Purpose: The purpose of this paper is to discuss the case-based reasoning (CBR) approach to improve microcredit initiatives by means of providing a borrower risk rating system.Design/methodology/approach: The CBR approach has been used to consider the Kiva microcredit system, which provides a characterization (rating) of the risk associated with the field partner supporting the loan, but not of the specific borrower which would benefit from it. The authors discuss how the combination of available historical data on loans and their outcomes (structured as a case base) and available knowledge on how to evaluate the risk associated with a loan request can be used to provide the end users with an indication of the risk rating associated with a loan request based on similar past situations.Findings: The adopted approach is applied and evaluated employing a selection of cases from individual loans. From this perspective, the case base and the codified knowledge about how to evaluate risks associated with a loan represent two examples of knowledge IT artifacts.Originality/value: The originality of the work lies in borrower risk rating in online indirect peer-to-peer microcredit lending platforms. The case base and the codified knowledge are the two contributions in knowledge IT artifacts.

The results reveal significant evidence of herding in the overall, bearish, and extended crisis market phases during extreme downturns, while the magnitude of market returns in the tail distribution is considered. Asymmetric herding behavior is more pronounced and prevalent, conditioned by market dimensions like return direction, trading volume, and volatility, with CSSD proving more effective than CSAD in detecting asymmetric patterns. Notably, herding strongly appears in the COVID-19 market during periods of abnormally high market volatility, reflecting heightened market sentiment. Applying Dow Theory to delineate bull and bear market phases significantly improved the methodological complexity and analytical depth related to herding behavior. These findings suggest policy implications for regulators and market participants in minimizing herding effects to create an efficient market environment through enhanced market surveillance, improved investor education, and the use of advanced technologies.

The study examines the dynamics of herding behavior in relation to macroeconomic shocks and monetary policy shifts in the Bangladesh equity market. By employing robust empirical methodologies across distinct market states including bullish, bearish, crisis, extended crisis, and COVID-19 phases, we first demonstrate that herding prevails under conditions of heightened uncertainty. Based on this foundation, we examine how exchange rate fluctuations and interest rate shifts alongside changes in deposit rates and reserve requirements serve as catalysts for collective investor behavior. The findings demonstrate that depreciation of the domestic currency and reductions in interest rates result in significant intensification of herding during vulnerable market phases. Moreover, monetary policy adjustments, predominantly changes in deposit rates and reserve ratios, trigger coordinated trading responses, especially during bearish and crisis markets. These results reveal the profound sensitivity of frontier equity markets to macro-financial signals and underscore the critical role of policy communication and stability in mitigating destabilizing herd dynamics. By bridging macroeconomic policy and investor psychology in a frontier market context, this research offers practical insights that can help regulators and policymakers improve market resilience.

The study investigates the state-dependent dynamics of overconfidence in the Bangladesh equity market by exploring the relationship between market returns and trading volume within a nonlinear information-theoretic framework. Building up on the traditional return–volume literature, the study differentiates between total market returns and unexpected returns, with the latter representing unexpected information shocks obtained using the Market Index Model. Transfer Entropy with bootstrap inference estimates the directional and asymmetric information flows across five different market states, namely: bullish, bearish, crisis, extended crisis, and COVID-19. The evidence suggests that the overconfidence biases in aggregate market returns are small and intermittent and are reflected in poor and unstable information flow between market returns and trading volume. In comparison, unexpected market returns have a directionally significant impact on trading behavior, which supports the behavior of state-dependent overconfidence. The findings also reveal that overconfidence is higher in normal and bullish market situations but drops significantly in crisis-based situations. The asymmetric analysis indicates increased trading responses to negative returns shocks, as it is more evident that investors are more sensitive to losses and recovery expectations. The research adds to behavioral finance literature on frontier markets through an unexpected return decomposition with nonlinear causality model. The results have serious implications on market surveillance, assessment of investor behavior and design of regulatory policies.

The study provides an intuitive investigation into the disposition effect within frontier markets such as Bangladesh, particularly focusing on its behavior during various market conditions. The study’s novelty lies in its application of a methodological framework of the disposition effect measure of Weber and Camerer, aiming to understand the disposition effect through different market conditions. Dow Theory is applied to disparate bullish and bearish intermediate periods. Disposition effects persist for the entire study period, as well as the different market conditions except for the bearish Bangladesh equity market. The bullish and crisis markets exhibit a rather high disposition effect due to their respective market volatility. Stronger disposition effects are more pronounced for a crisis market in relation to a bullish market. In addition, the disposition effect in Bangladesh’s equity market oscillates in crisis periods. The documentation of the disposition effect in the Bangladesh equity market across market conditions suggests that investors’ psychology plays a crucial role in their decision processes. Individuals and professional investors should carefully design an appropriate strategy to control their decision-making process since the presence of disposition effects may impair the risk-return payoffs.

The study investigates overconfidence bias in the Bangladesh equity market through the relationship between the market returns, and the trading volume in a nonlinear, information-theoretic model. Building upon the traditional literature on returns and volume, the study differentiates between the total market returns and unexpected market returns, the latter being the unexpected information shocks represented under the Market Index Model. Transfer Entropy with bootstrap inference is used to determine directional and asymmetric causality across various market states, including bullish, bearish, crisis, extended crisis, and COVID-19. The findings indicate that the total market returns give weak and inconsistent evidence of overconfidence, which is bi-directional but limited information flow. Conversely, unexpected market returns have a statistically significant directional effect on trading volume, which represents strong evidence of overconfidence. The results also reveal that overconfidence is conditional as it is stronger in normal and bullish market contexts, and weaker during times of crisis. Asymmetric analysis reveals that the overreaction of investors is more pronounced when the market trends are negative, implying that unexpected losses stimulate an amplified trading effect due to the feeling of mispricing and recovery hopes. The results have significant implications on market efficiency, investor behavior and regulatory policies to improve market stability and facilitate informed financial decision-making.