Nazrul Hisyam Ab Razak

This study explores current research trends on Trade-Based Money Laundering (TBML) within the context of environmental crime through a structured review of academic literature. It then investigates the association between TBML and environmental crime incidents across Southeast Asia. A two-stage methodology is adopted. The first stage systematically reviews studies published between 2006 and 2025, revealing that TBML research remains in its infancy and is principally focused on conceptual frameworks with limited empirical validation. The second stage applies a fixed-effects panel regression, using trade mis invoicing as a proxy for TBML and controlling for corruption, government intervention, and cross-border mobility. The results indicate a positive and statistically significant association between TBML and environmental crime, with the relationship strengthened in jurisdictions marked by higher levels of corruption and weaker enforcement. These findings underline the necessity for coordinated regional action and a holistic policy response to all TBML dimensions. Policymakers should conduct a comprehensive review of Southeast Asian regulatory frameworks to close loopholes, strengthen cross-border cooperation, and deploy real-time fraud detection systems to enhance transparency and deter illicit conduct. Enforcement must remain proportionate to avoid displacing laundering into clandestine channels, striking a balance between vigilant oversight and the facilitation of legitimate trade.

This study proposes a decision-making model based on deep reinforcement learning (DRL) for agricultural financial transactions, addressing core challenges such as significant data noise, strong time-series dependence, and limited strategy adaptability. We developed a multifactor dynamic denoising framework by integrating the Grubbs test for outlier detection and the median absolute deviation (MAD) method for noise handling. This framework categorizes agricultural financial indicators into six feature types, significantly enhancing robustness against data noise and improving model reliability. Furthermore, an long short-term memory (LSTM)-enhanced DRL architecture is employed, incorporating a sliding window mechanism to capture market timing features. This framework constructs a transaction cost-based reward function. It establishes an intelligent trading decision model based on the LSTM algorithm and the data query language (DQL). Experimental results demonstrate an annualized return of 45.12% and a 35% reduction in maximum retracement for Deere & Company and BAYN.DE. The Sharpe ratio reaches 1.51, reflecting a 62% improvement over the benchmark model. The results validate the robustness of the proposed decision-making model in the face of price fluctuations and policy interventions. This model addresses critical bottlenecks in the application of DRL in agricultural finance, facilitating the transition of agricultural economic management from empirical judgment to data-driven approaches. Through three key innovations—data denoising, time-series modeling, and domain adaptation—it provides a vital decision-support tool for advancing smart agriculture.

This paper provides an in‐depth examination of the role of rural financial institutions in rural development under deleveraging and greening externalities orientating China's economic transition. Specifically, we examine the impacts of leverage implementation and green finance on the multi‐dimensional efficiency (i.e., financial, social and comprehensive) of rural commercial banks (RCBs) in the following distinct ways. First, we account for the symmetric and asymmetric relationships between leverage and efficiency through a comparable analysis of using the ARDL model and the NARDL model, which follow dynamic heterogeneous panel data modelling by Shin et al. Second, we investigate the relationship between green finance and efficiency in an identical manner. Third, in order to capture potential nonlinearity, we quantify macroprudential green finance to ascertain its threshold effect on the leverage‐efficiency association by using the Hansen model. Our results depict that RCBs' efficiency, regardless of the dimensions involved, responds asymmetrically to changes in their leverage in both the long and short runs, while their asymmetric response to changes in green finance occurs solely in the long run. This finding is further corroborated by intuitive results of the cumulative sum of squares simulation and consolidates the observed financial accelerator effects in symmetric relationships. Additionally, the threshold role of macroprudential green finance triggers nonlinearity in associations of leverage with social and comprehensive efficiency. This finding suggests that, in light of the development of green finance, minor adjustments to macroprudential regulation below the threshold can facilitate micro prudence for RCBs' deleveraging.

