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https://doi.org/10.1016/j.intfin.2015.01.002

1

The Performance of Diversified Emerging Market Equity Funds

Abstract

We investigate the performance of globally diversified emerging market equity funds during

the first decade of the twenty-first century. A vast majority of these funds do not outperform

the market benchmark even before transaction costs. The systematic risk of most of the funds

is similar to that of the market benchmark portfolio, which may suggest that they aim to offer

diversification benefits rather than seeking superior risk-adjusted returns through active

management. We do not find any evidence of market timing ability among these funds.

Finally, whilst we detect persistence in performance, this result is driven mainly by the poorly

performing funds.

Keywords: emerging market, diversified equity funds, fund manager performance, market timing; persistence.

2

1. Introduction

Investing in emerging markets has been a major trend among investors well over last two

decades. Emerging markets reportedly offer investors in developed nations the potential of

higher returns as well as risk reduction benefits through portfolio diversification (see for

example, Ratner and Leal (2005)1. For investors in developed markets, mutual funds have

been one of the most important vehicles for investing in emerging markets. Most of these

funds are open-end equity funds (Kaminsky, Lyons, and Schmukler, 2001). In this paper, we

investigate the performance of globally diversified emerging market equity funds between

2000 and 2010. Specifically, we aim to address two important questions related to diversified

emerging market equity funds. First, do these funds produce superior risk-adjusted returns?

Second, is there any persistence in the performance of these funds?

Traditionally mutual funds have provided a low cost route to portfolio diversification.

However, with the spectacular growth of exchange-traded-funds (ETFs) in recent years,

investors now have an alternative vehicle to construct a low cost, well-diversified portfolio.

One important rationale for choosing to invest in traditional mutual funds over ETFs can be

the expectation of ‘abnormal’ returns resulting from the perceived informational advantages

or superior skills of fund managers. Hence it is important to evaluate whether these funds

deliver any (ex-post) positive abnormal performance. Second, from the investor’s

perspective, it is important to examine whether there is any evidence of persistence in the

performance of these funds. If there is predictability in performance of funds, investors may

1 The diversification benefits, arguably, have been less visible in recent years due to financial liberalization and integration of international markets. For example, Charitou, Makris, and Nishiotis (2006) find no significant international diversification benefits in the post 1993 period.

3

(ex-ante) reallocate their savings towards ‘winner’ funds and enhance abnormal returns

(Cuthbertson, Nitzsche, and O’Sullivan, 2008).

There has been a plethora of empirical research on performance of mutual funds in

United States (US), United Kingdom (UK) and other developed markets. Overall, the

evidence does not appear supportive of the claim of any superior performance. There is

limited evidence on persistence with most studies finding the phenomenon to be more

concentrated among the underperforming funds. These findings are generally consistent with

the ‘efficient market’ paradigm according to which asset prices quickly assimilate all

available information thereby making it impossible for any investor or group of investors to

systematically outperform the market on a risk-adjusted basis.

Yet whether the evidence gathered on fund performance from developed markets

applies to emerging market funds remains an open question. It is a commonly held notion that

many of the emerging markets have weaker regulatory environment relative to developed

markets and lack ‘informational efficiency’ due to poor information disclosure requirements

in emerging markets. If true, this may imply more opportunities for emerging market funds to

exploit these inefficiencies and deliver abnormal returns. On the other hand, even if

inefficiencies exist in these markets, the cost of collecting firm-specific information could be

sufficiently high to negate any attempt by funds to deliver superior returns (Chan and

Hameed, 2006).2

Despite the growth in diversified emerging markets funds, empirical studies on their

performance have been rather scarce. Past research conducted on performance of emerging

2 Empirical evidence on the issue of efficiency in emerging markets is divided. Many studies suggest inefficiency or a lower level of efficiency in emerging markets compared to developed markets (See for example, Fillis, 2006; Mobarek, Mollah, and Bhuyan, 2008, Risso, 2009) while others contradict this claim (see, for example, Karamera, Ojah, and Cole, 1999; Griffin, Kelly, and Nardari, 2010). However, there is agreement on the heterogeneous levels in efficiency across these markets.

4

market funds has focused on country specific (like Russia, Poland, India, Malaysia etc.) or

region specific (like Africa or Latin America) funds. Ours is among only a handful of studies

to analyse the performance of emerging market funds that are diversified across multiple

markets. The sample period of most of the past studies do not extend beyond 2006 and hence

they are almost a decade old. The exception is Eling and Faust (2010) whose sample period

extends to August 2008 but their study has an important shortcoming due to its choice of

inappropriate benchmark (S&P 500) in evaluating performance of emerging market funds.

Our study analyzes more recent data (2000-10) to investigate the performance of these funds

at an aggregate as well as individual level. To our knowledge, ours is the first study to

analyze the performance of emerging market equity funds since the onset of global financial

crisis (GFC). We find evidence that is different from some of the earlier findings on

performance of emerging market funds on several counts. Our study reveals greater

underperformance among emerging market funds compared to Eling and Faust (2010). In

contrast to Huij and Post (2011), we show that the persistence in performance is mainly

attributable to the underperforming funds as in developed markets. Also, we find the

abnormal returns spread between the best and the worst performing funds to be much smaller

than what they estimate.

The remainder of the paper is organized as follows. Section 2 briefly reviews the

literature on mutual fund performance in developed and emerging markets. Section 3

describes our data and methodology used in this study. Section 4 presents the empirical

results and Section 5 concludes.

2. Literature review

2.1 Mutual fund performance in developed markets

5

There is a long history of research in mutual fund performance in the US starting with Jensen

(1968) who samples 115 open-end mutual funds over a 20-year period (1945-64). Using the

single factor capital asset pricing model (CAPM), he reports an average excess performance

(alpha) of 0.4% and -1.1% gross and net of expenses respectively, which implies that on

average, the funds earned approximately 1.1% less per year than the benchmark index (S&P

500) on a risk adjusted basis. The results also find very little evidence of an individual fund’s

ability to outperform the market, except by mere chance. Of the 115 funds, only 3 funds show

significantly positive alphas.

Ippolito (1989) conducts a similar study of 143 mutual funds over a different 20-year

sample period (1965-84). Unlike Jensen (1968), he finds an average alpha of 0.81% for the

sample. However, only 12 out of the 143 funds show significantly positive alphas. The author

concludes that net of fees and expenses (excluding load fees), funds are able to outperform

the benchmark by a margin that is large enough to cover their load charges. However, Elton,

Gruber, Das, and Hlavka (1993) take issue with Ippolito’s benchmark and contest the

author’s results. After considering the mutual funds’ holding of non-S&P stocks, they find

the average alpha to be -1.49% with not a single fund producing significantly positive alpha.

Grinblatt and Titman (1989) compare gross and net returns of US mutual funds using

a single factor model over the 1975-85 period across four sets of benchmarks. For gross

returns, the funds outperform two indexes by 1.8% and 2.28% per annum and underperform

the other two indexes by 2.31% and 2.64%. However, outperformance diminishes when net

returns are used with no statistically significant alphas against any of the indexes.

Malkiel (1995) employs the CAPM model to evaluate performance of 239 mutual

funds in US between 1971 and 1991 and finds that these funds significantly underperform the

index both before and after expenses. The reported average alphas for gross and net returns

6

are -2.03% and -3.20% per annum respectively. As a result, he concludes that investors are

better off by investing in the market index rather than by investing with active managers.

Unlike Grinblatt and Titman (1989) who claim that the impact of survivorship bias is modest,

Malkiel finds that the potential upward bias in the estimated alpha due to non-inclusion of

non-surviving funds in the sample is quite severe.

Gruber (1996) employs both CAPM and a multifactor model with the inclusion of

‘small minus big’ (SMB) and ‘high minus low’ (HML) factors and also a bond index. The

funds in his study underperform by 1.56% and 0.65% p.a. against the single and the

multifactor model respectively. He also estimates the potential bias of performance

measurements when non-surviving funds are not included in the sample. The non-surviving

funds underperform the benchmark by 4.2% and 2.75% per annum against the single and

multifactor index respectively.

Daniel, Grinblatt, Titman, and Wermers (1997) use benchmarks based on

characteristics of stocks to evaluate whether active mutual funds outperform passive

strategies. They investigate a database of stock holdings covering 2,500 equity funds between

1975 and 1994. Their results show that whilst the average fund outperforms the passive

strategies, the magnitude of the outperformance (approximately 100 basis points) barely

outweighs the costs in the form of higher management fees. Kothari and Warner (2001) also

analyse simulated funds based on characteristics that mimic actual funds. They conclude that

past studies of mutual fund performance are prone to underestimate abnormal performance by

a large magnitude, particularly if a fund's style characteristics differ from those of the value-

weighted market portfolio.

