 0
*Survived funds refer to fund still in existence in December 2012. The attrition rate is computed as 1 −𝑁𝑟. 𝑆𝑢𝑟𝑣𝑖𝑣𝑒𝑑 𝑓𝑢𝑛𝑑𝑠𝑁𝑟. 𝑌𝑒𝑎𝑟 𝑒𝑛𝑑 𝑓𝑢𝑛𝑑𝑠 (%)
4.2 Sample characteristics
There are 464 funds in the initial list of funds that meet the foreign investment requirements. But most of these funds cease to existence after 2011, providing little time series data for individual fund analysis, and some of the funds lack of feasible history data for analysis. Consequently, there left 218 individual funds with a full set of monthly data over the period January 2008 to December 2012.
Sample funds cover 10 categories defined by CRSP mutual fund data set on fund style. Monthly returns are net return, which computed using NAVs after all management expenses and 12b-1 fees, but front and rear load fees are excluded. Continuously compounded daily mutual fund returns are computed as follows:
𝑅𝑡 = 𝑁𝑎𝑣𝑡∗𝑐𝑢𝑚𝑓𝑎𝑐𝑡
𝑁𝑎𝑣𝑡−1 − 1 (CRSP, 2012)
Where 𝑅𝑡 is the return on fund on month t, 𝑁𝑎𝑣𝑡 is the net asset value of the investment in the fund at time t, cumfact1 is an adjustment variable.
1
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As we can see on table 3, categories with the most funds are Emerging market funds and International multi-cap funds, which combined account for half of the total sample. In contrast, Latin American funds and Pacific Region Funds only make up 5 of the total sample. International mutual funds favor multi-cap stocks, because about 34% of sample funds fall within multi-cap category.
International mutual funds are an expensive investment to investors. The sample has an average expense ratio of 1.55%. In contrast, the average expense ratio for mutual funds in U.S. is about 1.34% (Mamudi2, 2010). Based on CRSP’s definition, the ratio of total investment that shareholders pay for the fund’s operating expenses, including 12b-1 fees. Commonly, financial media like to guide investors to choose funds with an expense ratio less than 1%. However, in my sample, no single category meets this requirement. In addition, 3-year annualized returns are performing better than 5-year annualized returns. This is possibly due to the financial crisis in 2008.
CRSP, 2012, “Survivor-Biased-Free US Mutual Fund Guide”, Page 6.
2
MAMUDI, S. (2010, May 3). Retrieved from
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Summary statistics of international mutual funds 2008-2012.
The table presents summary statistics of the funds in this study. The returns are annualized, and net of all management expenses and 21b-fees. Total net assets are in million US dollar.
Category Nr TNA ($ million) Expense ratio Management fee Fund turn-over Standard Deviation 3-Year Annualized return (2010-2012) 5-Year Annualized return (2008-2012)
China Region funds 11 131.94 1.93% 0.78 0.83 5.66% -2.99% -5.67%
Emerging Markets Funds 35 879.32 1.83% 1.04 1.03 6.33% 3.39% -3.79%
European Region Funds 12 195.18 1.56% 0.01 0.94 6.00% 5.72% -2.93%
Latin American Funds 2 31.19 1.82% 0.53 0.78 6.91% 1.31% -1.95%
Pacific Ex Japan Funds 5 47.79 1.73% 0.30 1.21 5.46% 4.83% -3.29%
Pacific Region Funds 3 212.63 1.69% 0.75 0.93 6.12% 3.94% -1.01%
International Real Estate
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4.3 Benchmarks
In the basic 1-factor Jensen’s alpha analysis, Morgan Stanley Capital International Europe, Australia, and the Far East (EAFE) index is used as performance benchmarks for the major international equity markets. The EAFE index is the most widely used proxy for the major stock markets outside of the United States. It includes common stocks that are traded on the exchanges of 18 different countries excluding the United States. This market value weighted index includes companies which encompass approximately 60 percentage of the total market value of shares for those 18 countries.
In order to compare the international mutual funds to the U.S. local market, I also choose S&P 500 indexes, which is the most widely used proxy for the U.S. stock market. It is the benchmark of the overall market; it is the most widely used proxy for the U.S. stock market and always used as the standard of comparison in terms of investment performance.
