at RiskMerger Arbitrage
Merger Arbitrage andManaged Futures
1 Month VaR@ 1% Confidence Level1 Month VaR@ 5% Confidence LevelMaximum Loss
−6.04000%
−3.1500%
−3.1400%−10.7400%
−1.7340%−5.5210%
Number of Simulations
10,000
10,000
Finally, in Figure 9.15, we present the distribution of returns associatedwith our combined portfolio managed futures and merger arbitrage. As canbe seen, the negative skewness has been reduced dramatically from that presented in Figure 9.14. The distribution in Figure 9.15 demonstrates greatersymmetry than that in Figure 9.14.
4000
3500
3000
Frequency
2500
2000
1500
1000
500
0–0.095
–0.08
–0.065
–0.05
–0.035
–0.02
–0.005
Return
FIGURE 9.14
Distribution of Returns for Merger Arbitrage
0.01
0.025
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RISK AND MANAGED FUTURES INVESTING
7,000
6,000
Frequency
5,000
4,000
3,000
2,000
1,000
0–0.0552
–0.0387
–0.0223
–0.0058
0.0107
0.0272
0.0437
0.0601
Return
FIGURE 9.15
Combined Portfolio Return Distribution
CONCLUSIONIn this chapter we demonstrate that managed futures or commodity tradingadvisors tend to be “long volatility” strategies. That is, the trend-followingor momentum strategies of CTAs provide an economic exposure that is similar to a long put option. This synthetic put option exposure can be used tooffset the short volatility exposure of other hedge fund strategies such asmerger arbitrage and event driven.When we formed our mimicking portfolios, we observed that the meanreturn to these portfolios was zero. This underlines the fact that the futuresmarket is a zero-sum game. However, managed futures should not be considered in isolation; their risk-reducing properties vis-à-vis short-volatilitystrategies provides measurable portfolio benefits. In sum, while the glorydays of global macro funds may be over, there is a new reason to seek thebenefits of CTAs.
CHAPTER
10
CHAPTER 10
The Interdependence of ManagedFutures Risk MeasuresBhaswar Gupta and Manolis Chatiras
ractitioners today are faced with a wide choice of methods to measurereturn and risk in portfolios, either in absolute or relative terms. TheSharpe ratio, maximum drawdown, and semideviation are common examples. We classify 24 such measures into six groups and attempt to gaugehow the measures interact, by using data on five different CTA strategies.For each measure, two groups of portfolios are created, containing CTAswith the lowest and highest values of the measure. We find evidence ofhigh correlation between the measures in some of the CTA strategies,pointing to information overlaps and suggesting that some of these measures may be redundant.
P
INTRODUCTION AND REVIEW OF THE LITERATUREThe managed futures industry has grown from just under $1 billion in 1985to more than $40 billion as of June 2003. This growth has led to closerscrutiny of the diversification properties as well as risk management of managed futures. The term “managed futures” represents an industry composedof professional money managers known as commodity trading advisors(CTAs) who manage client assets on a discretionary basis using globalfutures and options markets (CISDM 2002). The risks in managed futuresare inherently more complex than traditional investments as they undergorapid change over time. Hence a thorough understanding of the risks of thedifferent market segments CTAs trade in is essential to effectively managethese risks. This chapter examines risk surrogates for certain CTA portfolios.The risks in the different market segments have been explored in several articles. Tomek and Peterson (2001) have reviewed risk management
203
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RISK AND MANAGED FUTURES INVESTING
practices in agricultural markets. Their review highlights gaps between concepts and implementation and notes that even though many well-developedmodels of price behavior exist, appropriate characterization and estimationof probability distributions of commodity prices remain elusive. Their conclusions discuss what academic research can and cannot accomplish inassisting producers with risk management decisions.Risk surrogates also have been explored in several articles. Cooley,Roenfeldt, and Modani (1977), using returns of a sample of 943 firms having data for the period January 1966 to January 1974, calculate 11 riskmeasures to indicate the wide range of risk surrogates. Daglioglu and Gupta(2003b) study the interdependence of hedge fund risk measures. Using 330hedge funds that had complete data for the period January 1996 to September 2002, they construct 48 portfolios (24 top 50 percent and 24 bottom 50 percent) based on 24 risk measures. The 330 funds belonged toseven strategies. Their results had several implications:Although certain risk measures are relevant for some strategies, theyare not relevant for others.■ Certain risk measures for some strategies are perfectly correlated forboth the top and bottom portfolios. This suggests that there is stronginformation overlap and the use of any one would suffice.■ For some strategies (e.g., equity hedge and fund of funds), the riskmeasures are not perfectly correlated.■ The occurrence of low correlations is much greater for the marketneutral strategy than for any other strategy.■
