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This Bachelor's thesis by Emil Jonsson and Jesper Karlsson from the Centre For Finance – CFF, Gothenburg School of Business, Economics and Law examines the difference in performance of various hedge fund strategies in bull and bear stock market periods. The study provides insights into the best performing strategies in different market conditions and contributes to existing research by examining hedge fund performance during multiple long-term trends in the market.
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Programme in Business and Economics Spring 2016 15 hp
Emil Jonsson & Jesper Karlsson
Centre For Finance – CFF, Gothenburg School of Business, Economics and Law
Bachelor’s thesis
Supervised by Evert Carlsson
Hedge funds use a wide variety of investment styles, although many people have the perception that they are a relatively homogenous group with similar strategies to generate returns. Understanding the differences in the risk and return structure of hedge fund strate- gies is crucial to making a good investment decision. This paper examines the performance of 13 hedge fund strategies in the Credit Suisse Hedge Fund Index, during a 20-years long period ranging from 1995 to 2015. In contrast to previous research, our study encompasses performance analysis on multiple long-term bull and bear stock market periods. Our study provides potential hedge fund investors with valuable information about the best performing strategies in different market conditions. The results show that returns vary greatly between different strategies and time periods.
Keywords: Financial Markets, Hedge Funds, Hedging, Institutional Investors, Rate of Return Analysis, Risk Hedging, Risk Return.
JEL-Codes: G11, G
4 Performance of Hedge Fund Strategies in Bull and Bear Markets
The first hedge fund was founded in 1949 by Alfred Winslow Jones, who used a market neutral investment strategy by taking long positions in securities he found undervalued and at the same time taking short positions in overvalued securities. Still today, the popular per- ception is that hedge funds have a reasonably well-defined market neutral strategy (Brown & Goetzmann, 2001). While this strategy was used by the first hedge funds, modern hedge funds have evolved into a heterogeneous group with multifaceted organizational structures. The term ”hedge fund” now covers a broad range of investment philosophies that goes be- yond the original long-short strategy. However, there are a few common characteristics that help us define hedge funds. Some of them are: flexible investment strategies, unregulated or- ganizational structure, substantial managerial investment in the fund and strong managerial incentives (Ackermann et al. 1999, Brown & Goetzmann, 2001).
This study aims to examine the difference in performance of various hedge fund strategies in bull and bear stock market periods. As Edwards and Caglayan (2001) note, one of the primary motives for investing in hedge funds is to hedge against falling stock prices, it is of great importance to understand how different investment strategies perform in bear markets. We use data from Credit Suisse Hedge Fund Index and analyze the return of their 13 strategy indices to draw a conclusion about their ability to beat the market and also, which strategy is the most successful in both bull and bear market periods.
We test their performance by benchmarking the strategy indices to a global stock market index and see if the hedge fund strategies show statistically significant superior returns com- pared to the market. Furthermore, we also compare the risk-adjusted returns amongst the individual strategies in different time periods. An analysis of the estimated beta coefficients is also conducted as we make an attempt to derive two beta-neutral hedge fund portfolios. Our starting point in examining whether hedge funds are able to beat the market is the research conducted by M. C. Jensen in 1968 where he derived a risk-adjusted measure of portfolio performance, which is nowadays known as Jensen’s Alpha. He based his research on the CAPM-model which was introduced a few years earlier by Sharpe, Lintner and Treynor (Jensen, 1968). Jensen (1968) finds that the mutual funds in his sample are not able to outperform the market. Very little evidence is found that individual funds can perform significantly better than what could be expected (Jensen, 1968).
A few years later, Fama (1970) introduces the efficient market hypothesis (EMH), which states that the price of an asset fully reflects all available information to investors. The theory supports the findings of previous research that on average, mutual funds should not be able to outperform the market consistently on a risk-adjusted basis (Fama, 1970).
