CTA Strategies for Enhancing Diversification David Kuo Chuen Lee, Francis Koh, and Kok Fai Phoon Returns-In this chapter, we analyze the risk and performance characteristics of different
Trang 1CTA Strategies for Enhancing Diversification David Kuo Chuen Lee, Francis Koh, and Kok Fai Phoon
Returns-In this chapter, we analyze the risk and performance characteristics of different strategies involving the trading of commodity futures, financialfutures, and options on futures employed by CTAs Differing from previousstudies, we employ full and split samples to examine the correlations, andcompute risk and performance measures for various CTA strategies We rankthe returns of the S&P 500 and MSCI Global Indices from the worst to thebest months, and partition the sample into 10 deciles For each decile, wecompute the relationship between the CTA indices and the equity indices andcompare their risk and return characteristics We find that CTA strategieshave higher Sharpe and Sortino ratios compared to other asset classes for theentire sample period under study Further, unlike hedge funds, the correlationcoefficients between CTA and equity portfolios for the first decile (worst per-formance of the equity indices) are mostly negative The volatility (measured
by downside deviation) of CTA strategies is lower compared to equity indices.And, for the up-market months, CTA strategies are associated with highSortino ratios
Our results are consistent with previous findings that returns from CTAstrategies are less correlated with equity market indices during down marketsthan hedge fund strategies One possible explanation is that CTAs, unlikehedge funds, are exposed to lower liquidity risk in down markets and there-fore do not suffer any severe “liquidity” squeeze Our findings suggest thatthe negative correlations of CTAs with equity indices during periods of equitydownturns can provide an effective hedge against catastrophic event risks.Although hedge funds may provide diversification, they have positive corre-lation with equity indices in down markets, especially when extreme eventsoccur Hence, our findings suggest that adding CTA investments to an equityportfolio can improve the risk-return profile of a portfolio Such strategies notonly provide the usual portfolio diversification effects, but, given the negativecorrelation in down markets, the CTAs are returns-enhancing diversifiers
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INTRODUCTION
In recent years, there has been a marked change in the asset allocation strategy
in institutional investors, especially endowment funds In 2002 and 2003, itwas reported that many university endowment funds allocated, on average,about 5 percent and 7 percent, respectively, of their total investable funds toalternative investments Recently some endowments have increased their allo-cations to alternative investments significantly, to a figure as high as 40 percent
of their assets under management (Lee 2003) In particular, Vanderbilt versity (2002) has used alternatives since the 1970s and allocates just underhalf of its $2 billion endowment to them, including nearly 30 percent in hedg-ing and arbitrage strategies The endowment has returned 8 percent per annumover the past five years and 15 percent per annum over the past nine years(Vanderbilt University Endowment Review, “2002 Financial Report,” 2003).Alternative investments include hedge funds, private equity, and venturecapital as well as commodity pools, also referred to as commodity tradingadvisors (CTAs) In the current low-interest environment compounded bysomewhat bearish equity market sentiments, investors have been flocking toalternative investments to enhance their returns as well as to protect theirinvestments Institutional investors also have increased their demand foralternative investments in the search for absolute positive returns (Till 2004) Private equity and venture capital, in the main, provide “direct” invest-ment opportunities for the astute investor Conversely, alternative investmentslike hedge funds and CTAs add value “indirectly” through the use of a widerange of trading strategies, techniques, and instruments In this chapter, wefocus on the risk and returns performance of CTAs
Uni-LITERATURE REVIEW
A number of earlier researchers have analyzed CTAs, including Elton, Gruber,and Renzler (1987), who concluded that CTAs offer neither an attractive alter-native to bonds and stocks nor a profitable addition to a portfolio of bond andstocks Brorsen and Irwin (1985) and Murphy (1986), however, concludedthat commodity funds produce favorable and appropriate investment returns Schneeweis, Spurgin, and Potter (1996) found that a portfolio comprised
of equal investment in a managed future index outperformed a protective putstrategy consisting of the Standard & Poor’s (S&P) 500 index and a simulatedat-the-money put They concluded that managed futures may offer some of
1 Schneeweis and Spurgin (1998b) used a dollar-weighted index of CTAs published
by Managed Account Reports (MAR).