Daglioglu and Gupta (2003b) note that these results point to an importantconclusion: Risk measures should be chosen carefully for inclusion in performance reports so that redundancy is avoided.Gordon (2003) also examines several risk measures, such as historicalstandard deviation, downside deviation, semideviation, and maximumdrawdown. Using data from a large hedge fund of funds over the periodDecember 1991 to December 2000, he analyzes out-of-sample performanceto predict results in the nonoverlapping subsequent period of investment ineach hedge fund. He finds that historical standard deviation tends to besomewhat helpful in predicting future risk. He also finds that correlationbetween preinvestment standard deviation, downside deviation, and maximum drawdown is significant. Gordon concludes that standard deviationappears to be a better predictor of future losses than downside risk measuressuch as historical downside deviation and maximum drawdown. Althoughthis advantage is not statistically significant for some of the downside riskmeasures, he notes that standard deviation should probably be favored overall other downside risk measures because it is simple and well understood.
The Interdependence of Managed Futures Risk Measures
205
In this chapter we analyze the significance of the same 24 risk measuresused in Daglioglu and Gupta (2003b) for certain CTA portfolios. The 24measures are used as much in CTA performance reports as they are in hedgefund reports. Our results shed greater light on the implications of thesemeasures for particular CTA strategies. They also provide a clearer understanding of the interdependence of these two measures for certain CTAportfolios. We provide empirical evidence on the redundancy of certain risksurrogates, to help investors determine the relevance and applicability ofthese risk measures when evaluating CTA portfolios.In the next section we describe the methodology used for this study.Then we describe the data, present the empirical results, and conclude.
METHODOLOGYWe study the 24 risk measures that were analyzed in Daglioglu and Gupta(2003b) to ascertain the degree of informational overlap among them. Weuse correlation analysis in our study. We divide the degree of correlationinto four groups:1. (P) means Perfectly Correlated, correlation = 1.00.2. (H) means Highly Correlated, 0.90 < correlation < 1.00.3. (M) means Moderately Correlated, 0.65 < correlation < 0.90.4. (L) means Low Correlated, correlation < 0.65.The 24 risk measures are:1. Average Monthly Gain2. Average Monthly Loss3. Standard Deviation4. Gain Standard Deviation5. Loss Standard Deviation6. Semideviation7. Skewness8. Kurtosis9. Coskewness10. Sharpe ratio11. Calmar ratio12. Maximum Drawdown
13. Gain/Loss Ratio14. Beta15. Annualized Alpha16. Treynor Ratio17. Jensen Alpha18. Information Ratio19. Up Capture20. Down Capture21. Up Number Ratio22. Down Number Ratio23. Up Percentage Ratio24. Down Percentage Ratio.
These measures can be classified into six groups:1. Absolute return measures2. Absolute risk measures3. Absolute risk-adjusted return measures
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4. Relative return measures5. Relative risk measures6. Relative risk-adjusted return measures
DATAThe data for this study came from the Center for International Securitiesand Derivatives Markets (CISDM) database. We selected a sample of 200CTA managers who had complete return data for the period from January1998 to July 2003. The CTAs covered five strategies:1. Agriculture2. Currencies3. Diversified4. Financials5. StocksUsing these monthly rates of return, we calculated the 24 risk measuresfor the overall period, January 1998 to July 2003. These risk measures areindicative of the wide range of risk surrogates suggested in the literature onCTA analysis and portfolio management.We then ranked all of the CTAs by these 24 risk measures for the fivedifferent CTA strategies. Next, we took the first half and second half toconstruct bottom 50 percent and top 50 percent portfolios for these strategies. In other words, we created 48 portfolios (24 portfolios for bottom 50percent, 24 portfolios for top 50 percent) for each CTA strategy. Tables10.1, 10.3, 10.5, 10.7, and 10.9. present annualized returns, standard deviations, and Sharpe ratios of these portfolios and Tables 10.2, 10.4, 10.6,10.8, and 10.10 present the correlations between the portfolios.
