Asia-Pacific Journal of Financial Studies最新文献

This research examines the relationships among window dressing, fund performance, and fund flows in Taiwan's equity fund industry. The empirical findings reveal several important insights. Equity fund managers are actively involved in window dressing, with underperforming managers being more prone to such practices. These managers demonstrate a stronger preference for increasing their holdings of winner stocks more than avoiding loser stocks. Additionally, funds with a higher portfolio concentration and turnover rates are more likely to engage in window dressing activities compared to other funds. The study also highlights the adverse effects of window dressing on performance and fund flows. Window dressing negatively impacts both short- and long-term fund performances. Furthermore, window dressing fails to attract short-term fund inflows and instead exacerbates the decline in long-term outflows. This comprehensive analysis highlights the implications of window dressing on fund performance and investor behavior, shedding light on its prevalence and consequences within Taiwan's equity fund industry.

This study reveals a notable anomaly in Taiwan's stock market, where stocks with substantial directors' and officers' (D&O) insurance coverage tend to yield higher expected returns, partly due to their exposure to liquidity risk. First, firms purchasing extensive D&O insurance coverage experience increased stock illiquidity, which is influenced by the inadequate quality of information disclosure. This observation supports the moral hazard-based information opacity hypothesis. Second, we introduce an illiquidity-based mimicking factor and show that portfolios with higher (lower) D&O insurance coverage display positive (negative) loadings on this factor, underscoring the importance of liquidity risk in asset pricing. Third, this market-wide illiquidity mimicking factor explains approximately one-third of the observed premium associated with variations in D&O insurance coverage across certain model specifications. This research challenges the behavioral mispricing hypothesis by highlighting the liquidity-driven covariance risk linked to the D&O insurance anomaly, thereby providing fresh insights into asset pricing dynamics in emerging markets.

We examine whether board directors with previous experience as military officers improve corporate ethics. Using information about Korean board directors, we show that firms with ex-military-officer (EMO) CEOs and inside directors are substantially less likely to commit fraud, highlighting the importance of exposure to military values as former officers while serving as corporate executives. The positive impact of EMO directors' presence on corporate ethics is particularly prominent among Korean business group (chaebol) firms that are otherwise prone to fraudulent behaviors without their presence. Our results suggest that firms with board members of heightened personal integrity from their military officer experience have a higher standard in corporate ethics.

This study examines the effects of the 2007 antitrust regulations on the value of acquiring firms engaged in mergers and acquisitions (M&A) in Korea. By employing a difference-in-differences model and analyzing 5-day cumulative average abnormal returns (CAR), the study reveals that horizontal acquisitions lead to increased shareholder returns. However, there is no indication that market power or cost efficiency are contributing factors. For chaebol firms, M&A significantly decreases firm value, likely due to tunneling. Subsample analyses suggest that managerial entrenchment causes this decline, while non-chaebol firms show no empirical support for the market power or cost efficiency hypotheses.

This study uses China's stock exchange disclosure guidelines in a quasi-natural experiment to build a difference-in-differences model, examining how industry-specific disclosure regulation affect accounting estimation intensity. The results show these regulations reduce intensity due to better information comparability and stricter auditor oversight. The effect is stronger for firms with weak governance, in less competitive industries, or far from regulators. Tests also find such regulation lowers capital costs. The findings have implications for refining regulatory frameworks and capital market reforms in developing economies.

Under Article 27 of Taiwan's Company Act, juridical entities can designate representatives to act as directors of firms in which they invest. This institutional feature has raised concerns regarding the effectiveness with which such representative directors (particularly the friendly representative directors, FDIRs) can monitor and/or advise on firm operations. This study first examines whether FDIRs are associated with investee overinvestment. Prior studies have found that financial derivatives are important tools for risk management, reducing cost of capital and preventing underinvestment. This study extends these works and further examines whether the relationship between FDIRs and overinvestment is moderated by the use of hedging derivatives. Empirical results show that the proportion of FDIRs is negatively associated to firm overinvestment, suggesting that FDIRs improve the monitoring and/or advising functions of firm investment activities. A further test reveals that this negative relationship is pronounced for firms that are not engaged in derivative hedging activities. This implies that FDIRs are likely to incorporate managerial hedging programs into their monitoring and/or advising functions when examining firm investment decisions, thereby restraining overinvestment in firms that are not engaged in derivative hedging. Collectively, this study provides evidence that, in emerging markets like Taiwan, FDIRs tend to inhibit overinvestment, and this behavior is actually moderated by firm hedging derivatives activity.

