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.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

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.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

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.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

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.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

The National Pension Service(NPS) has increased the share of risky assets such as stocks to raise profitability with concerns over fund depletion. If so, has the NPS been making a profit on investments in risky assets so far? If the NPS are making a profit, where is the source of that ability? To answer this question, we directly analyzes the Korean stock portfolio held by the NPS. The objective of this study is to examine the operational performance of NPS investments in the Korean stock market over the past decade, as well as the sources for this performance. The NPS invests in a variety of risky assets, including stocks, bonds, and alternative investments. Of these, domestic stocks account for about 14% of the total as of the end of 2023, amounting to KRW 148 trillion in investments. The NPS is also the largest institutional investor in the domestic stock market and has a huge influence on the market, so it is very important. We obtain the monthly holdings of NPS in domestic stocks from 2009 to 2018 by combining the report on large holdings of stocks reported by the NPS and the trading data from the Korea Exchange (KRX). Specifically, we calculate the transaction history of the NPS from the KRX. The KRX's intraday trading data contains information on the seller and buyer for every transaction, and we use the seller and buyer information to identify the NPS accounts. The process is as follows. First, we obtain the name, dates, quantity and price of purchase and sale stocks from 72,622 reports from 2008 to 2018 in the name of the NPS through the “Report on Large Holdings of Stocks and Other Securities” and “Report on Ownership of Certain Securities by Officers and Major Shareholders.” Second, we identify the NPS accounts by matching the transaction details of each account with the transaction data of the KRX. Based on 72,622 reports, we identify 12,449 NPS accounts from 46 securities firms. This study analyzes the performance of NPS using two methods in addition to the raw portfolio return. First, we evaluate the performance of the NPS using various benchmarks. We examine risk-adjusted returns by using single factor or multi-factor models. However, it has the disadvantage of evaluating the performance based on the assumptions of the risk factor model. Second, we use the Daniel et al. (DGTW, 1997) methodology to decompose returns by controlling for a benchmark portfolio based on firm characteristics. This methodology overcomes the shortcomings of the traditional risk factor model. The main results of the empirical analysis are as follows First, the domestic equity portfolio held by the NPS has an average monthly return of 0.57% over the period from 2009 to 2018. This is equivalent to an annualized rate of 6.84%. The risk-adjusted return using a one-factor model of market returns is 0.31% per month, which is statistically significant. The risk-adjusted return using the Fama-French (1993) three-factor model is 0.49% per month and the risk-adjusted return using the Carhart (1997) four-factor model is 0.48% per month, both statistically significant. This shows that even after controlling for risk, the NPS is still generating significant returns. By year, the risk-adjusted returns are higher and statistically significant mainly in 2012 and 2013. Second, we separate the returns of the NPS into the KOSPI and the KOSDAQ market. The return on the KOSPI stocks is 0.58%, which is much larger than the return on KOSDAQ stocks (-0.01%). The NPS earns most of its returns from investing in the KOSPI market stocks and loses money in the KOSDAQ market. The risk-adjusted returns of the KOSPI stocks are also statistically significant at 0.33%, 0.51%, and 0.51%, respectively, while the risk-adjusted returns of the KOSDAQ stocks are not statistically significant. Overall, the performance of the NPS is better in the KOSPI market than in the KOSDAQ. Third, we decompose the investment performance of NPS according to the methodology of Daniel et al. (DGTW, 1997). DGTW decomposes fund performance into characteristic selectivity (CS), market timing (CT), and average style (AS). The analysis shows that the portfolio return is composed of 0.44% for CS, -0.16% for CT, and 0.30% for AS. The largest return comes from stock selection, while market timing is not significant with a negative return. This result is similar to other studies in the U.S. and Korea that decompose the performance of active funds. When we decompose the portfolio returns of the NPS into the KOSPI and KOSDAQ markets, we also find that the returns of the NPS are mostly due to stock selection ability (CS) and investment style (AS). Our results show that the majority of the NPS's investment performance is attributable to its ability to select Korean equities and average style performance.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

A recent study by Lee and Shin (2022) suggests that changes in the swap basis, defined as the difference between the cross-currency and interest rate swap rates, can predict the one-week ahead changes in the Korean Won-United States Dollar exchange rate. In this study, we propose using the cross-currency and interest rate swap rates as separate predictors, which corresponds to the unrestricted version of the swap basis model. The predictive power of the swap basis may not be stable depending on foreign exchange market and economic conditions, in which case the unrestricted model can better predict exchange rate changes. The unrestricted model shows superior performance in both in-sample and out-of-sample tests, and this result is robust when controlling for potential contemporaneous effects of the swap basis and other instruments, as the predicted variable instead of the original FX return. Our results are also consistent when we use daily and monthly data. In a monthly horizon, the cross-currency swap rate loses its predictive power and the interest rate swap rate tends to be a dominant predictor, which again makes the unrestricted model a better predictive model.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

