Earticle

현대 금융시장에서 신용평가는 금융건전성 평가뿐만 아니라 금융 기관의 대출 심사와 리스크 관리에도 필수적이다. 그러나 금융 환경이 급변하고 머신러닝 기술이 발전함에 따라 기존 신용평가 모형은 한계가 드러나고 있다. 본 논문에서는 2010년부터 2024년 까지 한국기업평가에서 부여한 제조업 기업들의 신용등급 데이터를 바탕으로 신용평 가 모형의 개선 방향을 논의하였다. 본 논문은 데이터 탐색을 통해 기존 신용평가 모형의 문제점을 파악하고, 다양한 머신러닝 기법을 적용하여 신용평가 모형을 개선하 고자 하였다. Random Forest, XGBoost, CatBoost를 활용해 주요 재무 변수의 중요도를 분석하고 신용위험 예측력을 향상시키는 데 초점을 맞추었다. 또한, 데이터 불균형 문제를 해결하기 위해 SMOTE를 적용하고, XAI 기법인 SHAP을 활용하여 신용등급 산정에 사용되는 재무 변수와 임계값 설정의 적정성을 평가하였다. 분석 결과, 실현된 신용위험과 기존 평가 방식에서 결정된 내재적 신용위험을 설명하는 주요 재무 변수가 다름을 확인하였다. 이는 특히 고 신용위험 기업의 평가 기준을 재정립할 필요성을 시사한다. 본 연구는 머신러닝 기반 신용평가 모형의 개선 가능성 을 제시하며, 금융 기관이 보다 정교한 신용위험 관리 전략을 수립하는 데 기여할 수 있다.

Credit scoring is essential for assessing financial soundness and serves as a fundamental tool for loan screening, capital allocation, and risk management in financial institutions. The accuracy and reliability of credit scoring models are directly linked to financial system stability, making their continuous improvement essential. Traditional models primarily rely on Generalized Linear Models (GLM), particularly Logistic Regression. While these models provide interpretable relationships between financial variables and default risk, they are constrained by their linear functional form and reliance on a limited set of features. This restricts their adaptability to evolving financial markets and the increasing availability of unstructured data sources. Advancements in machine learning (ML) and artificial intelligence (AI) have introduced various models to enhance predictive accuracy and address the limitations of conventional credit scoring models. ML-based approaches such as Random Forest, Support Vector Machines (SVM), XGBoost, and LightGBM, along with deep learning techniques, have been widely applied to credit risk modeling. These methods process large volumes of financial and transactional data, capturing complex patterns in credit risk assessment. However, their adoption requires further validation regarding interpretability and regulatory compliance. This study makes four key contributions to credit scoring research. First, unlike previous studies that relied on subjectively selected financial variables, we incorporate all financial features collected by credit agencies and adopt a data-driven selection approach, minimizing researcher bias and ensuring greater objectivity. This enables us to identify the most relevant predictors based on empirical evidence rather than predetermined assumptions. Second, we address the class imbalance issue, a common challenge in credit risk modeling. Since default cases are rare, traditional logistic regression models often suffer from biased estimates, where the model underweights defaulting firms. To mitigate this, we apply the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset before applying ML techniques. Third, we integrate multiple ML techniques to derive a comprehensive interpretation of feature importance. Specifically, we compare classification performance across Random Forest, Extreme Gradient Boosting (XGBoost), and Category Boosting (CatBoost). Unlike prior studies that analyze a single ML model independently, our approach integrates feature importance rankings across multiple models, providing a more robust estimation of the importance of financial variables in credit risk. Fourth, while ML models enhance predictive accuracy, their complexity can hinder interpretability, making adoption challenging for financial institutions. This study emphasizes the importance of explainable AI (XAI) in credit scoring. By applying Shapley Additive Explanations (SHAP), we provide insights into how key financial variables influence credit risk and default probabilities, offering practical guidance on the appropriateness of financial variables and threshold settings used in credit scoring. This study analyzes credit scoring data of manufacturing firms evaluated by Korea Enterprise Assessment from 2010 to 2024. By applying multiple ML techniques, we identify key financial variables influencing credit risk and integrate results for a comprehensive interpretation. Our analysis highlights differences between realized credit risk, which reflects actual defaults and missed payments, and implied credit risk, which is assessed by the current credit risk model. Realized credit risk is primarily driven by short-term liquidity and profitability indicators, such as inventory turnover period, current ratio, return on equity, and return on capital employed. In contrast, implied credit risk is largely influenced by firm size and long-term financial stability, with key variables including EBITDA, cost-to-sales ratio, pre-tax continuous operating income, total sales, and total liabilities. These findings suggest that while current credit scoring models emphasize long-term financial health, actual credit events are more influenced by short-term financial constraints. This discrepancy underscores the need to supplement credit scoring models by incorporating financial variables, particularly those related to short-term liquidity, especially for high-risk firms. Further analysis reveals that the importance of financial variables varies across rating levels. For A-level firms, short-term financial stability and debt repayment capacity are critical, emphasizing the importance of liquidity management. In contrast, B-level firms are more affected by structural financial indicators such as the debt-to-equity ratio and capital adequacy ratio, highlighting the significance of long-term solvency and debt management. These differences underscore the need to tailor credit scoring criteria based on risk levels. SHAP results indicate that while higher debt-to-equity and capital growth ratios generally reduce the likelihood of default, their impact on credit risk is nonlinear. This suggests that simple threshold-based classification may be insufficient for credit scoring. Instead, a more nuanced approach that accounts for interactions between financial indicators and their varying effects across credit risk levels is needed. Beyond feature importance analysis, we examine credit transitions. Credit scores evolve based on firms' financial conditions. Our findings show that while most firms maintain stable credit scores, downgrades occur more frequently than upgrades, particularly within the B-level category between 2022 and 2023. While some A-level firms experienced rating upgrades between 2019 and 2022, the trend shifted toward downgrades from 2022 to 2023. These patterns highlight the need for dynamic credit transition models that account for temporal changes in creditworthiness.

