This study investigates the relationships between three leading cryptocurrencies— Bitcoin, Ethereum, and Ripple—and six major implied volatility indices as proxies for stock market uncertainty: VIX, VXD, VSTOXX, VDAX, VXEFA, and VXEEM. We employ nonparametric causality-in-quantiles and quantile-on-quantile approaches to examine nonlinear causal effects and dependence structures across various cryptocurrency market states and levels of uncertainty. Our findings reveal a one-way information flow from cryptocurrency returns to stock market uncertainty, with stronger predictive power during periods of low uncertainty. This suggests that while stock market uncertainty may not reliably predict cryptocurrency returns, cryptocurrency-related information can significantly influence stock markets, particularly during low uncertainty, likely driven by investor attention to cryptocurrencies for diversification or speculative purposes. The quantile-on-quantile analysis shows that changes in implied volatility generally have a negative impact on cryptocurrency returns, with these effects being more pronounced at lower quantiles. Furthermore, cryptocurrencies’potential to act as hedges or safe havens against stock market uncertainty emerged only under extremely bullish market conditions and has significantly diminished over time due to the increasing integration of cryptocurrency and stock markets.

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Credit rating is an essential component of the financial sector, as it evaluates a firm’s capacity to meet debt obligations. In the Republic of Korea, rating agencies assign levels ranging from AAA to D, with additional modifiers, and these ratings significantly affect financing conditions. Traditional methods typically rely on logistic regression and selected financial variables, yet these approaches often face difficulties in capturing the intricate or nonlinear patterns present in corporate financial data. In response to these challenges, researchers have increasingly turned to advanced machine learning algorithms that can account for more complex relationships. Nevertheless, their deployment is limited by relatively small datasets—particularly among smaller firms—and by concerns regarding model interpretability. The present study proposes a machine learning framework, including a Graph Neural Network (GNN), to predict rating changes in Korean firms. The data set spans 2010 to mid-2024 and includes 182 firms, yielding 1,417 year-level samples. Each annual observation is labeled according to whether its credit rating was upgraded (+1), downgraded (–1), or left unchanged (0). Because most observations lie in the unchanged category, the data are highly imbalanced. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is employed, generating additional samples in the minority classes. In addition, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of sixteen indicators representing profitability, growth, stability, and activity. Six models are assessed: logistic regression, LASSO, random forest, support vector machine, Light Gradient Boosting Machine (LGBM), and a GNN. The GNN-based approach is noteworthy for modeling financial indicators or corporate attributes as nodes within a graph, with edges delineating the relationships among these nodes. Such a representation allows for the capture of latent dependencies that are difficult to detect in methods that treat predictors independently. Furthermore, each corporate sample belongs to discrete subgroups determined by listing market (KOSPI or KOSDAQ), data period, rating band, and industry classification. The proposed hierarchical GNN merges these subgroup-specific networks to produce a consolidated prediction, thereby incorporating both firm-level attributes and group-level characteristics. When compared to a benchmark that classifies samples randomly in proportion to the observed class distribution, all six machine learning algorithms demonstrate superior performance. The GNN shows the highest precision and F1-scores, suggesting that it is particularly effective at identifying upgrades and downgrades, which are far less common than no rating changes. Nonetheless, like other models, it finds rare rating shifts more challenging to predict, highlighting the impact of data imbalance and the difficulty of forecasting uncommon events. An inspection of feature importance across models underscores the significance of growth and activity metrics, implying that sales expansion, equity growth, and the efficient use of assets offer robust signals of rating volatility. Moreover, the GNN indicates that distinguishing firms by industry group is especially influential, possibly because each sector’s distinctive regulatory, economic, and financial traits shape its credit risk profile. Compared to certain deep neural networks that demand extensive datasets, the GNN-based method presented here is relatively more practical in settings with limited data, including smaller firms with incomplete rating histories. Additionally, this approach provides improved transparency, as the graph architecture clarifies how different financial indicators or subgroups collectively affect rating transitions. Future work may benefit from enlarging the dataset, experimenting with alternative oversampling strategies such as ADASYN, and examining cost-sensitive learning to mitigate the imbalance problem further. Investigations might also consider alternative graph structures that connect entire firms as nodes and delineate inter-firm relationships or incorporate advanced architectures such as Transformers or LSTM networks. In summary, the findings suggest that a GNN-based framework can improve credit rating predictions by capturing complex interactions that traditional or other advanced machine learning methods may overlook. While data imbalance is still a problem, its consequences are somewhat mitigated by SMOTE. The significance of growth, activity, and sector-specific characteristics suggests that more accurate and comprehensible rating projections can be produced by integrating richer and more interconnected data. In the end, more investigation and more extensive data gathering should improve the precision and dependability of credit rating systems, leading to a better comprehension of the dynamics of corporate finance.

