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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 current environment is unhelpful to female entrepreneurs, and they need to overcome numerous barriers when starting their own businesses. In this study, the researchers investigated the significant barriers that Pakistani female entrepreneurs require to overcome in this respect.

In this study, the researchers used a quantitative study and they used a questionnaire to survey the respondents and collect cross-sectional data. The researchers targeted female students who were undertaking bachelor’s and master’s degree programs in different Pakistani public and private sector universities. Accordingly, the researchers based this study’s findings on the usable samples received from 498 Pakistani female students.

The researchers used a structural equation model (SEM) in this study and its findings highlight that aversion to risk (ATR) has an insignificant impact on entrepreneurial inclinations (EI). In addition, fear of failure (FoF), lack of resources (LoR), aversion to hard work and stress (ASH) and the lack of social networking (LSN) have negative and insignificant effects on EI. The ATR factor has an insignificant effect on entrepreneurial success (ES), whereas FoF, LoR, ASH and LSN are negative and insignificant predictors of Pakistani female students’ ES.

This study’s findings may help Pakistani women to overcome the barriers to ES. In this respect, the researchers recommend that the Pakistan Government and policymakers develop significant strategies to provide the conducive business environment and to financially support Pakistani women to start their own businesses. Furthermore, this study’s findings contribute greatly to the vast amount of current literature and help to overcome the entrepreneurial conditions and barriers that potential entrepreneurs from advanced and developing countries experience frequently.

This study’s findings provide empirical evidence of EI and ES in Pakistan.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.

We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.

Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.

Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.

We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.

This paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.

By leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.

Results show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.

Annual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.

To the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.

This paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.

The investment behaviour of individuals has been a major area of interest for several researchers and policymakers due to its great impact on the economy. This study aimed to assess the investment behaviour of individuals in light of their risk appetite and how financial literacy regulates this relationship.

A self-administered structured questionnaire was used to collect responses from individuals using purposive and convenience sampling techniques. Individuals were presented with 16 investment avenues widely offered by the Indian financial market to choose from to construct a hypothetical portfolio. The association between risk appetite, financial literacy and the composition of the hypothetical portfolio was analysed using a gologit model.

Increased risk appetite increased the probability of respondents creating a portfolio with a greater proportion of risky assets and less diversification. Lower levels of financial literacy pointed towards portfolios with traditional and low-risk avenues. The results also revealed a significant moderating impact of financial literacy on risk appetite and the creation of the type of a hypothetical portfolio.

Even though the intended behaviour is a close estimate of actual behaviour, there is a possibility of deviation that cannot be ignored.

The present study provides insights into how individuals make portfolio choices by incorporating risk appetite and diversification factors whilst making investment decisions, thereby expanding the literature from an emerging economy perspective. The role of financial literacy as a moderator has not been studied in the domain of hypothetical portfolio creation in India, which has been empirically explored in the current study.

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

Purpose: This chapter examines the effect of COVID-19 on the stock market volatility (SMV) in the Colombo Stock Exchange (CSE), Sri Lanka.

Need for the Study: The study is necessary to understand investor behaviour, market efficiency, and risk management strategies during a global crisis.

Methodology: Utilising daily All Share Price Index (ASPI) data from 2 January 2018 to 31 August 2021, the data are divided into subsamples corresponding to the pre-pandemic period, the pandemic period, and distinct waves of the pandemic. The impact of the pandemic is investigated using the Mann–Whitney U test, the Kruskal–Wallis test, and the Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model.

Findings: The pandemic considerably affected CSE – the Mann–Whitney U test produced different market returns during the pre-COVID and COVID eras. The Kruskal–Wallis test improved performance during COVID-19 but did not continue to do so across COVID-19 waves. The EGARCH model detected increased volatility and risk during the first wave, but the second and third waves outperformed the first. COVID-19 had a minimal overall effect on CSE market results. GARCH and Autoregressive Conditional Heteroskedasticity (ARCH) models identified long-term variance memory and volatility clustering. The News Impact Curve (NIC) showed that negative news had a more significant impact on market return volatility than positive news, even if the asymmetric term was not statistically significant.

Practical Implications: This study offers significant insight into how Sri Lanka’s SMV is affected by COVID-19. The findings help create efficient mitigation strategies to mitigate the negative consequences of future events.