Purpose This study revisits the relationship between environmental, social and governance (ESG) activities and firm performance. More importantly, it tests whether this relationship is moderated by critical yet underexplored factors such as stakeholder engagement, financial constraints, and religiosity. Design/methodology/approach A wide range of estimation techniques, including pooled ordinary least squares (OLS), fixed effects, system generalized method of moments (GMM) and propensity score matching-difference-in-differences (PSM-DiD), are employed to investigate such issues in a large sample of firms from 31 countries. Findings ESG performance has a positive and significant impact on firm performance. While stakeholder engagement positively moderates this relationship, financial constraints and religiosity negatively moderate it. Interestingly, this positive linkage is driven by environmental and social performance rather than governance performance. Practical implications Firms should proactively engage in ESG initiatives and consider the intervening influences of stakeholder engagement, financial constraints and religiosity in making decisions to invest in ESG activities. Furthermore, our findings can help policymakers understand the financial consequences of ESG practices, which can be helpful in designing new policies to further promote corporate engagement in ESG practices. Originality/value First, our research findings help reconcile the long-standing debate about the value impact of ESG. Second, our paper investigates relatively new aspects of the ESG-firm performance relationship. Third, our study offers more insight into the ESG literature by showing that not all ESG dimensions equally impact firm performance.

Purpose By extending Edmans et al.’s (2021) music sentiment measures to the Vietnam market, the authors aim to investigate the impacts of music sentiment on stock market returns and volatility. Design/methodology/approach The authors adopted Edmans et al.’s (2021) music-based sentiment to proxy for investor mood. The current study uses linear regression analysis. Findings The authors find that music sentiment is significantly and positively related to both stock returns and stock market volatility. The authors also show that music sentiment has a contagious effect: Global music sentiment and those in the United States, France and Hong Kong are significant drivers of the Vietnamese stock market. The authors also examine the effect on different industry returns and find that returns on stocks of firms in the communication services, consumer discretionary, consumer staples, energy, financials, healthcare, real-estate, information technology and utility sectors are significantly related to music sentiment. In addition to valence, the authors find that other Spotify audio features can be used to quantify music sentiment. Originality/value This study contributes to the behavioral finance literature that focuses on investor sentiment. The authors address this topic in Vietnam using high-frequency data.

Purpose This study aims to investigate the moderating role of investor demand on the relationship between the investors' divergence of beliefs and the first-day initial public offering (IPO) return. Design/methodology/approach The study sample covers the period from 2010 to 2019 and consists of 117 IPOs that are priced using the fixed price and listed on the Malaysian stock exchange (Bursa Malaysia). This study employed both the ordinary least square (OLS) and the quantile regression (QR) methods. Findings Investor demand, proxied by the over-subscription ratio (OSR), plays a moderating role in increasing the effect of investors' divergence of beliefs on initial return, and the moderation effects vary across the quantile of initial return. Pure moderation effects are observed at the bottom and top quantiles, suggesting that investor demand is necessary for divergence of beliefs to influence IPO initial return. However, at the middle quantile of initial return, investor demand is a quasi-moderator. That is, the OSR not only moderates the relationship between the divergence of beliefs and initial return but also has a positive effect on the initial return. Practical implications Investors' excessive demand for an IPO issue exacerbates the IPO under-pricing issue induced by a divergence of beliefs amongst investors, thus rendering greater equity market inefficiency. Originality/value To the authors' knowledge, this study is amongst the first to empirically investigate the moderating role of investor demand on the investors' divergence of beliefs and IPO initial return relationship.

Purpose It has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets. Design/methodology/approach Two measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model. Findings The authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles. Originality/value This is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

Purpose Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction. Design/methodology/approach This study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity. Findings This study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics. Research limitations/implications The major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency. Practical implications Finally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020). Originality/value This study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.