Table 4
Summary statistics for benchmarks used in the Carhart 4-factor model 2008-2012 The excess return is calculated by subtracting the 1-month Treasury bill. SMB and HML are Fama and French’s factor-mimicking portfolios for size and book-to-market equity. MOM is a factor-mimicking portfolio for one-year return momentum.
Factor Monthly Excess return Standard deviation Cross correlations RMRF SMB HML MOM RMRF 0.0004 0.0553 1.0000 SMB 0.0036 0.0236 0.4309 1.0000 HML -0.0003 0.0292 0.4346 0.3139 1.0000 MOM -0.0055 0.0642 -0.4180 -0.2192 -0.4014 1.0000
In constructing the Carhart (1997) 4-factor model, I employ the Fama and French market index to approximate the market portfolio. Data on 1-month Treasury bills, SMB, HML, and MOM are gathered directly from Kenneth French’s website3. Summary statistics on the factor portfolios presents in table 4, it indicates that the
3
Data are downloaded from Kenneth French’s web site,
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4-factor model can explain considerable variation in returns. Firstly, the correlations between these four factors are low, which suggests the 4-factor model can explain sizeable time-series variation. Secondly, the low cross-correlations imply that the multicollinearity does not substantially affect the estimated 4-factor model.
5. Empirical results
5.1 Unadjusted performance
Table 5
Average monthly return and Standard Deviation Summary
Unadjusted total return is the monthly return including dividends and capital gain distributions, net of fees and expenses but before load charges
Category 2008-2012 2010-2012 Monthly return Standard Deviation Monthly return Standard Deviation
China Region funds -0.21% 7.94% -0.20% 5.66%
Emerging Markets Funds 0.17% 8.54% 0.49% 6.33%
European Region Funds -0.22% 7.09% 0.65% 6.00%
Latin American Funds 0.31% 9.43% 0.34% 6.91%
Pacific Region Funds -0.02% 7.08% 0.55% 5.46%
Pacific Ex Japan Funds 0.30% 8.51% 0.50% 6.12%
International Real Estate
Funds 0.17% 7.42% 1.00% 6.00%
International Large-Cap -0.11% 6.97% 0.45% 5.84% International Multi-Cap -0.18% 6.51% 0.52% 5.35% International Small/Mid-Cap -0.29% 6.81% 0.35% 5.40%
MSCI EAFE Index -0.04% 6.78% 0.49% 5.66%
S&P 500 Index 0.10% 5.50% 0.78% 4.43%
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Index during whole sample period. For the period between 2010 and 2012, the average monthly returns for 6 of the 10 categories better performance than the MSCI EAFE Index. Meanwhile only International Real Estate Funds were exceeded the S&P 500 Index. In both periods, average monthly returns on MSCI EAEF Index are lower than that of the S&P 500 Index. On an unadjusted return basis, the sample of international mutual funds appears to have not outperformed the domestic benchmark.
Standard deviations for the international mutual funds cover a narrow range of values. During the period of 2008 to 2012, Standard deviations of average monthly returns ranged from 6.51% to 9.43%, for 9 categories exceeded that for the MSCI EAFE Index, and all categories higher than that for the S&P 500 Index. Between 2010 and 2012, standard deviations of average monthly returns ranged from 5.35% to 6.91%, 7 categories outcomes exceeded that for the MSCI EAFE Index, and again all categories results higher than that of the S&P 500 Index. For both periods, the MSCI EAEF Index had a standard deviation of 6.78% and 5.66%, respectively, which are higher than that of the domestic benchmark.
5.2 All funds portfolio
In order to examine the power of a range of mutual fund performance models, I focus on the results at an aggregated point. To be more specific, I use an equally weighted portfolio of all funds. Table 6 reports the findings with respect to all funds portfolio. For each of the three models, I present alpha, beta, adjusted R2, and log-likelihood. The reason to perform Likelihood ratio (LR) test is in order to determine whether the explanatory power of the new model differs significantly from the previous one.
