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Chapter 2

An Application of Style Analysis to Middle East and North African (MENA) Hedge Funds

G.N. Gregoriou^*

M. Wu^**
^* State University of New York (Plattsburgh), School of Business and Economics, Plattsburgh, NY, United States
^** Clarkson University, Potsdam, NY, United States

Abstract

In this chapter we use style analysis to investigate if the returns of long-only equity hedge funds listed with the Middle East and North Africa (MENA) as their primary category classification in the Barclay Hedge Alternative Investment Database are driven by frontier market indexes and MENA indexes. Frontier markets are markets that are thinly traded and have poor liquidity as compared to traditional emerging markets. Our paper finds that our small group of hedge funds have high R-squared values, indicating that style indexes (MENA and frontier indexes) explain the variation in returns generated by these funds.

Keywords

style analysis

hedge funds

MENA

emerging markets

regression analysis

performance

1. Introduction

We begin with a definition as to what is meant by frontier markets and what the classification of a frontier market is. A frontier market, or a preemerging market, is usually a small country with an illiquid stock market composed of thinly traded stocks, which is typically too small and risky to be regarded as an emerging market, such as the MENA region. Frontier markets have excellent opportunities and are considered very volatile markets that can generate large positive as well as large negative returns (Oey, 2014). However, the benefit of producing low correlations with frontier markets versus developed markets—and to some extent against emerging markets^a—is a necessity for portfolio diversification (Speidell, 2012).

Frontier markets are considered a subcategory of emerging markets, with smaller market capitalizations, less accessibility to investors, and low stock turnover when compared to emerging markets, but which can be accessed through various exchange traded funds (Berger et al., 2011). In terms of trading costs, Marshall et al. (2011) measure transaction costs in 19 frontier markets and observe that the average trading costs are usually 300% higher than those in the United States. Our study notes that this was the price paid for diversification in the 2002–10 period; however, the diversification benefits of frontier markets vanished during the financial crisis of 2008. Standard and Poor’s (S&P) and the Financial Times Stock Exchange (FTSE) Group also places constraints on what it takes to be considered a frontier market. For example, countries too small to be regarded as emerging markets, as well as countries that experience a significantly lower rate of growth than traditional emerging markets, are categorized as frontier markets.

There is always the chance that an emerging market can slip back and be relabeled a frontier market due to economic weakening, as was the case with Argentina and Pakistan when Morgan Stanley Capital International (MSCI) downgraded both countries (Lesova, 2009). In addition, there are also other constraints concerning size: when an emerging market’s index drops below a certain threshold level it is downgraded back to a frontier market.

The push by institutional investors into frontier markets during the last decade has been due to the acceptance and understanding of the diversification benefits of these markets. Although it is difficult in many cases to get orders filled in frontier markets due to illiquidity, both institutional and retail investors are pouring money into them to get in early on potentially hot growth, as they did with emerging markets of the 1990s (Mobius, 2009). It is common knowledge that hedge funds^b are known to invest in all types of stocks and the MENA region is an exceptional way to realize high, double-digit returns.

When hedge funds report their primary category as the MENA region to database vendors such as Barclay Hedge, what markets do they really focus on? One way to investigate this question is to employ the style analysis used by Sharpe (1992). Asset allocation can explain a sizeable portion of the variability in returns of an investment portfolio (Brinson et al., 1991). In essence, Sharpe’s original idea was to figure out what style the fund manager used by comparing the manager’s returns to the returns of style indexes. The style analysis paper by Sharpe (1992) uses eight equity indexes and four bond indexes; however, a recent study by Mason et al. (2012) only uses four US equity indexes to analyze the style of US diversified mutual funds.

After describing the model in Section 4, we illustrate applications of a model with four style indexes to analyze a set of eight long-only equity hedge funds that focus on the MENA region. This is the first paper that applies Sharpe’s style analysis to long-only equity hedge funds investing in the MENA region. Section 2 reviews the current literature, Section 3 discusses the data, Section 4 (as mentioned) lays out the style analysis methodology, Section 5 presents results, and Section 6 summarizes our conclusions.

