This study examines the performance of regime-based portfolio rebalancing strategies that utilize hidden Markov models (HMMs) to dynamically adjust asset allocations in response to volatility regime shifts. Based on a globally diversified portfolio reflecting the investment context of institutional investors with significant cross-border exposure, the analysis yields three key findings. First, regime-based strategies consistently outperform static strategic asset allocation (SAA) and buy-and-hold approaches in terms of risk-adjusted returns, while effectively reducing downside risk measures such as conditional value-at-risk (CVaR), maximum drawdown (MDD), and volatility. Second, the benefits of regime-based strategies are more pronounced at higher adjustment intensities, confirming their adaptability under shifting market conditions. Third, the performance advantages of regime-based strategies persist even after incorporating transaction costs. By integrating regime-based signals into the rebalancing process, this study provides empirical implications for institutional investors managing globally diversified portfolios in volatility-sensitive environments.

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In recent years, digital commerce platforms have grown at an unprecedented pace, fundamentally reshaping retail and service industries worldwide. While these platforms prioritize rapid user acquisition and market share expansion, often at the expense of immediate profitability, their business models inherently carry heightened financial vulnerability. Aggressive marketing expenditures, heavy investments in logistics and technology, and extended deferred payment arrangements with vendors contribute to elevated cash burn rates and liquidity risk. Traditional insolvency prediction models, designed for asset-intensive or manufacturing firms, fail to capture the nuanced risk dynamics of digitally mediated, two-sided marketplaces. Against this backdrop, this study develops and empirically validates a hybrid risk-diagnosis framework tailored to digital commerce platforms, combining conventional insolvency scores with liquidity-specific indicators. Specifically, we integrate Altman’s Z″-Score, Ohlson’s O-Score, and Piotroski’s F-Score with burn rate and cash runway metrics to holistically assess structural solvency, operational health, and short-term survival capacity. Our empirical analysis covers eight major Korean platform firms—Homeplus, Woowa Brothers (Baemin), Coupang, TMON, Wemakeprice, Balan, skplanet, and Kurly—over the 2020–2024 period. Using publicly disclosed financial statements and, where necessary, reputable media reports to supplement missing data, we first employ logistic regression to identify the most predictive variables for default occurrence. The results reveal that the Ohlson O-Score (β≈+0.84, p≈0.10) and Cash Runway (β≈–0.60, p≈0.12) exert statistically meaningful effects on default odds, with one-unit increases in O-Score and one-month increases in runway corresponding to roughly 2.3× higher and 0.56× lower default odds, respectively. Conversely, Z″-Score and F-Score showed limited predictive power in this binary setting. To extend beyond binary classification and capture the temporal dimension of default risk, this study applies both frequentist and Bayesian Cox proportional hazards models. Consistent with prior logistic regression results, both models identify Ohlson O-Score and Cash Runway as statistically significant predictors of time-to-default. The frequentist model estimates a hazard ratio of approximately 2.22 for O-Score and 0.56 for Runway, indicating a higher default hazard with increased financial stress and a protective effect from greater liquidity. To enhance robustness under small-sample constraints and quantify uncertainty, we implement a Bayesian Cox model using Markov Chain Monte Carlo (MCMC) sampling via the No-U-Turn Sampler (NUTS). Posterior estimates confirm the predictive power of O-Score and Runway, with 95% credible intervals excluding zero and posterior probabilities exceeding 99%. This Bayesian approach provides not only directional validation but also interpretable uncertainty bounds critical for risk-sensitive policy decisions. Building on these insights, we propose a three-stage Early Warning System (EWS) and a complementary risk-based regulatory framework (“pinpoint regulation”). Stage 1 real-time monitoring flags acute liquidity stress via burn rate spikes or runway collapse. Stage 2 quantitative diagnosis confirms structural weakness using O-Score and Z″-Score thresholds. Stage 3 synthesizes signals to classify platforms into high-risk, recovering, and stable cohorts. For high-risk firms, we recommend emergency liquidity support (conditional lending, escrow mandate), shortened settlement cycles, and external management oversight. Recovering firms receive buffer instruments (guarantees, tax incentives) and quarterly indicator reviews, while stable firms benefit from annual stress tests and disclosure expansion to foster transparent, sustainable growth. Internationally, we compare EU, US, and Japanese regulatory approaches: the EU’s PSD2 and Late Payment Directive mandate client fund segregation and penalize payment delays; the US relies on private escrow services, trust-account rules under Money Transmitter Laws, and trade-credit insurance; Japan’s Transparency Law enforces upfront disclosure and administrative guidance. These case studies underscore the effectiveness of segmented regulation—combining pre-emptive oversight for high-risk entities with marketfriendly self-regulation for sound firms. Academically, our research enriches platform finance literature by adapting legacy credit risk models to the liquiditysensitive, digitally mediated economy. Practically, it delivers a replicable methodology and policy toolbox for regulators, investors, and platform operators to preempt financial distress, safeguard transactional ecosystems, and promote resilient, inclusive growth in the digital commerce sector.

