University of Oulu

Hedge fund return predictability with a random coefficient model

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Author: Vimpari, Janne1
Organizations: 1University of Oulu, Oulu Business School, Department of Finance, Finance
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: Oulu : J. Vimpari, 2013
Publish Date: 2013-06-10
Physical Description: 65 p.
Thesis type: Master's thesis
Tutor: Joenväärä, Juha
Reviewer: Perttunen, Jukka
Joenväärä, Juha
The recent academic literature has shown that some hedge funds are persistently able to provide superior risk-adjusted returns. Naturally such performance arises a question whether the performance could be predicted. This study proposes a predictive model to forecast future hedge fund returns using both macroeconomic and fund-specific characteristic predictive variables. With the proposed model I study in-sample, out-of-sample, and the economic value of predictability. The model I propose is based on a random coefficient model. It has appealing features to study return predictability. Contrary to time-series and cross-sectional models the random coefficient model is able to provide information at the individual hedge fund level and at the same time it takes into account all the information provided by the cross-section. To my best knowledge the random coefficient model has never been applied in hedge fund return predictability study before. In the proposed model I use a set of four economically motivated macroeconomic predictors: the default spread, the market return, the VIX, and the term spread. As fund-specific characteristic predictors I use the incentive fee, size, and age of an individual hedge fund. In this study I use a data sample provided by BarclayHedge database. My final data sample contains altogether over 6000 individual hedge funds from January 1994 to December 2010. I find that in the cross-section there are funds which are predictable in-sample with the used macroeconomic variables. The in-sample predictability varies clearly between distinctive strategy categories. It also has a very asymmetric nature; if there are positively predictable funds in a certain strategy category, it is unlikely that there are many negatively predictable funds. I study out-of-sample predictability of my model with portfolio sorting. I find that the decile my model predicts to perform the best also performs the best out-of-sample. This is actually true for the six highest decile portfolios; they all perform in the order predicted by my model. I study the economic value of predictability by constructing a hedge fund portfolio of 40 hedge funds selected by my model. I find that the mean annual excess return on the hedge fund portfolio selected by my model is 10%, clearly more than provided by any other strategy I consider except the VIX only strategy. In risk-adjusted basis my model performs much more poorly than the unconditional strategy which selects the best past performers. The results show that the random coefficient model can be used to predict future returns of hedge funds and possibly future returns of any asset class. The model I develop in this study could be used in a fund of hedge funds to select hedge funds to invest. However, it seems that the model has still room for improvements. In any case, the random coefficient model methodology looks promising for predicting future returns.
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Copyright information: © Janne Vimpari, 2013. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.