Search results

This study examines the five-factor model of Fama and French (2015) on the French stock market by comparing it to the Fama and French (1993)’s base model. The new Fama and French five-factor model directed at capturing two new factors, profitability and investment in addition to the market, size and book to market premiums. The pricing models are tested using a time-series regression and the Fama and Macbeth (1973) methodology. The regularities in the factor’s behavior related to market conditions and to the sovereign debt crisis in Europe are also examined. The findings of Fama and French (2015) for the US market are confirmed on the Paris Bourse. The results show that both models help to explain some of the stock returns. However, the five-factor model is better since it has a marginal improvement over the widely used three-factor model of Fama and French (1993). In addition, the investment risk premium seems to be better priced in the French stock market than the profitability factor. The results are robust to the Fama and Macbeth (1973) methodology. Moreover, profitability and investment premiums are not affected by market conditions and the European sovereign debt crisis.

The purpose of this paper is to examine alternative six- and seven-factor equity pricing models directed at capturing a new factor, aggregate volatility, in addition to market, size, book to market, profitability, investment premiums of the Fama and French (2015) and Fama and French’s (2018) aggregate volatility augmented model.

The models are tested using a time series regression and Fama and Macbeth’s (1973) methodology.

The authors show that both six- and seven-factor models best explain average excess returns on the French stock market. In fact, the authors outperform Fama and French’s (2018) model. The authors use sensitivity of aggregate volatility of a stock VCAC as a proxy to construct the aggregate volatility risk factor. The spanning tests suggest that Fama and French’s (1993, 2015, 2018) and Carhart’s (1997) models do not explain the aggregate volatility risk factor FVCAC. The results show that the FVCAC factor earns significant αs across the different multifactor models and even after controlling for the exposure to all the other in Fama and French’s (2018) model. The asset pricing tests show that it is systematically priced. In fact, the authors find a significant and negative (positive) relation between the aggregate volatility risk factor and the excess returns in the French stock market when it is rising (falling), in addition, periods with downward market movements tend to coincide with high volatility.

The authors contribute to the related literature in several ways. First, the authors test two new empirical six- and seven-factor model and the authors compare them to Fama and French’s (2018) model. Second, the authors give new evidence about the VCAC, using it for the first time to the authors’ knowledge, to construct a volatility risk premium.

The purpose of this paper is to test the new Fama and French (2015) five-factor model relying on a thorough sample of hedge fund strategies drawn from the Barclay’s Global hedge fund database.

The authors use a stepwise regression to identify the factors of the q-factor model which are relevant for the hedge fund strategy analysis. Doing so, the authors account for the Fung and Hsieh seven factors which prove very useful in the explanation of the hedge fund strategies. The authors introduce interaction terms to depict any interaction of the traditional Fama and French factors with the factors associated with the q-factor model. The authors also examine the dynamic dimensions of the risk-taking behavior of hedge funds using a BEKK procedure and the Kalman filter algorithm.

The results show that hedge funds seem to prefer stocks of firms with a high investment-to-assets ratio (low conservative minus aggressive (CMA)), on the one hand, and weak firms’ stocks (low robust minus weak (RMW)), on the other hand. This combination is not associated with the conventional properties of growth stocks – i.e., low high minus low (HML) stocks – which are related to firms which invest more (low CMA) and which are more profitable (high RMW). Finally, small minus big (SMB) interacts more with RMW while HML is more correlated with CMA. The conditional correlations between SMB and CMA, on the one hand, and HML and RMW, on the other hand, are less tight and may change sign over time.

To the best of the authors’ knowledge, the authors are the first to cast the new Fama and French five-factor model in a hedge fund setting which account for the Fung and Hsieh option-like trading strategies. This approach allows the authors to better understand hedge fund strategies because q-factors are useful to study the dynamic behavior of hedge funds.