Brown (1995) sets out to answer if the effect of persistence in fund performance is sig- nificant for investors to earn excess return over the market, just by choosing the previous
Bachelor’s thesis in finance 5
winners. He compares the return of a portfolio strategy where you invest in the highest performing fund of the previous year to one where you do the opposite. The results are that winning funds tend to deliver a higher return in future years, however they bear higher total risk. Ackermann et al. (1999) find that structural advantages of hedge fund strategies can help generate superior returns to the market. They also find that the strategies have an average significant excess return (alpha) of approximately half a percent. Furthermore, they note that there is substantial literature and research on mutual funds but less in the field of hedge funds. One reason for the lower number of studies on hedge funds could be that the industry is self-reporting. The lack of reporting requirements create a bias that makes it problematic for authors to come across reliable data (Capocci & Hubner, 2004). However, there still exists literature that examines the risk and performance of hedge funds. Brown and Goetzmann (2001) notes that the risk exposure from investing in a hedge fund largely depends on a funds style affiliation. This result builds on an earlier study by Brown et al. (1999) where they conclude that performance persistence in hedge funds is due to style effects rather than management skills. Hedge funds often have low correlation with other financial securities. Both Fung and Hsieh (1997) and Liang (1999) find that low correlation between hedge funds and other financial securities can significantly increase the risk-return profile in a portfolio. Most previous research on hedge fund performance have not distinguished between bull and bear markets since their time window under review (1990–2000 in most cases) has been a particularly bullish period on the stock market (Capocci et al, 2005). Moreover, hedge fund data collected prior to 1994 is likely to suffer from significant survivorship bias according to Capocci and Hubner (2004) and Fung and Hsieh (2000). Several studies choose not to include data prior to 1994. An exception to this is Edwards and Caglayan (2001), who studies the performance of hedge funds and commodity funds during 1990–1998 with periods of both falling and rising stock prices. The results from the research of Edwards and Caglayan (2001) reveal that only three hedge fund strategies^1 protect investors during bear stock market periods. They also find that the best performing strategies in bull periods are the best ones in bear markets. In their paper they define a bull stock market as a period when the monthly return of the S&P 500 is 1 % or more and a bear stock market when the S&P 500 drops by 1 % or more during a month. In our study, we use another definition to see how long-term bull and bear stock market periods affect performance of different hedge fund strategies. There are a few other studies examining hedge fund performance in longer lasting bear (^1) Market neutral, event driven and macro strategies were superior in bear markets.
Bachelor’s thesis in finance 7
The data we use when conducting our research comes from the Credit Suisse Hedge Fund Database which consists of approximately 9 000 funds. In more detail, we use data from an index called the Credit Suisse Hedge Fund Index (CSHFI), which is derived from their database and currently consists of around 375 hedge funds that meet the requirements of the index. The requirements for being in the index is that the fund has a minimum of $50 million in assets under management, a minimum one-year track record, and current audited financial statements.
Credit Suisse does not limit its index to any particular geographical region and may very well include funds that are managed in different countries around the world. The index was the industry’s first – and remains the leading – asset-weighted hedge fund index. In contrast to equal-weighted indices, which do not take the size of the fund into account, asset-weighted indices gives a more accurate depiction of the return in an asset class (Credit Suisse, 2016).
Apart from the composite Credit Suisse Hedge Fund Index, there are also strategy in- dices which has the same constituents as the CSHFI, but is divided into ten (13 including sub-strategies) indices depending on the hedge funds investment style. A calculation agent designates a strategy for every hedge fund that enters the index. The strategy is determined through examination of the hedge fund’s documentation, discussions with the fund’s per- sonnel and other relevant factors. To ensure the validity of the strategy classifications, a calculation agent conducts statistical checks on an ongoing basis in order to identify poten- tially misclassified hedge funds. Funds that appear to be statistical outliers are investigated to see if there is a more appropriate strategy for them (Credit Suisse Index Rules, September 2013).