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the coefficient of SMB is negative, this refers to the international mutual funds prefer to larger cap than small cap. The HML factor loading is negative as well, indicating the sensitivity to low book-to-market stocks (growth) instead of high book-to-market stocks (value). In addition alpha estimate rises from -0.16% to -0.148%. Last model, Carhart 4-factor model adds the momentum factor. The significantly positive mom factor coefficient signal the sensitivity of all funds portfolio for high momentum stocks. However, for the international mutual funds its shows negative. Based on the increase in Log Likelihood, Carhart 4-factor model is better at explaining mutual fund returns.
Table 6
Results for an equally weighted portfolio of all funds: 2008:01-2012:12
This table shows ordinary least squares estimates for the three different models employ in this study. The market factor is the excess return on the S&P 500 Index, which present as beta. The last two columns provide results to the question of whether the explanatory of the new model differs significantly from the previous model. If two times the difference in Log L between two models exceeds the corresponding critical value (5%), reports yes. Otherwise, reports no.
Model Alpha Market SMB HML MOM Adj.R2 Log L Significant ?
CAPM -0.160% 1.210*** - - - 0.812 122.274
3-factor -0.148% 1.309*** -0.049 -0.390*** - 0.827 125.736 yes 4-factor -0.226% 1.254*** -0.051 -0.485*** -0.160** 0.839 128.574 yes
- Not applicable.
***significant at the 1% level. ** significant at the 5% level.
5.3 Risk-adjusted performance
- 19 -
had a beta coefficient higher than the domestic benchmark beta.
Table 7
Monthly performance Measure for 218 international mutual funds 2008-2012
Category Beta R2 Jensen
Alpha
Sharpe Ratio
Treynor Measure Panel A. EAFE index
China Region funds 1.0181 0.7572 -0.0017 -0.0302 -0.0024 Emerging Markets Funds 1.1722 0.8664 0.0022 0.0155 0.0011 European Region Funds 0.9934 0.9018 -0.0018 -0.0352 -0.0025 Latin American Funds 1.2310 0.7836 0.0036 0.0290 0.0022 Pacific Region Funds 0.9960 0.9086 0.0002 -0.0071 -0.0005 Pacific Ex Japan Funds 1.1282 0.8079 0.0035 0.0311 0.0024 International Real Estate
Funds 1.0335 0.8920 0.0021 0.0183 0.0013
International Large-Cap 1.0196 0.9840 -0.0007 -0.0208 -0.0014 International Multi-Cap 0.9455 0.9680 -0.0014 -0.0319 -0.0022 International
Small/Mid-Cap 0.9751 0.9430 -0.0025 -0.0472 -0.0033 MSCI EAFE Index 1.0000 1.0000 - -0.0110 -0.0007 Panel B. S&P 500 index
China Region funds 1.1427 0.6294 -0.0032 -0.0302 -0.0021 Emerging Markets Funds 1.3447 0.7519 0.0004 0.0155 0.0010 European Region Funds 1.1346 0.7763 -0.0033 -0.0352 -0.0022 Latin American Funds 1.4241 0.6913 0.0017 0.0290 0.0019 Pacific Region Funds 1.1436 0.7900 -0.0013 -0.0071 -0.0004 Pacific Ex Japan Funds 1.2820 0.6881 0.0017 0.0311 0.0021 International Real Estate
Funds 1.2167 0.8151 0.0005 0.0183 0.0011 International Large-Cap 1.1765 1.1761 -0.0023 -0.0208 -0.0012 International Multi-Cap 1.1097 0.8789 -0.0029 -0.0319 -0.0019 International Small/Mid-Cap 1.0967 0.7868 -0.0040 -0.0472 -0.0029 S&P 500 Index 1.0000 1.0000 - 0.0130 0.0007 Notes: MSCI EAFE Index returns: five-year -0.04%.
S&P 500 Index return: five-year 0.10%
- 20 -
international mutual fund categories exceed that of S&P 500 Index. Besides, the domestic benchmark had a greater Sharpe ratio than the EAFE Index. Five of the ten international mutual fund categories had Treynor measure higher than that of the EAFE Index, and four of the ten international mutual fund categories’ Treynor measures were greater than S&P500 Index.
