Portable alpha through MANAGED FUTURES

Portable alpha through MANAGED FUTURES an effective platform by Aref Karim, ACA, and Ershad Haq, CFA, Quality Capital Management Ltd. In this article we highlight how managed futures strategies form a critical component of an effective portable alpha platform. We also take a holistic approach to portfolio management in combining a robust portable alpha portfolio with a robust beta portfolio. This shows how managed futures strategies add considerable value to the whole investment process. In building a robust portable alpha portfolio we add a hedge fund component to the managed futures part to complete the universe of alternative investments. We proxy the hedge fund component by the CSFB Tremont Hedge Fund Index; and represent managed futures by the CSFB Tremont Managed Futures Index. Arguably, the former represents predominantly short volatility strategies, largely playing convergence trades, and managed futures proxy long volatility playing divergence. The two in combination gives a robust portable alpha platform. They are complementary in the portable alpha paradigm. PORTABLE ALPHA IS LINKED TO BETA THROUGH LOW CORRELATION The main criterion in forming a long-term strong portable alpha platform is that the components in the portable alpha framework should be strategies that in the whole have low correlation to the asset classes in the beta platform. As such, dedicated active strategies on various asset classes that seek absolute returns will seek a place in the portable alpha framework. The beta portfolio, on the other hand, will compose of distinct asset classes, not strategies. Passive management of the asset classes cheaply is the basis of the formation of the beta platform. Statistically, the link between them has to be weak or non-existent, i.e. low correlation. This is possible as successful active management strategies, through sound research and shorting ability, can generate absolute returns that have low correlation to the beta platform. As a result the benchmark in the portable alpha platform is quite different from the benchmark in the beta platform. The beta platform will be expected to generate returns but through exposure to asset classes. Hence it is not expected to generate absolute returns. Portable alpha should represent a set of pure manager skill and needs to be bought at an agreeable price and an 5

investor needs to recognise this. Beta, on the other hand, should be passive exposure in asset classes, implemented for little or no cost. A GENERAL FRAMEWORK FOR COMBINING THE PORTABLE ALPHA VEHICLE WITH AN EFFICIENT BETA PLATFORM On the basis of the above an investor s cash can be mainly, or near-enough wholly, applied to generating a robust portable alpha framework. It is possible to gain exposure to a beta platform through efficient instruments like futures or swaps requiring little or no funding. Futures need small funding through margin but swaps do not. Swaps may require periodic payments and futures may also require variation margins but on the whole they require little to no cash. Assuming beta requires no funding and therefore the entire cash is used in the portable alpha platform, the total return to the investor will be: the risk free rate then the total return to the investor will be: Total Return = Beta Return + 0.5 (Portable Alpha Return Risk Free Rate) The attraction to managed futures as an alpha source is also scalability. Futures can be levered up efficiently because of low funding requirement. Therefore, the managed futures component of the portable alpha platform can thus be partly or largely notionally funded. This allows considerable flexibility in funding the beta exposure as well as increasing the returns in the alpha portfolio for the same resources. In this paradigm, as absolute return generators, the effective hurdle rate for the portable alpha vehicle becomes the risk free rate. MANAGED FUTURES STRATEGIES IN THE PORTABLE ALPHA PLATFORM Total Return = Beta Return + Portable Alpha Return Risk Free Rate If one were to have 50 of the capital invested in the portable alpha vehicle and the balance in cash earning We begin by examining managed futures in detail. Sources of returns in managed futures Long volatility in nature, the sources of returns in managed futures strategies are mainly derived from the Correlation matrix - monthly returns (Jan 1995 to Sep 2006) 1 Exhibit 1 CS-THF CS-TMF SP500 US bonds GSCI CS-THF 1.000 CS-TMF 0.176 1.000 SP500 0 0.480-0.125 1.000 US bonds 0.041 0.355-0.149 1.000 GSCI 0.181 0.299-0.005 0.050 1.000 6