국내 주식시장에서 배당이 주식수익률에 대한 예측력을 가지는지에 있어 학계에서는 상반된 의견들이 대립해왔다. 이에 본 연구는 2010년부터 2024년 10월까지 KOSPI 및 KOSDAQ 배당주들의 월별 실적자료를 대상으로 배당 관련 주요 시점 (배당 기준, 공시, 확정 및 지급일)에 따른 배당수익률과 주식수익률간의 관계를 실증분석하였다. 그 결과, KOSPI 및 KOSDAQ 주식시장에서는 배당 기준일과 배당 공시일을 기점으로 배당수익률과 주식수익률간 통계적으로 유의한 관계가 나타나고 있음을 확인하였다. 또한 배당수익률과 다음달 주식수익률 간의 이러한 유의한 관계는 연간배당 기업들을 대상으로 발견되며, 배당수익률 상위 또는 하위 산업군에 국한되어 있거나 배당수익률의 개념에 따라 유의미함이 변동하는 것이 아닌 일관된 효과라는 결과를 얻었다. 한편 금융위원회에서는 작년 초 배당절차를 개선하겠다고 발표한 이래, 현재 기업들이 개선된 배당절차를 적극 채택할 수 있도록 유도하고 있다. 주주들에게 배당이 주기적으로 수익을 제공하는 원천일 뿐만 아니라 국내 주식시장에서 배당수익률을 기반으로 포트폴 리오 구성시 주가 차익도 얻을 수 있음을 나타내고 있는 본 연구결과는, 배당을 투자의사 결정에 영향을 미치는 의미 있는 요소로 활용할 수 있는지를 실증적으로 제시하고 있다는 점에서 더욱 그 의미가 부각되는 바이다.