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This study empirically examines the relationship between dividend policy and management risk in Korean banks from 1999 to 2024, using fixed-effects panel regression analysis. Z-Score and standard deviation of ROE served as a proxy for bank management risk, with the study analyzing the effects of dividend payout ratio, BIS capital ratio, and their interaction on Z-Score and standard deviation of ROE. As a result, first, depending on bank characteristics, the dividend payout ratio may increase management risk or have no effect, but capital regulations can reduce this risk in banks where it increases. Second, non-linearity was found for the dividend payout ratio, also the BIS ratio exhibited non-linearity, suggesting an optimal BIS ratio can minimize management risk and promote bank stabilization. Third, for banks with lower capital adequacy (bottom 50% BIS), increasing dividends significantly amplified management risk, indicating a need for strict dividend restrictions linked to the BIS ratio for these banks. In conclusion, while a bank's dividend policy can elevate management risk, this can be mitigated through appropriate capital regulations. Financial authorities can preemptively regulate bank dividend policies via capital regulations to prevent increased management risks and stabilize the financial system.

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Our study challenges the belief that the mandatory bid rule (MBR) raises acquisition costs and deters takeovers. Leveraging the staggered adoptions of the mandatory bid rule (MBR) globally, we find that the MBR reduces the control premium, a key component of acquisition costs. Moreover, evidence does not support the claim that MBR discourages acquisitions above the threshold. While post-MBR ownership levels from block trades decline slightly, the likelihood of crossing the threshold remains unchanged. In the U.K., private deals above the threshold are fewer than below, but this gap is even larger in the U.S., which does not have an MBR.

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This study addresses a long-standing policy blind spot in Korea’s retirement pension system—namely, the absence of a structured decumulation phase. While the accumulation phase has been the central focus of most domestic pension research, with emphasis on asset management, product design, and return maximization, little attention has been paid to how pension assets are converted into stable retirement income. This study seeks to fill this gap by investigating how decumulation can be institutionalized to better support income security for retirees, particularly by enhancing fiduciary responsibility and adopting structured payout frameworks. One of the primary issues in Korea’s retirement pension system is the high proportion of lump-sum withdrawals at retirement. Despite decades of efforts to promote annuitization, the uptake of life annuities remains minimal. This is partly due to the underdevelopment of the payout phase and the absence of default decumulation pathways or fiduciary oversight during retirement. In contrast to other advanced economies, Korean pension plans lack institutional mechanisms for guiding retirees through complex income planning decisions after retirement. As such, retirees are often left to navigate investment, longevity, and liquidity risks entirely on their own. To explore institutional strategies that can improve the structure and governance of the decumulation phase, the study employs a comparative policy analysis of major pension systems. Specifically, the research examines the experiences of the United Kingdom, the Netherlands, and Australia—three countries that represent different models of decumulation governance. These cases were selected for their advanced policy frameworks in areas such as default drawdown design, collective risk-sharing, and fiduciary protection. In each of these countries, recent reforms have moved away from binary choices between annuities and full drawdowns, and toward hybrid structures that combine longevity protection with investment flexibility. For instance, the UK has introduced “investment pathways” as a default drawdown mechanism, supported by regulatory oversight to ensure product suitability. The Netherlands has transitioned to a collective defined contribution (CDC) framework, where decumulation is based on pooled longevity risk and conditional benefit adjustments. Australia, while maintaining flexibility through its superannuation system, is now moving toward setting a standard retirement income strategy (RIS) for each fund. Across these jurisdictions, fiduciary responsibilities are expanding to include not only asset accumulation but also the adequacy and stability of retirement income. Drawing on these insights, the study evaluates the structural limitations of Korea’s current decumulation landscape. Korea’s retirement pension market is dominated by defined contribution (DC) and corporate defined benefit (DB) plans operating in account-based frameworks. There is no system-wide default payout structure, nor are employers or trustees mandated to provide income guidance post-retirement. The paper analyzes the institutional factors that contribute to this gap, including fragmented plan governance, limited annuity supply, and a lack of fiduciary engagement during decumulation. To develop a practical design model suited for Korea, the study presents an institutional case analysis of the Small and Medium Business Retirement Pension Fund (SMBRPF)—Korea’s first fund-type occupational pension plan. As a centralized, trustee-governed fund with pooled asset management, the SMBRPF represents a unique testbed for integrating fiduciary-driven decumulation strategies. Based on interviews with stakeholders and analysis of fund design features, the study proposes a two-stage payout model for the SMBRPF. The first stage involves a default drawdown strategy offering flexibility within actuarially guided withdrawal limits. The second stage introduces a pooled income payout structure, akin to a tontine or CDC model, for long-term longevity protection. The paper further supports its proposal with a conceptual simulation framework. While a full stochastic simulation is reserved for subsequent research, the paper outlines key parameters to be tested, such as payout sustainability, income volatility, and longevity-adjusted income efficiency across different payout rules. The simulation design is based on the application of national life tables, projected investment returns, and policy-based mortality pooling rules. Policy recommendations include introducing fiduciary obligations for decumulation oversight, adopting standardized payout options as default mechanisms, and enabling collective risk-sharing structures, such as CDCs or longevity pools, within institutional plans. The study emphasizes that pension trustees must not only protect accumulated assets but also ensure that those assets translate into sustainable income streams after retirement. In conclusion, this study contributes to the academic and policy discourse by reframing the decumulation phase as an institutional design problem rather than an individual choice dilemma. It calls for a shift from product-centered approaches to governance-centered models in the Korean retirement pension system. By embedding fiduciary oversight and pooled payout mechanisms into the decumulation phase, the study offers a pathway for transforming Korea’s retirement pensions into a true income security system for an aging population.

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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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