2. Literature Review

The studies investigating frontier markets are relatively few and recent, and only a handful focuses on the MENA region. Traditionally, in times of crisis in developed markets, frontier markets can ameliorate the diversification of a traditional stock and bond investment portfolio as well as its risk-return characteristics (Gupta, 2011). Frontier markets usually do not move in tandem with developed and developing markets, and thereby are less exposed to market downturns in bear markets. Since companies from developed and emerging markets are not cross-listed on MENA markets, these markets provide the low correlation investors want. Not only do frontier markets offer low correlation with emerging markets (Jayasuriya and Shambora, 2009), but they also provide low correlation among themselves. In addition, during the Jan. 1, 2012 to Dec. 31, 2014 period the correlations among Egypt, Morocco, Tunisia, and Kuwait ranged from −0.02 to 0.37, making the case for investing in the MENA region even stronger. Investors caring about diversification should include both frontier and emerging markets in traditional stock and bond investment portfolios (Speidell, 2012). Furthermore, de Groot et al. (2012) observe that the correlation between developed and emerging markets is 0.90, while the correlation of frontier markets with emerging and developed markets is even lower, at 0.50. Likewise, Berger et al. (2011) find that the correlation between frontier markets and emerging markets is somewhat less than 0.10 and recognize that frontier markets are inclined to be insulated from global markets.

Shocks to frontier markets are influenced more by US markets in periods of crisis than in typical periods, but they are ordinarily small in scale in both (Samarakoon, 2011). In addition, including frontier government bonds in an investment portfolio also provides diversification benefits, according to Piljak (2013). In terms of diversification, Bley (2007) finds that investing in the MENA region provides substantial benefits. In a recent study, Paskelian et al. (2013) highlight that MENA stock markets during and after the 2008 crisis have offered investors exceptional investment opportunities along with appropriate portfolio diversification. Furthermore, Berger et al. (2011, p. 230) note that there is an absence of integration in global stock markets after inspecting several frontier market indexes. In terms of volatility, Knapp and Mansharamani (2013) observe that over the 2002–13 period frontier markets exhibited a lower standard deviation than emerging markets.

As for war and instability, Al Refai (2011) notes that four countries—Egypt, Morocco, Tunisia, and Kuwait—in the MENA region have been slightly impacted, but growth is now back on track. Although there was an outflow of money from the MENA region in the 1981–2008 period, according to Al-Fayoumi et al. (2012) it appears that capital is reentering due to superior political stability, enhanced economic conditions in the area, and higher levels of GDP leading to less capital flight. It is also widely known that in the MENA region corruption does indeed exist but has a minor impact on long-run growth performance, as highlighted by Guetat (2006). In the past MENA markets were frequently controlled by a handful of influential people and government officials, but according to the Corruption Perceptions Index (2014), the rankings of MENA countries have improved since 1993.

Yu and Hassan (2010) examine eight MENA markets with pre-2003 data and find that there does not appear to be any type of speculative bubbles in the region. Numerous MENA exchanges are attempting to create derivatives exchanges, but it appears they are falling behind emerging markets, and it could take several years before these exchanges become entirely functional once the regulation aspects have been addressed and approved (Al Janabi, 2012).

There are approximately 60 stock markets that are considered frontier markets, which range from market capitalization of $5 million to $25 billion, with a total market capitalization of near $500 billion (Speidell and Krohne, 2007). Turnover rates in frontier markets during the Jan. 1997 to Nov. 2008 period was roughly 15%; turnover rates in developed markets are between 50% and 150%, and for emerging markets they are between 25% and 75% (de Groot et al., 2012). In addition, de Groot et al. (2012) discover that the standard deviation of frontier markets is 4.2%, which is rather lower than that of emerging markets (7.2%) and developed markets (4.4%); they note that in a large majority of frontier markets and MENA markets it is difficult to sell stocks short.