The relationship between dividend yield and stock returns has been extensively studied, particularly in the U.S. market. Prior research, including Campbell and Shiller (1988) and Fama and French (1993), suggests that dividend yield predicts future stock returns. However, studies on the Korean stock market have produced mixed results. While some, such as Kim and Kim (2004) and Jung and Kim (2010), find no predictive power, others, like Oh (2021), provide empirical evidence supporting its significance. Given these conflicting findings, this study aims to clarify the issue by examining whether dividend yield predicts stock returns at specific dividend-related events, hypothesizing that its predictive power varies depending on event timing. To address this question, this study analyzes dividend-paying firms listed on KOSPI and KOSDAQ from January 2010 to October 2024, using financial and stock market data from FnGuide. Dividend yield is measured at four key dividend-related events: the ex-dividend date, the announcement date, the record date, and the payment date. The ex-dividend date marks the point at which shareholders eligible for dividends are determined. The announcement date is when firms publicly disclose their dividend decisions. The record date finalizes the dividend amounts, and the payment date is when dividends are distributed to shareholders. To examine whether dividend yield can predict stock returns, this study employs Fama-MacBeth (1973) cross-sectional regressions while controlling for firm characteristics such as profitability, asset growth, market capitalization, book-to-market ratio, and past stock returns. Additionally, five portfolios ranked by dividend yield are constructed to analyze return patterns across different yield levels. The results indicate that dividend yield significantly predicts stock returns under certain conditions. Specifically, dividend yield strongly predicts stock returns around the ex-dividend and announcement dates, with firms offering higher yields experiencing greater subsequent stock returns. This relationship remains statistically significant even after controlling for the Fama-French three-factor model and momentum effects. However, dividend yield does not predict stock returns around the record or payment dates, suggesting that market reactions occur primarily when dividend information is disclosed rather than when dividends are distributed. This study also examines whether dividend yield’s predictive power depends on payment frequency. The results show a strong and statistically significant relationship for firms with annual dividends, but not for those issuing quarterly or interim dividends. Furthermore, an industry-level analysis reveals that the predictive power of dividend yield is not confined to specific industries but applies broadly across the market, reinforcing its relevance in explaining stock return variations. To ensure robustness, this study tests alternative definitions of dividend yield, including expected future dividends rather than past dividends. The results remain consistent, confirming that the observed predictive effect is not driven by a specific measurement method. These findings provide strong empirical support for dividend yield as a meaningful indicator of stock return predictability in the Korean market. This study makes several key contributions to the literature on dividend yield and stock returns. First, it reconciles conflicting findings by distinguishing dividend yield’s effects at different event dates. By demonstrating that dividend yield significantly predicts stock returns around the ex-dividend and announcement dates, this study clarifies when dividend yield provides valuable information for investors. Second, it provides empirical evidence that dividend yield can enhance portfolio investment strategies, suggesting that investors can improve decision-making by incorporating dividend yield into stock selection criteria. Third, it challenges the notion that dividend yield is solely an industry-specific factor or a function of a firm’s dividend policy. Instead, it plays a crucial role in explaining stock return variations, reinforcing the idea that dividends are not just a mechanism for distributing earnings but also a valuable source of market information. This study also has policy implications for ongoing discussions on dividend reforms in Korea. As the Korean government seeks to enhance transparency and align domestic dividend policies with global standards, this study highlights the importance of clear and timely dividend announcements. By demonstrating that dividend yield contains meaningful information about future stock returns, it provides empirical support for policies encouraging firms to disclose dividend-related information timely and consistently. Improved disclosure practices could help investors make more informed decisions and enhance overall market efficiency. In conclusion, this study offers valuable insights for both policymakers and market participants. For investors, the findings suggest that dividend yield can be a useful tool in constructing profitable stock portfolios. For regulators, the study highlights the need for policies promoting better dividend disclosure. By addressing these issues, it contributes to a deeper understanding of dividends’role in stock return predictability and offers practical implications for investment strategies in the Korean stock market.

The global shift from defined benefit to defined contribution pension plans has emphasized the importance of individual retirement saving decisions. In response, India introduced the NPS All India model to address the retirement needs of its unorganized labour force, offering subscribers a choice between the Auto Choice (life-cycle approach) and the Active Choice (customized allocation). Recognizing the financial literacy gap among subscribers, this study employs a Genetic Algorithm (GA) to determine the optimal asset allocation weights for Active Choice. The results demonstrate that the GA-optimized portfolio outperforms the Auto Choice in terms of expected returns, accumulated retirement wealth, and monthly pension, while maintaining manageable risk levels. These findings have important implications for policymakers and fund managers in enhancing retirement outcomes for unorganized sector workers.

본 연구는 투자자 심리가 주식 수익률에 미치는 영향을 탐구하기 위해 새로운 안도감 변수(REL)를 제시하고, 이를 후회 변수와 비교하여 분석한다. REL 변수는 동일 산업 군 내 최저 수익률과 개별 자산의 수익률을 비교하여 투자자가 느끼는 심리적 안정성 을 반영한다. 연구 결과, 투자자는 안도감이 높은 자산에 대해 낮은 기대 수익률을 수용하며, 반대로 안도감이 낮은 자산에는 더 높은 위험 프리미엄을 요구하는 경향이 있다. 이러한 효과는 후회 변수와 기업 특성 요인을 통제한 후에도 지속되며, 특히 소규모 기업이나 고유 변동성이 큰 자산에서 더욱 두드러진다. 행동 재무학의 관점에 서 본 연구는 투자자의 효용함수가 심리적 요인에 의해 어떻게 조정되며, 이것이 투자 행동과 자산 가격 형성에 미치는 영향을 분석한다. 특히, 후회와 안도감이 투자자의 효용을 변화시키며 자산 가격 결정 과정에 미치는 상호작용을 설명한다. 또한, 심리적 가격 장벽이 투자자의 감정을 증폭시켜 REL 변수의 영향을 강화한다는 점도 밝혀냈 다. 본 연구는 투자 전략 수립과 리스크 관리에서 심리적 요인을 반영한 새로운 접근을 제안하며, 개인 투자자가 보다 합리적이고 감정적으로 균형 잡힌 의사결정을 내릴 수 있도록 돕는 방향성을 제시한다.

