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This paper has analysed the impact of cultural dimensions, investor sentiment and uncertainty on bank stock returns. Also, the study examined the influences of the interaction between cultural dimensions and individual (private) sentiment (investor sentiment).

To meet the study's objectives, a two-step generalised method of moments estimator was applied to the study sample, which included 105 banks in the nine Middle East and North African region countries between 2010 and 2020.

The cultural dimensions of individualism and masculinity were found to have a positive and significant effect on banks' buy and hold stock return (BUH). At the same time, power distance and uncertainty avoidance were discovered to have negative effects. Besides, the findings revealed that the interactions of power distance, individual sentiment and uncertainty avoidance had positive and significant relationships with banks' BUH. However, individualism, individual sentiment and masculinity had inverse relationships with banks' BUH. Furthermore, the findings revealed that investor sentiment positively influenced banks' BUH. Finally, uncertainty influenced banks' BUH stock returns positively.

Important implications for participants in the financial sector and governments may be learnt from this study's conclusions. Due to cultural biases, this study's findings suggested that investors overreact in the stock market.

Additionally, this research comprises one of the few studies that have overviewed the link between classical and behavioural finance in MENA countries with distinctive cultural characteristics.

The dissemination of misleading and false information through media can jeopardize a company’s reputation, thus posing a threat to its stock and performance. Institutional investors are known to influence capital markets. Therefore, this paper investigates whether institutional investors engage in shaping the media sentiment stock nexus, stabilize company stocks and enhance performance.

We first investigate the effect of media sentiment on market reactions by using panel regression models. To examine the role of institutional investors, we design a quasi-experiment by exploiting the Financial Crisis of 2008 and go further by examining the heterogeneity across levels of institutional ownership. Due to risk-averse, investors may respond asymmetrically to pessimistic and positive sentiment. Accordingly, we split the sample into two sub-types, good news and bad news, based on keywords representing positive or negative content.

We find supportive evidence that institutional investors have impacts on how the markets react to media news, and the impacts are heterogeneous in the face of bad and good news. We conjecture that institutional investors act as a stabilizer of stock prices through media sentiment management.

This paper confirms the distinctive effects of institutional investors on capital markets, and uncovers the behind-the-scenes intervention and possible causal link running from institutional investors to media sentiment management. It contributes to the broad field of institutional investors' behavior, media news involvement in capital markets and market efficiency.

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 systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

The study examines the IPO resilience grounded on the firm’s intrinsic factors.

We examine the association of IPO performance and post-listing firm’s performance with issuers' pre-listing financial and qualitative traits using panel data regression.

IPOs floated in the Indian market from July 2009 to March 31, 2022, evince the notable influence of issuers' pre-IPO fundamentals and legitimacy traits on IPO returns and post-listing earning power. Where the pandemic’s favorable impact is discerned on the post-listing year earning power of the issuer firms, the loss-making issuers appear to be adversely affected by the Covid disruption. Perhaps, the successful listing equipped the issuers with the financial flexibility to combat market challenges vis-à-vis failed issuers deprived of desired IPO proceeds.

High initial returns followed by a declining pattern substantiate the retail investors to be less informed vis-à-vis initial investors, valuers and underwriters, who exit post-listing after profit booking. Investing in the shares of the newly listed ventures post-listing in the secondary market can shield retail investors from the uncertainty losses of being uninformed. The IPO market needs stringent regulations ensuring the verification of the listing valuation, the firm’s credentials and the intent of utilizing IPO proceeds. Healthy development of the IPO market merits reconsidering the listing of ventures with weak fundamentals suspected to withstand the market challenges.

Given the tremendous rise in the new firm venturing into the primary market and the spike in IPOs countering the losses immediately post-opening, the study examines the loss-making and young firms IPOs separately, adding novelty to the study.

As cross-cultural mergers and acquisitions (M&A) have learning effects on organisations, assessing their impacts on corporate performance is crucial. This study aims to explore the impact of inter-firm cultural differences on long-term post-M&A stock market performance.

The authors select domestic M&A transactions of Chinese listed companies during 2010–2021 as the sample. Then, the authors use the partial least squares structural equation model (PLS-SEM) to construct the latent variable of cultural differences in four dimensions to explore long-term stock market performance.

Cultural differences first positively and then negatively impact post-M&A performance. Three transmissions mechanisms are identified: investor sentiment, takeover premiums and information disclosure quality. Further analysis reveals that acquirer stock performance improves with higher analyst coverage and non-local shareholders but worsens if there are business affiliations between the acquirer and target firms.

This study can help optimise information disclosure systems in M&A transactions for regulatory authorities and aid investors’ understanding of post-M&A performance changes. Furthermore, it can improve acquirers’ understanding of the risks and opportunities in cross-cultural M&A, thereby facilitating the adaptation of management practices to the im-pacts of cultural differences.

By integrating the theories of resource dependence and transaction costs, this study examines the reversal effect of cultural differences between merging companies on post-M&A performance. The authors use a PLS-SEM to empirically analyse the main effects and reveal three transmission mechanisms.

The purpose of this study is to examine the behaviour of retail investors while making an investment decision and how it gets affected by the behavioural biases of the investors using a moderated-mediation framework.

A mixed method approach has been used to fulfil the objectives of the study. In the first study, a qualitative analysis of the interviews with 15 retail investors was conducted. As part of the quantitative study, a total of 201 responses from Indian retail investors were collected using systematic sampling and analysed using structural equation modelling and Process Macro.

The results indicate that anchoring bias, availability bias, herding bias, switching cost, sunk cost, regret avoidance and perceived threat have a significant effect on retail investors’ investing intention. The attitude of the investors towards investing decisions mediates the effects of behavioural bias and the status quo on investment intention. The results of the moderated-mediation analysis indicate that mediating effect of attitude varied at the low and high-risk aversion of investors.

The findings of this study will help regulators and retail investors to understand the critical behavioural biases which affect the investors’ investing intention.

The paper contributes to the literature on investors’ behaviour, status quo bias theory (SQB) and behavioural bias. This study uniquely proposes a moderated-mediation framework to understand the effects of biases on retail investors’ investment intention.

The paper's purpose is to investigate the effects of digital finance on the risk of stock price crashes and the underlying transmission mechanisms, and to provide suggestions to inhibit the stock crash risk (CR).

This paper selects all companies that were listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2011 to 2020. It then uses the two-way fixed effect model and the intermediary effect model to verify such effects.

The overall outcomes demonstrate such a result that the CR of listed companies in China can be significantly reduced by the development of digital finance, and the overall transparency of business financial information and the equity pledge of controlling shareholders are the two underlying transmission mechanisms that digital finance can cause effects on the CR of stocks.

The main limitations are that there may exist some problems in the method for evaluating the CR of stocks. And there may be a problem of endogeneity caused by the empirical model cannot control all correlation variables.

This paper would provide policy implications, for different roles, to inhibit the stock CR and to make the development of the economy more stabilize.

Digital finance can promote economic development while restraining financial risks at the same time. Therefore, although this study is based on the relevant data from China, it can also provide a reference for other economies with different basic conditions from China, to promote the overall development of the world economy.

The current academic research on digital finance or stock price CR has been relatively sufficient, but there are few papers that combined both. By combining digital finance with stock CR, this paper researches the influence of digital finance on the CR of stocks through empirical analysis. So, this paper would provide new research ideas and evidence for potential influence factors of the CR of stocks, fill the gap in this research field and provide certain help for subsequent scholars to conduct relevant research.