Intelligent Hedge Fund Investing
Successfully Avoiding Pitfalls through Better Risk Evaluation
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Milind Sharma


The recognition by researchers and investors that the standard tools of portfolio theory and performance measurement are not generally applicable to hedge fund has resulted in a significant shift in the focus of research.

The good news is that investors armed with this knowledge are less likely to make incorrect inferences from the standard metrics. For example, Sharma notes that optimization of Fund of Funds portfolios on the basis of Sharpe Ratios will result in over-weighting non-directional strategies, which tend to have higher Sharpe Ratios (arising from low market betas), which in turn may result in unintended illiquidity and short volatility bias in the portfolio (as these are prominent characteristics of some non-directional strategies).

The bad news is that it takes a while before new metrics are developed, comparative studies are done, and conclusions are drawn about what’s best.

In this paper, Sharma uses a new performance measure (one of several new approaches presented in this volume) that addresses some of the known issues, namely, negative skewness and excess kurtosis in returns, the effect of leverage, and applicability when measured mean returns are negative.

Sharma’s method accomplishes this by assuming that investor’s utility for risk and return can be expressed by a particular, fairly general, class of utility functions. From this assumption it is possible to go in several directions. For example, it becomes simple to infer the risk premium that investors would demand from a hedge fund given its actual observed returns. Among other things, this allows investors to compare performance in much the same way that would be done using the Sharpe Ratio.

Sharma shows that traditional measures like, the Sharpe Ratio, Jensen’s Alpha and the Treynor ratio yield qualitatively very different conclusions about hedge fund performance than does the new measure, A.I.R.A.P.

Still, A.I.R.A.P.-measured performance of various styles reveals that hedge funds do indeed provide risk adjusted added value for investors. Additional support for this conclusion is found in Sharma’s analysis of persistence in hedge fund returns. Sharma finds strong persistence in returns on all hedge fund styles with the exception of two notoriously volatile (and typically highly directional) styles, Global Macro and CTA.

The expanded use of alternative metrics like A.I.R.A.P. for alternative investments, both in performance evaluation and in portfolio construction, can only benefit investors’ decision making.

