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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.

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.

We examine explicitly priced financial distress risk in post-1990 equity markets. We add a financial distress risk factor to Fama and French's (1993) three-factor model, based on Griffin and Lemmon's (2002) findings that financial distress is not fully captured by the book-to-market factor. We test three-factor and four-factor capital asset pricing models using both annual buy-and-hold analysis and monthly time series analysis across portfolios adjusted for common book-to-market, size, and financial distress factors. We find empirical support for an Ohlson (1980) O-score-based financial distress risk four-factor asset pricing model in the U.S. and Japanese markets.

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 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.

This paper aims to investigate the explanatory power of the Fama-French five-factor model and compares it to the other asset pricing models. In addition, the paper examines the contributions of two additional factors: profitability and investment factor. The authors test the alternative four-factor models.

The authors use stock returns data of BSE-500 listed firms for the Indian market, an emerging market, from 1999 to 2020, thus covering the post-Asian crisis and pre- and post-financial crisis (2007–2008) periods. The authors employ 75 and 96 portfolios based on different factors. To check the performance of asset pricing models, the authors also used the GRS F-statistics and factor spanning tests.

The authors find that the five-factor model and alternative four-factor model outperform the three-factor model. Contrary to the findings for the US, but similar to the Chinese stock market, the value factor is significant for the Indian stock market. Simultaneously, the authors also find that the investment factor has no explanatory power in the presence of the profitability factor in their sample.

To the best of the authors' knowledge, this is the most comprehensive study using data more than two decades. These results are based on 75 (25 × 3) portfolios based on size, value, profitability and investment. The authors also tested these results based on 96 (32 × 3) portfolios to check robustness, and these results still hold. Furthermore, the authors find that factors based on 2 × 3 sorting have higher explanatory power than those based on 2 × 2 and 2 × 2 × 2 × 2 sorting.

The purpose of this paper is to examine the level of foreign exchange exposure of listed nonfinancial firms in South Africa. The study spans the period January 2002 and November 2015. Foreign exchange risk exposure is estimated in relation to the exchange rate of the South African Rand relative to the US$, the Euro, the British Pound and the trade-weighted exchange rate index.

The study is based on the augmented-market model of Jorion (1990). The Jorion (1990) is a capital asset pricing model-inspired framework which models share returns as a function of the return on the market index and changes in the exchange rate factor. The market risk factor is meant to discount the effect of macroeconomic factors on share returns, thus isolating the foreign exchange risk factor. In addition, the study further added the size, value, momentum, investment and profitability risk factors in line with the Fama–French three-factor model, Carhart four-factor model and the Fama–French five-factor model to account for the fact that equity capital markets in countries such as South Africa are known to be partially segmented.

Foreign exchange risk exposure levels were estimated at more than 40% for all the proxy currencies on the basis of the standard augmented market model. However, after controlling for idiosyncratic factors, through the application of the Fama–French three-factor model, the Carhart four-factor model and the Fama–French five-factor model, exposure levels were found to range between 6.5 and 12%.

These results indicate the importance of controlling for the effects of idiosyncratic facto0rs in the estimation of foreign exchange risk exposure in the context of emerging markets of Sub-Saharan Africa (SSA).

This is the first study to apply the Fama–French three-factor model, Carhart four-factor model and the Fama–French five-factor model in the estimation of foreign exchange exposure of nonfinancial firms in the context of a SSA country. These results indicate the importance of controlling for the effects of idiosyncratic factors in the estimation of foreign exchange risk exposure in the context of emerging markets.

This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.

The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios.

The findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space.

To the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.

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 discuss a multiscale pricing model for the French stock market by combining wavelet analysis and Fama‐French three‐factor model. The objective is to examine the relationship between stock returns and Fama‐French risk factors at different time‐scales.

Exploiting the scale separation property inherent to the maximal overlap discrete wavelet transform, the data set are decomposed into components associated with different time‐scales. This wavelet‐based decomposition scheme allows the three Fama‐French models to be tested over different investments periods.

The obtained results show that the explanatory power of the Fama‐French three‐factor model becomes stronger as the wavelet scale increases. Besides, the relationship between the portfolio returns and the risk factors (i.e. the market, size and value factors) depends significantly upon the considered time‐horizon.

The proposed methodology offers investors the opportunity to construct dynamic portfolio management strategies by taking into account the multiscale nature of risk and return. Moreover, it gives a new insight to fund rating and fund selection issues in relation to heterogeneous investments periods.

The paper uses wavelets as a relatively new and powerful tool for statistical analysis that allows a new understanding of pricing models. The paper will be of interest not only for academics in the field of asset pricing but also for fund managers and financial market investors.