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This study examines whether country-level financial integration affects firms' accounting choices and the quality of financial information.

This study employs Propensity Score Matching (PSM), and panel regressions of a large sample of data from 20 emerging markets over the period 1987–2018.

This study finds evidence that increased level of financial integration is significantly positively associated with firms' accruals earnings management (AEM) and real earnings management (REM).

Findings in the study have implications for standard-setting bodies that aim to enhance the usefulness of financial reporting quality. The study also has implications for various initiatives by governments in emerging markets aimed at raising investor confidence and fostering stock market development through greater financial integration.

Findings in the study have implications for standard-setting bodies that aim to enhance the usefulness and quality of financial reporting. The findings can be of interest to analysts, auditors and other monitoring institutions who play a crucial role in detecting earnings management and reducing information asymmetry. Finally, the study has implications for various initiatives by governments in emerging markets aimed at raising investor confidence and fostering stock market development through greater financial integration.

Findings in the study reveal how country-level financial integration affects accruals and real earnings management in a sample of firms from 20 emerging markets. Further, the study adds to the growing body of literature on emerging markets where capital markets mechanisms, regulatory environment and firm's corporate governance are distinct to developed markets.

The phenomenal proliferation of crowdfunding platforms raises concerns on the heightened occurrence of financial crimes since billions of funds are exchanged through these online systems frequently. Accordingly, some countries have implemented legislative responses to address these risks, although each countries’ laws have varying degrees of severity. Hence, the purpose of this study is to assess the efficiency and robustness of Mauritian laws to combat financial crimes that may arise from a crowdfunding transaction with a particular emphasis on money laundering and tax evasion.

To achieve this research objective, the black letter approach was used to analyse Mauritian rules and regulations on the researched topic and a comparative analysis was carried out against the corresponding laws on crowdfunding in some other jurisdictions, notably the UK and the USA with the view of suggesting the policy recommendations to Mauritian authorities.

It was found that there is still scope for improving the existing legal and regulatory framework on crowdfunding in Mauritius to prevent instances of money laundering and tax evasion. The paper suggests that a crowdfunding operator must be categorised as a reporting person and must carry out regular due diligence checks. There must also be more collaboration in terms of information exchanges and training sessions among the tax authority of Mauritius, crowdfunding operators, fund seekers and investors to shed light on the tax treatment of income and deductions to avoid issues of tax evasion.

At present, to the best of the authors’ knowledge, this study is amongst the first academic writings on the efficiency of Mauritian laws in dealing with the risk of financial crimes through crowdfunding, and also, because existing literature is quite scarce on assessing the adequacy of crowdfunding rules in developing countries, this research aims at filling in the gap in literature. The study is carried out with the aim of combining a large amount of empirical, theoretical and factual information that can be of use to various stakeholders and not only to academics.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.

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.

This study analyses the role of individuals' objective financial knowledge in shaping preferences for ethical intermediaries and sustainable investments in Italy. Another goal of this study is to assess the impact of individuals' misperceptions about their own financial knowledge and to test for gender-related differences in attitudes towards socially responsible investing (SRI).

Using nationally representative microdata from the Bank of Italy’s “Italian Literacy and Financial Competence Survey” (IACOFI), the authors use probit models, extended to account for potential endogeneity issues, to assess the causal effects of financial knowledge and confidence on stated preferences for SRI. Empirical models also allow to explicitly assess the moderating role of gender on the effects of financial knowledge and confidence on attitudes towards sustainable investing.

Results indicate that individuals' preferences for sustainable finance significantly increase with financial knowledge, suggesting that inadequate financial competencies represent a barrier to participation in SRI. At the same time, lack of confidence in one’s own financial knowledge significantly hampers attitudes towards sustainable investments. Furthermore, the authors show that women have a greater preference for sustainable finance than men and point out that financial knowledge and confidence exert heterogenous effects on attitudes towards SRI.

This study provides several contributions to the literature on SRI. First, the authors give evidence of the causal effect of financial knowledge on preferences for both ethical financial intermediaries and sustainable investments. Moreover, this is the first study to investigate the role of financial underconfidence bias in shaping individuals' SRI attitudes. Finally, extending previous research, the authors assess differences in SRI preferences between women and men and provide novel evidence on gender-related heterogeneity in the effects of financial knowledge and underconfidence.

