Presentation on theme: "Performance Persistence of Short-Biased Hedge Funds Meredith A. Jones."— Presentation transcript:
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. BenchmarksAlpha Annualized AlphaBetaR S&P 500 TR0.56%6.87% DJIA TR0.66%8.25% MSCI WEI0.62%7.68% Annualized Risk TableSBIS&P 500DJIA MSCI WEI Compound ROR4.86%-0.42%1.73%0.55% Standard Deviation15.83%16.11%15.62%16.55% Semi Deviation14.21%18.95%17.12%19.35% Gain Deviation11.78%8.81%9.00%8.53% Loss Deviation8.56%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%) Sortino(10.00%) Sortino(5.00%) Sortino(0%)
Forward Simulations of the SBI Monte Carlo simulations (below) and multi-factor forward stress testing were used to determine that performance is likely to persist in the future. All Portfolio Statistics Annualized Return Annualized Standard Deviation Annualized Sharpe (RF) Maximum Drawdown Number Simulations10,000 Mean5.01%15.70% % Median4.84%15.68% % Standard Deviation5.20%1.32% % Maximum27.86%21.04% % Minimum-12.88%11.02% % 99th Percentile17.54%18.87% % 95th Percentile13.81%17.91% % 90th Percentile11.75%17.43% % 80th Percentile9.38%16.80% % 75th Percentile8.48%16.57% % 70th Percentile7.67%16.37% % 60th Percentile6.11%16.02% % 50th Percentile4.84%15.68% % 40th Percentile3.56%15.36% % 30th Percentile2.19%14.99% % 25th Percentile1.38%14.79% % 20th Percentile0.61%14.58% % 10th Percentile-1.53%14.00% % 5th Percentile-3.20%13.56% % 1st Percentile-6.46%12.76% %
Portfolio Simulations with SBI Compound ROR5.65% Standard Deviation3.04% Gain Deviation2.10% Loss Deviation2.10% Down Dev.(10.00%)2.88% Down Dev.(5.00%)2.11% Down Dev.(0%)1.51% Sharpe(5.00%)0.22 Sortino(10.00%)-1.41 Sortino(5.00%)0.29 Sortino(0%)3.65 Sterling0.19 Calmar0.37 Maximum 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 ROR6.84% Standard Deviation4.78% Gain Deviation2.91% Loss Deviation3.43% Down Dev.(10.00%)4.02% Down Dev.(5.00%)3.31% Down Dev.(0%)2.69% Sharpe(5.00%)0.39 Sortino(10.00%)-0.73 Sortino(5.00%)0.53 Sortino(0%)2.47 Sterling0.18 Calmar0.19 Maximum 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.