Asia-Pacific Journal of Financial Studies最新文献

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.

This paper utilizes the implementation of circuit courts in China to identify a causal relationship between judicial effectiveness and corporate debt financing costs. We provide firm-level evidence demonstrating that enhanced judicial effectiveness leads to lower debt costs, with reduced credit discrimination and improved contract enforcement being key mechanisms. Further analyses reveal that the impact of judicial effectiveness on debt costs is more pronounced for firms that initially experienced higher levels of credit discrimination, and firms with lower credit quality, weaker collateral capacity, and higher reliance on bonds. We also find that improved judicial enforcement increases firms' total loans and credit loans, while reducing collateral loans. Additionally, firms reduce operating expenses to strengthen their financial condition and increase R&D investment after securing more debt at lower costs. This paper offers novel evidence highlighting the critical role of judicial reform in shaping the debt market.

Based on a sample of listed companies in China's A-share market between 2009 and 2019, we analyze the tone of the Management's Discussion and Analysis (MD&A) section of 15 743 annual reports through machine learning. The MD&A tone displays a significantly positive association with the firm's future financial performance, proving that the textual tone of MD&A includes forward-looking aspects regarding management's intentions for the company's prospects, and investors can use it to forecast the firm's future performance. Additionally, higher levels of internal control quality, stronger analyst following, and higher holdings by institutional investors can enhance the correlation between the MD&A tone and the company's future performance.

This paper investigates the effects of a co-CEO structure on earnings smoothing and its implications for the informativeness of earnings. While the consequences of a co-CEO structure have been relatively underexplored in prior research, existing studies suggest potential benefits, such as higher valuations and stronger internal governance. This paper delves into the specific consequences of a co-CEO structure, focusing on earnings smoothing practices. Using data from Korean firms, where co-CEO structures are prevalent, we find that firms with a co-CEO structure exhibit smoother earnings compared to those with a sole-CEO structure. However, the informativeness of these smooth earnings is reduced under a co-CEO structure, suggesting that co-CEOs engage in earnings smoothing primarily for their own benefit, rather than to provide more information to investors. Additional analyses reveal that co-CEOs' earnings smoothing leads to adverse consequences for investors, including lower earnings quality, higher costs of capital, increased financial distress, greater credit risk, and higher stock price crash risk. Our study contributes to the literature by highlighting the potential costs associated with co-CEO structures, particularly in terms of reduced earnings informativeness.

As environmental, social, and governance (ESG) becomes an increasingly popular criterion for investment screening, drivers of ESG performance have attracted attention from a wide range of stakeholders. In this study, we test whether corporate site visits can improve ESG ratings using a unique dataset of site visits from Chinese listed firms. We find that corporate site visits can lead to improved ESG performance for listed firms, which is robust even after adding a series of control variables, alternative model specifications, and accounting for endogeneity issues. This effect is attributed to the mediating effects of ESG information disclosure and corporate internal control quality. We also find that the more ESG-related discussion during site visits the better managers learn to improve ESG performance of their firms. Furthermore, there is evidence that the positive effect of site visits on ESG are more pronounced among firms that are not audited by Big Four accounting firms, not cross-listed, and when Qualified Foreign Institutional Investors are involved. Overall, this paper contributes to the literature by providing complementary evidence that site visits are an important platform for communicating between investors and corporate management, which can have profound implications on incentivizing managers to deliver better ESG performance. Our findings also offer practical and policy implications to regulators on the promotion and implementation of ESG in listed firms.