Mutual fund performance has also been researched in other developed markets

outside the US. Cumby and Glen (1990) examine 15 internationally diversified mutual funds

7

with Morgan Stanley World Index as the benchmark. The authors find no evidence of fund

mangers outperforming the relevant benchmark. The average alpha of the funds in their study

is reported to be is -2.28% p.a. using net returns. Only 3 individual funds show postive alpha

but none of these are statistically significant. Cai, Chan and Yamada (1997) investigate the

performance of 64 Japanese mutual funds over a 10-year period (1981-92). They find the

alphas for the funds to be consistently negative. The alphas using the CAPM model vary

between -6.01% and -7.33% per annum whilst the alphas for the Fama and French three

factor (3F) model range from -5.53% to -6.19%. Blake and Timmermann (1998) study 2,300

UK mutual funds between 1972 and 1995 with the funds sub-divided into four categories:

growth, general, income and small companies. Their results show negative alphas across all

fund categories and estimated to be about -1.8% p.a. Cesari and Panetta (2002) examines

Italian mutual funds and finds positive alpha even after accounting for their non-equity

investments. 3 However, Matallin-Saez (2006) reports an average alpha of -1.2% among

Spanish mutual funds using CAPM. The alphas from other multifactor measures are

substantially lower but the estimates are not statistically significant.

One of the few studies that find mutual funds outperform benchmark net of expenses

is Otten and Bams (2002), who examine the performance of 506 mutual funds in various

European countries including France, Germany, Italy, Netherlands and the UK from 1991 to

1998. They employ Carhart (1997) four factor model and report positive alphas for funds in

all countries except Germany. However, only UK funds significantly outperform benchmark

by 1.33% per year after expenses. Their findings contradict those of Blake and Timmermann

(1998) who do not account for the momentum factor, the main driver for fund performance in

Otten and Bam’s study.

3 The alpha net of expenses, however, is not significantly different from zero.

8

2.2 Performance of funds in emerging markets

Empirical research studies conducted on the performance of emerging market fund managers

have been mainly country specific. For example, Lai and Lau (2010) examine performance of

311 mutual funds in Malaysia and find the average returns to be superior against market

benchmark, particularly during bull markets. Sehgal and Jhanwar (2008) investigate 60

growth and growth income mutual funds in India and find about 25% of the sample exhibit

significantly positive alphas. Swinkels and Rzezniczak (2009) explore security selection and

market timing ability for Polish mutual funds. Their results indicate selection skills but

negative market timing ability.

Among a handful of studies that investigate funds investing in multiple emerging

markets (rather than specific countries), Abel and Fletcher (2004) analyse the performance of

UK-based unit trusts with emerging market equity investments. They do not find any

evidence of superior performance. Gottesman and Morey (2007) examine the performance of

diversified emerging market equity funds over three consecutive periods: 1997-2000, 2000-

2003 and 2003-2005. The authors find some support for the claim that actively managed

funds generally underperform passive benchmarks with the proportion of funds with negative

alphas ranging from 45.03% to 80.90% for the different sample periods. Michelson,

Philipova, and Srotova (2008) examine the performance of 55 diversified emerging markets

funds between September 1999 and January 2005. Their findings suggest that these funds

outperform the MSCI Index and the S&P 500 Index, but underperform the emerging market

index. But since their sample includes only funds with data for the full sample period of 64

months, their results are clearly suffer from survivorship bias.

Eling and Faust (2010) study performance of mutual (and hedge) funds in emerging

9

markets between January 1995 and August 2008. They find that these funds outperform the

benchmark by 0.48% p.a. against the CAPM but underperform by 0.84% per annum against

the FF model. However, the alphas under both models are not statistically significant. As for

the distribution of statistically significant alphas, 14.15% of the funds have positive CAPM

alphas while 3.97% show negative CAPM alphas. The corresponding estimates using the FF

model are 10.33% and 3.97% respectively. However, the authors’ use of a broadly diversified

portfolio of US stocks (with a correlation of 0.97 with S&P 500) as the market factor in their

models to evaluate the performance of emerging market funds raises some concern about the

validity of their conclusions.

Hayat and Kraeussl (2011) analyzes the risk and return characteristics of a sample of 145

Islamic Equity Funds (IEF) that focus mainly on global as well as region/country specific

emerging markets over the period 2000 to 2009. Their results show that IEFs underperform

both Islamic as well as conventional equity benchmarks with underperformance increasing

during the recent financial crisis. They also find that IEF managers have poor market timing

ability.

Whilst studies examining the performance open-end equity funds are of utmost relevance

to our paper, there is also a section of literature that has looked at performance of closed-end

funds. But the conclusions have been similar. For example, Chang, Eun, and Kolodny (1995)

find closed-end emerging market funds fail to generate alphas for US-based investors. Lee

(2001) observes that closed-end country funds in emerging markets underperform their

benchmark IFCI indices. Movassaghi, Brahmandkar, and Shilkov (2004) also could not find

any evidence of superior performance of closed-end funds in any particular region or country

within the emerging markets.

ETFs offer an alternative route for investing in emerging markets through tracking of

passive benchmark indices in these markets. Blitz and Huij (2012) evaluate the performance

10

of such funds in global emerging markets (GEM). They find that, on average, GEM ETFs fall

short of their benchmark indexes by around 85 basis points per annum, which is consistent

with the expected drag on return due to fund expense ratios plus the impact of withholding

taxes on dividends.

Finally, while our study focusses on equity funds, when investing in emerging markets,

some investors also use hedge funds as investment vehicles. Strömqvist (2007) presents

evidence that emerging market hedge funds do not outperform their underlying benchmarks

during the period 1994-2004. This evidence is further supported by Peltomäki (2008) who

finds inferior performance of emerging market hedge funds at the index level. However, he

also finds that nearly 40% of emerging market hedge funds shows positive abnormal returns.

A further study by Abugri and Dutta (2009) also shows that emerging market hedge funds are

unable to consistently outperform their underlying benchmarks. Kotkatvuori-Örnberg,

Nikkinen, and Peltomäki (2011) hypothesize that the poor aggregate performance of

emerging market hedge funds may be due to lack of focus of these funds. Their results

suggest that a portfolio of emerging market hedge funds, which are geographically focused,

outperform their underlying stock market indices.

2.3 Performance persistence

The issue of persistence is of critical importance to investors. If there is evidence of

persistence in performance, they would allocate funds to those with superior past

performance and punish poorly performing funds through redemption. There are a vast

number of studies that examines persistence in performance of mutual funds.

The traditional approach to detect persistence involves splitting a fund’s performance

into two periods and utilizing the rank correlation coefficient to investigate the statistical

11

relationship between performances of the two periods. A positive correlation would indicate

persistence is evident. Sharpe (1966) rank performance based on Sharpe ratio for two decades

and reports a rank correlation of 0.36 but no statistically significance. Jensen (1968) finds

evidence of positive correlation in alphas but it is largely attributed to persistence of inferior

performance. Lehmann and Modest (1987) report some degree of persistence but find it to be

highly dependent on the performance measure employed. Elton et al. (1993) also provide

evidence of positive persistence but mostly among underperforming funds.

A second method to examine persistence in performance is by sorting winners and

losers over successive periods and determining the statistical significance of repeated winners

or losers. Goetzmann and Ibbotson (1994) employ this methodology to examine performance

persistence of funds between 1976 and 1988. Their results suggest that past performance do

entail some predictive power on future performance across various time periods tested.

Brown and Goetzmann (1995) find clear evidence of persistence but most of this

phenomenon is attributed to underperformers. Malkiel (1995) finds considerable persistence

in funds during the 1970s but not in the 1980s. Lastly, Droms and Walker (2001) test for long

term and short term persistence over two decades. They find evidence of short term but not

long term persistence.

Persistence can also be estimated by regressing current performance of a fund on its

lags. Hendricks, Patel and Zeckhauser (1993) follow this method to estimate quarterly

performance of mutual funds by incorporating up to eight lags. Their results show significant

positive persistence in the first four lags but insignificant negative persistence for the

following four lags suggesting persistence up to one year. Quigley and Sinquefield (2000) use

the FF model with yearly lags to detect persistence in the performance in UK equity funds.

12

They report evidence of persistence only among the losers and largely concentrated on small

cap funds.

Finally, another strand of literature uses the rank portfolio approach to examine

persistence. This is done by forming winner-loser portfolios of funds based on ranking

criteria such as past returns or alphas. Consequently, a time series of returns or alphas is

generated from the portfolios and persistence is determined by their ability to outperform the

benchmark in the post-formation period. Hendricks et al. (1993) sort mutual funds into

octiles based on past performance and find evidence short term persistence, particularly

among poor performers. Overall, they find stronger support for inferior persistence than

superior persistence. Elton, Gruber and Blake (1996) investigate persistence of funds by

forming deciles and affirm persistence over one year. Carhart (1997) estimate performance

using a four factor model and suggest that size and momentum factors can explain short-term

persistence in performance. Again, persistence is more visible amongst poor performers.

