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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