The purpose of this study is to examine whether ESG plays a positive moderating role in the negative relationship between financial constraint, the Kaplan-Zingales (KZ) and Whited and Wu (WW) indexes, and firm performance: Return of Asset (ROA) and Return of Equity (ROE). This study uses information from the Thomas Refinitiv database, which covers the Association of Southeast Asian Nations (ASEAN-5): Indonesia, Malaysia, Singapore, Thailand, and the Philippines non-financial firms from 2011 to 2019. Fixed-effects (FE) are used as the baseline model, and random-effects (RE) act as the robustness of methods. The results show that the main effect of financial constraints is to act as an obstacle to firm performance. However, the marginal effects of financial constraints can be improved in the presence of ESG. Firms with a high ESG score are better at alleviating the adverse impact of financial constraints as compared to those with a low ESG score. When the ESG score is further broken down into three sub-pillar dimensions, the S-score is of the greatest magnitude in its moderating role in the ESG breakdown. The findings have important implications: effective financial support and the source of funding from the government are crucial to supporting firm performance. ESG-compliant strategies should also be formulated to encourage ESG disclosure, which leads to increased capital allocation efficiency. The firms should be stringent on S-score, which helps drive the company as employees respond by giving their best. Governments and firms need to deploy ESG guidelines in order to succeed in thriving competitive firm performances.

The paper assesses the moderating role of firm age in the relationship between financial constraints, as measured by the KZ index, and the WW index on three alternative measures of firm growth (Asset growth, ROA, and ROE) in the ASEAN–6 region. This study employs a sample of ASEAN–6 listed firms over the period 2009–2019 using Fixed Effects and the System GMM model. Firm age is found to interact positively with financial constraints in their effects on firm growth. The negative impact of financial constraints on firm growth is less severe with older age firms.

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

Money laundering in international trade facilitates the concealment of illicit funds through the manipulation of trade invoices and the exploitation of regulatory gaps. Addressing this issue involves improving traceability through financial technology, a promising solution. However, the adoption of such technology poses inherent risks and challenges that require careful consideration. This review synthesises knowledge on trade-related money laundering and financial technology, highlighting critical areas for additional research and improvement. Employing a Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) approach, academic databases, including Scopus and Web of Science (WoS), were systematically searched to identify relevant studies and articles. Advanced search techniques using keywords such as money laundering, trade-based money laundering, and financial technology resulted in the identification of 57 articles for eligibility. After excluding 20 articles due to being out of the field, having titles not significantly related, or abstracts not aligning with the study's objective, 37 eligible articles and conference proceedings were further examined. Two main themes were developed, concluding that the convergence of money laundering through digital trade and financial technology requires collaborative efforts among regulatory bodies, financial institutions, and technology experts. Such collaboration is essential to balance innovation and security, ensuring stability and prosperity in global economies.

Manuscript type: Research paper Research aims: This study aims to investigate the volatility characteristics and risk-return trade-off of Islamic and conventional indexes in the Malaysian market. Design/Methodology/Approach: The research covers the daily data of the period from August 2007 to December 2022, divided into four distinct periods: the full sample, the period during and after the 2007- 08 financial crisis, and the period during the COVID-19 pandemic. The research employs a hybrid model that combines ARIMA with GARCH family models. Research findings: In this research, both Islamic and conventional indexes in the Malaysian market demonstrate a memory effect, emphasising the persistence of market volatility through the influence of past volatility. Additionally, historical data, represented by lagged values, significantly shape volatility, while negative shocks have an immediate and pronounced impact compared to positive shocks, providing valuable insights for investors and risk management. Lastly, during the COVID-19 crisis, the conventional index showed no leverage effect, and the Islamic index lacked safe haven characteristics, making this crisis unique in the Malaysian financial context. Theoretical contribution/Originality: This research contributes to the understanding of market volatility dynamics in the Malaysian context by utilising a hybrid ARIMA-GARCH model. The identification of memory effects, asymmetric responses, and the unique characteristics observed during the COVID-19 crisis adds to the body of knowledge on financial market behaviour. Practitioner/Policy implication: The findings of this study have practical implications for investors and supervisory entities operating in the Malaysian market. Understanding the persistence of volatility and the differential impact of positive and negative shocks can help investors make more informed decisions, while regulators can use this information to quantify and manage market volatility effectively. Research limitation/Implications: The limitation of the present study is that the results may be influenced by the selection of the sample period, potentially yielding different outcomes depending on market conditions, such as the presence of bull or bear markets, periods of high or low volatility, or other contextual factors.