timing of momentum or trends in various time frames by going long and short those markets appropriately. Research is dedicated to finding robust methods of capturing those trends with risk management being applied to curb the downside through either diversification of time frames and assets as well as capital management through downside management techniques. Substantial impetus is established due to diversification of markets traded for generating not only long run sustainable absolute returns but also providing a solid base for having a low correlation with the asset classes in the beta platform. The numbers confirm that conviction. Exhibit 1 shows a correlation matrix of the managed futures strategy against the hedge fund strategy as well as those with the asset classes in the beta platform. The upside in managed futures is mainly from price persistence in markets, while the downside is contained through diversification of assets and the use of exit strategies designed to curb exposure when markets reverse. This typically represents trend-based strategies which are most common in managed futures. The robustness of managed futures is because the strategies are generally self correcting. Further, at the core the strategies do not fight the markets if the signals are wrong. Self correcting market timing approaches form the profile for the long volatility managed futures strategies. Funding in managed futures Due to use of futures, managed futures strategies do not tie up much cash to generate returns. In the portable alpha vehicle, adding managed futures strategy will thus increase returns, especially where other hedge fund strategies are assumed to be cash intensive. This is an extremely powerful tool for adding value to increasing returns and reducing downside through managed futures. What helps here is also the low correlation of the two portable alpha strategies. Exhibit 1 shows this to be an attractive 0.176. Tail correlation in managed futures Managed futures strategies generally provide large gains in crises thus serving as a strong hedge for event risk. The scalability of managed futures and its self correcting nature gives the strategy the ability to perform in a severe bear market or in a widespread financial crisis. In 2002 when the S&P 500 had one of its worst years managed futures had one of its strongest years. The S&P 500 was down 22.70, with the CSFB Tremont Managed Futures Index being up 17.91. Comparatively, the CSFB Tremont Hedge Fund Index, a composite based on all hedge fund strategies, gained 3.03 in 2002. In 1998 during the emerging market collapse, managed futures again provided strong returns. This shows how managed futures provide great diversification benefits particularly at the tail. The correlation to hedge fund strategies and beta asset classes at the tail is generally negative. Tail risk and managed futures (Jan 1995 to Sep 2006) Exhibit 2 Month SP500 CS-T MF CS-T HF 31/08/1998-14.46 9.95-7.55 30/09/2002-10.87 4.11 0.08 28/02/2001-9.12 0.15-0.59 30/09/2001-8.08 3.65-0.83 30/11/2000-7.89 6.68-1.55 31/07/2002-7.80 6.12-1.35 30/06/2002-7.08 8.63-0.84 31/03/2001-6.34 4.88-0.34 31/08/2001-6.26 2.52 0.92 30/04/2002-6.06-1.61 0.79 7

8 The nature of managed futures thus lends itself to reducing systematic risk during crises. Exhibit 2 shows the 10 worst months in the equity market and the protection gained from managed futures during those. HEDGE FUND STRATEGIES IN THE PORTABLE ALPHA PLATFORM Hedge fund strategies, unlike managed futures, tend to derive returns from specific risk premia related to certain asset classes. Employing active management strategies, the sources of returns and the strategies are quite different in hedge funds. Hedge funds tend to employ a long/short approach in specific asset classes where generally a short volatility exposure of varying degrees results. This reduces the overlap in the hedge fund strategies and managed futures strategies as the two broad classes of strategies pursue absolute returns in completely different styles. Global macro strategies have overlap in style to managed futures strategies due to the directional bets made on broad asset classes. The difference between the two stems largely from the fact that fundamental data is used in global macro strategies, while managed futures strategies are primarily price driven. LONG-TERM ROBUST PORTABLE ALPHA AND BETA COMPONENTS We now look to combine managed futures and hedge funds to establish a robust long-term portable alpha platform. Portable alpha is composed of strategies not asset classes The portable alpha components compose of strategies that aim to achieve an absolute return in all economic environments. In the broadest spectrum, scalable strategies that are long term viable, sustainable and robust will be considered in the portable alpha platform. Managed futures together with other hedge fund strategies form a robust and viable portable alpha vehicle. The CSFB Tremont Managed Futures Index is used for the analysis to represent the managed futures strategy, while the CSFB Tremont Hedge Fund Index (a composite hedge fund index) is used for the analysis to represent the hedge fund strategy. As the portable alpha vehicle is defined in relation to the beta platform, a robust beta platform is imperative. Beta portfolio is composed of asset classes not strategies What belongs in the beta platform is always a question being asked by portfolio architects. The answer to this question is crucial in developing a successful holistic approach to create sustainable investment programmes. A viable portable alpha portfolio together with a robust and viable beta portfolio is the basis for such an investment programme. Commodities in the beta platform as asset class Beta, sometimes, starts off perceptibly exotic, until it becomes more scalable and markets begin to provide cheap liquidity to the asset class in question. This then leads to a degree of recognition and acceptance in the investor community as a potential asset class. Finally, it gets accepted enough to be taken onto the mainstream beta platform. The commodities asset class is such an example. The Goldman Sachs Commodity Index (GSCI) is a commodity index that can be accessed through efficient instruments and should be available at a cheap cost or one can construct this with relative ease. Arguments prevail on whether the GSCI is representative of commodities with a high weight to energies, or whether another index or a combination

