本研究是針對避險基金資料庫中的五大策略來找出各策略的最適改良套層線性模型，樣本期間從1995年7月到2012年12月共210個月。本文將從過去文獻中最常被引用的Brown et al.(1999)的一因子模型、Fung and Hsieh(2004)七因子模型、Fung and Hsieh(2001)的Emerging Market與Capocci and Hubner(2004)十一因子模型進行改良，改良的方式是在舊的模型上增加其他兩個因子模型的因子並使用Calhoun OOS test進行模型的改良，最後對各種避險基金策略找出最佳的因子模型。 The aim of this study is to find the best nested linear model for each strategy from the HFR Hedge Funds database. The datasets are chosen during July 1995 to December 2012, including 210 months in total. This study improves the models in references which are highly used by researchers, including the one-factor model by Brown et al. (1999), the 7-factor model by Fund and Hsieh (2004), the MSCI Emerging Market Index by Fung and Hsieh(2001) and 11-factor model by Capocci and Hubner (2004). We employ the Calhoun OOS test to investigate if any of the factors from other models has to be added to each model so as to significantly improve its out-of-sample forecasting ability.