This study examines how capital intensity and growth opportunities influence the relationship between dividend payouts and firm value. Although previous research has extensively explored dividend policy and firm value, little attention has been given to how firm-specific traits, particularly capital intensity, affect the value implications of dividend payouts. Using signaling theory and agency theory, this study proposes that dividends may serve as stronger signals of financial stability and managerial discipline in firms with higher capital intensity and more growth opportunities. By analyzing a sample of 7,822 firm–year observations of Korean listed firms from 2014 to 2023, we empirically test whether capital intensity moderates the link between dividend payout ratios and firm value. Firm value is assessed using Tobin’s Q and the price-to-book ratio. The findings indicate that dividend payouts are positively related to firm value, and this relationship intensifies for firms with higher capital intensity and greater growth prospects. These results suggest that tangible assets in capital-intensive firms enhance dividend signals’credibility and lower financing frictions, thus boosting investor confidence. This research adds to the literature by emphasizing the moderating role of capital intensity in the dividend–firm value relationship and providing new evidence from Korean listed firms.

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This study empirically investigates the long-term impact of listing regulation relaxation on corporate risk, focusing on South Korea’s unique "Technology-Based Special Listing" system. In line with the global trend of lowering entry barriers for innovative startups, such as the U.S. JOBS Act, South Korea introduced this scheme to enable technologically advanced firms to be listed on the KOSDAQ market. This system is particularly vital for R&D-intensive sectors such as biotechnology, artificial intelligence, and semiconductors, providing them with access to essential funding even before meeting traditional financial milestones, including profitability or specific revenue targets. While this policy fosters innovation, it also introduces significant investment risks driven by financial instability and heightened information asymmetry. Using stock return volatility as the primary proxy for investment risk, this research compares technology special listing firms with general IPO firms listed on the KOSDAQ market from 2014 to 2022. The research dataset was constructed by integrating listing data from the Korea Exchange’s (KRX) KIND system with financial and stock price information from the TS2000 database provided by the Korea Listed Companies Association. The final sample consists of 527 firms, including 149 technology special listing firms. To analyze firm-level risk, the study employs a multi-regression framework, controlling for firm characteristics such as firm size, leverage, return on assets (ROA), cash flow, and market-to-book ratios. The empirical results reveal that technology special listing firms exhibit significantly higher stock return volatility during their first year of listing compared to general IPO firms. This elevated initial risk is primarily attributed to two structural factors: the absence of conventional financial requirements leading to lower financial stability, and substantial information asymmetry inherent in firms whose value is primarily driven by intangible assets like technological potential. For these firms, the lack of a proven financial track record and a reliance on future growth expectations make their stock prices more sensitive to external shocks and market sentiment. However, a key finding of this research is that this volatility gap narrows considerably over the subsequent two to three years. As firm-specific information regarding technological progress and business milestones is disseminated into the market in a timely manner, the initial uncertainty is mitigated, leading to a stabilization of stock prices. Interestingly, while information asymmetry—measured through proxies like trading volume—significantly reduced over time, the study did not find a statistically significant improvement in the fundamental financial soundness of these firms during the same period. This suggests that the long-term reduction in stock return volatility is driven more by the resolution of information uncertainty through market learning than by fundamental financial recovery. To ensure the robustness of the findings, the study implemented several advanced econometric tests. First, the baseline risk measure was supplemented by employing market-adjusted return volatility and idiosyncratic risk. Second, the analysis was restricted to specific industries, such as medical and R&D sectors, where special listings are most prevalent, to control for industry-specific effects. Finally, to mitigate potential selection bias and omitted variable concerns, the research utilized Propensity Score Matching (PSM) and industry-specific 1:1 matching to compare special listing firms with general IPO firms of similar characteristics. The results remained consistent across all robustness checks. The findings of this research offer several contributions to both academia and practice. First, it adds to the global literature on the effects of listing regulation relaxation by providing detailed empirical evidence from the Korean market, highlighting the dual nature of capital access and investor protection. Second, unlike previous studies that focused primarily on the short-term performance or qualitative characteristics of special listing firms, this study provides a comprehensive long-term analysis of investment risk dynamics. It highlights the role of timely and transparent disclosure in reducing market uncertainty over time. For policymakers and regulators, these results suggest that while relaxing financial entry barriers is an effective tool for promoting industrial innovation, such measures must be complemented by robust disclosure requirements and institutional monitoring to protect investors and maintain market trust. For investors, the research provides a balanced perspective, emphasizing that the high initial volatility of technical listing firms is a characteristic of their unique information environment rather than a permanent state of instability. Ultimately, the study confirms that as the market processes and incorporates firm-specific information, the risks associated with innovative but financially unproven firms tend to stabilize.