Prior studies use two measures of firm-specific return variation (FSRV): idiosyncratic volatility in absolute and relative terms, the latter of which is also termed stock price nonsynchronicity. Whereas most research focuses on investigating the idiosyncratic volatility puzzle, the authors carry out comparison of these two measures and further investigate which of the two constituents of nonsynchronicity explain the association between FSRV and stock returns, emphasising the importance of assessing which component drives stock returns.

The authors use the US individual stock returns from 1925 to 2016 and define the two measures of FRSV based on the Fama and French (1993) model. Specifically, the authors decompose the relative measure into two components: (i) absolute idiosyncratic volatility and (ii) systematic volatility. The authors conduct various tests based on high-minus-low, zero-investment quintile portfolio sorts and perform the Fama–MacBeth analysis by singling out each component.

The authors find a positive return on the portfolio sorted on relative idiosyncratic volatility or on systematic volatility, but find a negative return sorted on absolute idiosyncratic volatility. The results are robust after controlling for size, BM and other risk characteristics using a double-sorting approach. The Fama–MacBeth regression results show that a positive association between the relative measure and stock returns is driven primarily by the low-systematic-volatility anomaly across firms. The findings are robust to controlling for return residual momentum, skewness, jumps and information discreteness.

Extant research posits the idiosyncratic volatility puzzle and the low-volatility anomaly. The authors emphasize the importance of integrating these two streams of research. This study enhances the understanding of the driving force underlying the relationship between FSRV and cross-sectional stock returns.

This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.

The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.

The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.

This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.

The purpose of this study is to examine how lenders alter their behavior when faced with real earnings management.

This study uses the incremental R-square approach as in Kim and Kross (2005) to examine how much lenders rely on income statement and balance sheet ratios as the degree of real earnings management increases.

As real earnings management affects mostly the income statement, the authors find that lenders rely less on income statement ratios in making credit decisions in the presence of real earnings management. The authors also find that lenders do not alter their reliance on balance sheet ratios when faced with real earnings management.

This paper is the first to study how lenders alter their reliance on financial statements in making credit decisions in the presence of real earnings management. The findings of this paper could help the regulators set standards to improve the usefulness of financial statements. The findings of this paper could also help practitioners (borrowers and lenders) understand how real earnings management affects credit decisions.

This study examines the relationship between investors' familiarity bias, including the home bias and endowment bias, and their financial situations, expectations and personal characteristics.

Using the 2019 Survey of Consumer Finances, the authors utilize an ordinary least squares regression to identify the presence of endowment bias and home bias in individual investors' direct stock holdings and use a Heckman selection model to examine determinants of the extent of endowment bias and home bias.

This study finds that investors with higher income and more education, men, non-white investors and people with greater risk tolerance are actually at a greater risk of endowment bias. This study also identifies a profile of investors that are more likely to have a home bias: with less financial sophistication, lower net worth, older, female, more risk-averse, with a positive expectation about the domestic economy and a relatively shorter investment horizon.

This paper is among the first to use US investors' directly reported stock holdings to examine the individual characteristics that are correlated with greater familiarity bias, providing financial professionals with information about how to allocate their limited time in providing education to a variety of clients.

Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market.

Need for the Study: The external market’s internal/own shocks and volatility spillovers influence portfolio choices in domestic stock market returns. Hence, it is required to investigate the internal shock in the domestic market and the external volatility spillovers from other countries.

Methodology: This study employs a quantitative method using ARMA(1,1)-GARCH(1,1) model. All Share Price Index (ASPI) is the proxy for the Colombo Stock Exchange (CSE) stock return. It uses daily time-series data from 1^st April 2010 to 21^st June 2023.

Findings: The findings revealed that internal/own and external shocks substantially impact the stock price volatility in CSE. Significant volatility clusters and persistence with extended memory in ASPI confirm internal/own shock in the market. Furthermore, CSE receives significant volatility shock from the USA, confirming external shock. This study’s findings highlight the importance of considering internal and external shocks in portfolio decision-making.

Practical Implications: Understanding the influence of internal shocks helps investors manage their portfolios and adapt to market volatility. Recognising significant volatility spillovers from external markets, especially the USA, informs diversification strategies. From a policy standpoint, the study emphasises the need for robust regulations and risk management measures to address shocks in domestic and global markets. This study adds value to the literature by assessing the sources of volatility shocks in the CSE, employing the ARMA-GARCH, a sophisticated econometrics model, to capture stock returns volatility, enhancing understanding of the CSE’s volatility dynamics.