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  1. Ackermann C., McEnally R., and Ravenscraft D., 1999, "The Performance of Hedge Funds: Risk, Return, and Incentives", Journal of Finance, 54(3), pp. 833-874.
  2. Agarwal, V. and Naik, N.Y., 1999, “On Taking the ‘Alternative’ Route: Risk, Rewards, Style and Performance Persistence of Hedge Funds”, LBS working paper.
  3. Agarwal, V. and Naik, N.Y., 2000, “Multi-Period Performance Persistence Analysis of Hedge Funds”, Journal of Financial and Quantitative Analysis, 35(3).
  4. Amenc, N., L. Martellini, L., and M. Vaissi, 2003, "Benefits and Risks of Alternative Investment Strategies", Journal of Asset Management, 4(2), 96-118.
  5. Anson, M. J. P., 2000, "Hedge Fund Risk Management for Institutions", in Managing Hedge Fund Risk (London: Risk Books).
    view at
  6. Brown, S. J., and W. Goetzmann, 2003, "Hedge Funds with Style", Journal of Portfolio Management, 29(2), pp. 101-112.
  7. Campbell, J., A. Lo, and C. MacKinlay, 1997, The Econometrics of Financial Markets, (Princeton: Princeton University Press).
    view at
  8. Embrechts, P., A. McNeil, and D. Straumann, 2001, “Correlation and Dependence in Risk Management: Properties and Pitfalls”, in Risk Management: Value at Risk and Beyond, M. Dempster, Ed. (Cambridge: Cambridge University Press).
    view at
  9. Fung, W., and Hsieh D., 1997, "Empirical Characteristics of Dynamic Trading Strategies", Review of Financial Studies, 10, pp. 275-302.
  10. Fung, W., and Hsieh D., 2001, "The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers," Review of Financial Studies, 14 (June), 313-341.
  11. Fung, W., and Hsieh D., 2002, "Benchmarks of Hedge Fund Performance: Information Content and Measurement Biases," Financial Analyst Journal, 58, 22-34.
  12. Grinold, R., 1999, “Mean–Variance and Scenario-Based Approaches to Portfolio Selection”, Journal of Portfolio Management, 25(2), pp. 10–22.
  13. Jacobs, B. I., D. Starer, and K. N. Levy, 1999, "Long-Short Portfolio Management: An Integrated Approach", Journal of Portfolio Management, 25(2), pp. 23-32.
  14. Jurcenzko E., and Maillet, B., 2002, “The Four-Moment Capital Asset Pricing Model: Some Basic Results”, Edhec Publications working paper.
  15. Kat , H., 2003, “Taking the Sting out of Hedge Funds”, Journal of Wealth Management, 6(3), pp. 67-76.
  16. Kat , H., and Lu, S., 2002, "An Excursion into the Statistical Properties of Hedge Fund Returns", working paper.
  17. Kinlay, J., 2003, “Long Memory and Regime Shifts in Asset Volatility”, Wilmott, pp. 78-82.
  18. Kritzman, M. and Rich, D., 2002, “The Mismeasurement of Risk”, Financial Analysts Journal, 58(3), pp. 91-99.
  19. Krause, A. and Litzenberger, R., 1976, “Skewness Preference and the Valuation of Assets”, Journal of Finance, 31, pp. 1085-1100.
  20. Levy, H., and Markowitz, H.M., 1979, “Approximating Expected Utility by a Function of Mean and Variance”, American Economic Review, 69(3), pp. 308-317.
  21. Lhabitant, F., 2001, "Hedge Fund Investing: A Quantitative Look Inside the Black Box", Journal of Financial Transformation 1, pp. 82-90.
  22. Lo, A., 2001, “Risk Management for Hedge Funds, Introduction and Overview”, Financial Analysts Journal, (Nov/Dec), pp. 16-23.
  23. Mandelbrot, B., 1971, “When Can Price Be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models”, Review of Economics & Statistics, 53, pp. 225–236.
  24. Mitchell, M., and Pulvino T., 2001, “Characteristics of Risk in Risk Arbitrage, Journal of Finance, 56, 2135-2175.
  25. Park J., S., Brown, and W. Goetzmann, 1999, "Performance Benchmarks and Survivorship Bias for Hedge Funds and Commodity Trading Advisors", Hedge Fund News, August.
  26. Schachter, B., 2003, “Theory and Practice of Risk Management in Hedge Funds”, presentation, Yale University.
  27. Scott, R. and Horvath, P., 1980, “On the Direction of Preference for Moments of Higher Order than the Variance”, Journal of Finance, 35(4), pp. 915-919.
  28. Sharma, M., 2004, “A.I.R.A.P. - Alternative RAPMS for Alternative Investments”, Journal of Investment Management
  29. Sharpe, W., 1994, “The Sharpe Ratio”, The Journal of Portfolio Management, 20(1), 49-59.
  30. Vaissie, M., 2003, “A Detailed Analysis of the Construction Methods and Management Principles of Hedge Fund Indices”, working paper, Edhec Publications

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  1. Other research by Milind Sharma:
    • Sharma, M., 2005, "‘Sharper’ Risk Adjusted Performance Measures (RAPMs): From Omega to AIRAP", HedgeQuest (Summer), pp. 13-16
      download paper (205K)
    • Sharma, M., 2005, "Hedge Fund Investing", in Risk Management: A Modern Perspective, M. Ong, Editor, Amsterdam: Elsevier.
    • SSRN website
  2. Related Research:
    • Gregoriou, G., Editor, 2005, Hedge Funds: Insights in Performance Measurement, Risk Analysis, and Portfolio Allocation
      buy at Amazon
  3. Hedge Fund Indices:

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"How exciting to read a book that is so timely and practical"
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Publisher: Risk Books
Hardcover: 470 pages
ISBN: 19044339220

Editor: Barry Schachter
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© 2004 Barry Schachter