Skip to main content
Log in

Analysis of hedge fund strategies using slack-based DEA models

  • Theoretical Paper
  • Published:
Journal of the Operational Research Society

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2

Similar content being viewed by others

References

  • Adler N and Golany B (2002). Including principal component weights to improve discrimination in data envelopment analysis. J Opl Res Soc 53: 985–991.

    Article  Google Scholar 

  • Agarwal V and Naik N (2004). Risks and portfolio decisions involving hedge funds. Rev Financ Stud 17: 63–98.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Artzner P, Delbean F, Eber JM and Heath D (1999). Coherent measures of risk. Math Financ 9: 203–228.

    Article  Google Scholar 

  • 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 

  • Banker RD and Morey RC (1986). Efficiency analysis for exogenously fixed inputs and outputs. Mngt Sci 43: 513–521.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bawa VS and Lindenberg EB (1977). Capital market equilibrium in a mean-lower partial moment framework. J Financ Econ 5: 189–200.

    Article  Google Scholar 

  • 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 http://www.sinopia-group.com/internet/Sinop2007/docs/NewRiskIndicator.pdf.

  • 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.

    Article  Google Scholar 

  • Charnes A, Cooper WW and Rhodes E (1978). Measuring the efficiency of decision making units. Eur J Opns Res 2: 429–444.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Connor G and Lasarte T (2005). An introduction to hedge fund strategies. Research report by Institute of Asset Management, London School of Economics.

  • Cooper WW, Seiford LM and Tone K (2000). Data Envelopment Analysis. Kluwer Academic Publishers: USA, pp 42–46.

  • Ding B and Shawky HA (2007). The performance of hedge fund strategies and the asymmetry of return distributions. Eur Financ Mngt 13: 309–331.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Eling M (2006). Performance measurement of hedge funds using data envelopment analysis. Financ Markets Portfolio Mngt 20: 442–471.

    Article  Google Scholar 

  • Eling M and Schuhmacher F (2007). Does the choice of performance measure influence the evaluation of hedge funds? J Bank Financ 31: 2632–2647.

    Article  Google Scholar 

  • Estrada J (2001). The cost of equity of internet stocks: A downside risk approach. Working paper, IESE Business School, Spain.

  • Fama E and French K (1992). The cross-section of expected stock returns. J Financ 47: 427–465.

    Article  Google Scholar 

  • Fung W and Hsieh DA (1997). Empirical characteristics of dynamic trading strategies: The case of hedge funds. Rev Financ Studies 10: 275–302.

    Article  Google Scholar 

  • 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 

  • Goetzmann W, Ingersoll J and Ross S (2003). High-water marks and hedge fund management contracts. J Financ 58: 1685–1717.

    Article  Google Scholar 

  • Gregoriou GN (2003). Performance appraisal of funds of hedge funds using data envelopment analysis. J Wealth Mngt 5(4): 88–95.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gregoriou GN and Lhabitant FS (2009). Madoff: A riot of red flags. Research report, EDHEC Risk and Asset Management Research Center, February 2009.

  • Gregoriou GN, Sedzro K and Zhu J (2005). Hedge fund performance appraisal using data envelopment analysis. Eur J Opl Res 164: 555–571.

    Article  Google Scholar 

  • Harper D (2003). Introduction to Hedge Funds—Part Two, Investopedia, http://www.investopedia.com/articles/03/121003.asp, accessed on 31 August 2007.

  • Jorion P (2000). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw Hill: New York.

    Google Scholar 

  • Justin P (2005). Bayou Hedge Fund: The story so far, Hedge Fund Street, http://www.fundstreet.org/2005/09/bayou_hedge_fun.html, accessed 1 October 2007.

  • Kao DL (2002). Battle for alphas: Hedge funds versus long-only portfolios. Financial Analysts Journal 58(2): 16–36.

    Article  Google Scholar 

  • 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 

  • Liang B and Park H (2007). Risk measures for hedge funds: A cross-sectional approach. Eur Financ Mngt J 13: 317–354.

    Google Scholar 

  • Lo A (2001). Risk management for hedge funds: Introduction and overview. Financ Anal J 57: 16–33.

    Article  Google Scholar 

  • Lovell CAK and Pastor JT (1995). Units invariant and translation invariant DEA models. Opns Res Lett 18: 147–151.

    Article  Google Scholar 

  • Mitchell M and Pulvino T (2001). Characteristics of risk and return in risk arbitrage. J Financ 56: 2135–2176.

    Article  Google Scholar 

  • Naik N and Tapley M (2007). Demystifying hedge funds. Bus Strategy Rev 18(2): 68–72.

    Article  Google Scholar 

  • Nayar S (2009). US hedge fund industry: Experts predict growth by fourth quarter. Hedge week special report, February 2009, pp 3–4.

  • Nguyen-Thi-Thanh H (2006). On the use of data envelopment analysis in hedge fund selection. Working paper. Université d'Orléans: Orléans, France.

  • Norman M and Stocker B (1991). Data Envelopment Analysis: The Assessment of Performance. John Wiley and Sons: Chichester, UK.

    Google Scholar 

  • Pastor JT, Ruiz JL and Sirvent I (2002). A statistical test for nested radial DEA models. Opns Res 50: 728–735.

    Article  Google Scholar 

  • Prosser D (2007). David Prosser's outlook: Hedge funds have plenty to answer for. The Independent on Sunday, http://news.independent.co.uk/business/comment/article2851457.ece, accessed on 17 April 2008.

  • 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.

    Article  Google Scholar 

  • Saranga H (2009). The Indian auto component industry—estimation of operational efficiency and its determinants using DEA. Eur J Opl Res 196: 707–718.

    Article  Google Scholar 

  • Scholz H (2007). Refinements to the Sharpe ratio: Comparing alternatives for bear markets. J Asset Mngt 7: 347–357.

    Article  Google Scholar 

  • Serrano Cinca C and Mar Molinero C (2004). Selecting DEA specifications and ranking units via. PCA J Opl Res Soc 55: 521–528.

    Article  Google Scholar 

  • Sharpe WF (1966). Mutual fund performance. J Bus 39(1): 119–138.

    Article  Google Scholar 

  • Sharpe WF (1994). The Sharpe ratio. J Portfolio Mngt 21(1): 49–58.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Seiford ML and Zhu J (2002). Modelling undesirable factors in efficiency calculations. Eur J Opl Res 142: 16–20.

    Article  Google Scholar 

  • Sortino FA and van der Meer R (1991). Downside risk. J Portfolio Mngt 17(Spring): 27–31.

    Article  Google Scholar 

  • Taleb NN (2004). Blowup versus Bleed: What does empirical psychology say about the preference for negative skewness? J Behavioral Financ 5(1): 2–7.

    Article  Google Scholar 

  • Tone K (2001). Slack based measure of efficiency in data envelopment analysis. Eur J Opl Res 130: 498–509.

    Article  Google Scholar 

  • Tone K (2002). A slack-based measure of super-efficiency in data envelopment analysis. Eur J Opl Res 143: 32–41.

    Article  Google Scholar 

  • White B (2006). Amaranth outlines its liquidation plans. Financial Times, http://www.ft.com/cms/s/0/540c3fd8-4dbc-11db-8704-0000779e2340.html, accessed 1 October 2007.

  • 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 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H Saranga.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kumar, U., Roy, A., Saranga, H. et al. Analysis of hedge fund strategies using slack-based DEA models. J Oper Res Soc 61, 1746–1760 (2010). https://doi.org/10.1057/jors.2009.143

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2009.143

Keywords

Navigation