This paper investigates the effects of the 2022 Shanghai lockdown and its subsequent lifting on global supply chains during the COVID-19 pandemic. We first identify four focal firms—Tesla, Volkswagen, SAIC Motors, and General Motors—most frequently cited in the news articles as experiencing supply chain disruptions due to the lockdown. Using an event study methodology, we estimate abnormal stock returns for these focal firms and their tier-1 global suppliers following the lockdown and its lifting announcements. We find significant and systematic stock market reactions for the suppliers of the focal firms, whereas the focal firms themselves do not exhibit statistically significant stock reactions. Specifically, suppliers experienced significantly negative abnormal returns during the lockdown, with the opposite effect observed upon the lifting of the lockdown. Furthermore, supplier characteristics such as capital expenditure and inventory turnover significantly influenced cumulative abnormal returns during the lockdown, while leverage and R&D intensity affected returns during the lifting event. Compared to the suppliers of benchmark firms, those of the focal firms experienced significantly more negative cumulative abnormal returns in the lockdown case, while no significant difference was observed between them in the lifting case.

Risks of abrupt changes in economic policies not only affect many segments of firms' operations, but also compromise the stability of the supply chain. Based on the data from listed companies in China between 2001 and 2020, this paper examines how firms stabilize their supply chain structure amidst growing economic policy uncertainty. Our findings reveal that, in response to increasing policy uncertainties, companies tend to stabilize supply chains by diversifying their supplier base. This impact is more prominent when firms face greater supply risks and a higher degree of market uncertainty. In addition, we discuss the timeliness and structural changes of firms' supplier-base in detail. Our results remain consistent across various endogeneity tests, providing empirical evidence for how economic policy uncertainty shapes firms' supply chain decisions. Policymakers can enhance understanding of firms' supply chain adjustment strategies in the face of rising policy uncertainty.

This study investigates the impact of a firm's environmental, social, and governance (ESG) controversies on its value and examines the moderating role of ESG initiatives in mitigating this impact. It further explores how this moderating effect varies based on factors such as reputational risk, external monitoring, agency motivations, and the recurrence of controversies. Using a novel machine learning approach, this study measures ESG controversies among publicly traded Korean firms by analyzing over 20 million news articles from major Korean media outlets. The findings indicate that ESG controversies negatively affect firm value, but this adverse impact is mitigated by stronger ESG performance, underscoring the insurance-like role of ESG. The moderating effect is particularly significant for firms facing high reputation risk, greater external monitoring, or with ESG activities that are less likely to be driven by agency motives. Moreover, non-repetitive ESG controversies exert a more substantial negative effect on firm value compared to repetitive ones, highlighting the crucial role of ESG in these situations. Consistent results are observed using short-term firm value measures, such as cumulative abnormal returns surrounding the release of ESG controversy news. Overall, this study contributes to the ESG literature by demonstrating that ESG can provide shareholders with an insurance-like benefit, supporting stakeholder theory.

The wide applications of machine learning techniques to big data allow researchers to dig deep into novel large-scale data sets, such as job postings, earnings calls, and news reports. They also equip researchers with powerful tools to study important but subtle/challenging topics that are impossible to explore before on a large scale, such as corporate culture and climate risk exposure. In this review, I survey various applications of different machine learning techniques in ESG research, beginning with foundational methods such as bag-of-words, progressing through topic modeling, word embedding, and BERT, and culminating with generative artificial intelligence (AI) and other advanced machine learning approaches. I conclude by outlining future directions for using big data and machine learning in ESG research.