We exploit Japanese mutual fund flows to demonstrate a strong investor demand for dividends in low-interest-rate environments. According to recent studies, investors who seek current income are more likely to invest in dividend-paying assets in such circumstances. These investors often shift their investments from bank deposits to dividend-paying assets, which offer more attractive cash flows despite higher risk. Investors’ such behavior, commonly referred to as "reaching-for-income," has been examined in various studies. Beyond the previous finding that dividend yields drive this "reaching-for-income" behavior, we show that the frequency of dividend payouts also plays an important role in this behavior. Even after controlling for dividend yields and other crucial factors that impact mutual fund flows, funds that pay dividends on a monthly basis receive more significant inflows than funds that pay out dividends less frequently, particularly during periods of economic expansion. Our findings suggest that, in the presence of incomechasing investors, the amounts and frequency of dividends have implications for asset prices.

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This study investigates the impact of a mobile information environment on information asymmetry and investor sentiment among individual investors. To this end, we use a mobile stock trading ratio as a proxy for exposure in the mobile information environment and the Amihud (2002) daily illiquidity ratio, volatility, and difference in net buying dollar volume between individual and institutional investors to measure the degree of information asymmetry. This study also creates an investor sentiment index using the adjusted turnover rate, individual investors’ buy–sell imbalance, and a relative strength index. We find that the mobile information environment significantly decreases individual information asymmetry and investor sentiment. Moreover, the effect in the KOSDAQ market, with high individual investor participation, is more significant than in the KOSPI. The results imply that individual investors may become gradually informed through information processing and cool down the over–heated investor sentiment in the mobile information environment.

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This study utilizes new data across countries on bank supervision for the years 1999–2016 to examine the impact of supervisory power and institutional characteristics of supervision on the key stability measures of the banking sector. Our results reveal that the degree of supervisory power and changes in financial supervisory architecture following the global financial crisis tend to enhance the banking sector stability. This study also finds that central banks’ monopolistic involvement in banking supervision appears to strengthen the banking sector stability and the independence of supervisory authority. However, this effect of monopolistic status of central banks in banking supervision countervails the positive effect of the degree of supervisory power and changes in financial supervisory architecture on the soundness of banking sector. These findings shed light on the significance of bank supervisory power and institutional architecture in ensuring the banking sector stability.

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There has been an ongoing debate on the impact of insidertrading on financial markets. Insiders can easily access inside information and make abnormal profits through this information advantage, consequently decreasing the confidence of investors in financial markets. In contrast to this negative side, insider trading can positively affect financial markets. Since insider trading provides investors with crucial information that is not publicly released about firms’performance or financial policies, insider trading reduces information asymmetry between firms and investors (Ke, Huddart, and Petroni, 2003; Lustgarten and Mande, 1998; Sivakumar and Vijayakumar, 2001; Kim and Choi, 2010; Sohn, Kim, and Goh, 2009). Insider buying (selling) signals that firms are possibly undervalued (overvalued) or may have good (bad) news, suggesting that insider trading benefits financial markets by decreasing information asymmetry. However, this negative relationship between insider trading and information asymmetry is likely to depend on information quality. Because insider trading in firms that disclose transparent information conveys valuable information to financial markets, the positive effect of insider trading on information asymmetry is expected to increase in these firms. In this reasoning, the relationship between insider trading and information asymmetry is likely to depend on the quality of information that firms provide. Meanwhile, a firm’s environmental, social, and governance (ESG) has received a growing attention from both practitioners and academics over the past decade. ESG is regarded as an important factor for firms to improve a good reputation and achieve sustainable survival (Leem, Cheung, and Son, 2019). Existing studies has examined the relationship between ESG and information asymmetry (Cho, Lee, and Pfeiffer, 2013; Kim, Park, and Wier, 2012; Yoon and Lee, 2019). These studies find that information asymmetry between firms and investors tends to decrease in firms with greater EGS because these firms provide markets with trustworthy information, implying that ESG is likely to improve the quality of information that firms disclose. In line with this extant literature, information quality can affect the relationship between insider trading and information asymmetry, but relatively little is known about this research. Thus, we examine how a firm’s ESG affects the relationship between insider trading and information asymmetry. Since the information quality of firms with more ESG is higher than that of firms with less ESG, the positive effect of insider trading on information asymmetry will be pronounced in firms that invest in more ESG. Based on this discussion, we propose the hypothesis that the negative association between insider trading and information asymmetry strengthens in firms with greater ESG. To test our hypothesis, we use data on Korean firms over the 2011-2020 period. Following existing literature (e.g., Lang and Lundholm, 1996; Krishnaswami and Subramaniam, 1999; Drobetz, Grüninger, and Hirschvogl, 2010), we measure information asymmetry using analysts’forecast errors and dispersion. In addition, we measure insider trading as net insider trading, which is the difference between insider buying and selling in a given year. Furthermore, given that our ESG data comprises six ratings, we define firms with A+, A, or B+ of ESG rating as high ESG firms and these with B, C, or D of ESG rating as low ESG firms, respectively. Our final sample includes 3,531 firm-year observations from 2011 to 2020. We find that the negative relationship between insider trading and analysts’ earnings forecast errors and dispersion is stronger in firms that engage in more ESG. Our results suggest that firms’ ESG is likely to improve the transparency and credibility of insider trading’s information, resulting in a greater decrease in information asymmetry. Further, our findings persist after addressing endogeneity issues such as reverse causality and omitted variable bias concerns. Specifically, our results hold when we conduct various robustness tests including regressions with firm fixed effects, change regressions, and System Generalized Method of Moments (GMM) estimations, and alternative measures of analyst forecasts. While previous studies on insider trading have examined a negative relationship between insider trading and information asymmetry, this paper contributes to the corporate finance literature by showing that the association between insider trading and information asymmetry depends on the information quality of insider trading. Moreover, this study adds to the literature on ESG by focusing on how a firm’s ESG affects information asymmetry.

