Mini KOSPI 200 options are derivatives based on the KOSPI 200 index, just like the regular KOSPI 200 options. While the contract terms, such as expiration dates and trading hours, are identical, mini options are structured to allow smaller investments by reducing the contract size to one-fifth (from a multiplier of 250,000 KRW to 50,000 KRW). This makes them accessible to smaller investors, aiming to increase retail participation. However, the question remains: have mini options successfully attracted retail investors as originally intended? As market data reveals, the share of retail investors in the mini options market is significantly lower than in the regular options market, contrary to the product’s initial purpose. Institutional investors, particularly securities firms, dominate the mini options market, contributing to its low liquidity. As a result, the liquidity premium in mini options leads to higher pricing compared to regular options with the same strike prices and expiration dates. This creates a barrier for retail investors, who generally prefer long positions and are sensitive to price disparities. Therefore, mini options have not achieved their goal of increasing retail participation and are instead primarily traded by institutional investors. The difference in liquidity and the composition of market participants leads to differing price determination mechanisms between mini and regular options. The distinct characteristics of the mini options market suggest that the optimal option pricing models suitable for regular options may not be applicable to mini options. The Black-Scholes (BS) option pricing model, introduced in 1973, has long been a fundamental tool in the options market, but its limitations have led to the development of many alternatives. The BS model, while advantageous for its simplicity and closed-form solution, fails to accurately reflect real-world variables like volatility and risk-free interest rates. Implied volatility, which is calculated based on option prices, tends to vary with strike prices and expiration times, a phenomenon known as the volatility surface, indicating that the BS model does not fully capture market realities. To overcome these limitations, various alternative models have been proposed, including stochastic interest rate models, stochastic volatility models, jump diffusion models, variance gamma models, and regime-switching models that assume sudden changes in volatility. GARCH models that assume conditional heteroscedasticity in the underlying asset are also used. Additionally, the Ad-Hoc Black-Scholes (AHBS) model is popular among market participants for estimating implied volatility using simple regression analysis. Previous research has shown that stochastic volatility models offer the greatest improvement over the BS model. Studies focused on the regular options market found that the AHBS model outperforms more mathematically complex models, including stochastic volatility models, in both pricing accuracy and hedging performance. Given that mini options display characteristics distinct from regular options, it is necessary to question whether the optimal pricing models used for regular options can still be applied effectively. This study aims to identify the optimal option pricing model for the mini KOSPI 200 options market. By comparing in-sample and out-of-sample pricing accuracy and hedging performance, we aim to recommend the most suitable model for participants in the mini options market. The models under consideration include the BS model, the AHBS model, and models that account for stochastic volatility and jumps. Through this comparison, we examine how differences in liquidity and the composition of market participants affect option pricing and hedging. The study suggests that markets with a high proportion of retail investors may favor simpler models like the BS model, while markets dominated by institutional investors might see better performance with more mathematically complex models. Given that the mini options market has a lower proportion of retail investors compared to regular options, it is expected that more sophisticated models that account for stochastic volatility and jumps will outperform simpler models like the BS model. This study provides several contributions to existing research. First, it is the first to explore the optimal options pricing model specifically for the mini KOSPI 200 options market. While mini options share many characteristics with regular options, their smaller contract sizes and different participantcomposition create unique market dynamics. The study shows that institutional investors play a larger role in the mini options market than in the regular options market, and that mini options have lower liquidity. These factors must be considered when selecting the optimal pricing model. Second, this research utilizes long-term data spanning 90 months, from the market’s inception to the present, offering a comprehensive view of the market’s evolution. Most previous studies focused on the early stages of the mini options market, where liquidity was insufficient. By incorporating a longer time frame, this study is able to capture the effects of market maturity and changes in volatility and liquidity over time. The results are as follows. Models that account for both stochastic volatility and jumps show the best performance in both in-sample and out-of-sample pricing tests. In terms of hedging performance, the AHBS model that includes both first-order and second-order strike prices performs the best. However, the differences in hedging performance across models are relatively small. When the forecasting period is extended to one week, the results remain consistent with the one-day forecast. Monthly performance also shows consistency, with statistically significant differences between the models' performance over the entire sample period. Compared to previous studies on the regular options market, which found that simpler AHBS models provided the best pricing and hedging performance, this study reaches different conclusions. The differences in liquidity and participant composition between the mini and regular options markets play a significant role in the selection of the optimal pricing model.

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This paper explorers the contributing factors to the Terra (UST)-Luna crash in May 2022. We argue that the crash was primarily fueled by the unsustainable interest rates offered by the Anchor Protocol. Initially, Terra was designed to facilitate fee-free payments and ensure stability through a diversified demand base from payment services. However, the launch of the Anchor Protocol in 2021, with its 19.45% interest rate, shifted UST's focus towards speculative demand. This change led to an unsustainable increase in UST's supply, ultimately triggering a run from Terra. We discuss flaws in the governance mechanism for setting Anchor Protocol's interest rates. The exorbitant yield offered by the Anchor Protocol significantly contributed to undermining Terra's long-term stability. The speculative demand incited by the Anchor Protocol's high interest rates was likely a major factor in the Terra collapse in May 2022.

