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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Main Authors: Rahul Roy, Santhakumar Shijin
Format: Article
Language:English
Published: Elsevier 2018-09-01
Series:Borsa Istanbul Review
Online Access:http://www.sciencedirect.com/science/article/pii/S2214845017301916
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spelling 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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