The MENA region is currently producing a great deal of interest from institutional investors for its investment opportunities and for being to some degree cointegrated when Paskelian et al. (2013) examined nine stock markets^c during the Jan. 2000 to Feb. 2012 period. However, Rengasamy (2012) investigated five Gulf Cooperation Council (GCC)^d markets during the Apr. 2009 to Mar. 2012 period and found they are not cointegrated.

Agarwal and Naik (2000) apply style analysis and discover that it cannot be used for the examination of hedge funds due to the restrictions of style weights, which have to be positive and add up to 100%. However, Schwindler and Oehler (2006) use Sharpe style analysis to examine funds of hedge funds, while Weng and Trueck (2009) apply style analysis to Asian hedge funds and observe that a high amount of cash is being held, along with high-quality rated bonds. In a well-known study, Fung and Hsieh (1997) find that 48% of hedge funds produce low R-squared values of less than 25%. In addition, Dor and Jagannathan (2002) demonstrate how important it is to select the correct style indexes, since using the wrong ones can lead to incorrect conclusions. Due to illiquidity and transparency issues in the MENA region, many hedge funds are opting to invest via index funds due to the ease of access to stocks.

3. Data

We used the Barclay Hedge database and focused on 35 active and inactive hedge funds which list the MENA region as their primary category or area of investment focus, while the second category lists their investment strategy as either (1) long-only equity, (2) long-short, or (3) multistrategy. We selected only active, long-only equity funds to perform our analysis and further restricted our selection to funds whose returns are in US dollars, leaving only eight funds for our analysis. We found that these eight funds did not have an accurate description in the Barclay database as to which specific countries the funds invest in.

Four equity-style indexes were selected for our analysis and are all based in US dollars. We used monthly returns net of management and performance fees for the Jan. 2012 to Dec. 2014 period. In addition, the indexes we used have no overlap in terms of region and have low positive correlation with each other, as determined by variance inflation factors (VIFs). The style indexes and definitions of each are identified in the appendix and reproduced from http://www.standardandpoors.com; http://www.hfr.com, http://www.barclayhedge.com, and http://www.ftse.com. A 3-year period was selected due to MENA indexes being relatively new; going back further would have resulted in fewer funds for the analysis. The exact MENA countries that the hedge funds trade in is not known, nor are their portfolio allocations supplied in our database. The MENA style market indexes we used in this paper are all investable. The indexes were provided by S&P and FTSE, with each firm having diverse construction methodologies and different country allocations to various markets in the MENA region.

4. Methodology

Sharpe style analysis (or returns-based style analysis) is a statistical method traditionally used in research on mutual funds that breaks down the returns of various investment strategies using various independent variables (style indexes). Sharpe (1992) style analysis allows one to get a glimpse into the exposure of a mutual fund’s strategy to numerous stock or bond style indexes, which are used as a way to determine a hedge fund manager’s style. We used Sharpe (1992) style analysis to investigate which MENA markets hedge funds invest in, which allowed us to see which indexes could closely reproduce the performance of the funds during the period studied. Style analysis works well for long-only mutual funds, as investigated by Sharpe (1992), but not that well for hedge funds that are known to use short-selling, derivatives, and leverage.

In this paper we compared hedge funds that invest in the MENA region to the returns from four MENA and frontier style indexes. We examined the variation in hedge fund returns (dependent variable) that are explained by style index returns (independent variables). The hedge funds that trade MENA stocks are long-only equity; therefore using style analysis should produce strong results. We examined a few funds separately with a long/short bias and a multistrategy focus in the MENA region and found their R-squared values low, as we had expected with Sharpe style analysis. The monthly returns (exposure) of each hedge fund were examined with respect to the different style indexes. The objective of style analysis is to build a benchmark portfolio from a set of style indexes and compare the portfolio to each hedge fund. We selected four MENA and frontier indexes and made sure they were not similar and that their correlations were not high. Furthermore, the VIFs were all less than 10 (6.42 is the highest), which implied that there is no multicollinearity and that the indexes were satisfactory, suggesting that the independent variables should not be removed from the analysis.