The coefficient of determination R2 is used to measure the efficiency with which a portfolio is diversified. When this measure is closer to 1, the portfolio diversification is closer to the market index. This means only systematic risk remains for a well-diversified portfolio. R2 values which were based on the MSCI EAFE Index had a range from 0.7572 to 0.9840. High R2 suggests that some funds elected to retain a sizable systematic risk component. R2 values for the international mutual funds were computed using the S&P 500 Index had a range from 0.6294 to 1.1761. Again, the high R2 suggests the diversification closer to market index.
5.4 Multi-factor models results
Since this study investigates international mutual fund performance I will focus on 4-factor alpha. Based on the results presented on Table 8, I observe only international mutual fund invested in Latin American and Real Estate shows positive alpha, the rest of sample shows negative alpha. However, none of these alphas are significantly different from zero. The adjusted R2 outcomes all above 0.7 suggest that the Carhart 4-factor model does a good job in explaining fund performance.
As noted in Costa and Jakob (2006), the factors coefficients on RMRF, SMB, HML, and Mom should be related to the investment strategy of a particular fund. Funds with a focus on larger (smaller) capitalization stocks are expected a negative (positive) SMB factor coefficient.
- 21 -
negative HML coefficients and eight are significantly different from zero.
Finally, the factor coefficient for Jegadeesh and Titman’s (1993) momentum should depend on the amount of momentum stocks in the funds. All categories in the sample have a negative coefficient and most of them are significantly different from zero on MOM factor. Using this 4-factor model analysis, most funds in the sample appear to be using some sort of mean reversion strategy.
Table 8
Multi-factor models summary results
This table presents the results of the estimation of Eq. (5) for the period 2008:01-2012:12. Reported are OLS estimates for each category funds. Alpha is the intercept of the model. The t-statistics are in parentheses.
Category 4-factor model
Alpha RMRF SMB HML MOM ADJ.R2
China Region funds -0.44% (-0.741) 1.256*** (9.568) -0.026 (-0.094) -0.816*** (-3.481) -0.172 (-1.642) 0.679
Emerging Markets Funds -0.14% (-0.279) 1.371*** (12.043) 0.033 (0.136) -0.669*** (-3.291) -0.257*** (-2.832) 0.791
European Region Funds -0.39% (-0.877) 1.172*** (11.930) -0.069 (-0.330) -0.298* (-1.700) -0.098 (-1.255) 0.775
Latin American Funds 0.07% (0.115) 1.538*** (10.787) -0.134 (-0.442) -0.826*** (-3.245) -0.211 (-1.858) 0.730
Pacific Region Funds -0.26% (-0.634) 1.175*** (12.902) 0.067 (0.348) -0.451*** (-2.777) -0.132* (-1.814) 0.806
Pacific Ex Japan Funds -0.09% (-0.153) 1.289*** (10.297) 0.183 (0.687) -0.797*** (-3.563) -0.300*** (-3.001) 0.746
International Real Estate Funds 0.06% (0.143) 1.226*** (13.676) -0.327* (-1.719) -0.118 (-0.736) -0.166** (-2.321) 0.828 International Large-Cap -0.24% (-0.723) 1.232*** (16.785) -0.168 (-1.078) -0.258 (-1.968) -0.077 (-1.310) 0.869 International Multi-Cap -0.34% (-1.149) 1.140*** (17.359) -0.041 (-0.294) -0.232 (-1.977) -0.070 (-1.329) 0.881 International Small/Mid-Cap -0.49% (-1.201) 1.136*** (12.726) -0.032 (-0.170) -0.387** (-2.427) -0.121* (-1.702) 0.798
- 22 -
5.5 The influence of fund characteristics
In this section, I examine whether the performance of international mutual funds can be affected by their basic characteristics such as the net value of assets, expense ratio, turnover, and management fee. Since previous sections only considered international mutual fund returns net of cost, which means management fees were already deducted from the fund’s return. Based on above results, international mutual funds are quite able to follow the market, because their alphas are not significantly different from zero.
Table 9
The influence of fund characteristics on risk-adjusted performance
This table gives the estimated coefficients with heteroskedasticity robust t-statistics within parentheses. I computed a White test and the results show no significant effect on this model (Appendix B). Hence, the requirement of homoscedasticity is met. This table only takes into account funds invested in Latin American, Pacific Ex Japan Funds and Pacific Region into All funds category, due to the small size.