Blake and Timmermann’s (1998) study of UK mutual funds supports persistence in

performance. In contrast to most other studies, they observe persistence amongst both top and

bottom performing funds. Finally, Bollen and Busse (2005) find evidence of persistence

among top decile funds over one quarter post ranking but not over longer periods.

The question of persistence of performance among diversified emerging market funds

has been barely investigated. Among the two notable studies, Gottesman and Morey (2007)

find no evidence to support any relation between past and future performance.4 The other

study is by Huij and Post (2011) who examine persistence of 137 diversified emerging

market mutual funds between 1993 and 2006. The authors find evidence of short term

persistence in the return spread between winner and loser funds. Interestingly, they find that 4 The authors find expense ratios of funds as the only significant predictor of their future performance. Funds with lower expenses, on average, show better performance.

13

the contribution of winner funds to persistence is significantly higher in emerging markets

compared to US market.

3. Data and Methodology

3.1 Sample Data

The sample data comprises of all open-end5 (including SICAV and ICVC6) emerging market

mutual funds available on Morningstar Direct’s database over the period from August 2000

to July 2010. These mutual funds are equity funds with a small portion of assets invested in

cash (approximately 6%). Whilst these funds are classified under diversified emerging

markets covering Asia, Latin America, Eastern Europe, Africa, and Middle Eastern countries,

a small proportion of assets are allocated to developed markets.7 The domiciles of these funds

are also diverse including the US, UK, Luxembourg, Sweden, Denmark, Finland, Spain,

Italy, Ireland, Australia, Austria and Chile. For funds whose assets are denominated in

currencies other than US Dollar, we convert them into equivalent US Dollars using prevailing

exchange rates.

To tackle survivorship bias in our sample we include all funds that have at least 12

months of returns reported during the sample period with some exceptions. We omit funds

without any reported returns as well as those with fragmented return records. Regional or

country specific funds (for example, JP Morgan Middle East Africa or BNP Brazil Equity)

are also excluded. For funds under the same management with identical or near-identical

returns, only the fund with the oldest history is selected. The resultant sample consists of 498

5 Open-end refers to funds that continuously offers new shares to investors and redeems them on demand. Close-end funds, however, issues limited shares and does not redeem. Bekaert and Urias (1999) find open-end funds track the emerging market benchmark index better than closed-end funds. 6 SICAV and ICVC are Western European and UK equivalent of open-end funds. 7 We do not include funds that invest in specific regions such as Africa, the Middle East and Eastern Europe because it can produce alpha estimates resulting from model misspecification rather than managerial skill.

14

funds comprising 202 funds with full data over the sample period (i.e. surviving funds with

full data), 72 funds that became defunct (i.e. non surviving funds) and 224 funds that were

incepted during the sample period and are still in existence (i.e. new funds that survived).

Monthly net returns are calculated by taking the change in monthly net asset value for

the fund, reinvesting all income capital gains distribution during that month, less the expenses

incurred during that month such as operating, management and other asset related costs, and

then dividing by the starting net asset value. Monthly gross returns are then derived by adding

back the most recent expense component to net returns. The returns are not adjusted for any

sales charges such as loading fees and redemption fees.

Table 1

Descriptive statistics for fund returns during the sample period. This table provides descriptive statistics of gross returns for mutual funds during the sample period (August 2000-July 2010). The reported minimum (Min), maximum (Max), median, mean, standard deviation (Std. Dev.), are calculated from each funds’ average returns, while fund size and net expense ratios are also averaged over the sampled period.

Number of Funds

Returns Fund Size ($

Million)

Net Expense

Ratio (%) Min (%)

Max (%)

Median (%)

Mean (%)

Std. Dev. (%)

All Funds 498 -5.71 3.39 1.19 1.05 0.75 714.83 2.08

Surviving Funds (Full Data) 202 0.44 2.06 1.22 1.22 0.22 1,418.65 2.19

Surviving Funds (New) 224 -2.12 3.38 1.17 1.00 0.72 271.30 1.96

Non Surviving Funds 72 -5.71 3.39 1.06 0.66 1.38 82.98 2.16

Table 1 provides summary statistics of the funds in our sample. Monthly returns for

funds that are operational through the full sample period average 1.22% with a standard

deviation of 0.22%. All of these funds provide positive returns within the range of 0.44% and

2.06%, a much smaller spread compared to new funds and non-surviving funds. Furthermore,

these funds also represent largest fund size category with an average of $1.42 Trillion assets

under management. The average net expense ratio at 2.19% is slightly higher than the other

15

categories. A large number of funds in our sample are new funds i.e. funds that were incepted

during the sample period. For 224 such funds that survived till the end of the sample period,

the average return is slightly lower but standard deviation higher compared to the older

surviving funds. However, these newly established funds have a slightly lower average net

expense ratio of 1.96%. The funds that do not survive for the full period, not unexpectedly,

perform the worst. The average return for these funds is only 0.66% with a high standard

deviation of 1.38%. The range of returns for this category is from 3.39% to -5.71%.

3.2 Methodology

We use two performance measurement models in our empirical analysis. The first is the well-

known Jensen’s alpha based on the single factor Capital Asset Pricing Model (CAPM).

Initially the following regression is performed following Jensen (1968).

(1)

Where is the monthly return of the fund i in the month t, is the one month is the risk-

free rate of return and is the monthly return on the benchmark index. The Jensen’s alpha

is given by which gives the excess return of the fund that is unexplained by the excess

return of the benchmark index.

Second, we apply the FF three factor model of Fama and French (1993) to regress

returns of the fund to allow for risk adjustment after controlling for ‘size’ and ‘value’ factors.

. . (2)

In addition to the excess return of market index as in (1), the FF model captures the excess

return of small stocks over large stocks (SMB) and that of value stocks over growth stocks

(HML).

16

If emerging market fund managers have market timing ability, they will switch

portfolios to high or low beta securities depending on whether future returns are expected to

be high or low respectively. Unless this timing ability is explicitly accounted for in our

performance measurement model, the beta coefficient will increase (decrease) in case of

positive (negative) forecast of excess market return and as a result systematically downward

bias the alpha estimate. In order to avoid the possibility of such bias, we also conduct

nonlinear regressions of realized returns of the fund against contemporaneous market returns

following Henriksson and Merton (1981). This also enables us to explicitly test for market

timing ability of the funds.8 The Henriksson-Merton market-timing measure allows for the

beta risk to be different in rising and falling markets. Specifically,

∗ (3)

where D is a dummy variable that equals 1 for (RMt - Rft) > 0 and zero otherwise, and and

are the selectivity and market timing coefficients respectively. Under the null hypothesis of

no market timing is expected to be zero, whereas for a successful market timer it should

exhibit a positive value.

Finally, to investigate persistence in fund performance, we employ the rank portfolio

approach. Starting November 2000, we rank individual funds every month based on their

average cumulative excess returns over the previous quarter and group them into deciles. The

portfolios are equally weighted comprising the winners (decile 10) through to the losers

(decile 1), and these are held for 3 months and 6 months following formation. The portfolios

are revised every month as fund rankings change thus generating a time series of monthly

8 An alternative test of market timing ability is given by the quadratic regression model of Treynor and Mazuy (1966).

17

excess returns for each decile portfolio from. Funds that do not survive till the end of our

sample period are included until they disappear. We compute the alphas for the decile

portfolios by regressing their excess returns against excess returns of the market index as per

(1). A positive and statistically significant difference between the top and bottom decile alpha

estimates would suggest persistence.

We use the MSCI Emerging Markets Investable Index denominated in US

dollars as the benchmark for emerging market returns. The index aims to capture 85% of total

stock market capitalisation of global emerging markets. We use the one month US Treasury

bill rate as the proxy for the risk-free rate. Currently there is no available data for Fama

French factors for emerging markets as a whole. Cremers et al. (2010) suggest constructing

factors based on common and easily tradable size and style indices. Common style indices

like MSCI are not only convenient to use for ‘emerging markets’ (which cover many national

markets) but can also provide for construction of reasonably good proxies for the Fama

French factors. Dyck, Lins and Pomorski (2013) use local MSCI indices to calculate local

Fama French factors in their study of the international mutual fund industry. Cuthbertson and

Nitzsche (2013) also do the same in their study of the German equity mutual fund industry.

More recently, Bruckner, Lehmann, Schmidt, and Steale (2014) include SMB and HML time

series based on MSCI indices for Germany.