We used multiple regression analysis to determine a hedge fund’s exposure to changes in the returns of several style indexes (Sharpe, 1992) and observe the strength of the results. With Sharpe style analysis the goal is to find out how much of the hedge fund manager’s fund can be attributed to stock selection and how much can be attributed to style (unexplained variation); the total must be 100%. In addition, we used an unconstrained regression when using the Sharpe style analysis. A fund attaining an R-squared value of 92% implies that the returns of the manager can be explained by the style indexes, while the remaining unexplained portion is attributed to stock picking by the hedge fund manager. The following is the multiple regression model for Sharpe (1992) style analysis; the notation is reproduced from Mason et al. (2012, p.173):

$r_{i} = [b_{i 1} f_{1} + b_{i 2} f_{2} + b {}_{i 3}f_{3} + b_{i 4} f_{4}] + e_{i}$ $r_{i} = [b_{i 1} f_{1} + b_{i 2} f_{2} + b {}_{i 3}f_{3} + b_{i 4} f_{4}] + e_{i}$

We can rearrange the equation:

$e_{i} = r_{i} - [b_{i 1} f_{1} + b_{i 2} f_{2} + b {}_{i 3}f_{3} + b_{i 4} f_{4}]$ $e_{i} = r_{i} - [b_{i 1} f_{1} + b_{i 2} f_{2} + b {}_{i 3}f_{3} + b_{i 4} f_{4}]$

where r_i is the return of the ith hedge fund; b_ij is the weight of the ith hedge fund in the jth style index; e_i is an error term (tracking variance), which is the difference between the hedge fund’s return and the style index portfolio; and f₁…f_n are style indexes (ie, the S&P GCC Composite Shariah Index, the S&P Frontier BMI Index, the Pan Arab Index, and the FTSE NASDAQ UAE).

We then add a constraint:

$\sum_{i = 1}^{n} β_{i} = 1$ $\sum_{i = 1}^{n} β_{i} = 1$

$β_{i} \geq 0, i = 1,..., n$ $β_{i} \geq 0, i = 1,..., n$

The regression coefficient for each style index measures the hedge fund’s allocation for that specific style. The equation in square brackets is the return of the style index portfolio, where the weights add up to 100% but the weights of each style index ranges from 0% to 100%. The error term (tracking error) e_i is the difference between the returns for the hedge fund (r_i) and that of the portfolio with a style similar to that of the hedge fund created by the four style indexes just mentioned. The objective of Sharpe style analysis is to carefully select appropriate style indexes to diminish the hedge fund’s tracking error. The approach pinpoints each hedge fund’s exposure to the variations in returns for each of our style indexes and provides an idea of what MENA countries each manager has an exposure to.

5. Results

Table 2.1 provides descriptive statistics of the eight active hedge funds. All funds are domiciled in offshore jurisdictions and are small in size, ranging from $7.5 million to $86 million. The assets under management highlight the fact that the MENA region is slowly attracting attention and capital is slowly and carefully being invested in it. The yearly average standard deviation ranges from 8.91% to 15.95%, with an average of 12.13% for our sample. The average annual return for the MENA group of hedge funds is 10.84%, while the cumulative return is 36.99%. However, the average annual return for the Barclay hedge fund index is 7.42%, while the average cumulative return is 23.75%; thus the eight MENA funds as a group performed better than the average hedge fund. In terms of comparing the average return of the eight MENA hedge funds to the HFRX MENA Hedge Fund Index, the average MENA fund did better than the HFRX MENA index.