Category Constant LN TNA Expense ratio Management fee Turnover ratio China Region funds 5.703%
(-0.110) -0.336% (-0.130) -2.806 (1.745) 1.274% (-1.866) -0.405% (-0.454) Emerging Markets Funds 0.249%
(0.712) 0.001% (-0.003) -0.294* (-1.752) 0.170% (1.439) 0.000% (-0.006) European Region Funds -0.005%
(-2.109) 0.039% (0.948) -0.115 (2.501) 0.009% (-1.630) -0.051% (-0.469) International Real Estate
- 23 -
For the all funds sample, the results of international mutual funds related to fund’ expenses display in Table 9. The size of assets is the only variable which has statistically significant negative impact on all fund performance. The results present the negative relationship between fund performance and expense ratio. This negative relationship was previously reported for U.S. industry by Elton et al. (1993), Malkiel (1995) and Carhart (1997). They all found a negative correlation between expense ratios and risk-adjusted performance.
For the funds invested in different foreign region, the results do not appear strong relationship between expense ratio, assets under management, management fee and turnover ratio. There are four out of seven regions shows positive relationship between expenses ratio and performance (alpha), which is the same as mutual fund managers often claim. Five out of seven regions shows a positive relationship between the log of fund assets and fund performance. The potential explanation maybe exists of economies of scale.
Finally, the influence of fund turnover ratio is considered. Turnover can be interpreted as a measure of the net costs of trading; because it reveals the marginal performance effect of small change in turnover, therefore, the turnover estimate implies transaction costs (Carhart, 1997). These results suggest that U.S. international mutual funds seems not sufficiently successful in finding and applying new information to offset funds’ expenses in order to add value for the investor.
5.6 Robustness test
- 24 -
level, respectively. The year 2011 leads to an annual alpha of -2.78%, which is much lower compared to the alpha of other years, this is maybe the reason that the alpha of period 2008-2012 and 2009-2012 is negative. However, there is no clear explanation to be given for this significant difference. Overall, the robustness tests uphold the initial findings- there is no evidence of outperformance for any sample of funds.
Table 10
Robustness results for international mutual funds
Alpha Market SMB HML MOM
Panel A. Sample exclude year 2008 (i.e. 2009-2012)
CAPM 0.090% 1.145*** - - -
3-factor -0.020% 1.228*** 0.001 -0.251 -
4-factor -0.170% 1.167*** 0.002 -0.354** -0.201*** Panel B. 4-factor model results for separate years
2009 0.910%* 1.114** -0.117 -0.267 -0.160
2010 0.510% 1.257*** -0.697** -0.487** 0.024 2011 -2.780%** 1.292*** 0.077 -1.374** 0.522
2012 0.860% 0.604 -0.787 0.185 -0.932**
- Not applicable.
***significant at the 1% level. ** significant at the 5% level. * significant at the 10% level
6. Conclusion
- 25 -
500 index, and trend to earn returns consistent with their risk exposure.
The international mutual fund industry provides an excellent opportunity to test performance of diversification outside of the U.S. market, and to see whether the addition choices are available to small investors to make sense. On one hand, the “home bias” might lead investors to keep investing within the domestic market, which reduced the available amount invest into international mutual funds. On the other hand, the small size of the industry out of U.S. compared to the total market capitalization and the presumably less efficient market creates opportunities to beat the market.
This research provides an assessment of existing mutual fund performance models by using a survivor-bias free database over period 2008 through 2012. Starting with the most basic single factor model — CAPM. Then introduce extra variables such as size, book-to-market, momentum and benchmarks. In addition, I also consider the other characteristics may significantly affect the fund performance.
