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Fama–French model (FFM) has been successful in helping to predict the financial markets, but investors have been interested in creating more sophisticated models to better predict the performance of the stock market. The objective of the extended version is to create a more robust econometric model to better predict the performance of the Mexican Stock Market.

The study divides the Mexican Stock Market into six different portfolios. The criteria to build those portfolios are the same one used in Fama–French (1992). The study comprises 78 stocks listed in the Mexican Stock Market that are analyzed monthly during 1997–2018. The study analyzes the period before and after the 2008–2009 financial crisis to identify whether there are important changes. The estimation applies the traditional and an extended version of the FFM that include macroeconomic variables such as country risk, economic activity, inflation rate, and exchange rate and some financial variables recommended in the literature.

Results indicate that classic FFM variables are statistically significant in most cases, but relevant macroeconomic variables such as the interest rate, exchange rate and country risk stand out for being weakly relevant in most of the portfolios. However, it is noticed that some of these macroeconomic variables became relevant for different portfolios only after the 2008–2009 crisis, especially in portfolios which include small market capitalization firms.

The study includes the stocks listed in the Mexican Stock Market. One limitation is the small number of stocks available, which reduces the possibility of creating well diversified portfolios. This study includes 78 stocks. The stocks removed from the sample are from firms that were not listed during six consecutive months or whose market capitalization did not change in the same period. Outlier data were removed from the sample to capture in better way the general performance of the stock market.

The objective of the extended version is to create a more robust econometric model than the traditional model. It is expected that such estimations can be helpful to investors to make better decisions when they try to predict performance in the stock market.

An extended version of the FFM can be helpful to investors to make better decisions when they try to predict performance in the stock market.

To the best of our knowledge there are no more studies in the literature of the Mexican financial market that apply the same methodology.

The purpose of this paper is to reinvestigate the performance of common stock returns with respect to two popularly known firm level characteristics: size and book‐to‐market ratio.

All of New York Stock Exchange, American Stock Exchange, and National Association of Securities Dealers Automated Quotations stocks between July 1926 and June 2007 are used, and divided into various size and book‐to‐market equity groups. The extension of the various versions of the simple Fama‐French model is implemented.

From the findings, it is inferred that: two risk factors based on the mimicking return for the size and book‐to‐market ratio play a significant role in capturing strong variation in stock returns; and volatility persistence can significantly improve the common risk factors' impact in explaining the time series variation in size and book‐to‐market sorted portfolios.

In some sense, the model is based on only two firm level variables. In reality there exists plenty of other sources of average return anomalies. For a clearer understanding, an integration of various firm level characteristics would be an interesting issue to explore. A general equilibrium model that incorporates volatility exposure in a Fama‐French framework would be a challenging task as well.

The approach will help scholars and investment professionals make robust quantification of risk and average returns with respect to various measures of fundamental value.

The patterns in the monthly and yearly average excess returns with respect to two firm level characteristics, which documented are consistent with earlier studies. Even though the important role of firm level characteristics on the average‐return anomalies of common stocks is widely known, the approach is the very first that extends its support with respect to volatility models.

Despite the extensive academic interest in social media sentiment for financial fields, multimodal data in the stock market has been neglected. The purpose of this paper is to explore the influence of multimodal social media data on stock performance, and investigate the underlying mechanism of two forms of social media data, i.e. text and pictures.

This research employs panel vector autoregressive models to quantify the effect of the sentiment derived from two modalities in social media, i.e. text information and picture information. Through the models, the authors examine the short-term and long-term associations between social media sentiment and stock performance, measured by three metrics. Specifically, the authors design an enhanced sentiment analysis method, integrating random walk and word embeddings through Global Vectors for Word Representation (GloVe), to construct a domain-specific lexicon and apply it to textual sentiment analysis. Secondly, the authors exploit a deep learning framework based on convolutional neural networks to analyze the sentiment in picture data.

The empirical results derived from vector autoregressive models reveal that both measures of the sentiment extracted from textual information and pictorial information in social media are significant leading indicators of stock performance. Moreover, pictorial information and textual information have similar relationships with stock performance.

To the best of the authors’ knowledge, this is the first study that incorporates multimodal social media data for sentiment analysis, which is valuable in understanding pictures of social media data. The study offers significant implications for researchers and practitioners. This research informs researchers on the attention of multimodal social media data. The study’s findings provide some managerial recommendations, e.g. watching not only words but also pictures in social media.

As investors' expectations shift toward corporate sustainability, many corporations have jumped on the bandwagon of being “green” by issuing green bonds. However, as a recent green financing tool, little attention has been paid on the value that green bonds actually deliver. This causes the problem of greenwashing, in which firms pretend to be environmentally responsible when in reality they are not. This study therefore aims to explore green bonds' impact on issuers' corporate environmental and financial performance.