Table 2.1

Descriptive Statistics Jan. 2012 to Dec. 2014

Hedge fund	Domiciled	Ending assets under management	Cumulative return (%)	Average annual return (%)	Yearly average standard deviation	Sharpe ratio annualized (5.00%)	Monthly skewness	Monthly kurtosis
TNI MENA UCITS Fund	Ireland	$38,700,000	41.94	12.81	13.36	0.58	−.54	−.21
TNI MENA Hedge Fund	Bermuda	$7,520,000	−2.19	−0.27	8.91	−0.59	−1.40	3.10
QUAM Middle East (QME) Segregated Portfolio	Cayman Islands	N/A	79.29	22.39	14.25	1.10	−.21	−.47
Duet MENA Horizon Fund	Luxembourg	N/A	90.33	24.48	10.30	1.68	−.22	−.47
Silk Road Frontiers Fund	Luxembourg	$10,355,000	42.81	12.88	10.01	0.75	−.77	−.09
Silk African Lions Fund	Luxembourg	$85,699,000	28.35	9.10	11.86	0.35	−.61	.19
Africa Sustainability Fund	Mauritius	$24,093,000	21.20	7.00	12.41	0.18	.03	−.21
Deutsche Invest I Africa	Luxembourg	$14,178,000	−5.78	−1.65	15.95	−0.35	.04	.03
Average			36.99	10.84	12.13	0.46	−.46	.23
Barclay Hedge Fund Index		—	23.75	7.42	4.27	0.54	−.56	.98
Hedge Fund Research HFRX MENA Index		—	34.69	10.69	5.56	0.94	−1.20	.08

The Duet MENA Horizon Fund has the highest cumulative return (90.33%) and also has the highest Sharpe ratio (1.68) (Table 2.1), while the Deutsche Invest I Africa Fund is ranked last in terms of annual and cumulative returns. The standard deviation is lower for both the Barclay and HFRX MENA indexes than for the MENA group of funds. However, skewness is negative on average (−0.46), with a positive average kurtosis (0.23), which is typical for hedge funds.

Table 2.2 displays the regression results. The average R (correlation coefficient) displays the strength of the relationship, while the average R-squared value equals 0.8511 for all funds, implying that 85.11% of the total variation in average returns is explained by the four style indexes, whereas the remaining 14.89% remains unexplained. For example, the TNI MENA UCITS Fund (Table 2.2) has an R-squared value of 0.8814, indicating that 88.14% of this fund’s variation in returns is explained by its style indexes and only 11.86% is unexplained and attributed to manager stock selection. The TNI MENA Hedge Fund has the lowest R-squared value, signifying that only 41.66% of its returns are explained by its style indexes, whereas 58.34% is unexplained and attributed to manager selection, suggesting that the manager is doing a poor job of stock selection. The low standard errors (Table 2.2) highlight the accuracy of the regression coefficients, with the F-tests demonstrating that the multiple regression model is highly significant due to the very small probability that all the regression outputs occur by chance (p-values are all significant at the 10% level). Our results reveal that a major portion of the variation in hedge fund returns is explained by style index (MENA indexes) returns.

Table 2.2

Overall Model Fit: Regression Results

	Multiple R	R-squared (style)	Adjusted R-squared	Standard error	F-test	Significance F
TNI MENA UCITS Fund	.9388	.8814	.8661	.0141	57.59	0.0000
TNI MENA Hedge Fund	.6455	.4166	.3414	.0209	5.54	0.0020
QUAM Middle East (QME) Segregated Portfolio	.9302	.8652	.8479	.0160	49.76	0.0000
Duet MENA Horizon Fund	.8121	.6596	.6156	.0184	15.02	0.0000
Silk Road Frontiers Fund	.9210	.8482	.8287	.0120	43.32	0.0000
Silk African Lions Fund	.8853	.7837	.7558	.0169	28.08	0.0000
Africa Sustainability Fund	.8890	.7903	.7632	.0174	29.20	0.0000
Deutsche Invest I Africa	.7866	.6187	.5695	.0362	12.58	0.0000
Average	.8511	.7330	.6985	.0190

Significance level: 0.10.