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This study proposes a machine learning approach to understand how post-earnings-announcement drift (PEAD) works. We analyze when PEAD, combined with other factors, becomes more pronounced. To accommodate diverse variables and more complex specifications, two tree-based machine learning approaches including eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) are used to examine the relationship between PEAD and 89 variables. The long-short portfolio produced by LightGBM model reports 2.1 times higher returns than the portfolio’s returns, based on the conventional measure of earnings surprise. The model enhances the economic and statistical significance of the long-short portfolio returns. SHapley Additive exPlanations (SHAP) analysis determines feature importance and shows that liquidity, firm size, profitability ratios, share turnover, net trading flows by retail investors, and earnings surprises, play an important role in the prediction of PEAD.

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This study demonstrates that the underinvestment problems due to a higher debt-equity ratio in corporate investment decisions can be mitigated by the overconfidence of managers. It can also mitigate the negative effect of a high debt ratio on cash dividends. The empirical results are as follows. First, we observe a decrease in capital expenditure and R&D investment, say "investment", and cash dividend with higher debt-asset ratios, resulting in significantly decreased firm values, consistent with existing literature. Second, however, higher management overconfidence results in lesser problems of underinvestment and passive dividend payout policy due to creditors’ influence. Third, even if the debt ratio is high, the level of the decrease in firm value is significantly reduced if management overconfidence is high. Fourth, however, the mitigating role of the management overconfidence is observed only in the firms belonging to highly competitive product markets, not the other way around. In short, higher management overconfidence can have a positive effect on corporate investment and firm value if product market competition imposes external discipline.

