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This study investigates the influence of nonfinancial 8-K disclosures released during the earnings announcement window on the abnormal trading activities of individual investors.

We employ regression analysis in this empirical study to examine the impact of nonfinancial 8-K filings on individual investors' abnormal trading activities.

Our results reveal that individual investors exhibit higher levels of abnormal trading activities when firms release nonfinancial 8-Ks during the (0,1) window of earnings announcements. This effect is observed for both buyer-initiated and seller-initiated transactions and is particularly pronounced for firms reporting an operating loss. Negative sentiment in 8-Ks significantly intensifies such effect. Additionally, we find that buy-sell consensus increases significantly with concurrent nonfinancial 8-Ks. This suggests that 8-Ks may reduce information noise, leading individuals to trade with greater conviction.

Our study examines the joint influence of nonfinancial 8-Ks and earnings announcements on individual investors' trading activities, thereby providing a novel perspective on the mechanisms through which 8-K filings affect individual investors' trading behaviors.

This study examines the influence of the ownership structure of banks on investors' behavior by dissecting the investors' response to news regarding performance indicators in private and government-owned banks.

The event study methodology is used for the analysis. The data for 35 banks (out of 38), listed on the National Stock Exchange (NSE) for a duration of 230 months (January 2001 to February 2020) is collected. A set of cross-sectional regression analyses is done to identify variables influencing the returns under differential circumstances.

Private banks seem to display a sharper response to negative changes in earnings, while government-owned banks show a more robust reaction to a positive change. The contrast is seen in the variables, having a bearing on the abnormal returns After controlling for a set of factors, the regression analysis shows the ownership structure may not matter on abnormal returns (on event day), the factors such as a change in quarterly earnings, firm-size and three-year average-sales growth influence the positive and negative changes in abnormal returns of government banks, and predictability for private banks is found to be poor regarding selected indicators.

The study evaluates the role of ownership structure on the heterogeneity in investors' responses to the financial performance of banks, thereby assisting in designing strategies to ensure the optimal outcome around the quarterly earnings announcements.

This study aims to investigate whether mandated disclosure of engagement quality review hours provides new information that affects investors’ decision-making.

In 2014, Korean authorities mandated that audit engagement quality review hours must be disclosed in their audit reports. Using this unique field setting in Korea, this study presents empirical evidence of the policy initiative’s effect on earnings reliability by examining both pre- and post-implementation periods.

Following the initial disclosure of engagement quality review hours in 2014, the authors observe that the capital market’s valuation of quarterly earnings surprises, measured by earnings response coefficients (ERCs), was significantly lower for firms with high levels of abnormal engagement quality review hours than for other firms. This paper also finds that the observed association between engagement quality review hours and ERCs in the postregulation period hinges on the probability of earnings management, proxied by discretionary accruals and just meeting or beating analyst earnings forecast.

This paper suggests that the policy mandating disclosure of engagement quality review hours provides original information that the market considers relevant for appraising the reliability of reported earnings.

This study aims to provide insights into different aspects of the extant literature on the effects of dividend announcements. Along with other outputs of a bibliometric study, this study provides deeper insights into the concentration of the extant literature and suggest future research agendas.

This study uses the bibliometric, network and content analysis of the dividend announcement literature indexed in Scopus. This study presents the temporal analysis, the network of authors, countries, author citations and the co-occurrence of author keywords. This study provides the concentration of the extant literature in three clusters and unearth some key future research areas. This study uses the latent Dirichlet allocation method for robustness.

A total of 54 documents examining the US sample have received 1,804 citations. Interestingly, the first article on emerging markets was published in 2002, when at least 34 articles on developed markets had already been published from 1982 to 2001. The content analysis of top-cited literature unveils diverse insights into dividend announcements’ effects on financial markets. Contagion effects negatively impact non-announcing banks, particularly larger ones. Dividend maintenance affects stock market momentum, influencing loser returns. While current dividend/earnings news may not predict future company performance, information content dominates bond market reactions to post-dividend announcements. Concomitantly, while financially constrained firms exhibit short-term gains but worse long-term performance following dividend increases, larger stock dividends send stronger market signals in China.

This study significantly contributes to the bibliometric and content analysis literature by analyzing the sample documents based on the sample examined. To the best of the authors’ knowledge, no previous bibliometric study in this domain has been conducted to explore the markets (developed and emerging) to which the samples examined belong and the quality of publications from developed and emerging markets.

COVID-19 and its accompanying lockdowns were arguably the most traumatic events of our times. This paper investigates the impact of COVID-19 on market efficiency.

I analyze all publicly traded U.S. equities for 2014–September 2021, using intraday data from TAQ, TRACE, I/B/E/S and Capital IQ and daily data from CRSP, Thomson Reuters, Compustat, CRSP-Compustat Merged Database and FRED, using a controlled contrast between absolute abnormal returns for relevant halfhours versus absolute abnormal returns in control halfhours, measured by the negative of the coefficient of the fixed effect of the interaction between the indicator variable, and as the case may be, ticker and/or time period of interest, in the regression of halfhour-level absolute abnormal returns on tickers, months and interactions.