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Summary statistics alpha and beta indices (Jan 1995 to Sep 2006) Exhibit 3 CS-T HF CS-T MF SP500 US bonds GSCI Annualised return 12.06 5.47 11.41 6.82 8.71 Annualised volatility 7.68 12.29 14.67 4.68 20.79 Worst drawdown -13.81-17.74-44.73-5.34-48.25 Efficiency 1.57 0.45 0.78 146 0.42 Return to worst drawdown 0.87 0.31 0.26 1.28 0.18 of indices should be considered. But for our purposes we will assume that some commodity representation as an asset class is essential in a long run robust beta platform. We will, therefore, proxy this by the largest of the commodity indices, the GSCI. Financials and commodities make winning combination on beta platform Commodities yield risk premium that is not correlated to equities, but yet deliver the returns and volatility that is similar to equities. This is S&P 500 equity beta component - five worst drawdowns Exhibit 4 GSCI commodity beta component - five worst drawdowns Exhibit 5 30/09/2000 - -44.73 30/11/1997 30/06/2000-48.25 30/06/1998 31/10/1998-15.37 30/11/2000 31/01/2003-35.42 30/11/1999 31/01/2000-6.82 31/08/2005 - -21.14 30/04/1999 31/08/1999-6.24 31/12/2002 30/11/2003-19.66 30/04/1997 31/07/1997-5.60 31/07/2004 30/11/2004-14.18 Jan 1995 to Sep 2006 Jan 1995 to Sep 2006 10

economically justified as the demand and supply conditions and hence price formation process is based on variables linked to real assets that do not have direct links to the price formation process in paper assets such as equities. As shown in Exhibit 1 the correlation between the GSCI and the S&P 500 is -0.05. Over the period January 1995 to September 2006, the annualised return and volatility for the GSCI is 8.71 and 20.79 respectively. Comparatively the S&P 500 yields an annualised return of 11.41 and volatility of 14.67 respectively. These are shown in Exhibit 3. To show how the two asset classes operate in different cycles and hence create low correlation, the five largest drawdowns based on monthly data are shown for the S&P 500 and the GSCI. As shown in Exhibits 4 and 5 the drawdowns occur in different cycles showing that equities, bonds and commodities form a winning combination and robust beta platform. We see that while the S&P 500 has been in a drawdown since September 2000, the GSCI has gone through highs and lows during that period and last went into a drawdown in August 2005. The robust diversification benefits are apparent from these statistics. A robust beta platform will be accessed cheaply. Skilled strategies in the portable alpha platform will be paid for by higher fees. A ROBUST PORTABLE ALPHA PORTFOLIO Modified value at risk 2 is a measure that adjusts VaR for the skew and kurtosis of the distribution. Based on monthly data from January 1995 to September 2006, and at a 95 confidence level, the minimum value for Five worst drawdowns Exhibit 6 Five worst drawdowns for hedge funds Exhibit 7 Five worst drawdowns for a 30:70 combination of managed futures and hedge funds 31/03/2000 31/12/2000-6.33 31/08/1998 30/04/1999-4.65 31/03/2004 30/11/2004-4.59 29/02/1996 31/05/1996-3.86 31/08/1997 30/09/1997-3.06 (CSFB Tremont Hedge Fund Index) 31/08/1998 30/11/1999-13.81 29/02/2000 30/04/2001-7.74 31/05/1996 31/07/1996-4.13 30/11/1995 31/01/1996-3.59 28/02/2002 31/08/2002-2.18 11