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Due to the influential works of Fama and French (1992, 1993), the value premium often refers to the book-to-market effect although there are alternative valuation measures. Recent evidence indicates that the book-to-market effect has weakened over time or even disappeared in the U.S. stock market. The literature suggests that, even in the Korean stock market, the value premium measured by an individual valuation ratio, including book-to-market, may significantly fluctuate over time, and a value factor constructed based solely on the book-to-market ratio may not capture the true value premium. This study aims to verify the predictability of value premiums based on various valuation measures, not just the book-to-market equity ratio, and to identify which value predictor is consistently useful in the Korean stock market over a long period from 1987 to 2023. We further attempt to investigate why the most useful valuation measure has a better predictive performance than others. To this end, we consider six valuation measures, including the book-to-market ratio, retained earnings-to-market ratio, contributed capital-to-market ratio, earnings-toprice ratio, cash flow-to-price ratio, and dividend-to-price ratio. The main findings are summarized as follows. First, except the contributed capital-to-market ratio, all alternative value measures can significantly predict the cross-section of stock returns when book-to-market is controlled for. The most significant is retained earnings-to-market, followed by dividend-to-price, and these two measures predict value premiums more strongly than the book-to-market ratio. The results are consistent with Ball et al. (2020) in that retained earnings-to-market is a strong predictor of the value premium whereas contributed capital-to-market is not, but controlling for the retained earnings-to-market ratio does not eliminate the predictive power of book-to-market in Korea. Second, we construct value factors based on each of the six valuation measures to compare their historical performance and find that the HML factor, which represents the book-to-market effect, has a Sharpe ratio of 0.35 in the period prior to July 2005 and 0.83 in the period after July 2005, indicating a stronger recent performance unlike in the U.S. market. However, when compared to alternative value factors based on useful valuation measures, the HML factor performs worst before July 2005 and best after July 2005, which implies that the book-to-market effect substantially varies over time, as in the U.S. market. On the other hand, the retained earnings-to-market factor has the highest Sharpe ratio of 0.64 throughout the sample period and almost equal Sharpe ratios during the periods before and after July 2005. In addition, the retained earnings-to-market factor has higher correlations with the earnings-to-price, cash flow-to-price, and dividend-to-price factors than the HML factor. Therefore, retained earnings-tomarket is the strongest and most reliable predictor of the true value premium in any period. The HML factor incorporates not only the useful information contained in retained earnings-to-market but also the information in contributed capital-to-market, which has little to do with the value premium, and as a result, it performs worse in periods when it is highly affected by contributed capital-to-market. In this sense, the HML factor may be a relatively unstable indicator of the true value premium. Third, in spanning regressions, the retained earnings-to-market factor subsumes all other value factors, including HML, and has additional information that the other value factors do not have. The next most valuable value factor is the one based on the dividend-to-price ratio. The remaining value factors and the HML factor do not seem to capture all of the information from each other and their priority is not clear. As a result, the value factor that is most useful as an asset pricing factor seems to be the retained earnings-to-market factor. Finally, considering that retained earnings represent the difference between accumulated earnings and accumulated dividends over the firm’s history, we investigate whether accumulated earnings or accumulated dividends over recent years are more responsible for predicting the cross-section of expected returns and find that accumulated dividends, rather than accumulated earnings, primarily drive the predictive power of retained earnings-to-market in Korea. Moreover, we find that retained earnings-to-market can strongly predict the growth in earnings over two to three years. In contrast, the book-to-market, cash flow-to-price, dividend-toprice ratios do not predict earnings growth. Therefore, the outperformance of retained earnings-to-market as a predictor of the value premium may be related to useful information about future earnings growth. Although we show that the book-to-market effect has recently become stronger in Korea, unlike in the U.S., our findings are consistent with recent evidence in the U.S. market in that the book-to-market effect may not always represent a true value premium. Our findings consistently show that the retained earnings-to-market ratio is the best predictor of the value premium among well-known and easily observable valuation measures. Moreover, our results of spanning tests suggest that it may be desired to construct a value factor based on retained earnings-to-market rather than book-to-market in asset pricing models.

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We investigate the factors influencing investors' decision-making in the Korean mutual fund market. Our findings indicate that investors prioritize simple signals when allocating capital to mutual funds, such as excess returns on benchmarks designated by fund rating companies or market indices like KOSPI and KOSPI200. Conversely, investors are less inclined to use sophisticated asset pricing models, including the CAPM (Capital Asset Pricing Model), Fama and French (1993) Three-factor model, and Carhart (1997) Four-factor model. Notably, institutional investors are more likely than retail investors to utilize these asset pricing models when selecting mutual funds. Our results remain robust even when accounting for observations following the Global Financial Crisis (GFC), extreme returns, and changes in fund ratings provided by rating agencies. Furthermore, we demonstrate that the weighting of time-series fund flow-performance sensitivity does not affect our conclusions. Our research suggests that in Korea's mutual fund market, investors tend to rely on straightforward indicators rather than the complex pricing models proposed by earlier studies. Importantly, our results suggest that this preference for simple indicators among retail investors is not unique to any specific country. We conclude that retail investors generally lack the level of financial literacy required to effectively use risk-adjusted performance measures.