Morningstar provides four subsets of MSCI emerging market indices which replicate

size segment and style segment indices. The MSCI Emerging Markets Large Cap Index

includes large cap emerging market firms with approximately 70% of market capitalization

coverage (MSCI Barra, 2010). Similarly, the MSCI Emerging Markets Small Cap Index

offers exposure to small cap companies across the countries listed on the MSCI emerging

market index aggregating to 15% market capitalization coverage. We proxy the SMB return

18

premium using the difference between monthly returns of these two indices. As for the HML

premium, we derive the proxy by taking the difference in returns generated between the

MSCI Emerging Markets Value Index and MSCI Emerging Markets Growth Index.9

4. Empirical Results

4.1 Performance of All Funds

Table 2 provides the mean returns for all 498 mutual funds during the full sample period.

Monthly excess returns of an equally weighted portfolio of all funds are regressed against

monthly excess benchmark returns using the single factor CAPM (panel A1) and the three

factor FF model (panel A2). Under the two models, the average fund underperformed the

benchmark by 0.11% and 0.15% per month (1.33% and 1.74% per annum) respectively.

However, neither of the alphas is significantly different from zero. The market beta

coefficient under both models is in excess of 0.96 suggesting most of the funds closely track

the returns of the MSCI Emerging Markets index. The SMB and HML coefficients in the

three factor model are statistically insignificant. The R2 for both the models indicate that

more than 90% of the variation in the emerging market funds’ returns is explained by the

broad market movement. This may indicate emerging market funds generally are more

focused on achieving diversification rather than taking active bets in these markets. The

insignificant coefficients for SMB and HML factors also suggest that emerging market funds

in general do not have any significant size or value/growth tilt in their portfolios.

9 The variables to assemble the value characteristics of the companies listed in MSCI emerging markets index are book value to price ratio, price to earnings ratio and dividend yields. As for growth characteristics, the determinates are book value to price ratio, long term forward earnings per share, short term forward earnings per share growth rate and may also consider the current internal growth rate and long term historical earnings per share trend (MSCI Barra, 2007).

19

Panel B1 and B2 provides the regression results of fund quartiles sorted by alpha of

individual funds. Each quartile estimates represent those of an equally weighted portfolio of

funds within each quartile. The average first quartile fund outperforms the market but not by

a statistically significant margin. The outperformance is also not large enough to outweigh

the negative impact of the fourth quartile funds as these underperform the index by 0.64%

and 0.71% per month (7.71% to 8.57% per annum) under the two models. The spread in

alphas between the first and fourth quartile funds are strongly significant.

Table 2

Average gross returns based performance: all funds. This table reports the regression estimates for an equally weighted portfolio of 498 funds in our sample between August 2000 and July 2010. Panel A1 and A2 depicts the regression estimates under the single factor CAPM and Fama and French (FF) three factor model for the entire sample respectively. Panel B1 and B2 reports regression estimates for fund quartiles formed by ranking the alphas of individual funds from the two models. Q1, Q2, Q3 and Q4 represents first, second, third, and fourth quartiles respectively. The spread between the top and bottom quartiles is denoted by Q4 – Q1. ***, **, * indicates significance at 1%, 5%, and 10% level respectively.

α β(Rm - Rf) βSMB βHML Adjusted R2

Panel A1: Full Sample - CAPM Coefficient -0.1106 0.9662*** 0.9081 Std Error 0.2706 0.0328

T-Stat -0.2532 42.5174

Panel A2: Full Sample: FF Model Coefficient -0.1453 0.9601*** 0.0847 0.0132 0.9122 Std Error 0.2725 0.0339 0.1124 0.1995

T-Stat -0.3214 41.9454 0.5206 0.1167

Panel B1: Quartile Analysis: CAPM Q4 0.3110 1.0108*** 0.9074 Q3 0.0264 0.9829*** 0.9536 Q2 -0.1361 0.9609*** 0.9430 Q1 -0.6426 0.9104*** 0.8288

Q4-Q1 0.9536*** 0.1004*** 0.0786

Panel B2: Quartile Analysis: FF Model Q4 0.2659 1.0084*** 0.0458 -0.0065 0.9222 Q3 0.0088 0.9815*** 0.0396 0.0406 0.9526 Q2 -0.1414 0.9578*** 0.0678 0.0004 0.9453 Q1 -0.7134 0.8929*** 0.1849** 0.0182 0.8292

Q4-Q1 0.9793*** 0.1155*** -0.1391*** -0.0248* 0.0930

20

An interesting trend observed in table 2 is the reduction in the estimated market beta

from the first to fourth quartile. The lower quartile funds are slightly less volatile than the

benchmark index. The difference in market beta between the top and the bottom quartile

funds is significant. This could indicate that underperforming funds either overweight their

holdings of stocks in defensive sectors or hold higher proportion of cash relative to the top

performers. The latter may be caused by higher liquidity requirements if these funds confront

a higher redemption rate. The results also show significant difference between top and bottom

quartile funds in terms of their exposure to size factor. The bottom quartile funds demonstrate

a higher tilt towards large cap stocks.

Comparing our results with those of pervious studies on emerging market funds, Eling

and Faust (2010) report postive alpha with CAPM and a much lower underformance of -

0.48% p.a. for the FF model. However, their results may be subject to model misspecfication

due to their selection of S&P 500 as benchmark rather than any index representing emerging

market stocks. Huij and Post (2011) estimate CAPM alpha on the terciles formed from

ranked returns of the funds. The spread between top and bottom terciles are much smaller

(4.44% p.a.) compared with our results of top and bottom quartiles ranked by individual

fund’s alpha (11.44% p.a.). On the other hand, there are also some similarities between our

results and those of Huij and Post (2011). The size and value factor coefficients are

comparable with their results as they are insignificant. The R2 values are very similar with

their models explaining more than 90% of the variation in returns. The alphas for the bottom

tercile in their study are significant similar to our results for net returns (which we do not

provide here), where the bottom quartile alpha was statisitically significant.

4.2 Performance of surviving funds with full data

21

We separately evaluate the funds with full data to see whether these funds earn positive

abnormal returns and also to determine the magnitude of survivorship bias in the sample.

This approach to estimate survivorship bias by taking the difference between the alpha for

surviving funds with full data and that for the entire sample is consistent with previous

studies (see Malkiel, 1995; Elton et al., 1996; Brown and Goetzmann, 1995).

As shown in Table 3, the alphas are still negative but statistically insignificant. There

is also noticeable improvement in their estimates. The annualized alpha estimate is -0.07%

and -0.13% for the single factor and the three factor model respectively. All other coefficient

estimates remain consistent with table 2. Whilst, the top quartile alpha is close to its

corresponding estimate for all funds, the bottom quartile alpha improves considerably. The

spread between the top and the bottom quartiles is almost halved to around 0.55 (6.4% p.a.).

Even though the magnitude of underperformance is greatly reduced, the funds that survived

the entire sample period still on average do not outperform the market. It could be because of

the difficulty to exploit price anomalies as the older funds tend to be funds with large

capitalisation and therefore closely following the market index. Overall, our results show that

if our sample consisted of only these surviving funds, the performance would be overstated

by nearly 3% per year i.e. the results would be subject to severe survivorship bias.

Table 3

Average gross returns based performance: funds surviving full sample period. This table reports the regression estimates for an equally weighted portfolio of 202 funds that survived through the full sample period (August 2000 to July 2010). Panel A1 and A2 depicts the regression estimate under the single factor CAPM and Fama and French (FF) three factor model for the entire sample respectively. Panel B1 and B2 reports funds quartiles formed by ranking the alphas of individual funds from the two models. Q1, Q2, Q3 and Q4 represents first, second, third, and fourth quartiles respectively. The spread between the top and bottom quartiles are denoted as Q4 – Q1. ***, **, * indicates significance at 1%, 5%, and 10% level respectively.