- 26 -
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Appendix
Appendix A: Sample Description
China Region funds
AIM Investment Funds: AIM China Fund
Columbia Funds Series Trust I: Columbia Greater China Fund Dreyfus Premier Investment Funds, Inc: Dreyfus Greater China Fund Eaton Vance Growth Trust: Eaton Vance Greater China Growth Fund
John Hancock Investment Trust III: John Hancock Greater China Opportunities Fund ING Mutual Funds: ING Greater China Fund
JPMorgan Trust I: JPMorgan China Region Fund Aberdeen Funds: Aberdeen China Opportunities Fund Old Mutual Funds I: Old Mutual Clay Finlay China Fund Parr Family of Funds: Parr USX China Fund
Templeton China World Fund
Emerging Markets Funds
AIM Investment Funds: AIM Developing Markets Fund Dunham Funds: Dunham Emerging Markets Stock Fund Allianz Funds: NACM Emerging Markets Opportunities Fund DWS International Fund, Inc: DWS Emerging Markets Equity Fund Delaware Group Global & International Funds: Emerging Markets Fund Dreyfus International Funds, Inc: Dreyfus Emerging Markets Fund Eaton Vance Special Investment Trust: Eaton Vance Greater India Fund
Eaton Vance Mutual Funds Trust: Eaton Vance Structured Emerging Markets Fund Evergreen International Trust: Evergreen Emerging Markets Growth Fund
Fidelity Advisor Series VIII: Fidelity Advisor Emerging Markets Fund Goldman Sachs Trust: Goldman Sachs Emerging Markets Equity Fund Goldman Sachs Trust: Goldman Sachs BRIC Fund
ING Mutual Funds: ING Russia Fund
JPMorgan Trust I: JPMorgan Emerging Markets Equity Fund JPMorgan Trust I: JPMorgan Russia Fund
Legg Mason Partners Equity Trust: Legg Mason Partners Emerging Markets Equity Fund MFS Series Trust X: MFS Emerging Markets Equity Fund
New World Fund, Inc
Oppenheimer Developing Markets Fund
RS Investment Trust: RS Emerging Markets Fund
Virtus Insight Funds Trust: Virtus Emerging Markets Opportunities Fund Pioneer Emerging Markets Fund
Principal Funds, Inc: International Emerging Markets Fund
RiverSource Global Series, Inc: Threadneedle Emerging Markets Fund Russell Investment Company: Russell Emerging Markets Fund
- 30 -
Templeton Global Investment Trust: Templeton Emerging Markets Small Cap Fund Templeton Global Investment Trust: Templeton BRIC Fund
UBS PACE Select Advisors Trust: UBS PACE International Emerging Markets Equity Investments
Van Eck Funds: Emerging Markets Fund
World Funds, Inc: Eastern European Equity Fund
Goldman Sachs Trust: Goldman Sachs Structured Emerging Markets Equity Fund Columbia Funds Series Trust I: Columbia Emerging Markets Fund
JPMorgan Trust I: JPMorgan India Fund
European Region Funds
AIM Funds Group: AIM European Small Company Fund AIM International Mutual Funds: AIM European Growth Fund DWS International Fund, Inc: DWS Europe Equity Fund
Fidelity Advisor Series VIII: Fidelity Advisor Europe Capital Appreciation Fund Henderson Global Funds: Henderson European Focus Fund
ICON Funds: ICON Europe Fund
Ivy Funds: Ivy European Opportunities Fund
JPMorgan Trust I: JPMorgan Intrepid European Fund Morgan Stanley European Equity Fund Inc
Franklin Mutual Series Fund Inc: Mutual European Fund Putnam Europe Equity Fund
RiverSource International Series, Inc: Threadneedle European Equity Fund
Latin American Funds
DWS International Fund, Inc: DWS Latin America Equity Fund JPMorgan Trust I: JPMorgan Latin America Fund
Pacific Ex Japan Funds
AIM International Mutual Funds: AIM Asia Pacific Growth Fund Fidelity Advisor Series VIII: Fidelity Advisor Emerging Asia Fund Goldman Sachs Trust: Goldman Sachs Asia Equity Fund
Ivy Funds: Ivy Pacific Opportunities Fund
Dreyfus Premier Investment Funds, Inc: Dreyfus Emerging Asia Fund
Pacific Region Funds
ICON Funds: ICON Asia-Pacific Region Fund Morgan Stanley Pacific Growth Fund, Inc
Wells Fargo Funds Trust: Wells Fargo Advantage Asia Pacific Fund
International Real Estate Funds
Cohen & Steers Asia Pacific Realty Shares, Inc Cohen & Steers International Realty Fund, Inc
- 31 -
Goldman Sachs Trust: Goldman Sachs International Real Estate Securities Fund ING Mutual Funds: ING International Real Estate Fund
