Performance Persistence of Short-Biased Hedge Funds

Meredith A. Jones

Reading questions

What is the difference between a short-biased hedge fund and a pure short seller?

What benefits do short-biased funds purport to offer over a pure short seller?

What factor(s) have most impacted the performance of short-biased hedge funds over the past ten years?

Have short-biased hedge funds added value over selected market indices over the past ten years?

Looking forward, does it seem likely or unlikely that the performance of short-biased hedge funds will persist?

What benefits do short-biased hedge funds add to diversified portfolios? What kind of “insurance premium” should investors expect to pay for these

benefits?

Summary

This paper investigates the historical and forward simulated performance persistence of short-biased hedge funds and attempts to establish their value in a diversified portfolio.

A short-biased hedge fund index (SBI) is constructed from multiple data sources to provide a historical return stream for analysis.

Traditional statistical analysis, Monte Carlo simulations, multi-factor forward stress testing and asset allocation models are used to draw conclusions.

Historical Performance of SBI

Historical Performance of SBI vs. Market Indices

Short-Biased Index vs. Benchmarks Alpha

Annualized Alpha Beta R

S&P 500 TR 0.56% 6.87% -0.763 -0.777DJIA TR 0.66% 8.25% -0.669 -0.661MSCI WEI 0.62% 7.68% -0.743 -0.777

Annualized Risk Table SBI S&P 500 DJIAMSCI WEI

Compound ROR 4.86% -0.42% 1.73% 0.55%Standard Deviation 15.83% 16.11% 15.62% 16.55%Semi Deviation 14.21% 18.95% 17.12% 19.35%Gain Deviation 11.78% 8.81% 9.00% 8.53%Loss Deviation 8.56% 12.02% 11.52% 12.81%Down Dev.(10.00%) 11.02% 13.50% 12.65% 13.83%Down Dev.(5.00%) 10.22% 12.79% 11.94% 13.13%Down Dev.(0%) 9.43% 12.07% 11.23% 12.42%Sharpe(5.00%) 0.07 -0.25 -0.12 -0.18Sortino(10.00%) -0.44 -0.74 -0.62 -0.65Sortino(5.00%) -0.01 -0.42 -0.27 -0.33Sortino(0%) 0.5 -0.03 0.15 0.04

Forward Simulations of the SBIMonte Carlo simulations (below) and multi-factor forward stress testing were

used to determine that performance is likely to persist in the future.

All Portfolio StatisticsAnnualized

Return

Annualized Standard Deviation

Annualized Sharpe

(RF)

Maximum Drawdown

Number Simulations 10,000 10,000 10,000 10,000 Mean 5.01% 15.70% 0.188 -31.99% Median 4.84% 15.68% 0.191 -30.30% Standard Deviation 5.20% 1.32% 0.317 -10.70% Maximum 27.86% 21.04% 1.361 -76.50% Minimum -12.88% 11.02% -1.015 -10.83% 99th Percentile 17.54% 18.87% 0.906 -62.09% 95th Percentile 13.81% 17.91% 0.706 -52.30% 90th Percentile 11.75% 17.43% 0.591 -47.13% 80th Percentile 9.38% 16.80% 0.458 -40.57% 75th Percentile 8.48% 16.57% 0.407 -38.41% 70th Percentile 7.67% 16.37% 0.358 -36.39% 60th Percentile 6.11% 16.02% 0.268 -33.12% 50th Percentile 4.84% 15.68% 0.191 -30.30% 40th Percentile 3.56% 15.36% 0.112 -27.79% 30th Percentile 2.19% 14.99% 0.026 -25.23% 25th Percentile 1.38% 14.79% -0.025 -23.92% 20th Percentile 0.61% 14.58% -0.078 -22.67% 10th Percentile -1.53% 14.00% -0.216 -19.61% 5th Percentile -3.20% 13.56% -0.336 -17.58% 1st Percentile -6.46% 12.76% -0.560 -14.28%

Portfolio Simulations with SBI

Compound ROR 5.65%Standard Deviation 3.04%Gain Deviation 2.10%Loss Deviation 2.10%Down Dev.(10.00%) 2.88%Down Dev.(5.00%) 2.11%Down Dev.(0%) 1.51%Sharpe(5.00%) 0.22Sortino(10.00%) -1.41Sortino(5.00%) 0.29Sortino(0%) 3.65Sterling 0.19Calmar 0.37Maximum Drawdown -6.58%

A portfolio maximizing low risk was constructed using traditional investments, global hedge funds, commodity trading funds and the SBI with possible inputs was created. The recommended allocation to the SBI was 21.7%. The insurance premium over the highest return portfolio was approximately three percentage points.

Portfolio Simulations without SBI

A portfolio maximizing low risk was constructed using traditional investments, global hedge funds, commodity trading funds but not the SBI with possible inputs was created. The return for this portfolio was approximately one percentage point higher than the SBI portfolio, but drawdown and standard deviation increased dramatically, making the insurance premium approximately one percentage point.

Compound ROR 6.84%Standard Deviation 4.78%Gain Deviation 2.91%Loss Deviation 3.43%Down Dev.(10.00%) 4.02%Down Dev.(5.00%) 3.31%Down Dev.(0%) 2.69%Sharpe(5.00%) 0.39Sortino(10.00%) -0.73Sortino(5.00%) 0.53Sortino(0%) 2.47Sterling 0.18Calmar 0.19Maximum Drawdown -14.16%

Conclusion

The past performance of short-biased hedge funds has been remarkably consistent, with patterns consistent with the correlation and beta profiles.

Over the past 10 years and several different market scenarios, short-biased managers have been able to generate alpha over the market indices.

It appears that short-biased hedge funds have the potential to perform in a similar manner going forward, based on both Monte Carlo simulations and multi-factor stress testing.

Based on their individual risk-reward mandate and view of the market, it may make sense to maintain a permanent allocation to short-biased hedge funds since the “insurance premium” for doing so is relatively low.