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Against the backdrop of growing institutional interest in digital assets, this study examines the implications of incorporating Bitcoin into a diversified multi-asset portfolio within a dynamic asset allocation framework that extends beyond static optimization. Rather than treating asset classes as constant components, the study explores how the integration of cryptocurrency interacts with traditional instruments such as equities, fixed-income securities, and commodities under time-varying market conditions using monthly excess return data spanning from January 2014 to October 2025. The study evaluates whether the return-risk profile of Bitcoin can improve portfolio efficiency and robustness under institutional investment constraints and whether dynamic asset allocation strategies offer advantages over their static counterparts in a Korean institutional investment context. To achieve a more stable and practical allocation, this research utilizes the Black–Litterman model as the primary optimization tool, specifically designed to address the limitations of traditional mean–variance optimization, such as the "corner solution" problem and the extreme sensitivity of portfolio weights to minor changes in input data. By incorporating market equilibrium and specific investor views into the optimization process, the Black–Litterman model produces portfolio allocations that are more stable and economically interpretable. This approach is particularly relevant when dealing with Bitcoin, as its high volatility could otherwise lead to impractical and overly concentrated positions. The model is implemented under realistic investment constraints, including the prohibition of short-selling and borrowing, to reflect the practical conditions faced by domestic institutional fund managers. The framework mitigates estimation error by reducing extreme portfolio weights, thereby improving the robustness of the resulting allocations and providing a more stable basis for investment decisions. This feature is particularly important in the context of digital assets, as the high volatility and non-normal return characteristics of Bitcoin may otherwise lead to unstable portfolio allocations and excessive concentration in optimization-based strategies. At the same time, this study incorporates the time-varying nature of financial markets by employing a Markov Regime-Switching Model (MSM). Unlike static models that assume constant return distributions, the MSM allows expected returns, volatilities, and correlations to vary across different market regimes, thereby capturing time-varying market conditions more effectively. The model identifies market regimes based on three distinct criteria: macroeconomic business cycles derived from the Composite Leading Index (CLI) published by Statistics Korea, equity market volatility measured by the V-KOSPI200, and the internal return dynamics of Bitcoin itself, capturing both macroeconomic and cryptocurrency-specific cycles. This framework enables regime-dependent portfolio adjustments as market conditions evolve over time. Such an approach is particularly relevant in financial markets characterized by structural breaks, asymmetric volatility, and rapidly changing cross-asset correlations, where static allocation strategies may fail to respond effectively to shifts in market conditions. The empirical findings suggest that the proposed framework enhances risk-adjusted portfolio performance. Even with relatively small allocations to Bitcoin, portfolios incorporating the digital asset exhibit higher Sharpe, Sortino, and Omega ratios compared to those composed solely of traditional assets, indicating enhanced portfolio efficiency across multiple performance dimensions. The improvement is most pronounced in downside-risksensitive measures such as the Sortino and Omega ratios, suggesting that Bitcoin inclusion improves downside-risk-adjusted performance. This enhanced efficiency stems not only from Bitcoin's high return potential but also from its diversification benefits, as evidenced by improvements in the Diversification Ratio (DR). Bitcoin maintains relatively low correlations with traditional asset classes, particularly sovereign bonds and commodities, highlighting the importance of accounting for cross-asset interactions when evaluating portfolio performance. While Bitcoin inclusion tends to increase tail-risk measures such as Maximum Drawdown (MDD) and Conditional Value-at-Risk (CVaR) under static allocation, the dynamic MSM framework helps mitigate these risks by tactically reducing Bitcoin exposure during high-volatility or bear-market regimes, including major Crypto Winter episodes, while maintaining exposure during recovery periods. The benefit of Bitcoin inclusion is not uniform across all regimes: during high-volatility or bear-market phases, dynamic strategies help preserve capital by reducing exposure, whereas maintaining Bitcoin exposure during low-volatility or recovery phases contributes to improved risk-adjusted