The performance persistence of equity longshort hedge funds

Journal of Derivatives and Hedge Funds

Volumn 15, Issue 1, 2009, Pages 51-69

  Manser, Samuel ^a,b   Schmid, Markus M ^b  

^a Credit Suisse Asset Management ^*  (Switzerland)

^b UNIVERSITY OF ST GALLEN   (Switzerland)

Author keywords

Equity long short; Hedge funds; Multifactor models; Performance persistence

Indexed keywords

EID: 67650248430     PISSN: 17539641     EISSN: 1753965X     Source Type: Journal    
DOI: 10.1057/jdhf.2008.28     Document Type: Article

References (42)

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15. The factors are based on returns to primitive trendfollowing strategies of rolling over lookback-straddles on commodity, foreign exchange and bond futures. The owner of a lookback call (put) option has the right to buy (sell) the underlying at the lowest (highest) price over the life of the option. The combination of these is a lookback-straddle.
16. These factors are based on returns from rolling over call and puts of different moneyness with a broad market index as the underlying.
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22. These factors are provided by Kenneth French on http://mba.tuck. dartmouth.edu/pages/faculty/ken. french.
23. We are grateful to Vikas Agarwal for providing the time series of returns on the option factors.
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26. Correcting for 'stale prices' by including lagged factors as additional regressors increases R2 adj. to 0.84 and decreases alpha from 0.33 to 0.28. The alpha, however, still remains highly significant.
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29. The difference between returns of surviving and all funds is a monthly 0.084 per cent considering all hedge funds and 0.074 per cent for equity long/short using data from 1973 to 2005. This is close to Eling's1 0.08 per cent for the CISDM database and at the lower end when compared to estimates from other databases. Restricting the time span to 1994-2005 increases the return difference to 0.12 and 0.11 per cent, respectively, as CISDM started to keep defunct funds in the sample.
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32. Agarwal, V., Daniel, N. and Naik, N. (2004) Flows, Performance, and Managerial Incentives in Hedge Funds. Georgia State University, Working Paper.
33. Herzberg, M. and Mozes, H. (2003) The persistence of hedge fund risk: Evidence and implications for investors. Journal of Alternative Investments 6(Fall): 22-42.
34. Strictly speaking, we cannot infer anything about the volatility of the underlying funds by looking at portfolio volatility because it is influenced by the covariance terms.
35. Liang, B. (2000) Hedge funds: The living and the dead. Journal of Financial and Quantitative Analysis 35(3): 309-326.
36. Brown, S., Goetzmann, W. and Park, J. (2001) Careers and survival: Competition and risk in the hedge fund and CTA industry. Journal of Finance 56(5): 1869-1886.
37. Jagannathan, R., Malakhov, A. and Novikov, D. (2006) Do Hot Hands Exist among Hedge Fund Managers? An Empirical Evaluation. Northwestern University, Working Paper.
38. These results are available from the authors upon request.
39. Kat, H. and Miffre, J. (2002) Performance Evaluation and Conditioning Information: The Case of Hedge Funds. University of Reading, Working Paper.
40. In unreported tests, we alternatively exclude the first 2 years of the second sub-period in order to rely exclusively on data from the second 'regiméalso when forming portfolios. The results, however, remain qualitatively unchanged.
41. A simple alternative is provided by Asness et al20 They propose to include lagged factors to correct for stale prices. Overall, the inclusion of lagged factors adds little explanatory power and the previously identified patterns all continue to hold. The results from this analysis are available from the authors upon request.
42. Using the more recent subsample (2000-2005), however, the largest part of the alpha spread comes from the negative alpha of portfolio 10.