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This study examines the implications of corporate ESG practices on credit risk and credit ratings of Korean listed firms. In particular, we investigate whether ESG performance is reflected in credit risk that is derived from stock price information and credit ratings assigned by credit rating agencies, respectively. This paper aims to advance our understanding of the relationship between ESG and credit risk and promote discussion about incorporating ESG factors into credit ratings. To measure the stock-based default risk, we calculate distance to default based on the widely used Merton (1974) bond pricing model. In the model, a firm’s equity can be viewed as a call option on the firm’s assets since at the maturity of a firm’s debt, the debt holders receive their debts, and the equity holders get the remaining amount. Our Merton distance-to-default measure is calculated using the numerical procedure of Bharath and Shumway (2008). Since the growth rate of a firm’s asset value is difficult to estimate, we use two estimates for the asset value drift rate, one using the risk-free interest rate and the other using CAPM. Regarding credit ratings, firms that issue corporate bonds in Korea are required to obtain credit ratings from at least two of the three separate official credit rating agencies (Korea Investors Service, Korea Ratings and NICE Investor Service). The lower of the two or three ratings is used as the bond credit rating. The credit rating system consists of a total of 22 rating grades ranging from AAA to D (10 investment grade ratings from AAA to BBB- and 12 non-investment grade ratings from BB+ to D). We convert the letter grades to numbers varying between 1 and 22 points, where 1 point corresponds to the highest rating, AAA. We obtain ESG ratings from the Korea Corporate Governance Service (KCGS). The KCGS’s ESG ratings include three categories: environmental, social and governance. Specifically, the environmental category includes environmental strategy and organization, environmental performance, and stakeholder relations. The social category includes employees, consumers, community, and partners and competitors. The governance category includes protections of shareholder rights, boards of directors, audit institutions and disclosure. The ESG rating isassigned in October every year, and after the final ratings are released, the ratings are adjusted in January, April, and July of the following year to reflect the latest issues if any. The KCGS ESG rating consists of seven grades: S, A+, A, B+, B, C, D, with S being the highest level. We convert the letter grades to numbers varying between 1 and 7 points, where 1 point corresponds to the highest rating, S. Our baseline panel regression model tests our hypothesis that ESG rating is negatively associated with the distance to default and positively associated with the credit rating. The dependent variables are the two distance-to-default measures and the credit rating. The independent variables are ESG ratings with a set of firm-level control variables. The firm-level control variables include size, leverage, cash flow, return on assets, stock beta, and asset volatility. We include firm and industry-year fixed effects for the regression. Using the abovementioned two measures of credit risk and ESG ratings from the KCGS, we find that corporate ESG performance is positively associated with the distance to default and a firm’s credit ratings. When we separate ESG into environmental (E), social (S), and governance (G) categories, we find that G is significantly associated with both the distance to defalutand credit ratings. On the other hand, S is significantly associated only with credit ratings. However, neither distance to default nor credit ratings are significantly associated with E, which has recently been attracting substantial attention. As such, credit rating agencies seem to incorporate ESG information when evaluating credit risk ahead of the stock market investors and considers governance ratings more importantly than the social and environmental ratings. It should be noted, however, that our results do not imply that corporate environmental performance is not an important factor when evaluating the credit risk of the companies. Given that credit rating agencies recently announced that they will considerenvironmental aspects of corporate activities when evaluating the credit risk of firms, the relationship between credit ratings and environmental performance will likely strengthen in the near future. Overall, depending on information availability and public attention toward ESG issues, there could be a time delay before environmental, social, and governance factors are fully incorporated into credit ratings. Last but not least, our findings suggest that ESG factors are increasingly important in corporate credit risk assessment and investors can improve credit risk management in corporate bond portfolios by considering ESG factors when evaluating corporate credit risk.

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Quantum computing is an emerging field that offers the potential to overcome the limitations of classical computers and is gaining attention as the next-generation computing platform. With the growth of interest in quantum computers, the competition to develop more powerful systems has intensified, with leading companies such as IBM, Google, Intel, Microsoft, and Samsung investing in the technology. They are working towards creating more advanced and practical quantum computers that can lead to new applications in various fields. Consequently, quantum computing has become a game-changer in recent years, with applications in cryptography, simulation, machine learning, and data retrieval. Quantum computing is also beingapplied to finance, giving rise to a new field known as quantum finance. This offers a unique way of processing data and information that is different from classical computing technologies. With its ability to perform complex optimization problems faster and more accurately than classical computers, quantum computing has significant potential in finance. Quantum finance has numerous advantages and limitless potential applications. For instance, it can help financial institutions better manage complex financial products like derivatives and structured products. By providing more accurate risk assessments, portfolio optimization, and trading strategies, quantum finance can enhance the efficiency and accuracy of financial markets. This enables financial institutions to make informed decisions, reduces the likelihood of financial loss, and ultimately benefits both the institutions and their customers. Quantum computing can also reduce the time and cost of complex financial calculations, such as Monte Carlo simulations. This is crucial for financial institutions, as the savings can be reinvested in other areas of the business. Additionally, quantum computing has the potential to increase the stability of financial markets by reducing market volatility and increasing investor confidence. The implementation of quantum finance requires cooperation between the financial sector and the government. Financial institutions should invest in quantum financial technologies and create the necessary infrastructure and regulations. Governments should also provide funding and support through regulation to encourage the development of quantum finance. Financial institutions, governments, and academia must work together to advance the field and maximize the potential of quantum computing in finance. However, implementing quantum finance requires expertise in both quantum computing and finance. Financial institutions need to secure human resources, such as quantum computing experts, to successfully implement quantum finance. This requires close collaboration between the financial sector and academia to develop the necessary skills. In conclusion, the potential benefits of quantum finance are vast, and its impact on the financial industry could be significant. By enhancing the efficiency, accuracy, and stability of financial markets, quantum finance has the potential to revolutionize the industry and benefit both financial institutions and their customers. It is crucial that financial institutions, governments, and academia work together to invest in and support the development of this emerging field. By exploring the potential and implications of quantum finance, this paper highlights the importance of supporting its growth, which has the potential to shape the future of finance and quantum computing.

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