英文标题：
《A six-factor asset pricing model》
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作者：
Rahul Roy, Santhakumar Shijin
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最新提交年份：
2018
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英文摘要：
  The present study introduce the human capital component to the Fama and French five-factor model proposing an equilibrium six-factor asset pricing model. The study employs an aggregate of four sets of portfolios mimicking size and industry with varying dimensions. The first set consists of three set of six portfolios each sorted on size to B/M, size to investment, and size to momentum. The second set comprises of five index portfolios, third, a four-set of twenty-five portfolios each sorted on size to B/M, size to investment, size to profitability, and size to momentum, and the final set constitute thirty industry portfolios. To estimate the parameters of six-factor asset pricing model for the four sets of variant portfolios, we use OLS and Generalized method of moments based robust instrumental variables technique (IVGMM). The results obtained from the relevance, endogeneity, overidentifying restrictions, and the Hausman\'s specification, tests indicate that the parameter estimates of the six-factor model using IVGMM are robust and performs better than the OLS approach. The human capital component shares equally the predictive power alongside the factors in the framework in explaining the variations in return on portfolios. Furthermore, we assess the t-ratio of the human capital component of each IVGMM estimates of the six-factor asset pricing model for the four sets of variant portfolios. The t-ratio of the human capital of the eighty-three IVGMM estimates are more than 3.00 with reference to the standard proposed by Harvey et al. (2016). This indicates the empirical success of the six-factor asset-pricing model in explaining the variation in asset returns.
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中文摘要：
本研究将人力资本成分引入Fama和French五因素模型，提出了均衡六因素资产定价模型。这项研究采用了四组组合，模拟了不同规模和行业的投资组合。第一组由三组六个投资组合组成，每个投资组合按规模与B/M、规模与投资、规模与动量排序。第二组由五个指数投资组合组成，第三组由四组二十五个投资组合组成，每个投资组合按规模与B/M、规模与投资、规模与盈利能力、规模与动量排序，最后一组由三十个行业投资组合组成。为了估计四组不同投资组合的六因素资产定价模型的参数，我们使用OLS和基于矩的稳健工具变量技术（IVGMM）的广义方法。从相关性、内生性、过度识别限制和豪斯曼规范测试中获得的结果表明，使用IVGMM的六因素模型的参数估计是稳健的，并且性能优于OLS方法。在解释投资组合回报变化时，人力资本部分与框架中的因素具有同等的预测能力。此外，我们评估了四组不同投资组合的六因素资产定价模型的每个IVGMM估计的人力资本组成部分的t比率。根据Harvey et al.（2016）提出的标准，83项IVGMM估计的人力资本t比率超过3.00。这表明六因素资产定价模型在解释资产回报变化方面取得了经验上的成功。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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