α β(Rm - Rf) βSMB βHML Adj R2

Panel A1: CAPM

22

Coefficient -0.0061 0.9704*** 0.9337 Std Error 0.1658 0.0217

T-Stat -0.0313 53.9328

Panel A2: FF Model Coefficient -0.0107 0.9698*** 0.0329 0.0002 0.9368 Std Error 0.1655 0.0215 0.0709 0.1108

T-Stat -0.0536 54.4810 0.2942 0.1200

Panel B1: Quartile Analysis: CAPM Q4 0.2726 0.9765*** 0.8944 Q3 0.0518 0.9949*** 0.9578 Q2 -0.0673 0.9701*** 0.9550 Q1 -0.2816 0.9407*** 0.9285

Q4-Q1 0.5542*** 0.0358** -0.0341

Panel B2: Quartile Analysis: FF Model Q4 0.2415 0.9908*** 0.0127 0.0221 0.9139 Q3 0.0478 0.9750*** 0.0274 0.0383 0.9387 Q2 -0.0587 0.9781*** 0.0109 -0.0184 0.9606 Q1 -0.2731 0.9356*** 0.0802 -0.0408 0.9345

Q4-Q1 0.5145*** 0.0553*** -0.0675** 0.0628* -0.0207

4.3 Non-Surviving Funds

Table 4 summarises the performance of non-surviving funds i.e. funds that do not exist at the

end of our sample period. These funds underperform the benchmark by as much as 4.25% per

annum before becoming defunct. It is also obvious that the distribution of returns is highly

skewed towards the left with an alpha below -13% per annum. Another interesting point

concerns the beta coefficient spread between top and bottom quartiles. Comparing the results

with the survivor funds, the spread is much larger for non-surviving funds due to the much

lower systematic risk of the bottom quartile funds.

Table 4

Average gross returns based performance: non-surviving funds. This table reports the regression estimates for an equally weighted portfolio of 72 funds that were deceased during the sample period. Panel A1 and A2 depicts the regression estimate under the single factor CAPM and Fama and French (FF) three factor model respectively. Panel B1 and B2 reports funds quartiles formed by ranking the alphas of individual funds from the two models. Q1, Q2, Q3 and Q4 represents first, second, third,

23

and fourth quartiles respectively. The spread between the top and bottom quartiles are denoted as Q4 – Q1. ***, **, * indicate significance at 1%, 5%, and 10% level respectively.

α β(Rm - Rf) βSMB βHML Adj R2

Panel A1: CAPM Coefficient -0.3299 0.9424*** 0.8867 Std Error 0.3288 0.0408

T-Stat -0.8060 32.7504

Panel A2: FF Model Coefficient -0.3545 0.9372*** 0.0734 0.0054 0.8914 Std Error 0.3327 0.0425 0.1331 0.2254

T-Stat -0.7801 31.6340 0.3331 0.0026

Panel B1: Quartile Analysis. CAPM Q4 0.2238 1.0111*** 0.9367 Q3 -0.1292 0.9814*** 0.9508 Q2 -0.2867 0.9527*** 0.9293 Q1 -1.1273 0.8243*** 0.7298

Q4-Q1 1.3512*** 0.1868*** 0.2069

Panel B2: Quartile Analysis. FF Model Q4 0.2136 1.0200*** 0.0626 -0.0198 0.9534 Q3 -0.1333 0.9621*** 0.0528 -0.0143 0.9449 Q2 -0.3091 0.9304*** 0.0041 -0.0113 0.9329 Q1 -1.1891 0.8363*** 0.1742 0.0669 0.7344

Q4-Q1 1.4027*** 0.1837*** -0.1116** -0.0867* 0.2189

4.3 Performance during the financial crisis sub-period

We measure the performance of the funds exclusively for the 2-year sub-period (August 2008

to July 2010) following the onset of the global financial crisis (GFC), a period marked with

high volatility in most equity markets. The selection of this sub-period is important for two

reasons. First, it enables us to observe the impact of the crisis and associated volatile market

conditions on the performance of emerging market funds. If fund managers possess market

timing skill, they will correctly adjust the portfolio risk anticipating the market movements.

Second, given the findings by Moskowitz (2000) who documented that during recessionary

periods US funds perform better due to information asymmetries as compared to poor

24

performance in expansionary periods, it would be of interest to find out whether emerging

market funds’ performance is consistent with this phenomenon. 62 funds are omitted from the

sample as they are either defunct or are due to defunct without at least 18 months of returns

reported.

Table 5

Average gross returns based performance: sub-period August 2008 to July 2010. This table reports the regression estimates for an equally weighted portfolio for 437 funds that had 18 months of returns data reported during the sub-period August 2008 to July 2010. Panel A1 and A2 depicts the regression estimates under the single factor CAPM and Fama and French (FF) three factor model respectively. Panel B1 and B2 reports funds quartiles formed by ranking the alphas of individual funds from the two models where the reported coefficients and adjusted R2 are based on quartile averages. Q1, Q2, Q3 and Q4 represents first, second, third, and fourth quartiles respectively. The spread between the top and bottom quartiles are denoted as Q4 – Q1. ***, **, * indicates significance at 1%, 5%, and 10% level respectively.

α β(Rm - Rf) βSMB βHML Adj R2

Panel A1: All Funds: CAPM Coefficient -0.2716 1.0015*** 0.9443 Std Error 0.5122 0.0464

T-Stat -0.3741 34.5738

Panel A2: All Funds: FF Model Coefficient -0.3917 0.9819*** 0.1287 -0.0838 0.9446 Std Error 0.6431 0.0646 0.2864 0.4625

T-Stat -0.4312 26.6638 0.4713 -0.4349

Panel B1: Quartile Analysis: CAPM Q4 0.4316 0.9860*** 0.9632 Q3 -0.0216 0.9827*** 0.9743 Q2 -0.2873 0.9921*** 0.9726 Q1 -1.2070 1.0450*** 0.8675

Q4-Q1 1.6386*** -0.0590*** 0.0956

Panel B2: Quartile Analysis: FF Model Q4 0.3279 0.9875*** -0.0142 -0.0346 0.9640 Q3 -0.0489 0.9850*** 0.0812 -0.1476 0.9782 Q2 -0.3501 0.9663*** 0.1855* -0.2773 0.9673 Q1 -1.4925 0.9887*** 0.2623** 0.1220 0.8696

Q4-Q1 1.8205*** -0.0012 -0.2765*** -0.1566* 0.0944

25

Table 5 presents results for the sub-period from August 2008 to July 2010. On

average, funds seem to underperform the benchmark for both the models but the negative

alphas are not statistically significant. The results are comparable with Eling and Faust’s

(2010) analysis of emerging market funds during the Asian crisis in 1997. Their results

showed an annual underperformance of 2.06% and 1.95% for the CAPM and the FF models

respectively. A possible reason for these results is that emerging market funds can face higher

redemptions during crisis periods. Given the risky nature of emerging markets, investors are

more likely to switch, by selling off assets and switch to more stable and liquid assets in

developed markets. In contrast, money only starts to trickle in emerging markets after

conditions stabilize and returns improve. This asymmetric nature of the fund flow may prove

to be a hindrance to the managers’ ability to time the markets and outperform the benchmark

index.

The performance of the different fund quartiles during the crisis period is revealing.

The funds in the top quartile have a slightly higher alpha during the crisis relative to that for

the full sample period under both models. The rest of the quartiles, however, have inferior

performance. The bottom quartile funds appear to experience a particularly rough period with

the magnitude of their underperformance almost doubling during the financial crisis

compared the full sample period. As a result, the difference between the alphas of the top and

bottom quartiles increases almost two-fold.

Furthermore, the beta estimates across the quartiles show an opposing trend compared

to the full period analysis. The beta coefficients for the top quartile funds are slightly lower

than the market whereas those for the bottom quartile funds are slightly higher. Therefore, we

can only conclude that the top performers seem to have adjusted their portfolio risk perhaps

26

by shifting allocations into safer assets like cash whereas the bottom performers were not able

appropriately adjust their funds’ beta during market downturns.

4.5 Performance of individual funds

Apart from the equally weighted portfolio of funds constructed for all of the above analyses,

we replicate the analysis by conducting time series regressions for individual funds. Figure 1

illustrates the distribution of monthly alphas for all funds under the two models that depict

very similar distributions. The peak of the distribution shows that the majority of alphas are

clustered around zero. The alphas for most funds lie within the -0.25 and 0.25 range. The

distribution is also skewed towards the left suggesting there are more underperforming funds

than there are outperforming funds relative to their benchmark. While we do not report the

full descriptive statistics for the distribution, the median alpha estimate is negative (-0.28%).

When we look at the distribution of funds which survive the full period as well as the

distribution of funds that do not survive, the median alpha estimates turns out to be negative

(-0.05% and -0.45% respectively).

27

a) CAPM Alpha

b) FF Alpha

Fig.1. (a) Distribution of CAPM alphas. This figure shows the frequency distribution of the estimated regression intercepts using Capital Asset Pricing Model. (b) Distribution of FF alphas. This figure shows the frequency distribution of the estimated regression intercepts using Fama and French three factor model.

0

20

40

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120

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‐4 ‐3.5 ‐3 ‐2.5 ‐2 ‐1.5 ‐1 ‐0.5 0 0.5 1 1.5 2 2.5 3 3.5 4

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‐4 ‐3.5 ‐3 ‐2.5 ‐2 ‐1.5 ‐1 ‐0.5 0 0.5 1 1.5 2 2.5 3 3.5 4

Monthly alpha estimate

Monthly alpha estimate

No.

