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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.

Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.

The purpose of this study is to explore the applicability of the Fama–French and Carhart models on the South African stock market (SASM). It examines the ability of the models to capture size, book-to-market (BM) and momentum effects on the SASM. The paper, additionally, explores the ability of the Fama–French–Carhart factors to predict the future growth of the South African economy.

The paper relies on data of 848 firms from January 1996 to April 2012 to examine the size, BM and momentum effects on the SASM. The paper constructs the test assets from a 3 × 3 sort on size and BM and a 3 × 3 sort on size and momentum. The paper estimates momentum as the past six-months’ cumulative return. The momentum portfolios are monthly rebalanced. Additionally, the size and BM portfolios are formed annually at the end of each June.

Evidence is provided that size, BM and momentum effects exist on the SASM; also, the small- and high-BM firm portfolios, respectively, appear riskier than the big- and low-BM firm portfolios. The paper provides evidence of past winners outperforming past losers aside from the small-firm group. Additionally, the models only partially capture the size and value effects on the SASM. The Carhart model partly captures the momentum effects, but the Fama–French model is unable to describe the returns to the momentum-sorted portfolios. The evidence shows that the models’ factors predict future gross domestic product growth.

The models do not fully describe returns on the SASM; any application of the models on the SASM should be done with caution. The Carhart model better describes returns than the Fama–French model on the SASM. The Fama–French–Carhart factors may relate to the underlying economic risk of the South African economy.

Lai, Li, Conover, and Wu (2010) propose a four-factor financial distress model to explain stock returns in the U.S. and Japanese markets. We examine this model in the stock markets of Australia, and six Asian markets (Hong Kong, Indonesia, Korea, Malaysia, Singapore, and Thailand). We find broad empirical support for the four-factor financial distress risk asset-pricing model in those markets. The four-factor financial distress asset pricing model improves explanatory power beyond the Fama–French (1993) three-factor asset pricing model in six of the seven Asian-Pacific markets (12 of 14 portfolio groupings), while the Carhart (1997) momentum-based asset pricing model only improves explanatory power beyond the Fama–French model in three of the seven markets (4 of 14 portfolio groupings).

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.

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.

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.

The purpose of this paper is to determine the best conditional asset pricing model for the Tokyo Stock Exchange sample by utilizing long‐run daily data. It aims to investigate whether there are any other firm‐specific variables that can explain abnormal returns of the estimated asset pricing model.

The individual firm sample was used to conduct various cross‐sectional tests of conditional asset pricing models, at the same time as using test portfolios in order to confirm the mean variance efficiency of basic unconditional models.

The paper's multifactor models in unconditional forms are rejected, with the exception of the five‐factor model. Further, the five‐factor model is better overall than the Fama and French model and other alternative models, according to both the Gibbons, Ross, and Shanken test and the Hansen and Jagannathan distance measure test. Next, using the final conditional five‐factor model as the de facto model, it was determined that the turnover ratio and the size can consistently predict Jensen's alphas. The book‐to‐market ratio (BM) and the past one‐year returns can also significantly predict the alpha, albeit to a lesser extent.

In the literature related to Japanese data, there has never been a comprehensive test of conditional asset pricing models using the long‐run data of individual firms. The conditional asset pricing model derived for this study has led to new findings about the predictability of past one‐year returns and the turnover ratio.

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.