Asian Economic and Financial Review

The impact of ESG factors on Taiwan ETFs’ performance

2025-11-04T13:15:23-06:00

This study investigates the impact of ESG (Environmental, Social, and Governance) factors on the financial performance of Taiwan’s index exchange-traded funds (ETFs). As ESG disclosure and assessment methodologies have become more comprehensive, and regulatory requirements have expanded, Taiwan’s central role in global supply chains makes its ESG data particularly valuable for research and investment analysis. The study employs difference analysis, linear, and nonlinear regression models, analyzing monthly data from 2016 to 2023 and calculating the ESG scores adjusted for the weighted stock holdings of each fund. It explores the effect of ESG scores on fund flow, excess returns, CAPM alpha, and Sharpe ratio. The main empirical findings are as follows. First, ESG-themed funds attract significantly more capital flow than non-ESG funds, indicating market recognition of sustainable investment. However, no significant impact was found between ESG scores and fund flow. Second, although ESG funds attract more capital, they exhibit significantly lower excess returns and risk-adjusted returns. Environmental and social scores show a negative impact on excess returns and CAPM alpha in linear regression, suggesting that transition costs and investment restrictions in ESG-focused ETFs can constrain short-term performance and reduce diversification. Third, ESG and governance scores show nonlinear effects: while moderate improvements enhance excess returns, excessively high scores diminish these benefits. Lastly, when market attention is low, excess returns significantly improve, indicating that less attention to ESG-related topics reduces overpricing of ETFs.

The intelligent finance ecosystem: AI applications in banking and fintech for enhanced decision-making

2025-11-05T01:32:06-06:00

The financial services sector is undergoing a significant transformation driven by artificial intelligence (AI), which is reshaping operational agility, real-time analytics, and intelligent decision-making. AI is being rapidly adopted across both traditional banks and fintech systems, offering substantial potential but also raising challenges related to equity, transparency, and regulatory compliance. This study evaluates the strategic role of AI across six major use cases: credit scoring, fraud detection, chatbot services, portfolio advisory, regulatory compliance, and decentralized finance (DeFi) smart contracts. A mixed-method approach, including literature review and simulated benchmarking with synthesized industry and academic data, was used. Key performance indicators such as accuracy, cost efficiency, and customer satisfaction were compared through weighted indices to analyze risk mitigation and operational impact. Results reveal that fraud detection achieves the highest AUROC (96.5%) and cost savings (23.5%), while chatbot services lead in customer satisfaction (89.6%). Regulatory compliance shows steady but moderate results, making it a suitable domain for automation and inclusion. DeFi smart contracts, however, perform poorly due to immature governance and integration. Overall, findings emphasize aligning AI potential with institutional readiness and domain needs. The paper proposes a framework for responsible AI adoption, stressing transparency, security, and ethical governance for future scalability.

Interest rate reforms and firm performance in Bangladesh’s manufacturing sector

2025-11-05T03:55:36-06:00

The study investigates the effects of a single-digit (6%–9%) interest rate set by the Bangladesh Bank in 2020, involving 94 listed manufacturing companies across 10 industries from 2018 to 2023. The key objective of the research is to examine the impact of such interest rate reform on financial performance, proxied by Return on Assets (ROA), Return on Equity (ROE), and Market-to-Book (M/B) ratio. Fixed and random effects panel data analysis techniques and the system generalized method of moments (GMM) have been applied to address heteroskedasticity, autocorrelation, and endogeneity. The results show that lower interest rates improve ROA and ROE, especially for capital-intensive industries like textiles and engineering, because of reduced borrowing costs. Higher lending rates and debt leverage adversely affect the M/B ratio, indicating that investors are concerned about the increase in debt levels. Price fluctuations in the exchange rate affect firm performance, which relies on imports. Additionally, firms with larger sizes and higher GDP growth perform better in the market. The study highlights the need for a dynamic and sector-focused interest rate policy to improve the resilience of the manufacturing sector, as well as strategies like capacity building in financial management to ensure long-term sustainability. To address the limitations of this study, future research should use probability sampling together with unlisted firm data and combine primary and secondary data while expanding the study period after the post-pandemic period. Moreover, cross-industry analysis will help achieve a better understanding of long-term interest rate effects on manufacturing firm performance.