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Schneeweis and Spurgin (1998b) further presented evidence that hedgefunds and managed futures may improve the risk-return profiles of equity,fixed income, as well as traditional alternative investments such as riskydebt Their findings were based on correlation analysis between the under-lying factors of:
■ Hedge fund indices from Hedge Fund Research and Evaluation ates Capital Management (EACM)
Associ-■ CTA indices (from MarHedge, Barclay Trading, and EACM)
■ S&P 500 and MSCI World indices for equities
■ Salomon Brothers Government Bond and World Government Bondindices for fixed income securities
Kat (2002) studied the possible role of managed futures in portfolios ofstocks, bonds, and hedge funds He found that managed futures appear to
be more effective diversifiers than hedge funds He found that adding aged futures to a portfolio of stocks and bonds will reduce a portfolio’sstandard deviation much more and quicker than hedge funds will, andwithout the undesirable side effects on skewness and kurtosis
man-For the period 1994 to 2001, Liang (2003) found that although CTAs
on a stand-alone basis underperformed hedge funds, returns from CTAs were negatively correlated with other instruments, making CTAs suitable forhedging against downside risks
Although the performance and risk characteristics of alternative ments as stand-alone investments are interesting and informative, analysis
invest-of the contribution invest-of CTAs to a portfolio invest-of traditional investments would
be instructive and functionally useful Finance theory has espoused the cept that the ability to diversify allows for a more efficient return-risk trade-off In the mean-variance framework, widely attributed to Markowitz(1952), an existing portfolio becomes more diversified upon the addition of
con-a new con-asset with con-a relcon-atively lower correlcon-ation
In this chapter, we attempt to differentiate three categories of assetdiversifiers:
1 Returns-protection diversifiers have relatively high correlations in both
the up and down markets with a generic asset class (such as the S&P
500 Index)
2 Returns-enhancing diversifiers possess correlations with the same
generic asset class in an up market but are relatively less correlated in
a down market
3 “Ineffective” diversifiers are assets that do not add value, even though
they may possess significant correlation coefficients with the genericasset class
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To illustrate, a hedge fund strategy that has a negative correlation ficient in an up-market regime and positive correlation coefficient in adown-market regime provides diversification with no incremental returns
coef-We classify this in the third category, that is, as an ineffective diversifier.Indeed, a strategy with such a characteristic will have the opposite effect of
a good diversifier as it weakens the returns on an uptrend and exaggeratesthe negative returns of the portfolio
We will show that CTAs are differentiated from hedge funds and arereturns-enhancing diversifiers
CTAs, HEDGE FUNDS, AND FUND OF FUNDS
There are many similarities between CTAs and hedge funds and hedge fund
of funds, including the management and incentive fee structures, high tial investment requirements, and the use of leverage and derivatives How-ever, significant differences also exist For example, hedge funds engage avariety of dynamic trading strategies using different financial instruments indifferent markets CTAs, however, mainly use technical trading strategies
ini-in commodity and fini-inancial futures markets The use of different marketsand instruments give rise to distinct differences in risk and returns profiles
On the regulatory side, CTAs must register with the Commodity FuturesTrading Commission (CFTC); hedge funds and fund of funds are largelyexempt from government regulations The CFTC is a federal regulatorybody established by the Commodity Exchange Act in 1974 It supervises aself-regulatory organization called the National Futures Association and has exclusive jurisdiction over all U.S commodity futures trading, futuresexchanges, futures commission merchants, and their agents, floor brokers,floor traders, commodity trading advisors, commodity pool operators, lever-age transaction merchants, and any associated persons of any of the forego-ing CTAs are subject to higher standard of compliance, including disclosurereporting, record keeping, and accounting rules These requirements are notrequired of hedge funds (which are not registered with CFTC) Many CTAsmay have been losing their assets and customers to hedge funds in recentyears partly due to restrictive regulations by the CFTC As a consequence,some CTAs have started emulating hedge funds, using similar trading strate-gies and instruments and getting more involved in equities If this trend con-tinues, the distinction between hedge funds and CTAs may become blurred
On the subject of returns, Liang (2003) and other past studies foundthat the correlations among the returns of hedge funds employing differentstyles are high But the correlations between the returns from different CTAstrategies and hedge fund styles are almost zero or negative This correla-tion structure points to a need to distinguish CTAs from hedge funds (aswell as funds of funds) in academic research
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The work of Liang (2003) analyzing CTAs and hedge funds separatelyalso provided several interesting results Table 19.1 summarizes the results
DATA AND METHODOLOGY
The S&P 500, MSCI Global, Lehman U.S Aggregate, and Lehman Globaldata for the period January 1980 until March 2003 were used in this study
We call these data sources as the benchmark group With the exception ofLehman Global, which starts from January 1990, we have 279 observationsfor each series There are only 159 observations for the Lehman GlobalIndex For the same period, we used returns data over differing periods offour CTA indices from MarHedge: Universe, Universe Equally-Weighted(EW), Future Funds Index, and Future Funds Equally Weighted (EW) Wealso conducted analysis on subindices from MarHedge covering six strate-
TABLE 19.1 Comparison between CTAs and Hedge Funds
Hedge Fund/Hedge
Risk-adjusted Lower on a stand- Hedge fund are highest followed returns alone basis.a by hedge fund of funds.