EMPIRICAL RESULTSAgricultureTable 10.1 presents summary statistics for the agriculture portfolios, andTable 10.2 presents the correlation matrix. The top 50 percent monthlystandard deviation, top 50 percent gain standard deviation, top 50 percentloss standard deviation, and top 50 percent semideviation yield exactly thesame results as do the bottom 50 percent portfolios for the four risk measures. Similarly the top 50 percent portfolio of the up percentage ratio yieldsthe same results as the top 50 percent portfolio of the down percentage ratio,and the bottom 50 percent portfolio of the up percentage ratio yields the
The Interdependence of Managed Futures Risk Measures
TABLE 10.1
Summary Statistics for Agriculture Portfolios
207
208
TABLE 10.2
Correlation Matrix for Agriculture Portfolios
The Interdependence of Managed Futures Risk Measures
209
same results as the bottom 50 percent portfolio of the down percentageratio. The top 50 percent up capture portfolio yields exactly the same resultsas the top 50 percent average monthly gain portfolio, and the bottom 50 percent up capture portfolio yields exactly the same results as the bottom 50percent average monthly gain portfolio. As expected, these portfolios areperfectly correlated with each other. There are also several high and moderate correlations and many low correlations. The low correlations can beexplained by the characteristics of our sample. Seven funds have completedata over the period of our study. Three are trend followers and four arenot. If the risk measures split the sample in a way that trend followers werein one sample and non-trend followers in the other for the top and bottom50 percent portfolios, then one would expect low correlations among theportfolios. However, if the portfolios were split in such a way that theycontain equal numbers of trend-following and non-trend-following funds,then one would expect moderate to high correlations.We also examined the sectors traded by these trading advisors. All sevenindicated that they traded grains; three said they traded meats; and threesaid they traded softs. One indicated that he traded currencies and interestrates, and another indicated that he traded energy and metals. Given thediverse characteristics of these portfolios, the low correlation between certain risk measures is a natural consequence.
CurrenciesTwenty-seven currency CTAs had complete data for the period of ourstudy. Table 10.3 presents the summary statistics for the currency portfolios; Table 10.4 presents the correlations among the portfolios. There wereonly two instances of perfect correlations, the top and bottom 50 percentmonthly standard deviation portfolios with the top and bottom 50 percentaverage monthly gain portfolios, and the top and bottom 50 percent semideviation portfolios with the top and bottom 50 percent loss standarddeviation portfolios. There were several instances of high, moderate, andlow correlations. Of the 27 funds, three indicated that their trades had ashort-term time horizon; four indicated that their trades had short-,medium-, and long-term horizons. Eight of the funds indicated that theirtrades had a medium-term horizon; four indicated that they had a longterm horizon. Two indicated that they traded intraday. Seven of the fundswere classified as discretionary, 15 as systematic, 2 as trend-based, and3 as trend-identifier.There is considerable variety even within the strategies. For example, acertain fund that was classified as systematic and short term had a correla-
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RISK AND MANAGED FUTURES INVESTING
tion of only 0.19 with another fund that was classified as systematic andmedium term for the time period studied. Another pair where both wereclassified as systematic and medium term had a correlation of 0.25. Systematic funds can be either trend followers or contrarian; in this case onewas a systematic trend follower and the other was a systematic non-trend
TABLE 10.3
Summary Statistics for Currency Portfolios
211
TABLE 10.4
Correlations for Currency Portfolios
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RISK AND MANAGED FUTURES INVESTING
follower. However, a pair where both funds were classified as systematictrend followers had a correlation of 0.47. As expected, the discretionaryfunds had low correlations. Given the diversity of the funds classified ascurrency, the correlation patterns of risk measures are along expected lines.
Diversified PortfoliosTable 10.5 presents the summary statistics for the diversified portfolios;Table 10.6 presents the correlations among the portfolios. For the periodof our study, 107 diversified CTAs had complete data. One interestingresult in the case of diversified CTAs is that no portfolios are perfectly correlated with each other. However, a majority of portfolios had high correlations, a few had moderate correlations, and none had low correlations.Of the 107 funds, 10 were classified as discretionary, 69 as systematic, 24as trend based, and 4 as trend identifier. Clearly since more than half of thefunds were systematic, these funds dominated the portfolios in all cases.Another reason why the portfolios exhibited high correlations is that manyof the funds had high correlations before analysis. Although there werepairs—for example, two funds classified as long-term systematic with acorrelation of 0.46—these did not impact the rankings enough to showthat the risk measures are not interdependent. Another reason for theseresults is the markets diversified CTAs trade in. Diversified CTAs encompass agriculture, currencies, financials, and stocks. Because most diversified CTAs trade in a majority of these markets, their return patternsshowed similar risk characteristics.