In terms of the four style indexes (Table 2.3), it appears that the S&P Pan Arab Index and the FTSE NASDAQ United Arab Emirates (UAE) 20 Index explain the majority of the style returns in all of the funds. The findings in Table 2.2 with high R-squared values leads us to believe that hedge funds focusing in this area may likely be trading indexes rather than buying MENA stocks.

Table 2.3

Sharpe Style Analysis Results

	S&P GCC Composite Shariah Index (%)	S&P Frontier BMI Index (%)	FTSE NASDAQ UAE 20 Index (%)	S&P Pan Arab Index (%)
TNI MENA UCITS Fund	52.34	0	26.93	20.73
TNI MENA Hedge Fund	0	11.93	38.67	49.39
QUAM Middle East (QME) Segregated Portfolio	33.61	41.32	18.40	6.67
Duet MENA Horizon Fund	0	6.42	29.03	64.56
Silk Road Frontiers Fund	0	0	11.38	88.62
Silk African Lions Fund	0	41.05	21.16	37.79
Africa Sustainability Fund	0	0	71.60	28.40
Deutsche Invest I Africa	0	60.16	0	39.84

6. Conclusions

In this short applied chapter, Sharpe style analysis reveals high R-squared values for equity long-only MENA-focused hedge funds. Although Sharpe style analysis is not perfect, it can supplement other statistical techniques and illustrate the strategy that closely tracks a hedge fund’s movement with the style portfolio. If the investment focus of the hedge fund is not representative in the memorandum, then style analysis may shed light on the countries the fund invests in. In terms of future research, frontier market researchers are warned that the number of hedge funds focusing on these markets is relatively few.

Acknowledgments

We thank Professor John McDermott of Fairfield University for the use of his style analysis spreadsheet. In addition, we thank PerTrac for the use of the PerTrac 7.2 style analysis platform, which is available at http://www.evestment.com. We also thank Sol Waksman, the president of Barclay Hedge (http://www.barclayhedge.com) and Beto Carminhato, information technology manager at Barclay Hedge, for providing us with the hedge fund data.

Appendix: Style Indexes

S&P Frontier BMI Index	The S&P Frontier BMI Index measures the performance of 34 relatively small and less liquid markets. The country indexes include all publicly listed equities representing an aggregate of at least 80% of the market capitalization available in each market. Calculated daily, the index is a fully float adjusted and market capitalization–weighted index.
S&P GCC Composite Shariah Index	The S&P GCC Composite Shariah offers investors a comprehensive Shariah compliant benchmark for the GCC region, including Saudi Arabia. The index reflects the float defined by foreign investment limits applicable to GCC residents, which is typically larger than that available to investors based outside the region.
FTSE NASDAQ Dubai UAE 20 Index	The FTSE NASDAQ Dubai UAE 20 Index comprises 20 stocks admitted to trading on NASDAQ Dubai, the Dubai Financial Market (DFM) and the Abu Dhabi Securities Exchange (ADX).
S&P Pan Arab Index	The S&P Pan Arab Index includes stocks from 10 Pan Arab markets. The index is designed for use by international investors, and reflects the float available to non-GCC residents. Saudi Arabia is excluded due its limited accessibility to foreign investors.
Hedge Fund Research HFRX MENA Index	The HFRX MENA (Middle East/Africa) Index is designed to reflect the performance of the Middle Eastern and African region of the hedge fund universe. Regional investment focus is designed to reflect the primary focus of the fund’s strategic exposure, over various market cycles, independent of the investment manager’s physical location or the domiciled registration location of the fund. Funds investing in Middle East/Africa typically have more than 50% exposure to either Middle Eastern or African regions. Hedge Fund Research, Inc. (HFR) utilizes a UCITSIII-compliant methodology to construct the HFRX hedge fund indexes.

Source: www.standardandPoors.com; www.hfr.com, www.barclayhedge.com; and www.ftse.com

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Table of Contents for Chapter 2: An Application of Style Analysis to Middle East and North African (MENA) Hedge Funds

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