returns. These regime-contingent results underscore the importance of treating Bitcoin not as a static allocation but as a dynamically managed component whose optimal weight varies with market conditions. Notably, even conservative investors with a high level of risk aversion (λ=10) can benefit from allocating as little as 1–2% of their portfolio to Bitcoin, as such a modest inclusion is sufficient to improve risk-adjusted performance. This finding has direct practical implications for institutional fund managers who face strict investment constraints yet seek incremental portfolio efficiency gains. Robustness checks using alternative estimation windows, rebalancing frequencies, and risk-aversion levels produce qualitatively similar results, suggesting that the findings are not overly sensitive to model specifications. During periods of significant market downturn, including major Crypto Winter episodes, dynamic strategies demonstrate lower cumulative losses compared to static allocation approaches. Overall, the results suggest that the observed performance improvement is driven not solely by higher returns but also by a dynamic risk adjustment process that responds to regime shifts. The role of Bitcoin in a portfolio is therefore not constant but varies with market conditions, and its contribution is closely linked to regime-dependent interactions with other asset classes. From a policy perspective, the results highlight the importance of establishing a clearer regulatory and institutional framework for digital asset investment in Korea, including standardized legal definitions, accounting treatment, and capital market guidelines, to support the responsible integration of Bitcoin into institutional portfolios. However, these findings should be interpreted within the context of the sample period and model specifications employed in this study, and caution is warranted when extrapolating the results to different market environments. Taken together, this study provides an empirically grounded framework for understanding the role of digital assets in dynamic portfolio management.

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This study examines ex-post responses in pension fund asset management when pre-determined asset allocation constraints are breached due to abrupt market fluctuations or unexpected expenditure shocks. In practice, Korean pension funds typically rely on static strategic asset allocation frameworks that specify target weights and allowable ranges for each asset class. While such frameworks play a central role in risk management and long-term portfolio governance, they are inherently static and provide limited guidance on how investment committees should respond once allocation constraints are violated. This limitation becomes particularly critical in the presence of alternative assets, which are characterized by substantial illiquidity and high transaction costs when liquidated prematurely. Hence, this study develops a formal decision-making model that explicitly incorporates the illiquidity of alternative assets and evaluates the economic implications of different ex-post responses from the perspective of the funding ratio. We consider a pension fund portfolio consisting of two asset classes: liquid traditional assets and illiquid alternative assets. In the presence of an exogenous expenditure shock that necessitates asset sales, the fund faces a discrete ex-post decision between two response strategies. The first strategy is immediate liquidation, in which both liquid and alternative assets are sold in proportions that strictly adhere to the original target allocation weights. This response maintains consistency with the pre-defined strategic asset allocation and the associated risk-return profile, but it entails potentially large and deterministic transaction costs due to the forced liquidation of illiquid assets. The second strategy is a temporary suspension of allocation constraints, under which the fund meets its liquidity needs solely by selling liquid assets while postponing the liquidation of alternative assets. This approach avoids immediate transaction costs but allows for deviation from target weights, potentially worsening the risk-return trade-off relative to the efficient frontier implied by mean-variance optimization. The model evaluates these two ex-post responses by comparing the expected utility of the funding ratio at the end of the period. Preferences are represented by a constant relative risk aversion (CRRA) utility function defined over the funding ratio, reflecting the objective of pension fund investment committees to balance return generation against the risk of underfunding. Within this framework, we examine how the optimal ex-post response depends on three key determinants: the transaction costs associated with alternative assets, the magnitude of the expenditure shock, and the pension fund’s constant relative risk aversion coefficient. Our analysis yields several notable findings. The relative risk aversion coefficient emerges