This study aims to investigate whether the zero-investment portfolio strategy generates higher excess returns for all listed companies in the Stock Exchange of Thailand (SET) or ESG100 stocks. The study period is from January 2016 to December 2020, a total of 60 months. The dividend yield is employed for categorizing the stock into value and growth stocks. The strategy of buying value stocks and short-selling growth stocks is then applied. The results show that investing using the zero-investment portfolio strategy can generate higher returns in an investment portfolio that consists of ESG100 stocks than in an investment portfolio that consists of all stocks in the SET. The optimal holding periods for investing in portfolios that consist of stocks in the SET are 6 months, 9 months, and 12 months, and the optimal holding periods for a portfolio that consists of ESG100 stocks is 6 months. To explain excess returns of stocks in the SET, the Fama and French (2015) five-factor model is employed. There is no relation between risk factors and excess returns for the holding period of 6 months and 12 months. However, excess return is found to have a negative relation with the market risk premium factor for a 9-month holding period. The excess returns of ESG100 stocks are also inversely correlated with investment factors for a holding period of 6 months.

The purpose of this paper is to focus on one of the major emerging Asian economies – India – to examine the role of human capital in asset prices.

The analysis uses various statistical techniques (e.g. multifactor regression model, 3D graphs, GRS test and residual graphs) to test the role of human capital in asset prices.

A six-factor model designed for capturing the size, value, profitability, investment and human capital patterns in average portfolio returns performs better than both Fama–French’s (1993) three- and Fama–French’s (2015) five-factor model. The main problem of six-factor model is its failure in capturing the average returns on “microcap with low-value stocks that are highly profitable invests aggressively for asset growth but invests much lesser for human growth” and “microcap with unprofitable stocks whose returns behave like those of low-value firms with conservative investment”. The study finds the investment factor (CMA) of Fama–French’s (2015) five-factor model as the redundant factor for describing the portfolio average returns in the study sample.

The paper argues that human capital also plays a role in predicting returns. This has significant public policy content.

The present study is novel for several reasons: first, it includes six-factor model descriptions; second, no comprehensive asset pricing study is done with human capital in Asian emerging markets, especially in India. Perhaps, this is the first study to examine whether portfolio returns are affected by the human capital in the Indian context. Third, the study period and methodology used are completely different from the previous studies.

The purpose of this paper is to investigate the return performance of a portfolio of US “vice stocks,” firms that manufacture and sell products such as alcohol, tobacco, gaming services, national defense and firearms, adult entertainment, and payday lenders.

Using daily return data from a portfolio of vice stocks over the period 1987-2016, the author computes the Jensen’s α (capital asset pricing model (CAPM)), Fama-French Three-Factor, Carhart Four-Factor, and Fama-French Five-Factor results for the complete portfolio, and each vice industry individually.

The results from the CAPM, Fama-French Three-Factor Model, and the Carhart Four-Factor Model show a positive and significant α for the vice portfolio throughout the sample period. However, the α’s significance disappears with the addition of the explanatory variables from the Fama-French Five-Factor Model.

The author provides academics and practitioners with results from a new model. As of this writing, the author is unaware of any articles published in peer-reviewed academic journals that investigate vice stocks within the framework of the Fama-French Five-Factor Model (2015). First, the existing literature does not shed light on the relationship between “profitability” and “aggressiveness” (the fourth and fifth factors of the Fama-French Model) and vice stock returns. Second, within the framework of the Fama-French Five-Factor Model, the author shows results not only from a portfolio of vice stocks, but from various vice industries as well.

This paper aims to study the cross section of expected returns on Shari’ah-compliant stocks in Pakistan by using single- and multi-factor asset pricing models.

To estimate cross section of expected returns of Shari’ah-compliant stocks, the study uses capital asset pricing model (CAPM), Fama-French three-factor model and Fama-French five-factor model. Data for the period 2001-2015 on 217 companies are used. For the market portfolio, PSX-100 and Dow Jones Islamic Index for Pakistan are used.

The study could not find empirical support for CAPM using Lintner (1965), Black et al. (1972) and Fama and Macbeth (1973) approach. Nonetheless, the relation between beta and returns is positive in up-market and negative in down-market. The results of Fama-French three-factor and five-factor models suggest that size premium is positive and significant for explaining the cross section of stock returns of small size stocks, whereas value premium is positive and significant for explaining the cross section of returns of high value stocks.