The publicly available data from Credit Suisse’s website comes in the form of monthly returns for each hedge fund strategy and the composite index. As Ackermann et al. (1999) points out, the use of monthly data has some strong advantages over yearly data. Using monthly returns leads to greatly enhanced accuracy of the risk measure in terms of standard deviation. Another negative consequence that arise from using yearly data is that large fluctuations in returns, due to external market forces and dynamic hedge fund strategies, can be smoothened out and thus overlooked if too few observations are used. This is not a problem, to the same extent, when monthly returns are used. Since risk-adjusted returns are an important part of our analysis, the accuracies of the risk measurements are critical.
8 Performance of Hedge Fund Strategies in Bull and Bear Markets
The following definitions are copied from Credit Suisse’s ”Hedge Fund Index Rules” available as a downloadable document on their website (see references).
. Convertible Arbitrage: Aims to profit from the purchase of convertible securities and the subsequent shortening of the corresponding stock when there is a pricing discrepancy made in the conversion factor of the security. Managers typically build long positions of convertible and other equity hybrid securities and then hedge the equity component of the long securities positions by shorting the underlying stock or options. The number of shares sold short usually reflects a delta neutral or market neutral ratio. As a result, under normal market conditions, the arbitrageur generally expects the combined position to be insensitive to fluctuations in the price of the underlying stock. . Fixed Income Arbitrage: Typically funds that attempt to generate profits by exploiting inefficiencies and price anomalies between related fixed income securities. Funds often seek to limit volatility by hedging exposure to the market and interest rate risk. Strategies may include leveraging long and short positions in similar fixed income securities that are related either mathematically or economically. The sector includes credit yield curve relative value trading involving interest rate swaps, government securities and futures; volatility trading involving options; and mortgage-backed securities arbitrage (the mortgage- backed market is primarily in the U.S. and over-the-counter). . Dedicated Short Bias: Takes more short positions than long positions and earn returns by maintaining net short exposure in long and short equities. Detailed individual company research typically forms the core alpha generation driver of dedicated short bias managers, and a focus on companies with weak cash flow generation is common. To affect the short sale, the manager borrows the stock from a counter- party and sells it in the market. Short positions are sometimes implemented by selling forward. Risk management consists of offsetting long positions and stop-loss strategies. . Emerging Markets: Invest in currencies, debt instruments, equities and other instruments of countries with ”emerging” or developing markets (typically measured by GDP per capita). Such countries are considered to be in a transitional phase between developing and developed status. Examples of emerging markets include China, India, Latin America, much of Southeast Asia, parts of Eastern Europe, and parts of Africa. There are a number of sub-sectors, including arbitrage, credit and event driven, fixed income bias, and equity bias. . Equity Market Neutral: Takes both long and short positions in stocks while minimizing exposure to the systematic risk of the market (i.e., a beta of zero is desired). Funds seek to exploit investment opportunities unique to a specific group of stocks, while maintaining a neutral exposure to broad groups of stocks defined, for example by sector, industry, market capitalization, country, or region. There are a number of subsectors including statistical arbitrage, quantitative long/short, fundamental long/short and index arbitrage. Managers often apply leverage to enhance returns. . Global Macro: Focus on identifying extreme price valuations and leverage is often applied on the an- ticipated price movements in equity, currency, interest rate and commodity markets. Managers typically employ a top-down global approach to concentrate on forecasting how political trends and global macroe-
10 Performance of Hedge Fund Strategies in Bull and Bear Markets
of traditional investors to continue holding them. This strategy is generally long-biased in nature, but managers may take outright long, hedged or outright short positions. Distressed managers typically attempt to profit from the issuers ability to improve its operation or the success of the bankruptcy process that ultimately leads to an exit strategy.
