Testing Fama–French’s new five-factor asset pricing model: evidence from robust instruments

Racicot, François-Éric; Rentz, William F.

doi:10.1080/13504851.2015.1080798

Cited by 22 publications

(21 citation statements)

References 17 publications

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“…Concurrently, Fama & French (2015) proposed a fivefactor model by adding profitability and investment factors to the FF three-factor model, which performs poorly because of its inability to capture the low average returns on small stocks. Inaddition, Fama & French (2017); Gregoriou, Racicot, & Théoret (2016); and Racicot & Rentz (2016) confirms the failure of the FF five-factor asset pricing model in asset return predictability.…”

Section: Introductionmentioning

confidence: 62%

Is Human Capital the Sixth Factor? Evidence from US Data

Roy

Shijin

2019

JOFRP

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Problem/Relevance: Measuring the risk of an asset and the economic forces driving the price of the risk is a challengingtask that preoccupied the asset pricing literature for decades. However, there exists no consensus on the integrated asset pricing framework among the financial economists in the contemporaneous asset pricing literature. Thus, we consider and study this research problem that has greater relevance in pricing the risks of an asset. In this backdrop, we develop an integrated equilibrium asset pricing model in an intertemporal (ICAPM) framework. Research Objective/Questions: Broadly we have two research objectives. First, we examine the joint dynamics of the human capital component and common factors in approximating the variation in asset return predictability. Second, we test whether the human capital component is the unaccounted and the sixth pricing factor of FF five-factor asset pricing model. Additionally, we assess the economic and statistical significance of the equilibrium six-factor asset pricing model. Methodology: The human capital component, market portfolio, size, value, profitability, and investment are the pricing factors of the equilibrium six-factor asset pricing model. We use Fama-French (FF) portfolios of 2  3, 5  5, 10  10 sorts, 2  4  4 sorts, and the Industry portfolios to examine the equilibrium six-factor asset pricing model. The Generalized method of moments (GMM) estimation is used to estimate the parameters of variant asset pricing models and Gibbons-Ross-Shanken test is employed to evaluate the performance of the variant asset pricing frameworks. Major Findings: Our approaches led to three conclusions. First, the GMM estimation result infers that the human capital component of the six-factor asset pricing model significantly priced the variation in excess return on FF portfolios of variant sorts and the Industry portfolios. Further, the sensitivity to human capital component priced separately in the presence of the market portfolios and the common factors. Second, the six-factor asset pricing model outperforms the CAPM, FF three-factor model, and FF five-factor model, which indicates that the human capital component is a significant pricing factor in asset return predictability. Third, we argue that the human capital component is the unaccounted asset pricing factor and equally the sixth-factor of the FF five-factor asset pricing model. The additional robustness test result confirms that the parameter estimation of the six-factor asset pricing model is robust to the alternative definitions of the human capital component. Implications: The empirical results and findings equally pose the more significant effects for the decision-making process of the rational investor, institutional managers, portfolio managers, and fund managers in formulating the better investment strategies, which can help in diversifying the aggregate risks.

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Section: Introductionmentioning

confidence: 62%

Is Human Capital the Sixth Factor? Evidence from US Data

Roy

Shijin

2019

JOFRP

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“…The explanatory power of the five factors model is more than 80% 3 . However, a recent study by Racicot and Rentz (2016) uses the improved GMM-based robust instrumental variables technique to evaluate the FF5F model. The study finds that the market returns are sufficient to explain the variation in portfolio returns of the investors and all other factors are redundant.…”

Section: Methodsmentioning

confidence: 99%

Do the Fama and French Five-Factor Model Forecast Well Using Ann?

Jan

Ayub

2019

Journal of Business Economics and Management

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Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors.

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“…On the one hand, only when the left-hand-side portfolios are formed on momentum factors is including a momentum factor imperative [8]. On the other hand, there is evidence to suggest that except for the market factor, all of the factors including liquidity are not significant at even the 5% level using their approach [21]. Specifically, Racicot and Rentz (2016) verify the five-factor model and compare this model to a six-factor model that includes the liquidity factor [17].…”

Section: B Limitationsmentioning

confidence: 98%

Are Profitability and Investment Good Proxies for Risk Factors The Analysis on Fama and French's Five-Factor Model

Huang¹

2016

Proceedings of the 2016 International Conference on Management Science and Management Innovation

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Abstract-The aim of this paper is to review a five-factor asset pricing model developed by Fama and French, which extends the three-factor model by incorporating two additional indicators that captured profitability and investment in average stock returns. Moreover, this paper provides a detail discussion and addresses some important issues and problems in capital asset pricing are becoming considerable topics for academic and practical research into the future.

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Testing Fama–French’s new five-factor asset pricing model: evidence from robust instruments

Cited by 22 publications

References 17 publications

Is Human Capital the Sixth Factor? Evidence from US Data

Is Human Capital the Sixth Factor? Evidence from US Data

Do the Fama and French Five-Factor Model Forecast Well Using Ann?

Are Profitability and Investment Good Proxies for Risk Factors The Analysis on Fama and French's Five-Factor Model

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