the Modified VaR is achieved on a combination of 30 for managed futures and 70 for hedge funds. When the two components are combined using the above weights, the annualised mean and volatility of the portfolio is 10.24 and 7.03 with an efficiency 3 therefore of 1.46, the same as US bonds. However, the worse drawdown of the combination reduces to 6.33, considerably better than the 13.81 and 17.74 that we have seen in Exhibit 3 for hedge funds and managed futures individually. Long volatility vs. short volatility Adding managed futures to hedge funds reduces overall drawdowns substantially and creates a more stable distribution of returns because the tails of the distributions are different. A histogram of the returns for managed futures shows the distribution having a large positive skew, which has different tails then the distribution of returns of hedge funds. Five worst drawdowns for managed futures (CSFB Tremont Managed Futures Index) Exhibit 8 30/04/1995 28/02/1997-17.74 31/12/1998 28/02/2001-14.23 Comparing the tails of the distribution shows that returns in excess of 5.60 in the hedge fund strategies is half in number as those in the managed futures strategies. Large positive skew incumbent in managed futures is an attractive property of systematic long volatility trading. When they are both combined in the proportions of 30 to managed futures and 70 to hedge funds, we get a distribution of returns that result in drawdowns shown in Exhibit 6. The drawdowns for hedge funds and managed futures are shown in Exhibits 7 and 8. We observe from Exhibits 6, 7 and 8 that in addition to reduced length of drawdowns in the combined portfolio, the magnitude of the drawdown has been scaled down substantially compared to the individual strategies. That is a direct result of the diversification benefits produced by absolute return strategies in different styles such as managed futures and hedge fund strategies. As the cycles of upside and downside are very different in the two strategies, the combination adds tremendous value in creating a robust portable alpha vehicle to produce stable long run absolute returns in all economic cycles. Using this criteria a typical long run robust portable alpha portfolio is arrived with 30 allocation to managed futures strategies and 70 to hedge fund strategies. This will be our portable alpha for the rest of the article. 12 31/01/2004 30/09/2005-13.92 31/08/2001 30/04/2002-12.31 30/04/1997 30/04/1998-7.27 A ROBUST BETA PLATFORM (THE INSTITUTIONAL BENCHMARK BETA) Construction of a typical institutional benchmark beta At the highest level the financials, comprising of equities and bonds, will be combined with commodities to form a robust beta platform. In this article, equities and bonds are considered at a typical institutional

60:40 allocation for financials. They are represented by S&P 500 and JPMorgan US Government Bonds Index. When different combinations of this packaged 60:40 financials portfolio and the GSCI are considered from January 1995 to September 2006, the minimum value for the modified VaR at 95 confidence level is achieved with 25 in the GSCI and 75 for the combination of financials. However, based on liquidity constraints, a 20 allocation to the GSCI will be used as a robust long term asset allocation to commodities in the beta platform. As a result, the weights in the beta portfolio will be 48 in S&P 500, 32 in US government bonds, and the balance of 20 in the GSCI. This combination will be referred to as the benchmark beta in the rest of the article. The annualised returns and volatility of the benchmark beta portfolio is 10.09 and 8.15 respectively. The five worst drawdowns are shown in Exhibit 9. COMBINING PORTABLE ALPHA AND BENCHMARK BETA FOR THE INSTITUTIONAL PORTFOLIO If we were now to consider a 50 cash investment in the portable alpha vehicle and the beta benchmark portfolio as above, the annualised return and volatility of the combined portfolio is 13.09 and 10.16 respectively. The efficiency of the combination portfolio now moves up to 1.29 from the 1.24 efficiency of the benchmark beta. And the annualised return increases from 10.09 to the 13.09 mentioned above, for an increase of 300 basis points. The five worst drawdowns of the combined portable alpha and the benchmark beta portfolio are shown in Exhibit 10. We also note how the length of the worst drawdown together with the magnitude reduces here, while annualised return increases by 3. The benchmark beta Exhibit 9 (48 equities, 32 bonds and 20 commodities) 30/09/2000 31/12/2003-18.94 31/07/1998 31/12/1998-9.11 28/02/1999 31/03/1999-3.27 Five worst drawdowns Exhibit 10 Five worst drawdowns when benchmark beta is ported with 50 alpha portfolio 30/09/2000 31/08/2003-16.54 31/07/1998 31/12/1998-10.55 31/07/1996 30/09/1996-4.65 30/09/2005 31/01/2006-3.19 31/08/1997 30/09/1997-4.29 31/07/1996 30/09/1996-2.98 31/10/2005 31/01/2006-4.12 13