. Multi-Strategy: Managers typically invest in a combination of event driven equities and credit. Within the equity space, sub-strategies include risk arbitrage, holding company arbitrage, equity special situa- tions, and value equities with a hard or soft catalyst. Within the credit-oriented portion, sub-strategies include long/short high yield credit (sub-investment grade corporate bonds), leveraged loans (bank debt, mezzanine, or self-originated loans), capital structure arbitrage (debt vs. debt or debt vs. equity), and distressed debt (workout situations or bankruptcies) including post-reorganization equity. Multi Strategy Event Driven managers have the flexibility to pursue event investing across different asset classes and take advantage of shifts in economic cycles.
The time period under analysis ranges from January 1995 to May 2015 and our goal is to analyze multiple bull and bear periods in the modern global stock market history. In order to do so, we divide the time period into three particularly bullish, as well as two particularly bearish periods. The cutting points for the different time periods are determined by observing monthly maximum and minimum values within certain time spans of a chart showing the monthly performance of Morgan Stanley’s World Index (MSCI WI). To verify that these time periods have strong trends, we calculate the percentage of months with positive and negative return within each chosen time period. We also calculate the yearly average returns and by looking at these results together, we can conclude that we have located significant trends, and that the time periods are strong enough to consider them as either a bull or a bear period. The results of these calculations are presented in Table I. This above mentioned method is previously used by Capocci et al. (2005). In their study they analyzed one bull period and one bear period, they set their cutting point to March 2000 when Russell 3000 peaked. They argued that the period before this date was a bull period since Russell 3000 reported positive returns in 70 % of the months and the average yearly return was 19.4 %. In the succeeding period, which they concluded was a bear period, Russell 3000 had positive returns in 39 % of the months and the average yearly return was -16.3 %. They argue that these trends are sufficiently strong to consider as a bull and a bear period on the stock market without having to use more complex rules for separating bull, bear and neutral months. In Table I we present the summary statistics of our chosen time periods and we see evidence for trends that are at least as strong as the ones in the study conducted by Capocci et al. (2005). We see for example in Bear 1, that the return of MSCI World Index was
Bachelor’s thesis in finance 11
positive in 30 % of the months and the average yearly return during that period was -22.3 %. These figures are significantly lower than the average for MSCI WI and therefore we conclude that we have located a time period with a strong down-trend.
Table I
The purpose of this table is to clearly show that the time periods we have distinguished are subject to strong trends. The columns contains the five different periods that we decided on, by observing maximum and minimum values of the MSCI World Index. The first row presents the percentage of months that MSCI WI reports a positive return within each of the five time periods. The second row reports the average yearly return for MSCI WI during each of these time periods. By looking at the obtained figures, there is no doubt that the time periods, which we found, had either unambiguously bullish or bearish trends on the global stock market. The exact time periods are shown in Table II.
Bull 1 Bear 1 Bull 2 Bear 2 Bull 3 Months with positive return (%) 70 30 70 19 60 Average yearly return (%) 18.3 -22.3 18.1 -44.0 16.
The periods are summarized in Table II. The first period we analyze is the bullish period in the 1990s, starting from January 1995 and lasting until February 2000. Following this period comes a sharp decline and a bearish period from March 2000 until September 2002. After that, we have a look at the bullish period from October 2002 all the way to the beginning of the financial crisis in October 2007. The financial crisis will be our next bearish period, which ranges from November 2007 to March 2009. Then we will have a look at the period from May 2009 to May 2015. We consider these six years as a bull market period, even though it contains a declining period in 2011. We chose to not consider this relatively short period as a separate bear period since we believe there to be too few observations. Looking at Table I, we also see that MSCI WI yielded 16 % in the third bull period when the decline in 2011 is included, therefore we do not see any particular need to divide it. We are aware that the stock market have been declining in the second half of 2015 as well as in the first months of 2016, and were considering to treat this as another bear period. Since we only have very few observations, we finally decided to not include it as another time period. All sources providing hedge fund performance may contain some conditioning biases, this is because reporting data on hedge funds is voluntary, therefore no source will be com- prehensive enough to include all the hedge funds (Ackermann et al. 1999). To minimize survivorship bias in the Credit Suisse Hedge Fund Database, funds are not removed from
Bachelor’s thesis in finance 13
the index until they are fully liquidated or fail to meet the financial reporting requirements. A closer look at biases will be presented in a section further down in the paper.