Explanation by CTA returns are Hedge fund returns cannot be factors explained by option explained by option trading
trading factors factors.
Attrition rate Generally higher Generally lower attrition rates
attrition rate Down-market conditions have Relatively lower greater impact on attrition rates attrition rates
in down markets.b
Correlation Low or negative Highly correlated with each structure correlation with other with other during down
other instruments markets.
Source: Bing Liang, “On the Performance of Alternative Investments: CTAs, Hedge
Funds, and Funds-of-Funds,” Case Western Reserve University, Working Paper,
2003, Cleveland, OH.
aLiang used Sharpe ratios after adjusting for autocorrelation in returns He explained that the difference may be due to the fee structure as well as the risks and autocorrelation structure
bUp and down markets are defined according to the S&P 500 index returns Up markets are periods when the monthly S&P 500 index returns are positive; down markets are defined as periods when the index returns are negative.
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gies: Currency-Sub, Diversified-Sub, Discretionary-Sub, Stock Index Sub,Systematic-Sub, and Trend Follower
The data were subsequently ranked according to the monthly ance of the two equity indices, the S&P 500 and the MSCI Global The worst-returns month was ranked first followed by the second worst The CTAsindices then are matched in that same order The ranked sample was thendivided into deciles As we are interested only in a two-asset class situation,
perform-we would observe the corresponding S&P 500 and CTAs returns ingly and calculate the linear correlation coefficient for each decile Forexample, analyzing the S&P 500 and Universe indices, we would compute
FINDINGS AND OBSERVATIONS
Table 19.2 presents the summary statistics and risk-adjusted returns Wereported the standard summary statistics associated with the first fourmoments for the whole period—mean, standard deviation, skewness, excesskurtosis (in excess of the normal distribution)—and the “down-side devia-tion” defined as the volatility of downside deviation below a minimumacceptable return of zero, the Sharpe and Sortino ratios, and the matrix ofcorrelations between the different CTA strategies with the stock and bondindices There are a number of interesting observations
Most of the CTA strategies have correlations with the equity indicesthat are close to zero or negative However, it is interesting to note that theDiscretionary Sub Index in Table 19.2 has a negative correlation with theS&P 500 but a high positive correlation with the MSCI Global
Most historical returns of the various CTA strategies (with the tion of Stock Index Sub) are higher than the benchmark group Corre-spondingly, the standard deviations are mostly higher than the benchmarkgroup (but comparable with equity indices with an absolute difference inthe order of less than 7 percent)
excep-All CTA strategies have skewness greater than 1 (with the exception ofthe Stock Index Sub Index strategy, which has negative skewness) Further,all CTA strategies have positive excess kurtosis (between 0.77 and 18.61)
2 We split the sample into deciles to study the relationships of the subsamples using the Pearson correlation coefficient It is well known that the correlation is much higher for hedge funds among themselves and with equity benchmarks during crisis than in normal times It is also known that the better-performing hedge funds have higher correlations with equity indices We acknowledge that there are other methods, such as Copula-based methods, that will give a more complete picture of the associations among several assets.
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