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Although the value effect is comprehensively investigated in developed markets, the number of studies examining the Vietnamese stock market is limited. Hence, the first aim of this research is to provide empirical evidence regarding returns on value and growth stocks in Vietnam. The second aim is to explain abnormal returns on Vietnamese growth and value stocks using both risk-based and behavioral points of view.

From the risk-based explanation, the Capital Asset Pricing Model (CAPM), Fama–French three- and five-factor models are estimated. From the behavioral explanation, to construct the mispricing factor, this paper relies on the method of Rhodes-Kropf et al. (2005), one of the most popular mispricing estimations in the financial literature with numerous citations (Jaffe et al., 2020).

While the CAPM and Fama–French multifactor models cannot capture returns on growth and value stocks, a three-factor model with the mispricing factor has done an excellent job in explaining their returns. Three out of four Fama–French mimic factors do not contain additional information on expected returns. Their risk premiums are also statistically insignificant according to the Fama–MacBeth second-stage regression. By contrast, both robustness tests prove the explanatory power of a three-factor model with mispricing. Taken together, mispricing plays an essential role in explaining returns on Vietnamese growth and value stocks, consistent with the behavioral point of view.

There are several value-enhancing aspects in the field of market finance. First, this paper contributes to the literature of value effect in emerging markets. While the evidence of value effect is obvious in numerous developed as well as international markets, both growth and value effects are discovered in Vietnam. Second, the explanatory power of Fama–French multifactor models is evaluated in the Vietnamese context. Finally, to the best of the author's knowledge, this is the first paper that incorporates the mispricing estimation of Rhodes-Kropf et al. (2005) into the asset pricing model in Vietnam.

Investors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.

On 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.

Our empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.

Individuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.

The study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

We form portfolios based on return and liquidity and examine the effects of liquidity and other risk factors on asset pricing in the Chinese stock market. Our results show that the past loser-and-illiquid stock portfolios tend to outperform the past winner-and-liquid stock portfolios in the 1–12 months holding period. The excess return is significantly associated with the market-wide liquidity factor even when we control the three Fama-French and momentum factors. Cross-sectionally, the liquidity beta significantly affects the excess return even with control of other risk betas and other traditional liquidity proxies.

This study aims to examine the relationship between investor gambling preferences and stock returns, using data for all firms listed in Shanghai A-share market during 2016 and 2021.

This study employs price and trading volume data to capture the behavioral characteristics and gambling preferences of investors. Using the Fama-French three-factor and five-factor models to estimate benchmark returns, this study investigates whether investing in gambling stocks can yield positive excess returns.

The study reveals that stocks identified as gambling stocks generate high returns in the month they are identified as such but subsequently experience a significant drop in excess returns compared to non-gambling stocks over the following one to six months. These results are found to be consistent across different methods used to classify gambling stocks and across various industry sectors.

This research provides insights into the risk-return tradeoff of different stock types and the factors that fuel irrational investment behavior. This research underscores the importance of considering the behavioral elements of investment, particularly in emerging markets where individual investors have a significant impact.

This study advises investors to avoid adopting a gambler or speculative mindset and instead make well-informed and calculated investment decisions that are in line with investors financial objectives and risk appetite. This approach can help create a more stable and sustainable financial market.

This study provides new evidence on the relationship between gambling preferences and future stock returns in financial markets and sheds new light on the important role of irrational factors in investment decisions.

Considering the standards developed by the Sustainability Accounting Standards Board (SASB), this study aims to examine whether the link between material sustainability and financial performance depends on the extent to which the company is oriented toward stakeholders.

To test the predictions, 13,942 firm-year observations from 43 different countries are used, covering the period from 2010 to 2019. Using a hand-mapping approach to match the indicators suggested by the SASB with those of the ASSET4, the authors realize that there are 170 material sustainability indicators among 466 indicators of the ASSET4. The authors use three different methods to verify if the materiality matters, including the alphas obtained from the Fama and French factor models, comparing the average abnormal returns of the portfolios and the bootstrapped Cramer technique.

The findings show that companies investing in material sustainability activities perform better than those investing in immaterial activities. Also, consistent with the theoretical foundations, the authors find that the effect of investing in material sustainability activities is more pronounced in stakeholder-oriented countries than that in shareholder-oriented countries. The results are robust to a battery of sensitivity tests.

Owing to COVID-19 in late 2019, data from 2020 to 2022 have not been used to obtain reliable results.

The results obtained in the current research provide valuable guidance for investors to make investments considering the degree of materiality of sustainability activities in different industries. It also helps managers to increase the company’s financial performance, make efficient decisions related to investment in sustainability activities and find investment strategies on the material sustainability issues in their industries.

This study provides a clearer understanding of investment in sustainability activities in different industries by separating material and immaterial sustainability activities in stakeholder and shareholder-oriented countries, and the results obtained can change the perspective of investors and company managers regarding investing in such activities in different countries. Investing in more materiality sustainability activities than the immateriality dimension can be new opportunities for companies to achieve predetermined goals, help retain and attract business partners or be a source of innovation for new product lines or services. Internal morale and employee engagement may increase while increasing productivity and firm performance. This discussion opens the way for future research.

This study provides insight into the effect of investing in material and immaterial sustainability activities in different industries on the company’s performance in shareholder and stakeholder-oriented countries.

Environmental, social, and governance (ESG) issues have recently received much attention. This research investigates the daily performance of socially responsible investment (SRI). To do that, the authors construct portfolios consisting of the SRI, non-SRI, and matched non-SRI. The portfolios can be compared with the market benchmark based on α adjusted asset pricing models. Due to using high-frequency data, the authors use ARCH/GARCH to deal with time-varying volatility. Moreover, the authors also utilized Fama–MacBeth pooled regression to confront the SRI stocks and the non-SRI counterpart. In sum, the findings of this study confirm the superior performance of the value-weighted (VW) SRI portfolio against the market. On a head-to-head basis, the SRI yields a higher return than the non-SRI. The results are robust in the quarterly analysis. It is essential for investors that put their money in socially responsible (SR) portfolios to either promote sustainable development or chase a return on it.

Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.

The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.

The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).

The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.

Prior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches.

We examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill.

We find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used.

This study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology.

In order to provide an updated view on the drivers of German stock returns, the authors evaluate the relative performance of nine competing neoclassical asset pricing models in the German stock market between November 1991 and December 2021.

The authors conduct asymptotically valid tests of model comparison when the extent of model mispricing is gauged by the squared Sharpe ratio improvement measure of Barillas et al. (2020).

The study finds that the Fama and French six-factor model with both traditional and updated value factors emerges as the dominant model.

The authors shed new light on the drivers of German stock returns through an updated and extended period of analysis, wider range of potential models and utilization of valid asymptotic tests of model comparison when models are nonnested (Barillas et al., 2020).