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This study explores the variance in investor responses to the corporate social responsibility (CSR) performance of firms, as influenced by information sources and investor types.

This study applies a short-term event study and cross-sectional analysis with unique CSR datasets obtained from newspaper articles and the Dow Jones Sustainability Index.

Investor reactions are significantly shaped by their sources of information. Individual investors are found to predominantly respond to accessible news announcements, whereas institutional investors show heightened sensitivity to adverse news from both scrutinized sources. Foreign investors, mirroring institutional investors' patterns, uniquely react positively to index additions.

Investors’ assessment of CSR activities varies due to the differing sources of information obtained; further, it is affected by the type of investor.

The findings guide public relation managers in strategizing CSR communication toward diverse investor types. This includes recommending targeted approaches for Japanese individual investors through newspapers and TV, exercising caution in disseminating adverse news to Japanese institutions, and promoting and justifying CSR actions to foreign investors. It underscores the need for a strategic investor relations frameworks that considers accessibility, literacy, and investors' interests.

This study examines the relationship between sources of information for CSR activities and investors’ responses, an area under-represented in the literature. The author uses CSR announcement data, collected from newspapers to make the results more accurate and relevant.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context.

The event study technique was used on data from 518 events that occurred in a 30-year period (1987–2016), comprising 167 stock splits and 351 reverse stock splits.

The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market.

This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct “zero” dates that of the ordinary general meeting that approved the share alteration and the “ex” date of the alteration, when the shares were effectively traded, reverse split or split.

The purpose of this article is to present the results of empirical research concerning the identification of the impact of the transfer of companies from the alternative market to the regulated market of the Warsaw Stock Exchange on their operating and net performance.

The study was conducted based on the empirical data of the companies that changed the listing place on the Warsaw Stock Exchange. Data regarding the years before the transfer were collected from the prospectuses of companies prepared mandatorily in connection with the transition to the regulated market. Data regarding the years of the event and subsequent years were obtained from companies' annual reports. As in other studies in the analysis, the operational metrics were used (operating return on sale, operating return on assets, total asset turnover), which was further extended to net profitability ratios (net return on ale, net return on asset, net return on equity). The significance analysis was based on the Student's t-test and Wilcoxon’s test.

The results show that before the transfer from the alternative market to the regulated market, companies improved financial performance. As a result of the change of listing venues, the results already collapsed in the year of the event. The downward trend continued in the following two years, with a noticeable improvement in the third year after the transfer.

The literature lacks such studies based on the Polish market. To the best knowledge of the author, this is one of the first studies in Poland showing the changes in operating and net performance of companies changing the stock listing venues. The research is based on a large group including all companies that have changed listing venues since the beginning of the alternative market in Poland. The article presents an original empirical result that can be used both by managers and investors in their decisions.

The study aims to determine whether a relationship exists between the potential significance of corporate controversies for stakeholders and how organisations respond to them in their annual and sustainability reports.

This paper employs content analysis on annual and sustainability reports of 48 listed companies from the Refinitiv database. The logit regression was used to estimate the model.

The study revealed that the main factors increasing the probability of a controversial issue being addressed in a corporate report are the controversy’s potential significance, companies’ financial performance and lawsuits.

Our study has three major limitations. These are a relatively small sample of companies and reports, focusing on disclosures made in corporate reports and omitting other channels of communication, for example, social media, and a certain amount of subjectivity in the process of coding information.

Former studies show that corporations face a serious risk of their hypocritical strategies becoming too evident for stakeholder groups. Our findings suggest that the risk is already materialising and may undermine the idea of CSR and sustainability reporting.

Our research focuses on high-profile adverse incidents widely reported in the media, the omission of which from corporate reports seems to constitute a particular case of organised hypocrite. It also demonstrates that companies use an impression management strategy to defuse adverse publicity and that major controversies cause minor ones to be omitted from their reports.

The authors’ goal is to provide an overview and historical context for the various alternatives to the efficient market hypothesis (EMH) that have emerged over time. The authors found eight current alternatives that have emerged to address the EMH's flaws. Each of the proposed alternatives improves some of the assumptions made by the EMH, such as investor homogeneity, the immediate incorporation of information into asset values and the inadequacy of rationality to explain asset prices.

To come up with the list of studies relevant to this review article, the authors used three databases, namely Scopus, Web of Science and Google Scholar. The first two were mostly used to get peer-reviewed articles while Google Scholar was used to extract articles that are still work in progress. The following words were used as the search queries; “efficient market hypothesis” and “alternatives to the efficient market hypothesis”.

The alternatives to the EMH presented in this article demonstrate that market efficiency is a dynamic concept that can be best understood with a multidisciplinary approach. To better comprehend how financial markets work, it is crucial to draw on concepts, theories and ideas from a variety of disciplines, including physics, economics, anthropology, sociology and others.

The authors comprehensively summarise the current state of the behavioural finance literature on alternatives to the EMH.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

This research mainly intends to ascertain the stimulus of investor investment tendencies on the amount of capital investment in the share market.

Utilizing a sample of 477 individual investors who actively trade on the Bangladesh capital market, this empirical study was conducted. The objective of this examination is to ascertain the investment trading behavior of retail investors in the Bangladesh capital market using multiple regression, hypothesis testing and correlation analysis.

The coefficients of market categories, preferred share price ranges and investment source reveal negative predictor correlations; all predictors are statistically significant, with the exception of investment source. Positive predictive correlations exist between investor category, financial literacy degree, investment duration, emotional tolerance level, risk consideration, investment monitoring activities, internal sentiment and correct investment selection. Except for risk consideration and investment monitoring activities, all components have statistically significant predictions. The quantity of capital invested in the stock market is heavily influenced by the investment duration, preferred share price ranges, investor type, emotional toleration level and decision-making accuracy level.

This investigation was conducted exclusively with Bangladeshi individual stockholders. Therefore, the existing study can be extended to institutional investors and conceivably to other divisions. It is possible to conduct this similar study internationally. And the query can enlarge with more sample size and use a more sophisticated econometric model. Despite that the outcomes of this study help the regulatory authorities to arrange more informative seminars and consciousness programs.

The conclusions have practical implications since they empower investors to modify their portfolios based on elements including share price ranges, investment horizons and emotional stability. To improve chances of success and reach financial objectives, they stress the significance of bettering financial understanding, active monitoring and risk analysis. Results can also be enhanced by distributing ownership over a number of market sectors and price points. The results highlight the value of patience and giving potential returns enough time.

This study on the trading behavior of investors in Bangladesh is unique and based on field study, and the findings of this study will deliver information to the stakeholders of the capital market regarding the investors’ trading behavior belonging to different categories, financial literacy level, investment duration, emotional tolerance level and internal feeling.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

National cultures significantly explain cross-country differences in the relation between asset growth and stock returns. Motivated by the notion that managers in individualistic and low uncertainty-avoiding cultures have a higher tendency to overinvest, this study aims to show that the negative relation between asset growth and stock returns is stronger in countries with such cultural features. Once the researchers control for cultural dimensions, proxies associated with the q-theory, limits-to-arbitrage, corporate governance, investor protection and accounting quality provide no incremental power for the relation between asset growth and stock returns across countries. Evidence of this study highlights the importance of the overinvestment hypothesis in explaining the asset growth anomaly around the world.