In this study, we investigate momentum strategies in Japan's stock market, which has historically diverged from global trends in effectiveness. We analyze the performance of three momentum factors—WML, MOM_6, and MOM_12, each differing in weighting, formation, and holding periods. Our findings reveal that while theWML factor with value-weighted portfolios shows positive average returns, their statistical significance is weak. In contrast, MOM factors constructed using equal-weighted portfolios yield negative returns. By examining momentum strategy returns across four market dynamics—BEARDOWN, BEARUP, BULLDOWN, and BULLUP—we find that momentum strategies produce positive returns during BEARDOWN market trends. However, mixed results are observed in BULLUP market between WML factor and MOM factors, and momentum strategies tend to underperform during market reversals, such as BEARUP and BULLDOWN. Given these findings, we applied the framework of Daniel and Moskowitz (2016) to assess whether the poor momentum returns in Japan can be attributed to momentum crashes. Although higher volatility and higher market beta for Loserportfolios are observed in BEAR markets, results suggest that significant momentum crashes do not necessarily coincide with the most volatile months or with the highest market beta for Loser portfolios, challenging the explanation by Daniel and Moskowitz (2016). Our findings indicate that Japan's distinctive market dynamics are key to understanding the underperformance of momentum strategies. While previous studies have emphasized socio-cultural factors, such as Japan's collectivist society, it is crucial to recognize the recent shifts towards individualism and corporate governance reforms. These changes suggest that traditional explanations for momentum strategy failures may not fully apply in Japan, where unique market conditions and an evolving socio-cultural landscape play a more critical role.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

In recent years, easing the estate tax burden has been consistently proposed as a solution to address the “°Korea Discount.”± The central argument is that reducing the current top marginal tax rate of 50% or eliminating the surcharge on shares bequeathed tothe largest shareholder group would help mitigate the tax burden associated with rising stock prices at the time of bequest. This, in turn, is expected to encourage controlling families to focus on enhancing firm value. Another argument is that the high estate tax burden incentivizes controlling shareholders and their families to engage in tunneling as a means of transferring corporate control. As a result, the stock prices of firms involved in such practices tend to be undervalued. It is also argued that reducing the estate tax burden could help mitigate this problem. However, empirical studies supporting this claim are scarce. The only study directly addressing this topic is Yeh and Liao (2018), who conducted an event study based on the Taiwanese government’s policy of reducing the top marginal estate taxrate from 50% to 10%. They found that during the [0, +5] event window, the stock prices of family-controlled firms rose by 1.44% relative to non-family firms, suggesting improved ownership structures in family firms. This study indirectly examines the effect of estatetax relief by analyzing stock price movements immediately following the assessment period for bequeathed share values, defined as two months after the valuation reference date. Prior to the end of the assessment period, stock price increases directly raise the estate tax burden, incentivizing controlling families to suppress stock prices. After the assessment period, however, this incentive disappears abruptly, producing an effect analogous to estate tax relief. This methodological approach offers several significant advantages: it allows research to be conducted even without an actual change in the top marginal tax rate, facilitates more precise estimation of event effects by leveraging the unpredictable nature of death, and expands the number of analyzable events (i.e., instances of death). To utilize this empirical research setting, we collected data on 36 deceased individuals—including group chairmen and their family members—from business groups designated for disclosure by the Korea Fair Trade Commission between 2000 and 2023. At the time of their passing, these individuals collectively held equity stakes in 73 publicly listed affiliate firms. Cases in which the decedent donated their equity holdings to public interest foundations or similar entities were excluded from the sample. Using this data, this study reveals that reducing estatetax burdens does not lead to an increase in stock prices for bequeathed shares. First, no statistically significant differences areobserved in stock price reactions between firms with bequeathed shares and those with non-bequeathed shares within the same business group immediately following the assessment period. This pattern holds consistently when examining buy-and-hold abnormal returns (BHAR) over 3-month, 6-month, and 1-year periods after the assessment. Furthermore, when comparing firms likely to increase dividends to pay estate taxes—those with bequeathed shares and those with family-owned shares but no new bequest—no statistically significant differences in stock price reactions were found. Neither the proportion of bequeathed shares nor the relative value of bequeathed stock shows a significant relationship with stock price movements. This pattern remained consistent even in firms where controlling families exert relatively greater influence on stock prices, such as those with smaller market capitalizations or lower price-to-book ratios (PBR). Similar results were also observed in post-2014 samples following amendments to the Monopoly Regulation and Fair Trade Act that curtailed unfair succession practices. Furthermore, this pattern was evident in firms where group succession was insufficiently advanced, potentially heightening incentives for stock price management. Additionally, the number of investor relations (IR) meetings, as well as R&D and capital expenditures,showed no substantial changes before or after the death of controlling shareholders, suggesting that efforts to enhance firm value were not significantly intensified following their death. Finally, while the age of controlling shareholders was associated with a decline in firm value, this trend was observed across all firms, regardless of whether the controlling shareholder held shares in the firm. This finding contrasts with the expectation that such effects would be limited to firms in which the controlling shareholder holds shares, where incentives to suppress stock prices may increase as mortality risk rises. These findings indicate that the observed decline in firm value is not the result of deliberate efforts by controlling families to suppress stock prices in response to estate tax burdens. Instead, the results challenge the argument that estate tax relief directly leads to significant stock price increases or enhanced firm value.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel

We examine a firm’s decision to choose between an equity spin-off and a captive spin-off and the decision’s announcement effect. A captive spin-off, which is a unique form of corporate divestiture in Korea and a few other countries, differs from an equity spin-off in that the spun-off entity remains a wholly owned subsidiary of the parent company and shareholders cannot directly hold ownership stakes in the subsidiary even after the spin-off. On the other hand, in the case of an equity spin-off, which is prevalent in many developed countries such as the U.S., incumbent shareholders receive proportional ownership in the spun-off entity, which typically becomes a publicly listed company after the spin-off. It is well-documented that corporate spin-offs can be beneficial to shareholders because they are likely to reduce negative synergies via refocusing, mitigate information asymmetry, and address relevant agency issues. However, many practitioners in Korea have criticized captive spin-offs, arguing that they can be detrimental to minority shareholders because the controlling owner can decide the spun-off entity’s eventual disposal without shareholder intervention. Despite the notable differences between these two spin-off methods, little has been examined regarding the determinants of firms’ spin-off method choices. We examine a large sample of Korean spin-offs, including both equity and captive spin-offs, from 1998 to 2022. Note that, in our sample, captive spin-offs account for 75.6% (704 spin-offs) of all spin-off activities in Korea, whereas equity spin-offs account for only 24.4% (227 spin-offs). This prevalence of captive spin-offs highlights the importance of our study examining what motivates a firm to choose such a controversial spin-off method. Examining simple mean differences between the two types of spin-offs, we find that captive spin-offs are preferred to equity spin-offs by firms that are smaller, less profitable, younger, investing more, and paying out less. These differences suggest that a firm’s choice of spin-off method can be driven by factors other than agency issues. In our multivariate analysis, we find evidence that a firm’s decision to choose between the two spin-off methods can be affected by market valuations. Specifically, an average parent firm is more likely to choose captive spin-off when it is overvalued relative to its industry peers, whereas it is more likely to choose equity spin-off when it is undervalued relative to its peers. Similarly, firms in overvalued industries are more likely to choose captive spin-offs, while those in undervalued industries are more likely to choose equity spin-offs. Moreover, firms with high growth rates and low cash flows, i.e., those likely in need of capital infusion, are more likely to choose captive spin-offs. Examining market reactions to spin-off announcements, we find that equity spin-off announcements, on average, attract more favorable market reactions in terms of announcement period cumulative abnormal returns than those of captive spin-offs. Further analysis reveals that the average announcement return is highest among undervalued parent firms that chose equity spin-offs, and the return is lowest among overvalued firms that chose captive spin-offs. Also, in the case of the former, announcement returns are on average higher when the spun-off entity is smaller relative to the parent, whereas in the case of the latter, announcement returns are negatively correlated with the spun-off’s relative size. We also examine the effects of a firm’s ownership structure on its choice of spin-off method, focusing on controlling family ownership, affiliated firm ownership, and blockholder ownership. Although we find that blockholder ownership concentration is negatively associated with the likelihood of captive spin-offs, we do not obtain consistent results regarding the other ownership variables. Note, however, that these results should be interpreted with caution because these ownership variables may not fully capture the complex ownership structure of Korean firms. Overall, our results are consistent with the notion that firms may strategically choose the method of their spin-offs to exploit market misvaluation, and such a choice can signal the market regarding their valuations. That is, equity spin-offs can be chosen to allocate undervalued shares to incumbent shareholders, which will attract positive market reactions, whereas captive spin-offs can be used as a means of raising capital using overvalued equities, which will result in less favorable market reactions. Adding to the prevailing view that captive spin-offs are used as a means of minority shareholder expropriation, this study offers another perspective that a firm’s choice between equity and captive spin-offs can be driven by a rational reaction to market misvaluation that is not necessarily disadvantageous to minority shareholders. That is, market reactions to corporate spin-offs in Korea can partly be driven by the signaling effect of firms’ spin-off method choices. Therefore, an assessment of whether a spin-off is detrimental to minority shareholders should consider not only the parent firm’s agency issues but also the market valuations leading up to the firm’s spin-off decision.

• EndNote
• RefWorks
• Scholar's Aid
• BibTeX

Export Citation Cancel