Financial PortfoliosTable 10.7 presents the summary statistics of the financial portfolios andTable 10.8 presents the correlations. In this case the portfolios were mostlyhighly or moderately correlated with only one perfectly correlated portfolio pair. The top 50 percent and bottom 50 percent information ratio portfolios were perfectly correlated with the top and bottom 50 percent Sharperatio portfolios. Thirty-nine CTAs had complete data for the period of ourstudy. Of these 5 were discretionary, 21 were systematic, 10 were trendbased, and 3 were trend identifiers. Clearly the systematic or trend-basedfunds dominated the portfolios. The return patterns of these portfolios suggest that they have similar risk characteristics.
The Interdependence of Managed Futures Risk Measures
TABLE 10.5
Summary Statistics for Diversified Portfolios
213
214
TABLE 10.6
Correlations for Diversified Portfolios
The Interdependence of Managed Futures Risk Measures
TABLE 10.7
Summary Statistics for Financial Portfolios
215
216
TABLE 10.8
Correlations for Financial Portfolios
The Interdependence of Managed Futures Risk Measures
TABLE 10.9
Summary Statistics for Stock Portfolios
217
218
TABLE 10.10
Correlations for Stock Portfolios
The Interdependence of Managed Futures Risk Measures
219
Stock PortfoliosTable 10.9 presents the summary characteristics of the stock portfolios;Table 10.10 presents the correlations. Several portfolios were perfectlycorrelated. For example, the top and bottom 50 percent gain standarddeviation portfolios were perfectly correlated with the top and bottom50 percent average monthly gain portfolios, and the top and bottom 50percent information ratio portfolios were perfectly correlated with thetop and bottom 50 percent compounded monthly rate of return portfolios. There were several instances of weakly correlated portfolios. Ofthe 15 funds that were analyzed, 3 were discretionary, 9 were systematic,and 3 were trend-based. The return patterns of stock futures can varydepending on the stock index; that is one explanation of the weakly correlated portfolios.
ImplicationsOne immediate application of the results of this analysis is in due diligence.Because the measures analyzed in this study are commonly used byinvestors to evaluate the performance of CTAs, perfect or high correlationscan lead to redundancy. Our results are also important for performancereporting. Investors may want to examine correlations between rankedportfolios of these risk measures to avoid redundancy.
CONCLUSIONThis research can be extended in many ways. For managed futures, wecould further classify the CTAs as systematic trend following, systematicnon-trend following, or discretionary. It would be interesting to attempt toidentify similar correlation patterns for discretionary and systematic CTAsin the different market segments. We also could explore performance characteristics of these portfolios to verify whether the top portfolios alwaysperformed better than the bottom portfolios for the whole period. In addition, we could perform out-of-sample testing to see whether the rankingshad any significance in other periods.
11
CHAPTERCHAPTER 11
Managing Downside Risk inReturn Distributions Using HedgeFunds, Managed Futures, andCommodity IndicesMark Anson
his chapter examines how alternative investments can provide downsidereturn protection in a portfolio composed of U.S. stocks and bonds.Adding active, “skill-based” strategies such as hedge funds or managedfutures to the portfolio leads to important improvements in downsidereturns, Sharpe ratio, and cumulative performance improvement, oftenwithout reducing upside expected returns. In some cases, the same benefitscan be realized by adding passive commodity futures indices instead of skillbased strategies.
