Earticle

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.

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.

본 연구는 기업의 환경, 사회, 지배구조(ESG) 활동이 신용위험과 신용등급에 미치는 영향에 대한 실증분석을 수행하였다. 구체적으로, 신용위험의 대용치인 Merton(1974) 의 부도거리와 신용평가사의신용등급에 기업의 ESG 평가가 반영되고 있는지, ESG 활동이 신용위험에 미치는 영향에 대한 시장의 평가와 신용평가사의 평가 간에 차이가 있는지를 분석한다. 분석결과, 기업의 ESG 활동은 신용위험 및 신용등급과 통계적으로 유의한 관계가 있음을 발견할 수 있었다. ESG 개별항목 중 지배구조부문은 신용위험 및 신용등급에 유의한 영향을 미치는 것으로 나타났으며, 사회부문은 신용등급에만 유의하게 반영되는 것으로 나타났다. 반면, 상대적으로 최근에 관심도가 높아지고 있는 환경부문은 신용위험, 신용등급 모두에 유의한 영향을 미치지 않는 것으로 나타났다. 이와 같이 ESG 개별 항목별로는 신용위험 및 신용평가 반영에 차이가 있는 것으로 보이며, 신용평가사가 시장에 비해 선제적으로 반영하는 것으로 해석된다. 또한, 최근 일부 신용평가사가 환경부문을 신용평가에 적극적으로 반영하겠다는 의지를 표명한 바, 향후에는 환경부문의 성과가 신용등급에 반영될 것으로 기대되며 시장도 이를 점차 반영할 것으로 기대된다.

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.

최근 인공지능과 머신러닝의 급속한 발전과 함께 양자컴퓨팅이 새로운 기술로 주목받 고 있다. 양자컴퓨팅의 개념은 양자 역학의 발전에서 시작되었으며, 이후 기술의 발전 과 함께 다양한 분야에 적용되기 시작했다. 양자컴퓨팅은 수많은 조합을 통해 계산되 는 문제 처리에 유용하다. 이러한 유형의 문제는 시뮬레이션, 암호, 머신 러닝, 데이터 검색과 같은 여러 분야에서 적용이 가능하다. 해외에서는 이러한 양자컴퓨팅에 대한 특징을 금융에 접목하려는 연구가 시작되고 있다. 양자 금융은 새로운 분야이며, 이는 국내 금융 분야에서 새로운 기회를 만들 수 있다. 양자컴퓨팅은 기존의 컴퓨팅 기술과 는 다른 새로운 방식으로 데이터와 정보를 처리한다. 이 과정에서 양자 알고리즘은 복잡한 금융 문제를 해결할 기회를 제공한다. 특히, 양자컴퓨팅은 최적화 문제와 몬테 카를로 시뮬레이션에 장점을 가지며, 투자 최적화, 리스크 관리, 트레이딩 시스템 등에 활용할 수 있다. 본 연구에서는 양자 컴퓨터를 금융에 적용하기 위한 연구 현황과 실무 현황을 분석하고, 금융 분야에 적용 가능성을 제시한다. 또한 양자 금융 시대의 준비를 위한 정책적인 시사점과 전략을 제언한다. 이를 통해 양자 금융의 미래에 대한 깊은 이해와 양자컴퓨팅의 활용에 대한 중요성을 강조한다.

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.