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fun

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No.

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28

Table 6 summarises the percentage of individual funds with alphas that are

insignificant as well as the percentage that are significantly positive and negative under the

two models. The results illustrate that the largest proportion of funds in our sample have

insignificant alphas. For funds with significant alphas, the proportion of funds with

significantly positive alphas is less compared to the proportion of those with significantly

negative alphas. 10 However, Eling and Faust (2010) reported a higher percentage of

statistically positive alphas than negative alphas for both performance measures on emerging

market funds. As discussed before, their results may be influenced by their selection of a

portfolio of US stocks as the market benchmark index.

As an exception to the general trend in the results, among funds that survive the full

sample period, the proportion of significantly positive alphas is greater than that of

significantly negative alphas. The proportion of funds with positive (negative) alphas was

8.91% (7.92%) for CAPM and 8.42% (6.93%) for the FF model. This may suggest that funds

with long tenure, to some extent, may have the expertise in capturing postive risk-adjusted

returns at the gross level.

Table 6

Alphas for Individual Funds. This table presents the percentage of individual funds with alphas that are statistically (i) insignificant (=0), (ii) significantly positive (> 0) and (iii) significantly negative (< 0) at 5% level. Panel A and B report alphas based on CAPM and Fama and French (FF) three factor model respectively.

Percentage of Funds with Alpha

Total =0 > 0) < 0

Panel A: CAPM

All Mutual Funds 498 85.35 5.42 9.23

10 Although we do not present the results for alphas net of expenses, for majority of the funds that exhibit positive alphas, we find they turn insignificant when expenses are factored in. This is consistent with Grinblatt and Titman (1989), Malkiel (1995) and Wermers (2000) who all reported an increase in statistically significant negative alphas under net returns evaluation.

29

Full Data Funds 202 83.17 8.91 7.92 Non-Surviving Funds 72 79.16 4.17 16.67 Sub-Period 437 90.85 3.20 5.95

Panel B: FF Model

All Mutual Funds 498 84.94 5.62 9.44 Full Data Funds 202 84.65 8.42 6.93 Non-Surviving Funds 72 75.00 6.94 18.06 Sub-Period 437 94.28 1.60 4.12

For non-surving funds, surprisingly a small proportion has positive alphas. A possible

explanation could be that these funds do not represent those that are forced to close down due

to bad performance but in fact, are mereged or taken over, possibly because of their strong

performanc and consequent attractiveness. For the financial crisis sub-period, we have the

largest porporption of statistically insignficant alphas under both models.

4.6 Nonlinear Regression Estimates

Table 7 reports the estimates for nonlinear regression model based on Henriksson and Merton

(1981) model as represented by equation (3). For the aggregated sample of 498 funds shown

in panel A, we do not find any evidence of selectivity or market timing skill as both the alpha

and lambda estimates are not significant. When we conduct the same analysis based on

quartiles (panel B), the results do not change. None of the alphas or lambdas is statistically

significant although the difference between the alphas of top and bottom quartile funds

remain significantly positive for this model. However, the difference in lambdas between the

two quartiles is negative (although not significant) and that somewhat counterbalances the

positive difference in alphas.

Table 7

Market timing performance: 498 mutual funds for the period August 2000 to July 2010. This table reports the regression estimates based on equation (3). Panel A1 shows the regression estimates for an equally weighted portfolio of all 498 funds. Panel A2 shows the regression estimates for equally

30

weighted portfolio of fund quartiles formed by ranking the alphas of individual funds given by equation (1). Q1, Q2, Q3 and Q4 represents first, second, third, and fourth quartiles respectively. The spread between the top and bottom quartiles are denoted as Q4 – Q1. ***, **, * indicate significance at 1%, 5%, and 10% level respectively.

α β(Rm – Rf) Adj R2

Panel A1: Full Sample Coefficient 0.1595 1.0019*** -0.0925 0.9100 Std Error 0.4191 0.0529 0.1070

T-Stat 0.3989 27.1912 -0.7615

Panel B1: Quartile Analysis Q4 0.8463 1.0966*** -0.2796 0.8457 Q3 0.2465 1.0215*** -0.0798 0.9497 Q2 0.0194 0.9782*** -0.0255 0.9497 Q1 -0.4739 0.9113*** 0.0143 0.8943

Q4-Q1 1.3202*** 0.1852*** -0.2938 -0.0486

Turning to the market timing coefficients of individual funds, Table 8 reports the

percentages of funds with lambdas that are insignificant as well as of those that have

significantly positive and negative lambdas. Less than 2% of the funds show evidence of

market timing ability with significantly positive lambdas. On the other hand, more than 16%

of the funds report negative lambdas.11 For funds that survive the full sample period, the

estimated percentage of funds with positive lambdas barely increase but that of funds with

negative lambdas decrease by about 25%. The trends are opposite for the non-surviving funds

with decrease (increase) in percentage of funds with positive (negative) lambdas. A very

interesting result is observed for the estimates during the GFC sub-period. The percentage of

funds with positive lambda during this 18-month period is 8.30% i.e. more than 4 times of

that during the full sample period. There is corresponding fall in the number of funds with

insignificant lambdas. It suggests that some emerging market funds may have been more

active in their market timing activities during the financial crisis.

11 Adequate caution should be exercised in interpreting such a perverse outcome. One explanation is that higher cash inflows (outflows) from investors during rising (falling) markets bias market timing coefficients downwards (See, for example, Bollen and Busse, 2001)

31

Table 8

Market timing coefficients for individual funds. This table exhibits the percentage of individual funds with market timing coefficient (lambda) that is statistically (i) insignificant (=0), (ii) significantly positive (> 0) and (iii) significantly negative (< 0) at 5% level.

No. of funds

Percentage of Funds with Lambda

=0 > 0 < 0 All Mutual Funds 498 81.93 1.81 16.26 Full Data Funds 202 85.64 1.98 12.38 Non-Surviving Funds 72 79.17 1.38 19.45 Sub-Period 470 74.90 8.30 16.80

Overall, the absence of market timing ability for emerging market funds is not entirely

unexpected. As discussed previously, most of the funds may just seek to have exposure in

these markets by simply mimicking the index rather than generate excess returns by timing

the market. Our evidence of only a small proportion of funds having market timing ability is

consistent with the findings of bulk of empirical studies in the developed markets (see for

example, Wermers, 2000; Cuthbertson, Nitzsche, and O’Sullivan, 2010).12

4.7 Persistence in Performance

For measuring persistence in performance of emerging market funds, we form decile

portfolios based on past quarter’s performance as described in section 3.2. Table 9 reports the

average alphas for the decile portfolios over holding periods of 3 and 6 months after portfolio

formation. The results show persistence for both the best and the worst performers as they

continue to remain in the top and bottom deciles respectively. The annualised average alpha

spread between the top and the bottom decile portfolios is 4.5% for the 3-months holding

12 Borensztein and Gelos (2000) examine the behaviour of emerging market funds with respect to movement of funds between different individual markets. They find evidence that funds withdrew large sums from the affected country in the month prior to the crisis. However, in many cases, the withdrawn money was invested in other countries that were seen as suffering from contagion effects. The study did not examine how fund flow behaviour affected performance.

32

period which is statistically significant at 1% level. For the 6-months holding period, the

annualized spread of 2.96% exhibits weaker significance at 10% level.

We find that approximately 35% of the 3-month holding periods exhibit

statistically significant positive persistence. This decreases to 29% for holding periods of 6

months. Conversely, there are also some holding periods periods where bottom performers

significantly outperform top performers. The proportions of such periods of negative

persistence are 14% and 19% for the 3 and 6 months’ holding period respectively.

Table 9

Performance of mutual fund decile portfolios formed based on performance over past quarter. Funds are sorted every month between November 2000 to April 2010 into equally weighted decile portfolios based on their previous quarter’s average cumulative excess return. Funds with the highest and lowest returns over past 3 months comprise Decile 10 and Decile 1 respectively. The average monthly excess returns of each decile for next 3 months and 6 months are shown below. The spread between Decile 10 and Decile 1 is denoted ‘Highest – Lowest’ with t-statistics indicated in parenthesis. The last two rows represent the proportion of persistence during the individual months of the sample period. ***, **, * indicates significance at 1%, 5%, and 10% level respectively.

