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If the superior return of hedge funds is attributable to better manager skill then one would expect the same funds to have persistence in returns year after year.

Investors are therefore questioning the literature as to whether hedge funds are still delivering performances; see for instance Bares et al. (2001), who have analyzed whether the performance of hedge funds in delivering subsequent returns is persistent across different time windows. Using a non-parametric approach (Kernel or Bayesian models) over the period January

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1992 to December 2000, they studied whether hedge fund managers added value to the performance delivered to investors by investigating different investment strategies and different time horizons.

Based on a different point of view, which stipulates that hedge funds may exhibit a higher degree of non-normality as well as a non-linear relationship with the stock markets, Kat and Amin (2003) developed a new approach using new dynamic trading-based performance measures instead of using the Sharpe ratio or even the Jensen alpha, suggesting that those traditional performance measures are no longer suitable to evaluate hedge fund performance. On the other hand, Agarwal and Naik (2004) examined the persistence of performance in hedge fund returns using a one-year moving average. Their results suggested that there is persistence in return by recreating the payoff distribution and compare the cost of the strategy with the price of a fund participation.

Previously, Agarwal and Naik (2000) had investigated the performance of returns in hedge funds using a multi-period framework, i.e. the performance of hedge funds is short term or long term. They examined short-term and long-term persistence by investigating their pre-fee and post-fee returns over quarterly, half year and yearly timeframe periods; also, the persistence was assessed by investigating the series of wins and losses for two, three and more consecutive time periods. They reached the conclusion that strong persistence can be noticed in the quarterly horizon and the persistence slowly starts to reduce when shifting towards yearly persistence, indicating that persistence among the hedge fund industry is primarily short term, in contrast with the finding of mutual funds or fund of funds where investors should preconize long-term persistence in the return up to two years. Additionally, they added that the persistence is sensitive to the return measurement interval: persistence decreases as the return measurement interval increases.

In addition, Baquero et al. (2005) suggested controlling for the look-ahead bias tests of persistence as standard persistence in hedge funds may be biased if the fund’s survival depends on historical performances. They have created by the use of information what would not have been known during the period analysed.

Edwards and Caglayan (2001) examined the persistence in hedge fund performance over the period January 1990 to August 1998 using alphas from a six-factor risk model( T-bill, HML, SMB, WML, long term debt corporate bond). Employing both a parametric and a non-parametric model they found persistence in the performance over one-year and two-year periods and

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suggested that the degree of persistence might vary with the investment strategy put in place. Also, Bares et al. (2003) appraised the persistence of hedge fund performance over short- and long-term horizons.

Ackerman et al. (1999) found that hedge funds earned better return than mutual fund over the period 1988 to 1995 despite hedge funds exhibiting more volatility than mutual funds. On the performance of hedge funds, Liang (1999) examined the relationship between hedge fund performance and fund characteristics such as the nature of watermark, hurdle rate73, and leverage. The results compared hedge fund against mutual fund. By using an asset class factor model and a mean variance efficient analysis, the paper tried to provide deep insight among the evaluation of hedge funds in terms of performance and risk. Liang reached the conclusion that hedge funds dominate mutual funds in the mean-variance and are different from mutual funds in the way they display their strategy. The results also found significant difference between the return of hedge funds with high watermarks74 and those without watermarks, and reveal as well that an incentive fee provides managers with strong incentive schemes; the higher the incentive fee, the better the fund performance.

Capocci and Hubner (2004) investigated hedge funds’ performance levels and persistence using various asset-pricing models. In the 1980s performance measures were based on the CAPM like the Jensen’s alpha (1969); it is with the recent interest in multi-factor models on the cross- sectional variations in stocks return that researchers have started to identify factors such as size, leverage, earnings/price, book to market, etc, as the US shows little relation to the betas of Sharpe (1964). Lintner (1965) stated, however, that there is no unanimously accepted model across the literature.

In the same manner, Capocci et al. (2005) tested the performance of hedge funds over a period of bullish and bearish market using the same methodology developed by Capocci and Hubner (2004) by applying a ten-factor composite performance model that appeared to achieve significant results. Their results indicated that most hedge funds outperformed the market during the whole period and no significant underperformances were observed during periods of downfall.

73 _{Managers can collect incentive fees only if the cumulative returns can make up for previous losses and} exceed the hurdle rate.

74 _{A high watermark ensures that a fund manager does not get paid large sums for poor performances. For} instance, if the fund manager loses money over a period, s/he must get the fund above the high watermark before receiving a performance bonus.

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Furthermore, investigating hedge funds’ performance through portfolio strategies that incorporate predictability in managerial skills, fund risk loading and benchmark returns was studied by Avramov et al. (2011). They examined the out-of-sample investment opportunity set and found that there exist subgroups of ex-ante identifiable hedge funds that can deliver subsequent return. The strategy of selecting those hedge funds based on the criteria set up are robust to various considerations such as backfill bias, incubation bias, illiquidity-induced serial correlation, fund fees, closed funds, alternative benchmark, etc.

Finally, by revisiting stylized facts about hedge funds Joenvaara et al. (2012) found evidence that on average hedge funds deliver economically and statistically abnormal return on an equal and value-weighted basis even after examining for different size, investment strategies and domiciles. Also, they suggested that hedge fund performance persists at annual horizons. Findings are in line with previous results suggested by Kosowski et al. (2007).

Koh et al. (2003) suggested exploring the persistence outside the US, by investigating persistence in the performance of hedge funds that invest in Asia and found that persistence occurs mainly at monthly horizons and at quarterly horizons.

To conclude, studies differ in the point of view regarding the persistence of performance in hedge funds; however, despite numerous studies using different approaches, the main caveats might be that hedge fund persistence in performance seems to be more present over a short- term window.