A six-factor asset pricing model
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...
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doaj-ff63549400ce4ef4a18d6f11ee6db2712020-11-24T21:17:48ZengElsevierBorsa Istanbul Review2214-84502018-09-01183205217A six-factor asset pricing modelRahul Roy0Santhakumar Shijin1Corresponding author.; Department of Commerce, School of Management, Pondicherry University, Pondicherry, 605014, IndiaDepartment of Commerce, School of Management, Pondicherry University, Pondicherry, 605014, IndiaThe 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. Keywords: FF portfolio, Human capital, IVGMM approach, Return predictability, Six-factor asset pricing model, JEL classification: G12, C36, J24http://www.sciencedirect.com/science/article/pii/S2214845017301916 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rahul Roy Santhakumar Shijin |
spellingShingle |
Rahul Roy Santhakumar Shijin A six-factor asset pricing model Borsa Istanbul Review |
author_facet |
Rahul Roy Santhakumar Shijin |
author_sort |
Rahul Roy |
title |
A six-factor asset pricing model |
title_short |
A six-factor asset pricing model |
title_full |
A six-factor asset pricing model |
title_fullStr |
A six-factor asset pricing model |
title_full_unstemmed |
A six-factor asset pricing model |
title_sort |
six-factor asset pricing model |
publisher |
Elsevier |
series |
Borsa Istanbul Review |
issn |
2214-8450 |
publishDate |
2018-09-01 |
description |
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. Keywords: FF portfolio, Human capital, IVGMM approach, Return predictability, Six-factor asset pricing model, JEL classification: G12, C36, J24 |
url |
http://www.sciencedirect.com/science/article/pii/S2214845017301916 |
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