JPMorgan Trust I: JPMorgan International Realty Fund Kensington Funds: Kensington International Real Estate Fund
Virtus Opportunities Trust: Virtus International Real Estate Securities Fund
International Large-Cap
Fidelity Advisor Series VIII: Fidelity Advisor Overseas Fund Fifth Third Funds: Fifth Third International Equity Fund GE Funds: GE International Equity Fund
GAMCO International Growth Fund, Inc
Goldman Sachs Trust: Goldman Sachs Concentrated International Equity Fund Hartford Mutual Funds, Inc: Hartford International Opportunities Fund
ICAP Funds, Inc: MainStay ICAP International Fund Ivy Funds: Ivy International Core Equity Fund Ivy Funds: Ivy International Growth Fund
Natixis Funds Trust I: Hansberger International Fund
JPMorgan Trust I: JPMorgan International Opportunities Fund JPMorgan Trust I: JPMorgan International Equity Fund JPMorgan Trust I: JPMorgan International Value Fund JPMorgan Trust I: JPMorgan Intrepid International Fund MFS Series Trust X: MFS International Growth Fund
MassMutual Select Funds: MassMutual Select Diversified International Fund RS Investment Trust: RS International Growth Fund
Pioneer International Value Fund
Prudential World Fund, Inc: Dryden International Value Fund Putnam Funds Trust: Putnam International Growth & Income Fund Putnam Investment Funds: Putnam International New Opportunities Fund Putnam International Equity Fund
SEI Institutional International Trust: International Equity Portfolio Saratoga Advantage Trust: International Equity Portfolio
Sentinel Group Funds, Inc: Sentinel International Equity Fund State Farm Mutual Fund Trust: State Farm International Equity Fund State Farm Mutual Fund Trust: State Farm International Equity Fund Strategic Funds, Inc: International Stock Fund
UBS Funds: UBS International Equity Fund
UBS PACE Select Advisors Trust: UBS PACE International Equity Investments Waddell & Reed Advisors International Growth Fund, Inc
Wells Fargo Funds Trust: Wells Fargo Advantage International Equity Fund Munder Series Trust: Munder International Fund - Core Equity
Nationwide Mutual Funds: Nationwide International Value Fund Davis New York Venture Fund, Inc: Davis International Fund Timothy Plan: Timothy Plan International Fund
- 32 - AllianceBernstein International Growth Fund, Inc
AllianceBernstein Trust: AllianceBernstein International Value Fund American Independence Funds Trust: International Equity Fund Sanford C Bernstein Fund, Inc
Sanford C Bernstein Fund, Inc
Columbia Funds Series Trust: Columbia Multi-Advisor International Equity Fund DWS International Fund, Inc: DWS International Value Opportunities Fund DWS International Fund, Inc: DWS International Fund
DWS Advisor Funds: DWS International Select Equity Fund
International Multi-Cap
AIM Growth Series: AIM International Allocation Fund Dunham Funds: Dunham International Stock Fund
AIM International Mutual Funds: AIM International Growth Fund Allegiant Funds: Allegiant International Equity Fund
Allianz Funds: NACM International Fund Allianz Funds: NFJ International Value Fund
Calamos Investment Trust: CALAMOS International Growth Fund Calvert World Values Fund, Inc: International Equity Fund
Columbia Funds Series Trust: Columbia Marsico International Opportunities Fund Columbia Funds Series Trust: Columbia Masters International Equity Portfolio Delaware Group Global & International Funds: International Value Equity Fund Optimum Fund Trust: Optimum International Fund
Domini Social Investment Trust: Domini European PacAsia Social Equity Fund Advantage Funds, Inc: Dreyfus International Value Fund
Eaton Vance Mutual Funds Trust: Eaton Vance Tax-Managed International Equity Fund EuroPacific Growth Fund
Evergreen International Trust: Evergreen International Equity Fund
Federated World Investment Series, Inc: Federated International Value Fund Fidelity Investment Trust: Fidelity Advisor Canada Fund
Fidelity Investment Trust: Fidelity Advisor International Value Fund Fidelity Investment Trust: Fidelity Advisor International Discovery Fund Fidelity Advisor Series VIII: Fidelity Advisor Diversified International Fund
