This paper is aimed to validate the four-factor asset pricing model as an improvement towards the standard Fama-French three-factor model. Using U.S. monthly stock returns data from period January 1963 to December 2010 , we construct 25 portfolios and t he four-factor model includes the market factor (beta), the size factor (SMB), the book-to-market factor (HML), and the ‘momentum' factor (MOM). Similar time series method as in Fama and French (1993) are employed to elaborate the three-factor model and the four-factor model regression. Our findings show that the four-factor model to some extent has significant capability in explaining the variations in average excess stock returns. Although the R 2 extracted from the four-factor model is just slightly higher than the three-factor model, yet it provides suggestive for the robustness of the four-factor model. In addition , our robustness test shows that January seasonal effect is absorbed by the risk factors including the market factors, SMB, HML, and MOM factor. The consistency of the four-factor model in explaining the U.S stock market return variations for the newest data, provide relevance to apply this model in emerging markets data in order to give guidance for investor in understanding the market condition.

中文翻译：

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扩展的法玛-法国三因素模型：再探讨

本文旨在验证四要素资产定价模型，作为对标准Fama-French三要素模型的改进。使用1963年1月至2010年12月期间的美国月度股票收益数据，我们构建了25个投资组合，并且四因素模型包括市场因素（beta），规模因素（SMB），账面市价因素（HML） ，以及“动量”因子（MOM）。采用与Fama和French（1993）中类似的时间序列方法来阐述三因素模型和四因素模型回归。我们的发现表明，四因素模型在某种程度上具有解释平均超额股票收益率变化的显着能力。尽管从四因素模型中提取的R 2略高于三因素模型，但它为四因素模型的鲁棒性提供了提示。此外，我们的稳健性测试表明，一月份的季节性影响已被包括市场因素，SMB，HML和MOM因素在内的风险因素吸收。四因素模型在解释美国股市最新数据的收益变化方面的一致性，为将该模型应用于新兴市场数据提供了相关性，从而为投资者了解市场状况提供指导。