T
INTRODUCTIONEvery investor is concerned with downside risk management. This is whydiversification is a uniform portfolio tool. The better diversified an investment portfolio, presumably, the less the portfolio is exposed to monthswhere the return is negative.Yet it is an unfortunate fact of life that when things hit the fan, theytend to do it all at the same time. For example, a number of studies haveexamined the correlation of the U.S. domestic and international equitymarkets during periods of market stress or decline. The conclusion is thatthe equity markets around the world tend to be more highly correlatedduring periods of economic stress. (See Erb, Harvey, and Viskanta 1994;
220
Managing Downside Risk in Return Distributions
221
Sinquefield 1996.) Therefore, international equity diversification may notprovide the requisite diversification when a U.S. domestic investor needs itmost—during periods of economic turmoil or decline.The equity markets have become a single, global asset class for fourreasons.1. Policymakers from major industrial nations regularly attend economicsummits where they attempt to synchronize fiscal and monetary policy.The Maastricht Treaty and the birth of “Euroland” is an example.2. Corporations are expanding their operations and revenue streamsbeyond the site of their domestic incorporation.3. The increased volume of international capital flows suggests economicshocks will be felt globally as opposed to locally.4. Nations such as Japan have undergone a “big bang” episode wheredomestic investors have greater access to international investments.This provides for an even greater flow of capital across internationalboundaries. As a result, distinctions between international and domestic stocks are beginning to fade.This diversification vacuum is one reason why “skill-based” investinghas become so popular with investors. Hedge funds and managed futuresand other skill-based strategies might be expected to provide greater diversification than international equity investing because the returns aredependent on the special skill of the manager rather than any broad macroeconomic events or trends. However, diversification need not rely solely onactive skill-based strategies. Diversification benefits also can be achievedfrom the passive addition of a new asset class such as commodity futures.This chapter examines the downside portion of the return distributionfor a diversified portfolio of stocks and bonds. We then blend in hedgefunds, managed futures, and commodity futures to see how the distributionchanges when these alternative asset classes are added.
DESCRIBING DOWNSIDE RISKThe greatest concern for any investor is downside risk. If equity and bondmarkets are indeed becoming increasingly synchronized, internationaldiversification may not offer the protection sought by investors. The abilityto protect the value of an investment portfolio in hostile or turbulent markets is the key to the value of any macroeconomic diversification.Within this framework, investment strategies and asset classes distinctfrom financial assets have the potential to diversify and protect an invest-
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RISK AND MANAGED FUTURES INVESTING
25
Frequency
20
15
10
5
0–8%
8%
7%
9%
–7%
6%
–6%
%
4%
3%
–5
–5%
4%
–3%
1%
2%
–2%
–1%
0%
1%
–0%
%–−1
2%
%–−2
3%
%–−3
5%
4%
%–−4
%–−5
7%
6%
%–−6
%–−7
%–−8
Return
FIGURE 11.1
Frequency Distribution, Portfolio with 60/40 Stocks/Bonds
ment portfolio from hostile markets.1 Hedge funds, managed futures, andcommodity futures are a good choice for downside risk protection.To demonstrate this downside risk protection, we start with a standardportfolio of stocks and bonds. We begin with a portfolio that is 60 percentthe Standard & Poor’s (S&P) 500 and 40 percent U.S. treasury bonds. InFigure 11.1 we provide a frequency distribution of the monthly returns tothis portfolio over the time period 1990 to 2000.Our concern is the shaded part of the return distribution, which showsboth the size and the frequency with which the combined portfolio of 60percent S&P 500 plus 40 percent U.S. treasury bonds earned a negativereturn in a particular month. It is this part of the return distribution thatcorresponds to downside risk and that investors attempt to avoid or limit.(See Strongin and Petsch 1996.)We measure downside risk two ways: First we take the average returnin the shaded part of the return distribution presented in the figure. Secondwe examine the number of months of negative returns associated with thedistribution of returns for the stock/bond portfolio.Table 11.1 shows that the average monthly return to a 60/40stock/bond portfolio in the shaded part of the distribution is −2.07 percent. In other words, when the standard stock/bond portfolio earned anegative return in any given month, on average the magnitude of thatreturn was −2.07 percent. These negative returns are exactly the downside1We argue that hedge funds represent alternative investment strategies within existing asset classes rather than a distinct asset class.