In the following section we describe the different methods used to measure performance. Although the retrieved data initially was on a monthly basis, we distinguish between monthly and annual returns in our equations. We have chosen to denote monthly returns with small r and annualized returns with capital R. Furthermore a bar over the letters, such as: r¯, denotes the arithmetic mean for the returns. To begin with, we calculate absolute returns for each hedge fund strategy index for every time period that we observe. To examine the exposure to systematic risk, we estimate the beta statistic using OLS-regression on a sample of all 13 strategy indices. The estimations are not made on every individual fund in an index. Instead we estimate the beta on a strategy index as a whole, where each index itself contains a number of funds. The number of individual hedge funds in each index is shown in Table III. The beta for a strategy index can be estimated by:
βˆj = covˆ(r, rm) covˆ (rm, rm) =
i=
(¯r − ri) · (¯rm − rm,i) (^245) ∑ i=
(¯rm − rm,i)^2
where r = return of the index, rm = return of the market proxy and i = one monthly observation. Both the expected market return and the expected risk-free interest rate are assumed to be constant throughout the time period. From the CAPM-equation, we can solve for ˆα, which is the mean excess return for a strategy index, during any time period:
αˆ = ¯r − r¯f − βˆ · (¯rm − ¯rf ) = ¯r − C, (2)
where C = c(β) is the security market line and rf = risk-free interest rate. To calculate the expected monthly market return, we simply calculate the average monthly return of MSCI World Index during our period from January 1995 to May 2015. To calcu- late the risk-free interest rate, we use the average monthly returns of a U.S. 90-days treasury bill. This is widely used as a measurement of the risk-free rate and is also used in an arti- cle by Ackermann et al. (1999). Since risk-free interest rates can be realized on a relative
14 Performance of Hedge Fund Strategies in Bull and Bear Markets
Figure 1
0%
50%
100%
150%
200%
250%
300%
350%
400%
450%
500%
J an/95 O ct/97 J ul/00 A pr/03 J an/06 O ct/08 J ul/11 A pr/
S&P 500 MSC I World Index C S Hedge Fund Index
This figure shows the cumulative returns over time of Credit Suisse Hedge Fund Index in comparison to the common market proxy indices: S&P 500 and MSCI World. Time is on the horizontal axis and accumulated return is on the vertical axis. The Credit Suisse Hedge Fund Index is a composite of all 13 strategy indices. A quick look at the graph reveals that the CS Hedge Fund Index reports higher returns than both S&P 500 and MSCI World. The returns in the figure are compounded on a monthly basis.
short-term basis of 3 months, we use the realized risk-free interest rate for every time period instead of using a predetermined rate. The risk-free interest rate is therefore adjusted in every time period, depending on the average yield of the U.S. 90-days treasury bill. In order to account for sampling errors in the estimation, statistical hypothesis testing is used to test whether the strategy indices report statistically significant excess return to the market. This is done using one-sided t-tests with the null-hypothesis: α = 0 and the alternative hypothesis: α > 0. From the tests we can see which strategies have outperformed the market the most during bull, bear and the whole period and whether the results are significant or not. We use the global MSCI World Index as our benchmark and proxy for
16 Performance of Hedge Fund Strategies in Bull and Bear Markets
Table IV reports the absolute returns of all hedge fund strategies during each time period. The ”Total”-column to the far right shows the total accumulated return for all time periods.
