Forecasting

• AMENC, Noöl, Sina el BIED and Lionel MARTELLINI, Evidence of Predictability in Hedge Fund Returns and Multi-Style Multi-Class Tactical Style Allocation Decisions, USC FBE Working Paper No. 02-5., 2002. [about 39]
  "Using multifactor models for the return on nine hedge fund indexes, where the factors are chosen to measure the many dimensions of financial risks (market, volatility, credit and liquidity risks), we find strong evidence of very significant predictability in hedge fund returns. We also find that the benefits in terms of tactical style allocation portfolios are potentially very large. Even more spectacular results are obtained both for an equity-oriented portfolio mixing traditional and alternative investment vehicles, and for a fixed-income oriented portfolio mixing traditional and alternative investment vehicles. These results do not seem to be significantly affected by the presence of reasonably high transaction costs."
  
• AMENC, Noel, Sina el BIED and Lionel MARTELLINI, Predictability in Hedge Fund Returns, Financial Analysts Journal, Vol. 59, No. 5, pp. 32-46, September/October 2003. [about 69]
  "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."
  
• DASH, Gordon H. and Nina KAJIJI, Forecasting Hedge Fund Index Returns by Level and Classification: A Comparative Analysis of Neural Network Topologies, 2003. [about 15]
  "This paper examines the predictive accuracy of three alternate radial basis function neural networks when applied to the returns of thirteen Credit Swiss First Boston/Tremont (CSFB) hedge fund indices. We provide evidence that the Kajiji-4 RBF neural network dominates within the RBF topology in the prediction of hedge fund returns by both level and classification. The results also show that the Kajiji-4 method is capable of near perfect directional prediction."
  
• FUNG, William and David A. HSIEH, The Risk in Fixed-Income Hedge Fund Styles
  "By linking these strategy risks to asset prices with long histories, we may even be able to predict hedge fund returns during market extremes."