Using two separate objective, systematic, independent and ordinal per se measures of market efficiency based upon market reactions separately to key developments and earnings announcements, I find that U.S. equities markets were statistically and economically significantly less efficient during the first two-three months of the COVID-19 lockdowns.

Efficient capital markets provide substantial social benefits and are a sine qua non for the democratization of markets and the protection of investors, and constitute a critical mission of regulatory bodies such as the U.S. Securities and Exchange Commission (SEC) and the U.S. Financial Industry Regulatory Authority (FINRA).

The impact on market efficiency provides one critical input into the social cost-benefit analysis of public health policy and that of government interventions in general.

There has been no previous work done on the systematic and objective characterization of the impact of COVID-19 and associated lockdowns on market efficiency.

Both the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.

Using approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.

Consistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.

This study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

This research employs a behavioural approach to investigate the determinants of CEO disclosure tone inconsistency. By examining CEO characteristics and psychological attributes, the study aims to unravel the complexities underlying tone variations in Management Discussion and Analysis (MD&A) reports. Through this exploration, the research seeks to contribute to understanding ethical considerations in corporate communications and provide insights into the nuanced interplay between personal, job-related and psychological factors influencing CEO disclosure tone.

The study utilises a dataset comprising 1,411 MD&A reports from 143 companies listed on the Tehran Stock Exchange between 2012 and 2021. Multiple regression analyses with year- and industry-fixed effects are employed to examine the relationships between CEO gender, tenure, duality, ability and psychological attributes such as narcissism, myopia, overconfidence and tone inconsistency. Data analysis involves MAXQDA software for analysing MD&A reports and Rahavard Novin software for document analysis, supplemented by audited financial statements.

The findings reveal significant relationships between CEO characteristics, psychological attributes and tone inconsistency. Female CEOs exhibit reduced tone inconsistency, contrasting with previous research trends. CEO tenure correlates negatively with tone inconsistency, whereas CEO ability shows a positive correlation, indicating a nuanced relationship with performance. However, CEO duality does not exhibit a significant association. Psychological attributes such as narcissism and myopia are positively associated with tone inconsistency, while no substantial connection is found with managerial overconfidence.

This research contributes to the inaugural exploration of CEO disclosure tone inconsistency through a behavioural lens, advancing measurement precision in the field. By delving into CEO characteristics and psychological attributes, the study offers unique insights into the roots of tone inconsistency. Applying comprehensive lexicon and phraseology enriches the methodological approach, fostering dialogue among diverse stakeholders and adding distinct perspectives to the discourse on ethical issues in business. Through its meticulous examination of behavioural underpinnings, this study becomes a catalyst for reflection, dialogue and progress in corporate communications and ethical considerations.

The study aims to investigate market reactions associated with the JUST 100 rankings published by JUST Capital, a non-profit organization, as well as differences in financial reporting quality and performance between selected firms and their industry peers.

This study uses a sample of 431 firms selected as the 100 America’s Most Just Companies between 2016 and 2020 by JUST Capital. This study performs both an event study to determine whether the rankings are useful to investors and cross-sectional regression analyses on the characteristics of selected firms compared to their peers.

This study finds that investors react positively to selected firms around the time of the release of the JUST 100 rankings, suggesting that the rankings are decision-useful. This study also finds that selected firms exhibit higher accounting quality and financial performance than their peers.

Rankings may not be free from bias because of JUST Capital’s ownership of an exchange-traded fund.

The findings validate the rankings as well as the methodology used by JUST Capital, as they show market participants value firms that engage in socially responsible actions through their commitment to positively impact five key stakeholder groups: employees, customers, communities, environment and shareholders.

To the best of the authors’ knowledge, this is the first study that shows the importance of the JUST 100 rankings for investment decisions. Considering the growing push for companies to disclose environmental, social and governance (ESG) activities, this study provides evidence to support ESG disclosure regulations.

This study examines whether investors value the timing and/or information of mandatory disclosures in a unique research setting of listed companies in Thailand.

The authors adopt an event-study based approach. Abnormal stock returns are calculated using an OLS market model to measure market reactions to three types of mandatory reports issued by listed Thai firms: financial statements, Form 56-1 and Form 56-2. These reports are released sequentially but contain overlapping information content. Multivariate regression models are employed to examine the market reactions to these regulatory reports and explore the characteristics of firms that affect the market response.

The stock market reacts differentially to these reports. The financial statements, which are filed the earliest and are the most concise, prompt the strongest reaction. Investors similarly react significantly to Form 56-1 and Form 56-2, although Form 56-2 provides additional information beyond Form 56-1. The market reactions to small firms are stronger. Collectively, equity investors focus on the timeliness of disclosures rather than the information disclosed in the mandatory reports.

The evidence provides support for ongoing regulatory initiatives aimed at improving the timeliness of mandatory disclosures in emerging economies.

Prior studies on disclosure regulation investigate either the effect of information content or the timing of mandatory disclosures in isolation. The authors differentiate the effect of information content from disclosure timing and extend the literature by suggesting that investors incrementally value timeliness of disclosures. Investors perceive the benefit of the timely release of quantitative information compared to subsequent narrative disclosures. Between Form 56-1 and Form 56-2, the earlier release of the narrative non-financial information is incrementally traded into share prices.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.