Table IV
This table reports the absolute return (%) of each strategy index and the composite Credit Suisse Hedge Fund Index (CSHFI) in each time period. The far right column reports the total return of each strategy index during the total time period. The table also reports the estimated betas, which measures how closely each strategy index follow the market. The beta of MSCI World Index is equal to 1.00 since it is our benchmark index and our proxy for market return. For example, a 1.00 % increase in MSCI WI should on average result in a 0.55 % increase of the emerging markets strategy index. All returns are compounded on a monthly basis.
β^ ˆ Bull 1 Bear 1 Bull 2 Bear 2 Bull 3 Total Convertible Arbitrage 0.19 91.4 30.4 43.9 -28.5 72.2 342. Dedicated Short Bias -0.80 -36.8 52.0 -36.5 35.7 -65.4 -71. Emerging Markets 0.55 37.5 -4.2 157.8 -32.3 60.7 269. Equity Market Neutral 0.20 103.0 27.2 47.7 -42.4 22.7 169. Event Driven 0.28 110.2 12.0 105.6 -19.1 43.2 460. –Distressed 0.27 132.1 15.2 114.0 -22.5 58.9 604. –Multi-Strategy 0.29 98.8 10.3 103.8 -17.3 36.2 403. –Risk Arbitrage 0.15 73.5 11.4 43.2 -3.6 24.1 231. Fixed Income Arbitrage 0.14 48.2 22.1 29.0 -27.8 75.6 196. Global Macro 0.14 128.1 48.3 89.1 -0.9 44.8 818. Long/Short Equity 0.45 223.9 -11.1 91.7 -22.0 63.4 604. Managed Futures -0.01 19.2 32.6 36.6 16.7 20.5 203. Multi-Strategy 0.16 88.6 13.7 75.9 -22.3 80.6 429. CSHFI 0.28 139.8 6.1 79.7 -19.5 52.5 460. MSCI World Index 1.00 131.5 -48.4 127.9 -55.4 106.1 150.
The return of the CSHFI, which includes all 13 strategies, is also displayed in this table next to the return of the MSCI WI, which we include for comparison purposes. It is obvious by looking at Table IV that there is a large variation in return between the strategies. The large differences are very clear when observing the total returns but they are
Bachelor’s thesis in finance 17
also distinguishable in each individual period. Ackermann et al. (1999) address the question of whether the structural advantages of hedge fund strategies can help generating superior returns to the market. They conclude that hedge funds’ ability to outperform the market is dependent on the time period and strategy, which is in line with what we can observe in Table IV.
Every hedge fund strategy index, except for dedicated short bias, earned higher returns than MSCI WI in the total period. The very poor performance for dedicated short bias in the total period is not surprising, since the funds within the index have a net short exposure to the market and lose money when the market rises. On the other hand, dedicated short bias outperforms all other strategy indices in bear stock market periods. Because of the nature of this strategy, it will always be superior to the others in the bear periods and an outlier in all our tests and estimations. We therefore do not put too much effort in analyzing this index in more detail in the rest of the paper.
The returns in the total period ranges from -71.4 % for the dedicated short bias index to +818.6 % for the global macro index. MSCI WI rose 150.1 % in the same time period. CSHFI outperforms MSCI WI in the total period with more than three times (460.8 %). In the bear periods all strategy indices performed better than MSCI WI, which indicates that to some extent, they manage to hedge against down-turns on the stock market. However, worth noting is that in the first bear period only two strategy indices had negative returns, while 11 strategy indices reported negative returns in the second bear period. The composite hedge fund index declined by -19.5 % in Bear 2, which indicates a poor ability for hedge funds in general to hedge their assets in this period.
Edwards and Caglayan (2001) note that the best performing strategies in bull periods are generally also the best ones in bear markets. We do not find any clear evidence of that just by looking at the absolute returns in Table IV. For example, the long/short equity index is the best performing strategy index in Bull 1, but the worst performing index in Bull 2. The strategy index with the third lowest total return (dedicated short bias excluded) was managed futures, but it is also the only strategy index which has positive return in all examined time periods.
