Journal of the Operational Research Society

, Volume 61, Issue 12, pp 1746–1760 | Cite as

Analysis of hedge fund strategies using slack-based DEA models

Theoretical Paper


Hedge funds have made a significant impact on the performance of world financial markets in recent times. Our objective in this paper is to develop a robust framework for the evaluation of hedge funds by incorporating a maximum number of performance measures through public data sources. We analyse the hedge fund strategies (styles) using a variety of classical risk-return measures with the help of slack-based Data Envelopment Analysis (DEA) models to determine a unique performance indicator. The main thrust is to investigate the risk return profile of 4730 hedge funds classified under 18 different strategies using multiple inputs and outputs. The originality of the work lies in applying Slack-Based DEA to decipher the risk-return profile of these strategies using advanced risk-return measures such as Value at Risk, drawdown, lower and higher partial moments and skewness. We find that the correlation between the ranking of hedge fund strategies based on Sharpe ratio and the DEA models is very low; at the same time, there is a significant correlation between rankings obtained by the application of DEA using different sets of input/output measures. We have also compared the DEA rankings with other traditional financial ratios such as modified Sharpe ratio, Sortino ratio and Calmar ratio. The paper also studies the impact of events such as the Asian financial crisis on the performance of hedge funds. The study around the event shows that only a relatively small number of strategies performed better during times of turmoil.


data envelopment analysis hedge fund Sharpe ratio Calmar ratio Sortino ratio 



We thank the anonymous referees and the editor for their constructive comments that have helped us to improve the quality of this research work.


