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Detecting Performance Persistence in Fund Managers
This paper provides the first systematic analysis of performance patterns for emerging funds and managers in the hedge fund industry. Emerging funds and managers have particularly strong financial incentives to create investment performance and, because of their size, may be more nimble than established ones. Performance measurement, however, needs to control for the usual biases afflicting hedge fund databases. After adjusting for such biases and using a novel event time approach, we find strong evidence of outperformance during the first two to three years of existence. Each additional year of age decreases performance by 42 basis points, on average. Cross-sectionally, early performance by individual funds is quite persistent, with early strong performance lasting for up to five years.
Since hedge funds specify significant lock-up periods, we investigate persistence in the performance of hedge funds using a multi-period framework in which the likelihood of observing persistence by chance is lower than in the traditional two-period framework. Under the null hypothesis of no manager skill (no persistence), the theoretical distribution of observing wins or losses follows a binormial distribution. We test this hypothesis using the traditional two-period framework and compare the findings with the results obtained using our multi-period framework. We examine whether persistence is sensitive to the length of return measurement intervals by using quarterly, half-yearly and yearly returns. We find maximum persistence at the quarterly horizon indicating that presistence among hedge fund managers is short term in nature.
The authors examine the performance of the off-shore hedge fund industry over the period 1989 through 1995 using a database that includes both defunct and currently operating funds. The industry is characterized by high attrition rates of funds, low covariance with the U.S. stock market, evidence consistent with positive risk-adjusted returns over the time, and little evidence of differential manager skill. Copyright 1999 by University of Chicago Press.
In this article, the authors study the persistence and predictability of several statistical parameters of individual hedge fund returns. The authors find little evidence of persistence in mean returns but do find strong persistence in hedge funds' standard deviations and their correlation with the stock market. Persistence in skewness and kurtosis is low, but this could well be due to the relatively small size of our sample. Despite the observed persistence, this study also shows that in absolute terms hedge funds' risk profiles are not easily predicted from historical returns alone. The true value of a hedge fund's track record therefore appears not to lie in its use as a predictor of future performance and risk, but primarily in the insight that it provides in a fund's risk profile relative to that of other funds in the same strategy group. The availability of a track record is important, but for a different reason than many investors think.
A significant amount of research has been devoted to the predictability of traditional asset classes, but little is known about the predictability of returns emanating from alternative vehicles, such as hedge funds. We attempt to fill this gap by documenting evidence of predictability in hedge fund returns. Using multifactor models for the return on nine hedge fund indexes,for which the factors were chosen to measure the many dimensions of financial risk, we found strong evidence of significant predictability in hedge fund returns. We also found that the benefits of "tactical style allocation" portfolios are potentially large.. We obtained even more spectacular results for an equity-oriented portfolio that mixed traditional and alternative investment vehicles and for a debt-oriented portfolio that mixed traditional and alternative investment vehicles. These results do not seem to have been significantly affected by the presence of reasonably high transaction costs.
Following a review of the data and methodological difficulties in applying conventional models used for traditional asset class indexes to hedge funds, this article argues against the conventional approach. Instead, in an extension of previous work on asset-based style (ABS) factors, the article proposes a model of hedge fund returns that is similar to models based on arbitrage pricing theory, with dynamic risk-factor coefficients. For diversified hedge fund portfolios (as proxied by indexes of hedge funds and funds of hedge funds), the seven ABS factors can explain up to 80 percent of monthly return variations. Because ABS factors are directly observable from market prices, this model provides a standardized framework for identifying differences among major hedge fund indexes that is free of the biases inherent in hedge fund databases.
The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economists, Incentives, Judgement, and the European CVAR Approach to Macroeconometrics” contrasting two different perspectives in Europe and the US that are currently dominating empirical macroeconometric modeling and delves deeper into their methodological/philosophical underpinnings. It is argued that the key to establishing a constructive dialogue between them is provided by a better understanding of the role of data in modern statistical inference, and how that relates to the centuries old issue of the realisticness of economic theories.