Average Excess Holding Period Alphas

Portfolio Decile 3 Months 6 Months

10 (Highest) 0.102 0.018 9 0.020 -0.093 8 -0.021 -0.089 7 -0.181 -0.083 6 -0.157 -0.100 5 -0.228 -0.132 4 -0.254 -0.168 3 -0.246 -0.216 2 -0.262 -0.202

1 (Lowest) -0.267 -0.226

Highest - Lowest 0.368*** (3.003) 0.244* (1.730) Proportion of Holding Periods

with Positive Persistence 35.09% (40 out of 114) 28.83% (32 out of 111) Proportion of Holding Periods

with Negative Persistence 14.06% (16 out of 114) 18.92% (21 out of 111)

33

Whilst our results support persistence in performance among emerging market mutual

funds observed by Huij and Post (2011), there are two important differences. Huij and Post

(2011) report the alpha spread between the top and bottom ninth of their fund sample to be

7.09% but we find the spread between the top and the bottom deciles to be significantly

smaller. One explanation of this difference could be our use of longer holding periods (3 and

6 months) for performance evaluation compared to the 1-month holding period in their study

as it is likely that the persistence would be more visible over shorter periods. This trend is

also apparent from our results as the return spread between the top and bottom decile

portfolios declines substantially over the 6-month holding period (relative to the 3-month

holding period) and is no longer statistically significant at 5% level.

The other point where we differ from Huij and Post (2011) is that we find that the

persistence in fund performance is driven more by the bottom performers than by the top

performers. Huij and Post (2011) claim the top ninth funds in their sample outperformed the

benchmark by 4.29% whereas the bottom ninth underperformed it by 2.8% on a risk-adjusted

basis. The corresponding annualized alphas for the top and bottom decile portfolios in our

study are 1.23% and -3.25% for the 3-month holding periods. Our result is consistent with the

evidence of persistence in the US like Carhart (1997) who find the lowest return funds to be

largely responsible for persistence in mutual fund performance and in UK where Cuthbertson

et al. (2010) find persistence among ‘loser’ funds but not among ‘winner’ funds.

34

Fig.2. Performance spread (cumulative) between best and worst performers. This figure shows the cumulative excess return spread between top and bottom decile funds ranked by past quarter’s performance.

Figure 2 demonstrates the cumulative excess return spread between top and bottom

decile funds ranked by their returns over the previous quarter throughout the entire sample

period. It is apparent that the cumulative excess return spread for the 3 months’ holding

period is generally higher compared to the 6 months’ holding period, indicating persistence is

stronger for shorter evaluation periods. The overall trend shows a gradual increase in the

spread over time, suggesting persistence in emerging market funds is not specific to a

particular sub-period. The exception to this is the period following the onset of the financial

crisis.13

5. Conclusions

13 One can conjecture that the spread net of fees and expenses would be smaller if the better performing ‘top decile’ funds increase their fees subsequently to reflect the value difference they create for investors compared to the underperformers. However, this is unlikely given the evidence of negative relationship between before-fee performance and fees documented in the mutual fund literature (see, for example, Gil-Bazo and Ruiz-Verdu, 2009).

‐5

0

5

10

15

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25

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40

Feb‐2001

Jul‐2001

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Jun‐2004

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um

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(%

)

3MonthsHolding6MonthsHolding

35

The purpose of this study was to examine the ability of diversified emerging market equity

funds to produce risk-adjusted returns over and above a comparable market benchmark index.

Amongst studies of fund performance in developed markets, most have reported no superior

performance, which is consistent with one’s expectation in informationally efficient markets.

Emerging markets, on the other hand, are commonly considered to offer greater opportunities

for fund managers to exploit stock mispricing and information asymmetries. Our results do

not support this notion as we demonstrate that on an average diversified emerging market

funds do not outperform their benchmark index. Our analysis of individual funds shows that

nearly 95% of emerging market funds fail to outperform the benchmark index. Most of these

funds have market beta risk that is close to the benchmark, which may suggest that these

funds mainly aim to offer diversification benefits to investors rather than seek superior risk-

adjusted returns through active fund management. We test for market timing skills of funds

but find that less than 2% of funds show evidence of superior ability at a statistically

significant level.

We conduct separate analysis of fund performance for the period since the onset of

financial crisis which marks a period of significant volatility. Emerging market funds,

expectedly, have inferior risk adjusted performance during this period. However,

surprisingly, the average alpha of the top quartile funds is higher during this sub-period than

that during the full sample period. The other quartiles, particularly the bottom quartile, is

however significantly poorer during this sub-period. Interestingly, the proportion of funds

showing superior market timing ability increase significantly during the sub-period, although

it is still less than one in ten.

We also look for the evidence of persistence in emerging market funds’ performance.

Consistent with past studies evaluating mutual fund performance, we find evidence of short

36

term persistence among emerging market funds. But unlike the recent findings on persistence

in emerging markets by Huij and Post (2011), we find that the poorly performing funds play a

greater role in driving persistence than the top performers. In this respect, our results are

more in agreement with the evidence of persistence in developed markets.

Why do most diversified emerging market funds fail to outperform the benchmark?

There are two possible explanations. First, emerging markets in recent times may be no less

informationally efficient than developed markets as shown by Griffin, Kelly, and Nardari

(2010). In that case, fund managers in emerging markets will find it almost as difficult to

outperform the benchmark index on a risk-adjusted basis as their counterparts in the

developed markets. If funds face higher transaction costs in operating in emerging markets,

they will find it even harder to beat the market on a net basis. Second, a body of research has

shown domestic fund managers to have informational advantages over their foreign

counterparts (Shukla and van Inwegen, 1995; Bialkowski and Otten, 2011). Since the

diversified equity funds in our study are generally domiciled in developed markets and

mainly run by foreign managers, they might be at a disadvantage in exploiting any potential

inefficiency in emerging markets.

The findings of our study have important implications for investors. Our evidence

suggests that attempts to earn superior risk-adjusted returns by investing in diversified

emerging market equity funds are likely to be disappointed. Whilst emerging markets can

offer potential diversification benefits to investors in developed markets, these benefits are

sensitive to the particular investment vehicle used by the investor (Bekaert and Urius, 1999).

In light of our evidence, it appears that equity funds that diversify across multiple emerging

markets may not be the best vehicle to access such opportunities. In absence of superior risk-

adjusted performance (alpha) by these funds, investors would be better off by allocating to

37

ETFs which can provide similar diversification opportunities through beta exposure at a

lower cost. The other alternative that investors can consider is to invest in funds that focus on

individual emerging markets as there is some evidence in the hedge fund literature showing

that funds with a geographical focus do better than those that invest across multiple emerging

markets. It is quite possible that funds will have informational advantages when they have a

clear geographical focus as suggested by Kotkatvuori-Örnberg et al. (2011).

One limitation of our study deserves particular attention. We have not considered the

heterogeneity across different geographical regions or markets comprising the ‘emerging

markets’. Whilst all funds evaluated in this study hold diversified portfolios across these

markets, there may be individual funds with greater (lesser) exposure to certain regional

markets. Broad emerging market indexes like MSCI may not capture the true systematic risk

of such funds. Future research could look more closely into the specific asset allocation of

emerging market funds and evaluate their performance by constructing more appropriate

benchmarks.

References

Abel, E., Fletcher, J., 2004. An empirical examination of UK emerging market unit trust

performance. Emerging Markets Review 5, 389-408.

Abugri, B. A., Dutta, S. 2009. Emerging market hedge funds: Do they perform like regular

hedge funds? Journal of International Financial Markets, Institutions & Money 19,

834-849.

Bekaert, G., Urias, M., 1999. Is there a free lunch in emerging market equities?, Journal of

Portfolio Management 25, 83–95.

38

Bialkowski, J. Otten, R., 2011. Emerging market mutual fund performance: Evidence for

Poland. North-American Journal of Economics and Finance 22, 118-130.

Blake, D., Timmermann, A., 1998. Mutual fund performance: Evidence from the UK.

European Finance Review 2, 55-77.

Blitz, D., Huij, J., 2012. Evaluating the performance of global emerging markets equity

exchange-traded funds. Emerging Markets Review 13, 149-158.

Bollen, N., Busse, J., 2001. On the timing ability of mutual fund managers. Journal of

Finance 56, 1075-1094.

Bollen, N., Busse, J., 2005. Short-term persistence in mutual fund performance. Review of

Financial Studies 18, 569-597.

Borensztein, E., Gelos, G., 2000, A panic-prone pack? The behavior of emerging market

mutual funds. IMF Working Paper No. 00/198.

Brown, S., Goetzmann, W., 1995. Performance persistence. Journal of Finance 50, 679-698.

Bruckner, R., Lehmann, P., Schmidt, M., Steale, R., 2014. Fama/French factors for Germany:

Which set is best?. SSRN Working Paper

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2390063

Cai, J., Chan, K., Yamada, T., 1997. The performance of Japanese mutual funds. Review of

Financial Studies 10, 237-273

Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance 52, 57-82.