  1. Adler N and Golany B (2002). Including principal component weights to improve discrimination in data envelopment analysis. J Opl Res Soc 53: 985–991.CrossRefGoogle Scholar
  2. Agarwal V and Naik N (2004). Risks and portfolio decisions involving hedge funds. Rev Financ Stud 17: 63–98.CrossRefGoogle Scholar
  3. Alexander G and Baptista A (2004). A comparison of VaR and CVaR constraints on portfolio selection with the mean-variance model. Mngt Sci 50: 1261–1273.CrossRefGoogle Scholar
  4. Artzner P, Delbean F, Eber JM and Heath D (1999). Coherent measures of risk. Math Financ 9: 203–228.CrossRefGoogle Scholar
  5. Bali TG and Gokcan S (2004). Alternative approaches to estimating VaR for hedge fund portfolios. In: Schachter B (ed). Intelligent Hedge Fund Investing. Risk Books: London, pp. 253–277.Google Scholar
  6. Banker RD and Morey RC (1986). Efficiency analysis for exogenously fixed inputs and outputs. Mngt Sci 43: 513–521.Google Scholar
  7. Banker RD, Charnes A and Cooper WW (1984). Some Models for estimating technical and scale efficiencies in data envelopment analysis. Mngt Sci 30: 1078–1092.CrossRefGoogle Scholar
  8. Bawa VS and Lindenberg EB (1977). Capital market equilibrium in a mean-lower partial moment framework. J Financ Econ 5: 189–200.CrossRefGoogle Scholar
  9. Bertrand J (2005). In the land of hedge fund, the Sharpe ratio is no longer the king, HSBC Global Asset Management article last cited on 4th March 2009 at
  10. Casu B, Shaw D and Thanassoulis E (2005). Using a group support system to aid input-output identification in DEA. J Opl Res Soc 56: 1363–1372.CrossRefGoogle Scholar
  11. Charnes A, Cooper WW and Rhodes E (1978). Measuring the efficiency of decision making units. Eur J Opns Res 2: 429–444.CrossRefGoogle Scholar
  12. Charnes A, Cooper WW, Golany LM, Seiford S and Stutz J (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. J Econ 30: 91–107.CrossRefGoogle Scholar
  13. Connor G and Lasarte T (2005). An introduction to hedge fund strategies. Research report by Institute of Asset Management, London School of Economics.Google Scholar
  14. Cooper WW, Seiford LM and Tone K (2000). Data Envelopment Analysis. Kluwer Academic Publishers: USA, pp 42–46.Google Scholar
  15. Ding B and Shawky HA (2007). The performance of hedge fund strategies and the asymmetry of return distributions. Eur Financ Mngt 13: 309–331.CrossRefGoogle Scholar
  16. Duzakin E and Duzakin H (2007). Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis: An application of 500 major industrial enterprises in Turkey. Eur J Opl Res 182: 1412–1432.CrossRefGoogle Scholar
  17. Eling M (2006). Performance measurement of hedge funds using data envelopment analysis. Financ Markets Portfolio Mngt 20: 442–471.CrossRefGoogle Scholar
  18. Eling M and Schuhmacher F (2007). Does the choice of performance measure influence the evaluation of hedge funds? J Bank Financ 31: 2632–2647.CrossRefGoogle Scholar
  19. Estrada J (2001). The cost of equity of internet stocks: A downside risk approach. Working paper, IESE Business School, Spain.Google Scholar
  20. Fama E and French K (1992). The cross-section of expected stock returns. J Financ 47: 427–465.CrossRefGoogle Scholar
  21. Fung W and Hsieh DA (1997). Empirical characteristics of dynamic trading strategies: The case of hedge funds. Rev Financ Studies 10: 275–302.CrossRefGoogle Scholar
  22. Getmansky M, Lo AW and Mei SX (2004). Sifting through the wreckage: Lessons from recent hedge-fund liquidations. J Investment Mngt 2: 6–38.Google Scholar
  23. Goetzmann W, Ingersoll J and Ross S (2003). High-water marks and hedge fund management contracts. J Financ 58: 1685–1717.CrossRefGoogle Scholar
  24. Gregoriou GN (2003). Performance appraisal of funds of hedge funds using data envelopment analysis. J Wealth Mngt 5(4): 88–95.Google Scholar
  25. Gregoriou GN and Gueyie JP (2003). Risk-adjusted performance of funds of hedge funds using a modified Sharpe ratio. J Wealth Mngt 6(Winter): 77–83.CrossRefGoogle Scholar
  26. Gregoriou GN and Lhabitant FS (2009). Madoff: A riot of red flags. Research report, EDHEC Risk and Asset Management Research Center, February 2009.Google Scholar
  27. Gregoriou GN, Sedzro K and Zhu J (2005). Hedge fund performance appraisal using data envelopment analysis. Eur J Opl Res 164: 555–571.CrossRefGoogle Scholar
  28. Harper D (2003). Introduction to Hedge Funds—Part Two, Investopedia,, accessed on 31 August 2007.
  29. Jorion P (2000). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw Hill: New York.Google Scholar
  30. Justin P (2005). Bayou Hedge Fund: The story so far, Hedge Fund Street,, accessed 1 October 2007.
  31. Kao DL (2002). Battle for alphas: Hedge funds versus long-only portfolios. Financial Analysts Journal 58(2): 16–36.CrossRefGoogle Scholar
  32. Koh Francis, Koh Winston TH, Lee David KC and Phoon K (2004). Investing in hedge funds: Risks, returns and performance management. In: Gregoriou G, Papageorgiou N, Hubner G and Rouah F (eds). Hedge Funds: Insights in Performance Measurement. John Wiley & Sons: USA, pp. 341–364.Google Scholar
  33. Liang B and Park H (2007). Risk measures for hedge funds: A cross-sectional approach. Eur Financ Mngt J 13: 317–354.Google Scholar
  34. Lo A (2001). Risk management for hedge funds: Introduction and overview. Financ Anal J 57: 16–33.CrossRefGoogle Scholar
  35. Lovell CAK and Pastor JT (1995). Units invariant and translation invariant DEA models. Opns Res Lett 18: 147–151.CrossRefGoogle Scholar
  36. Mitchell M and Pulvino T (2001). Characteristics of risk and return in risk arbitrage. J Financ 56: 2135–2176.CrossRefGoogle Scholar
  37. Naik N and Tapley M (2007). Demystifying hedge funds. Bus Strategy Rev 18(2): 68–72.CrossRefGoogle Scholar
  38. Nayar S (2009). US hedge fund industry: Experts predict growth by fourth quarter. Hedge week special report, February 2009, pp 3–4.Google Scholar
  39. Nguyen-Thi-Thanh H (2006). On the use of data envelopment analysis in hedge fund selection. Working paper. Université d'Orléans: Orléans, France.Google Scholar
  40. Norman M and Stocker B (1991). Data Envelopment Analysis: The Assessment of Performance. John Wiley and Sons: Chichester, UK.Google Scholar
  41. Pastor JT, Ruiz JL and Sirvent I (2002). A statistical test for nested radial DEA models. Opns Res 50: 728–735.CrossRefGoogle Scholar
  42. Prosser D (2007). David Prosser's outlook: Hedge funds have plenty to answer for. The Independent on Sunday,, accessed on 17 April 2008.
  43. Portela MS, Thanassoulis E and Simpson G (2004). Negative data in DEA: A directional distance approach applied to bank branches. J Opl Res Soc 55: 1111–1121.CrossRefGoogle Scholar
  44. Saranga H (2009). The Indian auto component industry—estimation of operational efficiency and its determinants using DEA. Eur J Opl Res 196: 707–718.CrossRefGoogle Scholar
  45. Scholz H (2007). Refinements to the Sharpe ratio: Comparing alternatives for bear markets. J Asset Mngt 7: 347–357.CrossRefGoogle Scholar
  46. Serrano Cinca C and Mar Molinero C (2004). Selecting DEA specifications and ranking units via. PCA J Opl Res Soc 55: 521–528.CrossRefGoogle Scholar
  47. Sharpe WF (1966). Mutual fund performance. J Bus 39(1): 119–138.CrossRefGoogle Scholar
  48. Sharpe WF (1994). The Sharpe ratio. J Portfolio Mngt 21(1): 49–58.CrossRefGoogle Scholar
  49. Sharpe JA, Meng W and Liu W (2007). A modified slacks-based measure model for data envelopment analysis with ‘natural' negative outputs and inputs. J Opl Res Soc 58: 1672–1677.CrossRefGoogle Scholar
  50. Seiford ML and Zhu J (2002). Modelling undesirable factors in efficiency calculations. Eur J Opl Res 142: 16–20.CrossRefGoogle Scholar
  51. Sortino FA and van der Meer R (1991). Downside risk. J Portfolio Mngt 17(Spring): 27–31.CrossRefGoogle Scholar
  52. Taleb NN (2004). Blowup versus Bleed: What does empirical psychology say about the preference for negative skewness? J Behavioral Financ 5(1): 2–7.CrossRefGoogle Scholar
  53. Tone K (2001). Slack based measure of efficiency in data envelopment analysis. Eur J Opl Res 130: 498–509.CrossRefGoogle Scholar
  54. Tone K (2002). A slack-based measure of super-efficiency in data envelopment analysis. Eur J Opl Res 143: 32–41.CrossRefGoogle Scholar
  55. White B (2006). Amaranth outlines its liquidation plans. Financial Times,, accessed 1 October 2007.
  56. Wilkens K and Zhu J (2005). Classifying hedge funds using data envelopment analysis. In: Gregoriou GN, Rouah F and Karavas VN (eds). Hedge funds: Strategies, Risk Assessment and Returns. Beard Books: Washington, pp. 161–175.Google Scholar

Copyright information

© Operational Research Society 2009

Authors and Affiliations

  1. 1.Indian Institute of ManagementBangaloreIndia

Personalised recommendations