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On April 29, 2019, the Korea Exchange (KRX) reduced the duration of the pre-market opening call auction—referred to as the opening call auction session—from 60 minutes to 30 minutes in its KOSPI market. This study examines the impact of this policy change by analyzing how foreign and institutional investors contribute to price discovery during the shortened auction period. Specifically, we divide the opening call auction session into 5-minute intervals and investigate investor-specific order submission patterns and the cross-sectional determinants of Weighted Price Contribution (WPC), both before and after the change. Using data from all submitted orders over 50 trading days before and after the event, we find the following key results: First, order submission behavior differs markedly across investor types. Retail investors predominantly submit their orders during the early part of the auction session, whereas foreign and institutional investors tend to concentrate their order submissions toward the latter part of the session. Moreover, the proportion of revised and canceled order by foreign and institutional investors increases significantly near the end of the session, suggesting active price adjustment behavior. This implies that these investors behave as informed traders, delaying order submission to the final stages in order to avoid premature information disclosure and to exert greater influence over price determination of the latter part of the session. As a result of these strategic behaviors, foreign and institutional investors exhibit significantly higher order execution rates, reinforcing their dominant role in the price discovery process. Second, the determinants of Weighted Price Contribution (WPC)—used to measure the contribution of submitted orders to the opening price—vary across time segments within the call auction and reveal contrasting dynamics between the early and late phases. Specifically, the initial stage of the auction (the “WPC initial computation interval”) is the first opportunity for market participants to incorporate overnight information that has become available since the previous day’s close. During this phase, price discovery tends to occur more rapidly in stocks with lower participation by foreign and institutional investors, smaller firm sizes, higher volatility, or greater liquidity. In contrast, the latter part of the call auction (the “WPC adjustment interval”) is characterized by a reversal in these determinants. In this phase, price discovery is primarily driven by stocks that have higher participation from foreign and institutional investors, lower volatility, or larger firm sizes. This suggests that investor composition, and stock characteristics exert different influences on price formation depending on the stage of the auction. Interestingly, at the moment of opening price determination—the final order-matching point—price discovery again accelerates in stocks with lower foreign investor participation or smaller firm sizes, diverging from the characteristics observed during the immediately preceding phase. This reversal implies a process similar to a tatonnement mechanism (as in a Walrasian auction), wherein the market undergoes a trial-and-error adjustment process to reach an equilibrium price. Third, the adjustment of WPC in the latter stage of the auction session is primarily driven by the strategic behavior of foreign and institutional investors. Initially, WPC often reflects overshooting caused by overreactions from retail investors, who act as noise traders. As the auction progresses and approaches the final price determination point, WPC declines—sometimes turning negative— indicating a correction process toward a more accurate equilibrium price. During this adjustment phase, foreign and institutional investors shift from a passive stance in the early phase to an active role in the latter part of the session, submitting a large proportion of new, revised, and canceled orders that play a crucial role in correcting earlier mispricings. This behavior is empirically supported by the statistical analysis showing that the share of foreign and institutional orders during this phase exhibits a strong and positive correlation with cross-sectional WPC values, reinforcing their pivotal role in leading the price discovery process. The overall analysis demonstrates that these patterns hold consistently before and after the 2019 policy change, in which the KRX reduced the length of the opening call auction from 60 minutes to 30 minutes. This consistency implies that the first 30 minutes of the original one-hour session may have contained a high degree of market noise and uncertainty, resulting in inefficiencies in the price discovery process. The shortening measure of the opening call auction period did not diminish the role of foreign and institutional investors; rather, their strategic contributions to price discovery remained intact. These findings suggest that the policy change had a positive effect on market efficiency of this session. In conclusion, the differentiated behavior of investor types, the shifting determinants of WPC, and the strategic timing of informed traders’ order submissions together illustrate the complex dynamics underlying price discovery in a compressed opening call auction environment.

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This study analyzes the stock market reaction to share sale announcements made by venture capital firms (VCs) and tests the monitoring role of VCs on publicly listed companies. We hypothesize that the divestment of shares by a VC signals the termination of its monitoring function, thereby inducing a negative market response. This effect is anticipated to be more pronounced when the divesting VC possesses stronger incentives to monitor the portfolio company. This study employs the ownership stakes and board representation of VCs as proxy variables for monitoring incentives. Analyzing 298 disclosures of share sales by VCs in the KOSDAQ market from 2010 to 2022, this study finds the following. First, a statistically significant negative cumulative abnormal return is observed from the disclosure date to five days afterward. Second, univariate analyses of subsamples indicate that the negative market reaction is more pronounced when the VC holds a larger ownership stake or a board seat. Third, regression analyses controlling for firm and VC characteristics confirm a negative correlation between the VC’s monitoring incentives and post-disclosure cumulative abnormal return, providing support for the monitoring hypothesis.

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I find that a more concentrated debt structure increases the likelihood of newly issued loans being sold in the secondary market. A one-standard-deviation increase in the Herfindahl-Hirschman Index of the seven debt types prior to loan issuance is associated with a 7% higher likelihood of a new loan being sold relative to the sample average. A more concentrated debt structure enhances coordination and bargaining power among existing creditors, which discourages the participation of new lenders and weakens their bargaining position. Consequently, new entrants tend to prefer more tradable loan structures and rely on active secondary markets to mitigate these constraints. This effect is less pronounced for loans involving relationship lenders or those structured as credit lines, which can mitigate conflicts between existing and prospective creditors. By contrast, the effect is more pronounced for firms with a higher proportion of secured debt, as the presence of collateral strengthens incumbent lenders’ incentives to monitor borrowers and enforce their claims in the event of default. Moreover, the effect is amplified among financially distressed firms, where disputes over asset distribution are more likely to occur. These results underscore the critical role of a borrower’s existing debt structure in shaping investor behavior.

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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.

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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.

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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.

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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.

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