Cesari, R. Panetta, F., 2002. The performance of Italian equity funds, Journal of Banking &

Finance 26, 99-126.

Chan, K., Hameed, A., 2006. Stock price synchronicity and analyst coverage in emerging

markets. Journal of Financial Economics 80, 115-147.

Chang, E., Eun, C. S., Kolodny, R., 1995. International diversification through closed-end

country funds. Journal of Banking and Finance 19, 1237-1263.

39

Charitou, A., Makris, A., Nishiotis, G., 2006. Closed-end country funds and international

diversification. Multinational Finance Journal 10, 251-276.

Cremers, M., Petajisto, A., and Zitzewitz, E., 2010. "Should benchmark indices have alpha?

Revisiting performance evaluation", Working Paper.

Cumby, R., Glen, J., 1990. Evaluating the performance of international mutual funds. Journal

of Finance 45, 497-521

Cuthbertson, K., Nitzsche. D., O’Sullivan, N., 2008. UK mutual funds performance: Skill or

luck? Journal of Empirical Finance 15, 613-634.

Cuthbertson, K., Nitzsche. D., O’Sullivan, N., 2010. The market timing ability of UK mutual

funds. Journal of Business Finance and Accounting 37, 270-289.

Cuthbertson, K., Nitzsche, D., 2013. Performance, stock selection and market timing of the

German equity mutual fund industry. Journal of Empirical Finance 21, 86-101.

Daniel, K., Grinblatt, M., Titman, S., Wermers, R., 1997. Measuring mutual fund

performance with characteristic-based benchmarks. Journal of Finance 52, 1035–

1058.

Droms W., Walker, D., 2001. Persistence of mutual fund operating characteristics: returns,

turnover rates, and expense ratios. Applied Financial Economics 11, 457-466.

Dyck, A., Lins, K. V., Pomorski, L. 2013. Does active management pay? New international

evidence. Review of Asset Pricing Studies 3, 200-228.

Eling, M., Faust, R., 2010. The performance of hedge funds and mutual funds in emerging

markets. Journal of Banking and Finance 34, 1993-2009.

Elton, E., Gruber, M., Das, S., Hlavka, M., 1993. Efficiency with costly information: A

reinterpretation of evidence from managed portfolios. Review of Financial Studies 6,

1-22.

40

Elton, E., Gruber, M., Blake, C., 1996. The persistence of risk-adjusted mutual fund

performance. Journal of Business 69, 133-157

Fama, E., French, K., 1993. Common risk factor in the return on stocks and bonds. Journal of

Financial and Economics 33, 3-56.

Fillis, G., 2006. Testing for market efficiency in emerging markets: Evidence from the

Athens Stock Exchange. Journal of Emerging Market Finance 5, 121-133

Gil-Bazo, J., Ruiz-Verdú, P., 2009. The relation between price and performance in the mutual

fund industry. Journal of Finance 64, 2153–2183.

Goetzmann, W., Ibbotson, R., 1994. Do winners repeat? Journal of Portfolio Management,

20, 9-18.

Gottesman, A., Morey, M., 2007. Predicting emerging market mutual fund performance.

Journal of Investing 16, 111-122.

Griffin, J., Kelly, P., Nardari, F., 2010. Do market efficiency measures yield correct

inferences? A comparison of developed and emerging markets, Review of Financial

Studies 23, 3225-3277.

Grinblatt, M., Titman, S., 1989. Mutual fund performance: An analysis of quarterly portfolio

holdings. Journal of Business 62, 393-416.

Gruber, M., 1996. Another puzzle: The growth in actively managed mutual funds. Journal of

Finance 51, 783-810.

Hayat, R., Kraeussl, R., 2011. Risk and return characteristics of Islamic equity funds.

Emerging Markets Review 12, 189–203.

Hendricks, D., Patel, J., Zeckhauser, R., 1993. Hot hands in mutual funds: Short-run

persistence of relative performance, 1974-1988. Journal of Finance 48, 93-130.

41

Henriksson, R., Merton, R., 1981. On market timing and investment performance II:

Statistical procedures for evaluating forecasting skills. Journal of Business 54, 513-

533.

Huij, J., Post, T., 2011. On the performance of emerging market equity mutual funds.

Emerging Markets Review 12, 238-249.

Ippolito, R., 1989. Efficiency with costly information: A study of mutual fund performance,

1965-1984. Quarterly Journal of Economics 104, 1-23

Jensen, M., 1968. The performance of mutual funds in the period 1945-1964. Journal of

Finance 23, 389-416.

Kaminsky, G., Lyons, R., Schmukler, S., 2001. Mutual fund investment in emerging markets:

An overview. The World Bank Economic Review 15, 315-340.

Karamera, D., Ojah, K., Cole, J., 1999. Random walks and market efficiency tests: evidence

from emerging equity markets, Review of Quantitative Finance and Accounting 13,

171-188.

Kothari, S., Warner, J., 2001. Evaluating Mutual Fund Performance. Journal of Finance

56, 1985–2010.

Kotkatvuori-Örnberg, J., Nikkinen, J., Peltomäki, J., 2011. Geographical focus in emerging

markets and hedge fund performance. Emerging Markets Review 12, 309-320.

Lee, S., 2001. Diversification benefits and investment performance of emerging market

funds: Evidence from closed-end country funds. PhD Dissertation, George

Washington University.

Lai, M., Lau, S., 2010. Evaluating mutual fund performance in an emerging Asian economy:

the Malaysian experience. Journal of Asian Economics 21, 378-390.

Lehmann, B., Modest, D., 1987. Mutual fund performance evaluation: a comparison of

benchmarks and benchmark comparisons. Journal of Finance 42, 233-265.

42

Malkiel, B., 1995. Returns from investing in equity mutual funds 1971 to 1991. Journal of

Finance 50, 549-572.

Matallin-Saez, J., 2006. Seasonality, market timing and performance amongst benchmarks

and mutual fund evaluation. Journal of Business Finance and Accounting 33, 1484-

1507.

Michelson, S., Philipova, E., Srotova, P., 2008. The case for emerging market funds. Journal

of Business and Economics Research 6, 81-88.

Mobarek, A., Mollah, A., Bhuyan, R., 2008. Market efficiency in emerging stock market:

Evidence from Bangladesh. Journal of Emerging Market Finance 7, 17-41.

Moskowitz, T., 2000. Mutual fund performance: an empirical decomposition into stock-

picking talent, style, transactions costs, and expenses: discussion, Journal of Finance

55, 1695-1703.

Movassaghi, H., Bramhandkar, A., Shikov, M., 2004. “Emerging” vs. “Developed” markets

closed-end funds: A comparative performance analysis. Managerial Finance 30, 51-

61.

MSCI Barra., 2007. MSCI Global Investable Market Value and Growth Indices.

http://www.msci.com/methodology/meth_docs/MSCI_May07_GIMIVGMethod.pdf

(Accessed on 08/08/2010)

MSCI Barra., 2010. MSCI Global Investable Market Indices Methodology.

http://www.mscibarra.com/eqb/methodology/meth_docs/MSCI_Sep10_GIMIMethod.

pdf (Accessed on 08/08/2010)

Otten, R., Bams, D., 2002. European mutual fund performance. European Financial

Management 8, 75-101.

Peltomäki, J., 2008. Emerging market hedge funds and the Yen carry trade. Emerging

Markets Review 9, 220–290.

43

Quigley, G., Sinquefield. R., 2000. Performance of UK equity unit trusts. Journal of Asset

Management 1, 72-92.

Ratner, M., Leal, R., 2005. Sector integration and the benefits of global diversification.

Multinational Finance Journal 9, 235-267.

Risso, W., 2009. The informational efficiency: the emerging markets versus the developed

markets, Applied Economics Letters 16, 485-487.

Sehgal, S., Jhanwar. M., 2008. On stock selection skills and market timing abilities of mutual

fund managers in India. International Research Journal of Finance and Economics,

15, 1-11.

Sharpe, W., 1966. Mutual fund performance. Journal of Business, 39, 119-138.

Shukla, R., van Inwegen, G., 1995. Do locals perform better than foreigners?: An analysis of

UK and US mutual fund managers. Journal of Economics and Business 47, 241–254.

Strömqvist, M., 2007. Should you invest in emerging market hedge funds? Working Paper.

Stockholm School of Economics.

Swinkels, L., Rzezniczak, P., 2009. Performance evaluation of Polish mutual fund managers.

International Journal of Emerging Markets 4, 26-42.

Treynor, J., Mazuy, F. 1966. Can mutual funds outguess the market? Harvard Business

Review 44, 131-136.

Wermers, R., 2000. Mutual fund performance: an empirical decomposition into stock-picking

talent, style, transaction costs, and expenses. Journal of Finance 55, 1655-1695.