The results suggest that fund managers can use Shari’ah-compliant stocks for portfolio diversification and for offering specialized investments given the positive market excess returns and the existence of size and value premium on Shari’ah-compliant stocks.

This is the first study on Fama-French (2015) five-factor model for Islamic capital markets in Pakistan.

The purpose of this paper is to examine whether Fama–French common risk-factor portfolio investors herd on a daily basis for five developed markets, namely, Europe, Japan, Asia Pacific ex Japan, North America and Globe.

To examine the herd behavior of common risk-factor portfolio investors, this paper utilizes the cross-sectional absolute deviations (CSAD) methodology, covering a daily data sampling period of July 1990 to January 2019 from Kenneth R. French-Data Library. CSAD driven by fundamental and non-fundamental information is assessed using Fama–French five-factor model.

The results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration. However, Fama–French common risk-factor portfolio investors mimic the underlying risk factors in returns related to size and book-to-market value, size and operating profitability, size and investment and size and momentum of the equity stocks in European and Japanese markets during crisis period. Also, no considerable evidence is found for herding (on fundamental information) under crisis and up-market conditions except for Japan. Ancillary findings are discussed under conclusion.

Further research on new risk factors explaining stock return variation may help improve the model performance. The performance can be improved by adding new risk factors that are free from behavioral bias but significant in explaining common stock return variation. Also, it is necessary to revisit the existing common risk factors in order to understand behavioral aspects that may affect cost of capital calculations (e.g. pricing errors) and valuation of investment portfolios.

This is the first paper that examines the herd behavior (fundamental and non-fundamental) of Fama–French common risk-factor investors using five-factor model.

Since the introduction of VIX to measure the spot volatility in the stock market, VIX and its futures have been widely considered to be the standard of underlying investor sentiment. This study aims to examine how the magnitude of contango or backwardation (MCB volatility risk factor) derived from VIX and VIX3M may affect the pricing of assets.

This paper focuses on the statistical inference of three defined MCB risk factors when cross-examined with Fama–French’s five factors: the market factor R_m–R_f, the size factor SMB (small minus big), the value factor HML (high minus low B/M), the profitability factor RMW (robust minus weak) and the investing factor CMA (conservative minus aggressive). Robustness checks are performed with the revised HML-Dev factor, as well as with daily data sets.

The inclusions of the MCB volatility risk factor, either defined as a spread of monthly VIX3M/VIX and its monthly MA(20), or as a monthly net return of VIX3M/VIX, generally enhance the explanatory power of all factors in the Fama and French’s model, in particular the market factor R_m–R_f and the value factor HML, and the investing factor CMA also displays a significant and positive correlation with the MCB risk factor. When the more in-time adjusted HML-Dev factor, suggested by Asness (2014), replaces the original HML factor, results are generally better and more intuitive, with a higher R² for the market factor and more explanatory power with HML-Dev.

This paper introduces the term structure of VIX to Fama–French’s asset pricing model. The MCB risk factor identifies underlying configurations of investor sentiment. The sensitivities to this timing indicator will significantly relate to returns across individual stocks or portfolios.

The aim of this study is to investigate the role of efficiency in capital asset pricing. The paper explores the impact of a four-factor model that involves an efficiency factor on the returns of Nasdaq technology firms.

The paper relies on data of 147 firms from July 2007 to June 2017 to examine the impact of efficiency on stock returns. The performances of the capital asset pricing model (CAPM), Fama–French three-factor model and the proposed four-factor model are evaluated based on the time series regression method. The parameters such as the GRS F-statistic and adjusted R² are used to compare the relative performances of all models.

The results show that all factors of the models are found to be valid in asset pricing. Also, the paper provides evidence that the explanatory power of the proposed four-factor model outperforms the explanatory power of the CAPM and Fama–French three-factor model.

Unlike most asset pricing studies, this paper presents a new asset pricing model by adding the efficiency factor to the Fama–French three-factor model. It is documented that the efficiency factor increases the predictive ability of stock returns. Evidence implies that investors consider efficiency as one of the main factors in pricing their assets.