as the dominant factor in determining the optimal ex-post response. While higher transaction costs generally increase the attractiveness of suspending allocation constraints by making immediate liquidation more expensive, their effect is often dominated by the fund's degree of risk aversion. Pension funds with higher levels of risk aversion exhibit a strong preference for immediate liquidation, even when transaction costs are substantial. This preference arises because highly risk-averse decision makers penalize the increased uncertainty and tail risk associated with deviations from the target allocation more heavily than the certainty of transaction costs incurred through liquidation. In contrast, the magnitude of the expenditure shock itself plays a relatively minor role in the decision, once the effects of risk aversion and transaction costs are taken into account. Furthermore, the study highlights the critical role of the gap between the initial allocation to alternative assets and the optimal allocation implied by mean-variance optimization. When the initial weight of alternative assets exceeds the mean-variance optimal level, suspending allocation constraints exacerbates the deviation from the efficient portfolio and further deteriorates the risk-return trade-off. In such cases, immediate liquidation is more likely to be optimal, particularly for risk-averse funds. Conversely, when the initial allocation to alternative assets is below the optimal level, a temporary suspension of allocation constraints can be welfare-improving. By selling only liquid assets, the relative share of alternative assets increases endogenously, moving the portfolio closer to the optimal allocation while simultaneously avoiding transaction costs. This result demonstrates that the desirability of ex-post flexibility is contingent upon the ex-ante positioning of the portfolio. These findings underscore the importance of linking ex-post decision rules to ex-ante asset allocation design. While ex-post flexibility can mitigate unnecessary costs and improve outcomes following shocks, such flexibility is not unconditional. Rather, it is supported by an appropriately calibrated strategic asset allocation that aligns initial portfolio weights with optimal risk-return trade-offs. In this sense, the study emphasizes that effective ex-post responses are not substitutes for sound ex-ante asset allocation, but complements that depend on the quality of the initial allocation. This study contributes to the literature by providing a structured and tractable framework for evaluating ex-post responses in pension fund asset management under illiquidity and allocation constraints. Unlike existing studies that primarily focus on ex-ante optimization or permanent changes in allocation policy, this paper formalizes the trade-off faced by pension funds when responding to realized shocks within a static strategic allocation framework. From a practical perspective, the results offer clear guidance for pension fund investment committees and outsourced chief investment officer (OCIO) managers. By jointly considering transaction costs, risk preferences, and the alignment between initial and optimal asset allocations, the proposed framework supports more disciplined and transparent ex-post decision-making. Ultimately, the analysis suggests that a model-based approach to ex-post responses can enhance the robustness of pension fund governance by preventing mechanically enforced rebalancing and by promoting economically justified flexibility under well-defined conditions.

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This paper studies within-exchange price dispersion between gold and mini-gold in Korea Exchange (KRX). Although the two products are claims on the same underlying commodity, trade on the same centralized exchange, are quoted in the same currency, and are traded in identical one-gram increments, mini-gold nevertheless trades at persistent discounts relative to gold. Using the full trading history of KRX mini-gold, I show that mini-gold discounts are strongly state dependent with respect to international parity deviations: when domestic gold trades below the international benchmark, the probability of a mini-gold discount rises sharply. I further show that domestic frictions explain the depth of the wedge. Relative illiquidity and mini-gold trading inactivity are both associated with wider spreads, and the pricing of illiquidity is materially stronger in discount states. These results are robust to alternative proxies based on relative participation and OHLC-implied intraday roughness. Taken together, the evidence supports a nested limits-to-arbitrage interpretation in which international dislocations affect the incidence of discount states, while domestic liquidity and participation frictions determine the magnitude of within-exchange price dispersion.

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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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