223
Managing Downside Risk in Return Distributions
TABLE 11.1
Downside Risk Exposure with Stocks and Bonds
Portfolio Composition
ExpectedReturn
StandardDeviation
SharpeRatio
AverageDownside
60/40 US Stocks/US Bonds
0.91%
2.60%
0.177
−2.07%
55/35/10 Stocks/Bonds/EAFE
0.86%
2.66%
0.155
−2.11%
Number ofDownsideMonths
CumulativeDownsideProtection
60/40 US Stocks/US Bonds
42
N/A
N/A
N/A
55/35/10 Stocks/Bonds/EAFE
44
−5.90%
−6.60%
−12.50%
Portfolio Composition
Cumulative CumulativeReturnPerformancePotentialImprovement
risk that investors want to reduce through diversification. In addition, thenumber of months of negative returns is 42 out of 132, a frequency of31.8 percent.To demonstrate the synchronization of the global equity markets, weblend in a 10 percent allocation to international stocks to our 60/40 U.S.stock/U.S. bond portfolio. The exact allocation is 55 percent S&P 500, 35percent U.S. treasury bonds, and 10 percent EAFE.2 We then calculate thereturn distribution for this new portfolio in the same manner by which weproduced the return distribution for the 60/40 U.S. stock/U.S. bond portfolio.Table 11.1 provides the statistics regarding the return distribution forthe 55/35/10 U.S. stock/U.S. bond/international stock portfolio. Again, weconcentrate on the downside portion of the distribution. The averagemonthly return to the downside portion of this distribution is −2.11 percent.That is, a 10 percent allocation to international stocks provided an additional monthly exposure to downside risk of −4 basis points, on average.Therefore, over this time period, an allocation to international stocksdid not diversify an investment portfolio comprised of domestic stocks andbonds. In fact, a 10 percent allocation to international stocks increased theexposure to downside risk. Also, the number of months with negativereturns increased to 44 (a 33.3 percent frequency) for the 55/35/10 U.S.stock/U.S. bond/international stock portfolio from 42 months for our initial 60/40 U.S. stock/U.S. bond portfolio.2Europe, Asia, and the Far East (EAFE) is an international stock index developedand maintained by Morgan Stanley Capital International.
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RISK AND MANAGED FUTURES INVESTING
Finally, the addition of international equities to the standard 60/40stock and bond portfolio resulted in a decline of the expected monthlyreturn down to 0.86 percent, a reduction in average monthly return of5 basis points, with a commensurate decline in the associated Sharpe ratio.Unfortunately, this is an example where international equity diversificationdid not provide downside risk protection.
MANAGING DOWNSIDE RISK WITH HEDGE FUNDSWe have described and demonstrated the risk that investors attempt toavoid through diversification. The question we now address is whetherhedge funds can help investors manage this risk. There has been some speculation as to whether hedge funds, in fact, can hedge an investment portfolio. (See Asness, Krail, and Liew 2001.)We use data on hedge funds from Hedge Fund Research Inc. (HFRI),and include several categories of hedge funds in our portfolio mix todetermine how each style changed the return distribution for the blendedportfolio. We begin with funds of funds (FOF). Using the HFRI FOFindex, we construct a portfolio of 55 percent U.S. stocks, 35 percent U.S.treasury bonds, and 10 percent FOF. We build the same frequency distribution as presented in the exhibit and focus on the downside portion ofthe return distribution.For hedge FOF, we find that the average downside return was −1.90percent. This indicates that, on average, the addition of hedge FOF to thestandard stock/bond portfolio provided 27 basis points of downside riskprotection. The number of downside months was the same at 42. Table11.2 presents the results of the blended portfolios of 55 percent U.S.stocks, 35 percent U.S. treasury bonds, and 10 percent hedge funds, foreach category of hedge fund.We also consider what trade-off might be necessary to achieve this levelof downside protection. It is possible some upside potential was sacrificedto provide the downside protection. In Table 11.1 we saw that the averagemonthly return to our initial U.S. stock/U.S. treasury bond portfolio was0.91 percent. In Table 11.2 we see that the average monthly return whenhedge fund of funds is added is 0.92 percent. Therefore, no upside returnpotential was sacrificed to achieve the downside risk protection. Last, theSharpe ratio increased for the portfolio with hedge FOF.We can calculate the cumulative performance improvement to thestock/bond/hedge fund of funds portfolio from downside risk protectionand upside return enhancement by:(−1.90% × 42 months) − (−2.07 × 42 months) + [(0.92% − 0.91%)× 132 months] = 8.46%
225
60/40 US Stocks/US Bonds55/35/10 Stocks/Bonds/FOF55/35/10 Stocks/Bonds/Equity L/S55/35/10 Stocks/Bonds/Convertible Arb55/35/10 Stocks/Bonds/Market Neutral55/35/10 Stocks/Bonds/Distressed Debt55/35/10 Stocks/Bonds/Event Driven55/35/10 Stocks/Bonds/Fixed Income Arb