Fidelity Advisor Series VIII: Fidelity Advisor International Capital Appreciation Fund First American Investment Funds, Inc: International Fund
First American Investment Funds, Inc: International Select Fund
Goldman Sachs Trust: Goldman Sachs Structured International Equity Fund Hartford Mutual Funds, Inc: Hartford International Growth Fund
Henderson Global Funds: Henderson International Opportunities Fund Eagle Series Trust: Eagle International Equity Fund
HighMark Funds: HighMark International Opportunities Fund ICON Funds: ICON International Equity Fund
ING Mutual Funds: ING International Value Choice Fund ING Mutual Funds: ING Diversified International Fund ING Mutual Funds: ING Foreign Fund
- 33 -
John Hancock Funds III: International Allocation Portfolio John Hancock Funds III: International Core Fund
John Hancock Funds III: International Growth Fund
Artio Global Investment Funds: Artio International Equity Fund II
Legg Mason Partners Equity Trust: Legg Mason Partners International All Cap Opportunity Fund
Lord Abbett Securities Trust: Lord Abbett International Core Equity Fund MFS Series Trust X: MFS International Value Fund
MFS Series Trust X: MFS International Diversification Fund MFS Series Trust I: MFS Research International Fund MainStay Funds: MainStay International Equity Fund
MassMutual Premier Funds: MassMutual Premier International Equity Fund MassMutual Premier Funds: MassMutual Premier Focused International Fund MassMutual Select Funds: MassMutual Select Overseas Fund
MEMBERS Mutual Funds: International Stock Fund Aberdeen Funds: Aberdeen International Equity Fund
Nuveen Investment Trust II: Nuveen Tradewinds International Value Fund Old Mutual Funds I: Old Mutual International Equity Fund
Oppenheimer Quest International Value Fund Inc Oppenheimer International Growth Fund
Oppenheimer International Diversified Fund
Virtus Opportunities Trust: Virtus Foreign Opportunities Fund Principal Funds, Inc: Diversified International Fund
Prudential World Fund, Inc: Dryden International Equity Fund Federated Equity Funds: Federated InterContinental Fund RidgeWorth Funds: International Equity Fund
SunAmerica Equity Funds: SunAmerica International Equity Fund Templeton Funds, Inc: Templeton Foreign Fund
Thornburg Investment Trust: Thornburg International Growth Fund Thornburg Investment Trust: Thornburg International Value Fund Transamerica Funds: Transamerica Multi-Manager International WT Mutual Fund: Wilmington Multi-Manager International Fund
Wells Fargo Funds Trust: Wells Fargo Advantage International Value Fund Fidelity Investment Trust: Fidelity Advisor International Growth Fund Fidelity Investment Trust: Fidelity Advisor Total International Equity Fund Dreyfus Premier Investment Funds, Inc: Dreyfus Diversified International Fund Neuberger Berman Equity Funds: Neuberger Berman International Large Cap Fund First Investors Equity Funds: International Fund
Ivy Funds: Ivy Managed European/Pacific Fund
Ivy Funds: Ivy Managed International Opportunites Fund
Janus Adviser Series: Janus Adviser INTECH Risk-Managed International Fund
International Small/Mid-Cap
Allianz Funds: NFJ International Value Fund
- 34 -
Delaware Group Global & International Funds: International Value Equity Fund Advantage Funds, Inc: Dreyfus International Value Fund
Fidelity Investment Trust: Fidelity Advisor International Value Fund First Eagle Funds: First Eagle Overseas Fund
Goldman Sachs Trust: Goldman Sachs Structured International Equity Fund
Legg Mason Partners Equity Trust: Legg Mason Partners International All Cap Opportunity Fund
MFS Series Trust X: MFS International Value Fund
MFS Series Trust V: MFS International New Discovery Fund
Nuveen Investment Trust II: Nuveen Tradewinds International Value Fund Oppenheimer Quest International Value Fund Inc
Putnam Investment Funds: Putnam International Capital Opportunities Fund RidgeWorth Funds: International Equity Fund
Wells Fargo Funds Trust: Wells Fargo Advantage International Value Fund Munder Series Trust: Munder International Small-Mid Cap Fund
Appendix B: Hereteroskedasticity Check