FINANCIALS BETA TOGETHER WITH PORTABLE ALPHA VEHICLE If the benchmark beta portfolio was to exclude commodities and 100 invested in financials in a 60:40 allocation to S&P 500 and JPMorgan US Government Bonds Index, the annualised returns and volatility for the combined portfolio with 50 in portable alpha are 12.91 and 10.46 respectively. The worst five drawdowns are shown in Exhibit 11. USING LEVERAGE FOR MANAGED FUTURES: A HUGE PLUS FOR BOOSTING PORTABLE ALPHA If the beta platform is taken as the benchmark beta with financials and commodities, and the managed futures exposure in portable alpha portfolio is increased by 1.5 times, the annualised return and volatility are 13.57 and 10.46 respectively. The annual return increases by 48 basis points for lower drawdowns. As mentioned managed futures require very little funding and can be easily levered up. Exhibit 12 shows the five worst drawdowns for this combination. We note that in this combination both the drawdown period and magnitude of drawdowns have reduced while returns have increased compared to the combination portfolio where the managed futures strategy is not levered. Indeed if managed futures was levered higher still at 2 times, say, the annualised return would increase to 14.04 and the largest drawdown reduce further to 13.10. Thus notionally levering up the managed futures component in the portable alpha has the effect in the combined portfolio of further reducing downside risk and increasing returns for the same capital. The flexibility of Five worst drawdowns Exhibit 11 Financials beta with 50 portable alpha Five worst drawdowns Exhibit 12 Five worst drawdowns for benchmark beta and 50 portable alpha with 1.5 times managed futures 30/09/2000 31/10/2003-19.82 30/09/2000 31/05/2003-14.83 31/07/1998 30/11/1998-9.55 31/07/1998 31/12/1998-9.90 30/04/2000 31/08/2000-5.86 31/08/1997 30/09/1997-4.84 31/08/1997 30/11/1997-5.55 31/07/1996 30/09/1996-4.64 31/03/2004 30/11/2004-4.76 31/10/2005 31/01/2006-4.26 14

Return/drawdown plot for benchmarks and combinations 4 Exhibit 13 Annualised return to average three worst drawdowns: Jan 1995 to Sep 2006 16 14 Portfolio A+B Annualised return 12 10 8 6 4 CSFB Tremont Hedge Funds Portfolio A Benchmark Beta JPMUSGovBonds S&P 500 CSFB Tremont Managed Futures GSCI 2 0 0 5 10 15 20 25 30 35 40 Average worst three drawdowns (absolute) leverage, combined with a different return distribution, makes managed futures strategies one of the most powerful portable alpha investments for any institutional portfolio. CONCLUSIONS Build a long-term benchmark beta Beta represents asset classes and not strategies and should be available at low cost. Diversification amongst the beta asset classes with low cost methods of accessing risk premia is at the core of efficient portfolio construction for successfully porting alpha. Commodities have attractive risk/return profile to add value to beta portfolio. Port a robust alpha portfolio Alpha represents strategies and not asset classes. A robust portable alpha portfolio, representing pure skill, combining the systematic long volatility characteristic of managed futures strategies together with hedge fund strategies is scalable and can perform in all economic cycles generating consistent absolute returns. Witness a large-scale improvement in overall investment With efficient use of resources and by forming a superior alpha portfolio consisting of managed futures strategies, it is possible to achieve large scale improvement in overall investment. A 30 allocation to managed futures strategies, with conservative allocations to the alpha programme (50) and no adjustment in the notional amount for the managed futures exposure, improves returns in the alpha/beta combination by 3 annually over the benchmark beta comprising equities, bonds and commodities. In the portable alpha portfolio managed futures strategies can create greater synergies 15

in reducing downside risk and increasing returns. The flexibility of leverage, combined with a different return distribution, makes managed futures strategies one of the most powerful portable alpha investments for any institutional portfolio. Notes: 1. CS-T HF is CSFB Tremont Hedge Fund Index; CS-T MF is CSFB Tremont Managed Futures Index; SP500 is the S&P 500 Total Return Index; US Bonds is the JP Morgan US Govt Bond Index; and GSCI is the Goldman Sachs Total Return Commodity Index. 2. Value at risk (VAR) calculates the maximum loss expected (or worst case scenario) on an investment, over a given time period and given a specified degree of confidence. 3. Efficiency is annualised return divided by annualised standard deviation. 4. Portfolio A represents the portable alpha portfolio with 70 hedge funds, 30 managed futures levered up 1.5 times, less risk-free. Benchmark beta is 48 S&P, 32 US govt bonds and 20 commodities. Portfolio A+B is the combination portfolio with portable alpha plus benchmark beta. Aref Karim Aref Karim, ACA, is Chief Executive Officer and Chief Investment Officer and Ershad Haq, CFA, is Research Director at Quality Capital Management Ltd. in Weybridge, UK. For further information, please telephone +44 (1932) 334 400 or e-mail: cb@qualitycapital.com 16