Soo-Wah Low

Purpose: In this study, IPO underpricing in a fixed-price market is examined through two distinct objectives: (1) a predictive objective identifying the machine learning model that best generalizes out-of-sample, and (2) an economic interpretation objective using model-agnostic interpretability tools (SHAP) and formal statistical tests (bootstrapped hierarchical regression) to assess which economic mechanisms are consistent with the observed predictive patterns. Conventional linear models with additive feature effects are inefficient for understanding moderation effects involving interactions. This is done through two methodological innovations: (1) explicit non-linear feature engineering (interaction terms to describe moderation effects), allowing linear models to learn complex relationships, and (2) model-agnostic interpretability of tree-based ensembles through SHAP analysis to numerically quantify interaction strengths. It combines the predictive stability of regularized linear models (SVR-Linear: R ² = 0.512) with the interpretability of non-linear models (Random Forest SHAP) for hypothesis testing. Design/methodology: The data employed in this paper include 350 Malaysian IPOs of the respective years 2004–2021. It uses seven machine learning algorithms: Support Vector Regression (SVR), Random Forest, XGBoost, LightGBM, k-Nearest Neighbors, Multilayer Perceptron, and Linear Regression. The evaluated algorithms are systematically assessed using 5-fold cross-validation. In terms of feature importance, SHAP (Shapley Additive exPlanations) values are used to quantify feature importance and characterize associations regarding investor demand, offer price, and listing board. Formal hypothesis testing is conducted via bootstrapped hierarchical regression. Findings: SVR-Linear offers better predictive performance (test R ² = 0.512, CV R ² = 0.486 ± 0.087), whereas ensemble techniques are prone to overfitting. SHAP analysis shows that the over-subscription ratio (OSR) contributes 40.7%, followed by a knowledge gap (15.6%). Hierarchical regression is used to establish strong moderating roles: investor demand is a significant moderator of the knowledge gap-underpricing relationship (β = 0.0284, p < 0.01), and intense demand (OSR > 30x) dilutes the effect of information asymmetry by 73%. There is marginal moderation in the offer price (β = 0.0198, p = 0.073) and no significant effect by listing board differences (β = 0.0089, p = 0.496). The threshold analysis shows that when investor demand is high, cascading demand effects lead to fundamental valuation concerns and patterns consistent with the predictions of Welch’s (1992) bandwagon theory. Practical implications: The findings from this research can benefit a variety of stakeholders. The stockholders can benefit from this research and minimize underpricing. Furthermore, regulatory bodies will consider book-building processes for large IPOs when there is a signal of demand. Additionally, the institutional investors should avoid oversubscription. Furthermore, it can be concluded that precise targeting policies can be achieved by measuring interaction effects. Originality/value: The study contributes in three ways, including (1) the first application of SHAP-based interpretability to IPO underpricing, which allows the identification of non-linear moderation effects that are not observed by conventional regression, (2) a rigorous overfitting diagnostic can be evaluated using hierarchical regression with bootstrapped confidence intervals, a feature that identifies the gap between black-box prediction and inferential rigor; and (3) formal statistical validation of SHAP results through bootstrapped hierarchical regression, a property that has not been previously available in financial machine learning studies. Explainable AI, coupled with causal inference, offers a transparent and replicable analytical framework for financial research that requires predictive validity and theoretical elucidation.

We explore the spillover among ASEAN-5 stock markets (Malaysia, Singapore, Thailand, the Philippines, and Indonesia) and the categorical economic policy uncertainty (EPU) of three major economies (China, Japan, and the United States), employing an R ² decomposed connectivity approach. Our empirical findings depict significant connectedness among ASEAN-5 stock markets and the categorical EPUs of three major economies, where instantaneous temporal spillovers are greater than lagged spillovers. We find that most categorical EPUs are shock receivers, whereas the ASEAN-5 stock markets are shock transmitters. Chinese monetary and exchange rate policy uncertainties, along with Japanese fiscal policy uncertainty, act as net shock transmitters. However, the roles of categorical EPUs and ASEAN-5 stock markets vary across total, contemporaneous, and lagged transmissions. We also find that the GFC, the Euro debt crisis, the U.S.‒China trade war, COVID-19, and the Russia–Ukraine conflict caused dynamic fluctuations in shock transmission. We also show that oil price changes, global financial stress, climate change, and gold market ups and downs are key factors that link economic policy uncertainties and the ASEAN-5 markets in different time frames. Hence, our empirical results provide valuable insights for managing risks and formulating strategies for addressing economic policy uncertainty and world crisis events.

This paper examines the impact of investor demand, measured by the over-subscription ratio (OSR), on the divergence of opinion among prospective investors. The sample comprises 131 IPOs listed on Bursa Malaysia from 2010 to 2018. The results indicate that OSR significantly amplifies investors’ divergence of opinions, contributing to their different trading behaviors, and leading to greater underpricing and market inefficiency on the first day of listing. The effect is particularly pronounced for IPOs listed on the ACE Board and during hot issue periods. These findings highlight the significant negative impact of OSR on market efficiency and investor behavior on the first day of listing. They suggest that future issuers should consider pricing their IPO issues using the book-building method, which could reduce divergence of opinion and enhance overall market efficiency, despite the costlier pricing method. Finally, market participants, including investors and issuers, can leverage these insights to make more informed decisions in IPO markets that rely on the fixed-price method.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

This study intends to investigate the impact of CSR practices on ERM implementation, focusing on Islamic banks operating in Gulf Cooperation Council (GCC) countries. Following the banking industry’s collapse during the 2008 financial crisis, there has been a notable rise in the enactment and literature of Corporate Social Responsibility (CSR). Although the influence of CSR practices on performance, trust, and reputation has been extensively researched, limited research studies have examined their impact on enterprise risk management (ERM). The study uses secondary data obtained from the annual reports of 23 Islamic banks between 2011 and 2020. CSR practices and ERM implementations were scored using content analysis. Using panel regression techniques, we find a significant positive relationship between CSR practices and ERM implementation for listed Islamic banks over the sample period. The relationship remains intact across two distinct ERM measures and various estimation methods. The findings of the study contribute to the literature by offering valuable empirical evidence of the effect of corporate social responsibility practices on enterprise risk management implementation in the GCC Islamic banks. The study suggests that executives, policymakers, and regulators of Islamic banks in the GCC should support and endorse socially related initiatives to aid in the successful implementation of ERM.

Enterprise Risk Management (ERM) is a contemporary trend that has gained momentum in recent years. ERM is a comprehensive approach to risk management that considers both operational and strategic risks that affect business objectives and success. The present research investigates the impact of ERM on the financial performance (FP) of listed Islamic banks in the Gulf Cooperation Council (GCC) countries. This study takes a quantitative approach, employing secondary data collection methods and content analysis. The study analyses a sample of 221 yearly observations from 23 listed Islamic banks in the GCC from 2011–2020. The ERM implementation and FP measures were derived from previous studies. The study discovered that ERM implementation is associated with an increase in accounting performance but not with changes in market performance for Islamic banks. Our study confirms that Islamic banks perform better in the short term after implementing ERM controls but have no impact on long-term value creation. This study provides valuable insight on the benefits of enhancing GCC Islamic banks' ERM implementation decision-making processes. Finally, the empirical findings can assist managers, policymakers, and regulators in improving bank performance by implementing best practices of ERM in GCC Islamic banks and countries with similar socioeconomic environments.