This study introduces a new sentiment-based variable called Relief (REL) to examine the impact of investor psychology—particularly emotions such as relief and regret—on asset pricing and decision-making in financialmarkets. Rooted in behavioral finance, the REL variable aims to capture a specific dimension of psychological stability that investors experience when their chosen asset performs better than the worst-performing peer in the same industry group. The REL variable is constructed by comparing the return of an individual asset with the lowest return among its industry peers. This comparison reflects an investor’s emotional comfort—or relief—from having avoided the worst-case scenario, even if the absolute return is modest or negative. Essentially, the REL variable quantifies a positive psychological payoff derived from relative outperformance, even when that outperformance is not impressive in absolute terms. Empirical analysis reveals a consistent pattern: investors tend to accept lower expected returns for assets with high REL values, indicating a stronger sense of emotional relief. In contrast, when an asset has a low REL value—meaning it did not outperform even the worst peer—investors demand a higher risk premium. This behavior highlights how emotional states influence investment decisions beyond traditional risk-return trade-offs. Importantly, the effects of the REL variable remain statistically significant even after controlling for the regret variable and firm-specific characteristics such as size, volatility, and the book-to-market ratio. This suggests that REL captures a unique behavioral dimension not fully explained by existing psychological or financial models. Moreover, the impact of REL is especially pronounced in small-cap stocks and assets with high idiosyncratic volatility—markets that are inherently more uncertain and thus more susceptible to emotionally driven behavior. The study also explores how psychological price anchors—such as recent highs—can amplify emotional reactions associated with REL. Investors become more sensitive to relative performance comparisons when asset prices move away from the psychological price barrier. In such contexts, feelings of regret or relief are intensified, causing greater deviations from rational investment behavior. These findings demonstrate that emotional biases are not isolated anomalies but are systematically embedded in market dynamics. One of the study’s key theoretical contributions lies in its challenge to traditional financial models that assume stable preferences and rational utility maximization. Instead, the REL variable supports a dynamic model of investor utility, where emotional responses like regret and relief actively reshape decision-making preferences. Regret reflects the emotional cost of missing a better opportunity, while relief provides a positive emotional benefit from avoiding the worst outcome. These emotions function as psychological forces that alter how investors evaluate gains and losses. Incorporating REL into behavioral finance expands the framework by accounting not only for negative emotions such as regret but also for positive emotions like relief. This dual-emotion perspective helps explain why investors may favor certain assets—not necessarily due to their fundamentals—but because of how those assets make them feel in relative terms. Investors may feel reassured knowing that their asset did not perform the worst, even if the overall return was subpar. From a practical perspective, the findings offer valuable insights for investment strategy, portfolio management, and risk assessment. Investment professionals who consider sentiment-based variables such as REL may be better positioned to understand market dynamics shaped by investor psychology. By identifying assets that generate strong feelings of relief, investors can anticipate lower return expectations, while also recognizing the increased return demands associated with assets that trigger regret or emotional discomfort. Additionally, understanding the emotional reinforcement embedded in relative performance may lead to more psychologically resilient portfolios. Rather than relying solely on traditional metrics like beta, volatility, or book-to-market ratios, integrating psychological measures like REL enables investors to develop strategies that reflect real-world investor behavior—where emotions often have a stronger influence than pure analysis. In conclusion, this study contributes to a more comprehensive understanding of investor behavior by introducing the REL variable as a meaningful tool for capturing emotional responses to relative performance. The REL framework enhances traditional regret-based models by including the often-overlooked role of positive emotional feedback. This research demonstrates that asset pricing is shaped not only by fundamentals or rational expectations, but also by how investors feel about their performance in relation to the worst possible outcomes. By identifying and quantifying this emotional comfort, the REL variable illuminates the complex ways in which sentiment influences market outcomes. It offers both theoretical insight and practical guidance for building emotionally aware investment models, reinforcing the view that both positive and negative emotions play a central role in financial decision-making.