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In the stock market, herding behavior occurs when investors mimic the actions of others in their investment decisions. As a result, the market becomes inefficient and speculative bubbles form. This study aims to investigate the relationship between information, overconfidence, market sentiment, experience and national culture, and herding behavior among Malaysian investors. A total of 400 questionnaires are distributed to bank institutions' investors. The survey design based on cross-sectional data is analyzed using the Partial Least Squares Structural Equation Model. The results indicate that information, market sentiment, experience, and national culture are positively related to herding behavior, while overconfidence has no effect. With this, the government should strengthen regulations to prevent the dissemination of misleading information. Moreover, investors are encouraged to overcome narrow thinking by expanding their understanding of different cultures when making investment decisions.

This research aims to determine the factors that can affect financial literacy in Micro, Small, and Medium Enterprises (MSMEs), especially regarding loans and budgeting. Data are obtained using a survey of owners or managers of MSMEs, which is then processed using multiple regression. This research contributes toward a deeper understanding of MSMEs’ financial literacy determinants, specifically regarding loans and budgeting, in a pandemic situation that differs from ordinary circumstances and encourages many financial activities to utilize technology. The research results indicate the role of Financial Education, Money Attitude, and Financial Socialization Agents in determining MSMEs’ financial knowledge and skills regarding loans and budgets.

Purpose: This work provides an empirical analysis of investor behaviour's simultaneous influence due to the surprise effect caused by COVID-19 cases and government responses to market risk. This analysis compares tourism assets risk with other sectors and different types of investors' assets and categories in Europe.

Design: The paper applies an ARIMA with a GARCH model to predict conditional volatility of models for market uncertainty. Nonlinear models, factor analysis and time series linear regression for stationary variables in first differences are applied to predict market uncertainty.

Findings: We demonstrate that market risk does not arise from COVID-19 cases but instead from the surprise effect, as the market accurately predicts future cases. Only the volatility of the sectors Travel, Airline, and Utility are influenced by both surprise effect and government response, but only the travel sector reveals an interaction effect with both government response effort and surprise effect.

Originality: The article mutually studies the simultaneous interactions among investor behaviour due to the surprised effect caused by COVID-19 and government responses to the pandemic and the influence on professional investors' volatility in two asset types and between different sectors.

Practical implications: With this model and results, investors and financial service providers may verify whether or not government intervention during pandemic periods is effective in reducing uncertainty and risk levels on sectors, types of investors and different sorts of assets.

The COVID-19 pandemic has recently had an impact on the stock market all over the globe. A thorough review of the literature that included the most cited articles and articles from well-known databases revealed that earlier research in the field had not specifically addressed how the BRIC stock markets responded to the COVID-19 pandemic. The data regarding COVID-19 were collected from the World Health Organization (WHO) website, and the stock market data were collected from Yahoo Finance and the respective country’s stock exchange. A random forest regression algorithm takes the closing price of respective stock indices as target variables and COVID-19 variables as input variables. Using this algorithm, a model is fit to the data and is visualised using line plots. This study’s findings highlight a relationship between the COVID-19 variables and stock market indices. In addition, the stock market of BRIC countries showed a high correlation, especially with the Shanghai Composite Stock Index with a correlation value of 0.7 and above. Brazil took the worst hit in the studied duration by declining approximately 45.99%, followed by India by 37.76%. Finally, the data set’s model fit, which employed the random forest machine learning method, produced R² values of 0.972, 0.005, 0.997, and 0.983 and mean percentage errors of 1.4, 0.8, 0.9, and 0.8 for Brazil, Russia, India, and China (BRIC), respectively. Even now, two years after the coronavirus pandemic started, the Brazilian stock index has not yet returned to its pre-pandemic level.

This chapter uses data from the Scopus database to present a comprehensive bibliometric analysis of fintech research, focusing on publication trends, citation patterns, and thematic clusters within the field. The analysis reveals notable trends, including influential publications, prolific authors, and their affiliations. It identifies and explores publications categorized into 23 distinct themes, representing key areas of inquiry in fintech, such as technological advancements, financial inclusion, innovation, data analytics, sustainability, and regulatory compliance. The study also identifies research gaps, indicating areas within fintech that have received limited scholarly attention. These findings provide valuable insights for researchers, policymakers, and industry practitioners. Researchers can better understand the fintech landscape, identify research gaps, and guide future inquiries. Policymakers can develop effective regulations and policies based on identified trends and challenges. Industry practitioners can leverage fintech developments to enhance their strategies and operations.

I propose a model of behavior in social interactions where individuals maximize a three-term utility function: a conventional consumption utility term and two “social” terms that capture social preference. One social term is a taste for desert, which is maximized when the individual believes the other person is getting what they deserve. The second social term measures the target individuals’ anger or gratitude from the interaction which is determined by a value function derived from prospect theory. After introducing the model and generating a series of comparative statics results and derived predictions, I report the results of a series of quasi-field experiments on social preferences. I discuss how the model explains several paradoxes of empirical moral philosophy that are less explicable by current economic models of social preference focusing on outcomes and intentions.

The authors believe the COVID-19 pandemic has an impact on supply and demand. The potential decline in real sector performance leads to lower expectations of securities performance. The uncertainty of future performance can change investor behaviour. This study tried to gain insight into stock investor behaviour during the COVID-19 pandemic. The results showed that the majority of the investor realized and believed the pandemic would affect the stock market performance. Hence, they did not show herding behaviour and were very confident during the COVID-19 pandemic. The survey also indicates that investors tend to avoid risk rather than take the opportunity to buy at a lower price. Moreover, investors believe that the COVID-19 vaccine will soon be found, and the economy will return to normal. Government and self-regulated organizations (SRO) are responsible for making effective policies to convince the investors about the future prospect.