The sample is collected from among the green bond and conventional bond issues between 2015 and 2019 issued by corporations from various countries. Using the propensity score matching (PSM) and then difference-in-difference (DiD) approaches, two sub-groups (green bond and conventional bond issuers) were generated for comparison. Changes in environmental and financial performance over time between the sub-groups are then examined.

The overall results show that green bonds are effective in improving environmental performance, but only when they are certified by third parties. Additionally, green bonds do not have an impact on financial performance. The findings imply that green bonds' dependency on external certification may be a consequence of an underdeveloped green bond market, where weak governance still dominates the green bond market. Because of this, corporations tend to take advantage of green finance's growing popularity, causing the greenwashing problem.

Green bonds are an extremely new area of research. Few research studies focus on the effectiveness of green bonds in impacting corporate financial and environmental performance. Therefore, this study strives to fill this research gap. It sheds light on the effectiveness of green bonds in supporting the development of green projects and provides a reference point for decision-making in strengthening transparency and accountability in environmental disclosure and helps regulating authorities develop tighter regulatory controls.

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.

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 study aims to document the time varying risk premia for market, size, value and momentum factors for an emerging market using a sophisticated conditional asset pricing model. The focus of this study is Turkish stock market denominated in local currency with its peculiar risk premia.

The authors employ Gagliardini et al.'s (2016) econometric method that uses cross-sectional and time series information simultaneously to infer the path of risk premia from individual stocks.

Using this methodology, the authors assess several conditioning information and conclude that local dividend yield, inflation and exchange rates have the most explanatory power. The authors document the time varying risk premia in Turkey over three decades.

Existing studies on dynamic estimation of risk premia lack a consensus as to which state variables should be included and to what extent they impact the magnitude of the premium. The authors extend the conditioning information set beyond the ones existing in the literature to determine variables that are specifically important for an emerging market.

The authors construct asset portfolios comprising small-sized companies and value stocks that provide with higher returns for the UK market based on a three-factor model with incorporated behavioural features. The authors were able to demonstrate that value factor model is vulnerable to behavioural patterns, especially corporate fraud. In all of the above, the authors utilised a new proportional sorting methodology against the value ranking approach, commonly employed in empirical studies. Strong evidence is observed that portfolio performance based on various syntheses of allocated assets reveals counter-intuitive results related to the BE/ME, namely, that expected returns based on size and BE/ME produce significant errors and small firms retain consistently better returns. The reason might be the unusual accounting techniques many firms follow to receive extended capital after management decisions. The paper aims to discuss these issues.

The authors were able to demonstrate that value factor model is vulnerable to behavioural patterns, especially corporate fraud. In all of the above, authors utilised a new proportional sorting methodology against the value ranking approach, commonly employed in empirical studies. Strong evidence is observed that portfolio performance based on various syntheses of allocated assets reveals counter-intuitive results related to the BE/ME, namely, that expected returns based on size and BE/ME produce significant errors and small firms retain consistently better returns. The reason might be the unusual accounting techniques many firms follow to receive extended capital after management decisions.

Value factor model is vulnerable to behavioural patterns, especially corporate fraud. In all of the above, the authors utilised a new proportional sorting methodology against the value ranking approach, commonly employed in empirical studies. Strong evidence is observed that portfolio performance based on various syntheses of allocated assets reveals counter-intuitive results related to the BE/ME, namely, that expected returns based on size and BE/ME produce significant errors and small firms retain consistently better returns. The reason might be the unusual accounting techniques many firms follow to receive extended capital after management decisions. Overall, asset pricing models with embedded risk factors which entail either shares or dividends are logically circular behavioural simultaneities, thus invalid when tested and estimated by statistical methods as an outcome of the EMH.

In distinctive contrast to the recent literature, the authors show that the returns from a size factor model of small stocks tend to outperform big stocks especially in crisis periods. Moreover, the authors were able to demonstrate that value factor model is vulnerable to behavioural patterns, especially corporate fraud. In all of the above, the authors utilised a new proportional sorting methodology against the value ranking approach, commonly employed in empirical studies. Strong evidence is observed that portfolio performance based on various syntheses of allocated assets reveals counter-intuitive results related to the BE/ME, namely, that expected returns based on size and BE/ME produce significant errors and small firms retain consistently better returns. The reason might be the unusual accounting techniques many firms follow to receive extended capital after management decisions. Overall, asset pricing models with embedded risk factors which entail either shares or dividends are logically circular behavioural simultaneities, thus invalid when tested and estimated by statistical methods as an outcome of the EMH.

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.