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This paper explores the relationship between size, book-to-market, beta, and expected stock returns in the U.S. Information Technology sector over the July 1990–June 2001 period. Two models, the multivariate model and the three-factor model, are employed to test these relationships. The risk-return tests confirm the relationship between size, book-to-market, beta and stock returns in IT stocks is different from that in other non-financial stocks. However, the sub-period results (the periods before and after the technology crash in April 2000) show that the nature of the relationship between stock returns, size, book-to-market, and market factors, or the magnitude of the size, book-to-market, and market premiums, is on average unchanged for both sub-periods. This result suggests the technology stock crash in April 2000 was not a correction of stock prices.

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 study is to investigate the effects of increased fund commitments on portfolio size and subsequent effects on portfolio success rates. This paper empirically analyzes the changes in average portfolio size over a 20‐year time period and how these changes affect the venture capitalists' ability to successfully exit their investments.

The authors utilize venture capitalists' fund level data and conduct both univariate and multivariate analysis. The multivariate analysis is conducted using a two‐limit regression tobit model. This is justified since the authors' dependent variable is a ratio bounded by zero and one, hence the tobit specification is the most appropriate methodology.

The authors find that increasing the size of portfolios not only leads to a decrease in the number of successful investments but also significantly affects portfolio success rates. They also find evidence which suggests that some optimal portfolio size exists.

The sample was limited to independent private partnerships that raised funds specifically for investment in US portfolio companies and it represents all funds maintained in the SDC database with non‐missing data on fund size and other fund characteristics.

There are three main practical implications derived from this study. First, venture capitalists overextend themselves by investing in too many portfolio firms. Second, some optimal portfolio size exists beyond which success rate of the venture capitalist's portfolio declines. Third, portfolio size is an important determinant of venture capital portfolio success rates.

The study presents new evidence that venture capitalists have a tendency to increase their portfolio size in years following growth in fund inflows, an idea that has not been investigated earlier. The authors also use data that is not adulterated by significant economic and financial conditions such as internet bubble burst of 2000 and financial crisis of 2007/2008.

Whether the risk factors or firm characteristics cause the value premium of stocks still needs further investigation. This paper shows that the factor‐based models are significant but not sufficient for the stock returns in Taiwan. Size or book‐to‐market ratio alone cannot influence the stock returns under a factor‐based model. However, size along with book‐to‐market is significant under a factor‐based model. Furthermore, the risk characteristics are more influential than the factor load in stock return behavior. We conclude that employing only a factor‐based model or only risk characteristics will not consider some important content in stock returns.

We would like to thank C. Y. Chen, Wenchih Lee, two anonymous referees and the seminar participants at the 2000 FMA annual meeting for their helpful comments and encouragement. All of the remaining errors are our responsibility.

We examine the incremental cross-sectional role of several common risk factors related to size, book-to-market, and momentum in size-and-momentum-sorted portfolios. Unlike the existing literature, which focuses on the conditional mean specification only, we evaluate the common risk factors’ incremental explanatory power in the cross-sectional characterization of both average return and conditional volatility. We also investigate the role of ex-ante market risk in the cross-section. The empirical results demonstrate that the size-and-momentum-based risk factors explain a significant portion of the cross-sectional average returns and cross-sectional conditional volatility of the benchmark equity portfolios. We find that the Fama–French (1993) factors and the ex-ante market risk are priced in the cross-sectional conditional volatility. We conclude that the size-and-momentum-based factors provide a source of risk that is independent of the Fama–French factors as well as ex-post and ex-ante market risk. Our results bolster the risk-based explanation of the size and momentum effects.

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.

This paper aims to examine the nexus between the pricing of market-wide volatility risk and distress risk in the cross-section of portfolio returns for the 1990-2011 time period. The author expands upon prior research by constructing an ex post factor that mimics aggregate volatility risk based on the new VIX index of the Chicago Board Options Exchange, termed FVIX, as well as focuses on volatility risk in crisis versus non-crisis time periods.