While CSHFI yields much lower positive returns in Bull 2 and Bull 3 than MSCI WI, the return in the total time period is still more than three times higher than MSCI WI. This seem to be because the hedge fund strategy indices declined less than MSCI WI in the bear periods. In the first bear period, the IT-crash, only two out of 13 strategy indices reported negative returns, while MSCI World Index declined -48.4 % during the period. The average return of the composite CSHFI was +6.1 % in the first bear period. Hedge funds’ superior returns in the total period seem to be more a result of their ability to limit losses
Bachelor’s thesis in finance 19
Table V
This table presents the excess return (Jensen’s alpha) for each strategy index in each time period relative to the market. We use the MSCI WorldIndex as a benchmark for market return. The obtained alphas should be interpreted as the average monthly excess return with regards to systematicrisk.
The test is done with a one sided
t-test, with the null-hypothesis being that the strategy indices do not report excess returns (
α^
= 0 in the
CAPM-model) and the alternative-hypothesis that
α >
p-value for these tests, with a * indicating significance on
a 5 % significance level.
Bull 1
Bear 1
Bull 2
Bear 2
Bull 3
Total period
α^
(p
-value)
α^
(p
-value)
α^
(p
-value)
α^
(p
-value)
α^
(p
-value)
α^
(p
-value)
Convertible Arbitrage
Dedicated Short Bias
Emerging Markets
Equity Market Neutral
Event Driven
–Distressed
–Multi-Strategy
–Risk Arbitrage
Fixed Income Arbitrage
Global Macro
Long/Short Equity
Managed Futures
Multi-Strategy
20 Performance of Hedge Fund Strategies in Bull and Bear Markets
Looking at the individual strategies, the best performing strategy index in Bull 1 was long/short equity, which exceeded similar investments with an average of 1.6 % per month. The global macro index had the second highest excess return in Bull 1(1.2 %). The worst performing strategy index in all of the bull periods is not surprisingly dedicated short bias. More interesting is to look at the second worst performing strategy, which in the first bull period was the managed futures index. This strategy index only exceeded expectations with 0.1 %. Further on, if we look at its p-value (0.379), which indicates non- significance, we can not say for certain that hedge funds in this index were able to beat the market in Bull 1. The emerging markets index had the highest alpha in Bull 2. This strategy index was up 0 .9 percentage points from Bull 1, amounting to a total average of 1.2 % in the second bullish period. Meanwhile the fixed income arbitrage funds performed second to worst, having the smallest positive alpha of 0.2 %. Most of the hedge fund strategy indices have lower excess returns in Bull 2 than in the in the first bullish period. Lastly in Bull 3, the best strategy indices were: convertible arbitrage and multi-strategy, with both of them exceeding their expectations with 0.6 % on average per month. Second worst were the equity market neutral and managed futures indices. It is worth noting that these two indices in Bull 3 were the only occasions when realized return equaled the expected return from an investment with similar systematic risk, such that ˆα = 0. To summarize, the strategy index with the highest excess return in bull markets is differ- ent in each of the three time periods. Long/short equity, emerging markets, multi strategy and convertible arbitrage are the strategy indices that are ranked number one in a bull pe- riod. None of the strategy indices that are ranked the highest in one of the bull periods are among the top three best performing in the other two periods. This finding is surprising and makes it hard for us to draw any conclusions. The worst performing strategy index in the bull periods (dedicated short bias excluded) is managed futures, which has the lowest excess return in both Bull 1 and Bull 3 and the third lowest in Bull 2.
In the first bear period, two strategy indices had a negative alpha: emerging markets and long/short equity. The other 11 of Credit Suisse’s hedge fund indices were able to outperform the market and report excess returns. We see this result as a sign of strength, in a period when the market declined by -48.4 %, most strategy indices seem to maintain excess returns. Capocci & Hubner (2004) concluded that most hedge funds outperformed the market in their whole test period, which was 1994–2002, and the best performing strategy was the