The author investigates the relationship between volatility and distress risk using several techniques in the empirical finance literature. Specifically, the author investigates the behavior of correlations between risk factors as well as the correlations between factor loadings when using the Fama and French research portfolios as our test assets for different time periods. Additionally, the author examines the variation in the volatility factor loadings across the size- and value-sorted portfolios and assesses whether augmenting conventional pricing models with a volatility factor leads to a higher goodness-of-fit in pricing the 25 size- and value-sorted portfolios.

The author’s results suggest that factor volatilities are high during periods of market turmoil. In addition, the author presents evidence indicating that a factor mimicking innovation in volatility (based on the new VIX) is correlated with the market and momentum factors, while exhibiting the uncorrelated behavior with respect to the size, value and liquidity factors when using data from 1990 through 2011. In this paper, the author finds that the aggregate volatility factor’s correlation with the market and momentum factors increases during crisis periods. In periods of relative market tranquility, correlations decrease significantly. In examining multivariate factor loadings for the test assets, the results provide no clear pattern with regard to the variation of the volatility loadings across the book-to-market and size dimensions. Furthermore, the author finds that conventional pricing models are comparable to FVIX-augmented pricing models, in terms of goodness-of-fit, when pricing the 25 Fama-French size- and value-sorted portfolios. Additionally, when using the FVIX volatility factor to proxy for aggregate volatility risk, the coefficients are never significant statistically, thus revealing that innovations in aggregate volatility based on the new VIX index do not constitute a priced risk factor in the cross-section of returns.

The author’ finding indicates an absence of strong variation of the volatility factor loadings across the Fama-French research portfolios. In particular, the asset pricing results cast doubt on whether a factor mimicking innovations in aggregate volatility based on the new VIX index is priced. In agreement with prior research, the author believes that the inseparability of volatility and jump risk in the VIX can be a possible explanation of the current findings in this paper.

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.

This paper aims to confirm the existence of size, book to market (BM) and momentum effects in the New Zealand (NZ) stock market. It also aims to compare the performance of the CAPM, the Fama‐French (FF) model, and Carhart's model in explaining the variation of stock returns.

The paper adapts the Fama and French methodology using a 2×3 size‐BM ratio sort. It also forms three portfolios based on past returns to verify the momentum effect.

The paper documents significant BM and momentum effects but a relatively weaker size effect. The paper finds some improvement in explanatory power provided by the FF model relative to the CAPM but it still leaves a large part of the variation in stock returns unexplained. The FF model is also unable to explain the strong momentum effect in New Zealand.

The findings imply that: cost of capital estimates would be more accurate using Carhart's model; portfolio managers can increase returns by investing in small and high BM firms that are recent winners; performance evaluation should take into account the size, BM, and momentum effects; and the existence of size and BM return premia appear to be rewards to risk bearing.

The existing literature testing the robustness of the FF model in markets outside the USA is sparse, especially in emerging markets, with most of these studies suffering from data problems. The NZ stock market provides an interesting setting for such a study because of its unique characteristics.

This study aims to investigate the market timing strategy in different market conditions (i.e. up, down, normal and in-financial-crisis situation) in the emerging market of Pakistan over the period 1995 to 2015. Furthermore, this study tests the validity of the capital asset pricing model (CAPM) and Fama and French model.

This study considers monthly stock returns of 167 firms and constructs six different portfolios on the basis of different size and book to market ratio. The Treynor and Mazuy model is used to capture the market timing strategy.

The results indicate evidence of the market timing in normal market conditions. However, there is less supportive evidence of market timing in up-market, down-market and in-financial-crisis situations. This study also confirms the validity of the capital asset pricing model and Fama and French three-factor model with strong support of value premium and size premium in the stock market.

The findings of this study are helpful to companies in estimating the cost of issuing equity more accurately. The investors can use market timing to make their investment in a more better and profitable manner.

Unlike other previous studies, this study considers an extended period to test the validity of the capital asset pricing model and Fama and French model. In addition, this study is novel in testing the marketing timing of the firms in the context of emerging economy of Pakistan.