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Research suggests that overconfident managers often harm firm value by taking excessive risks due to overly optimistic future outlooks or inflated self-assessments (Griffin and Tversky, 1992; Malmendier and Tate, 2008). Overconfident CEOs are reported to overestimate the expected returns from uncertain ventures. However, Hirshleifer, Low, and Teoh (2012) present evidence that overconfident CEOs, who tend to be more enthusiastic about challenging and risky projects, invest more in innovation and often succeed, introducing the "overconfident manager puzzle" where such managers enhance rather than damage firm value. This study aims to test these conflicting findings in the context of Korean firms. It explores the hypothesis that optimistic managers—unlike overconfidence, which is traditionally viewed as harming firm value through overinvestment—might instead be innovators who enhance firm value. The study utilizes a novel methodology involving machine learning to measure managerial overconfidence. Unlike U.S. studies that often use stock options as indicators, this research leverages text analysis of managerial opinions disclosed in business reports, using the BERT machine learning model to quantify optimism. The ambiguity in measuring overconfidence in previous studies is another consideration. Psychologically, overconfidence is seen as an irrational bias, but empirical variables might capture a manager’s rational optimism about the future. If a manager’s outlook is rational, overconfidence could lead to positive outcomes, unlike the negative connotations typically associated with it. Many studies interchange overconfidence with optimism, where the former implies irrational excessive confidence and the latter signifies a positive future outlook. Generally, overconfident CEOs are believed to make poor decisions by overestimating future performance and underestimating risks, leading to value-destroying mergers and acquisitions (Malmendier and Tate, 2005, 2008). Such CEOs are often reported to overpay in M&A deals, necessitating strict control through compensation and governance structures. Overconfident managers also tend to invest more than their less confident counterparts, potentially harming firm value through overinvestment (Moez and Amina, 2008; Chen, Ho, and Ho, 2014). Conversely, some researchers highlight the positive roles of overconfidence. It can enhance decision-making execution, encourage necessary risk-taking for shareholder benefit, and stimulate entrepreneurial activities (Russo and Schoemaker, 1992; Goel and Thaker, 2008; Bernardo and Welch, 2001). Hirshleifer et al. (2012) found that overconfidence negatively impacts acquisitions but positively influences innovation. They suggest that overconfident managers in firms with innovation opportunities can achieve significant success, unlike those in firms without such opportunities who might make detrimental acquisition decisions. The study’s empirical analysis yielded several key findings. First, contrary to expectations, there was a negative relationship between CEO Optimism and risk, as measured by stock return volatility, suggesting that optimistic CEOs do not prefer riskier projects unlike overconfident CEOs. However, this relationship turned positive when controlling for capital availability, indicating that optimistic CEOs choose risky projects when capital is accessible. This implies that the measure of optimism might reflect rational optimism rather than irrational bias. Second, optimistic CEOs were found to increase R&D investments and engage more in innovation activities, such as filing patents. Third, they tend to hold more cash to seize future investment opportunities. Last, optimistic CEOs were confirmed to enhance firm value, aligning with Hirshleifer et al. (2012), suggesting that they are rational optimists driving innovation rather than irrationally overconfident leaders. In conclusion, the study reaffirms that optimistic managers can be seen as rational optimists whose confidence drives innovation and firm value enhancement, challenging the traditional view of overconfidence as purely detrimental.

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Previous studies have examined how credit ratings are associated with corporate financial decisions by focusing on their linear relationship (e.g., Baghai, Servaes, and Tamayo, 2014; Khieu and Pyles, 2016; Jung and Kim, 2018; Kim and Kim, 2019; Jeon and Lee, 2020). However, this relationship is not likely to be linear because market recognition on credit ratings and financial regulations are affected by alphabetic ratings (Kisgen, 2006). For example, firms near credit rating upgrade (a rating designated with "+") or downgrade (a rating designated with "-") have stronger incentives to obtain a credit rating jump or avoid a credit rating downgrade than these not near credit rating change issue, resulting in different corporate financial decisions. In this paper, we thus examine how the likelihood of credit rating changes affects corporate financial policies. We hypothesize that firms near credit rating changes are more likely to have conservative financial policies than these not near rating changes. Our hypothesisis based on two reasoning. First, firms closer to credit rating changes are more likely to pursue conservative financial decisions to reduce the cost of raising capital. Because credit ratings are a significant factor to determine the cost of raising capital, firms near a rating change issue are more concerned about possible changes in their cost of capital. Hence, firms with a plus (minus) rating will be more conservative regarding their financial decisions to obtain the higher rating (maintain the current rating) than these without a plus (minus) rating. Second, firms near a change in rating are likely to choose more conservative policies to meet the investment criteria of financial institutions. Financial institutions face several regulations with respect to their investments. Under the regulations, a firm’s credit rating is one of important criteria to decide the investments of financial institutions. Therefore, firms with a minus (plus) rating are motivated to set greater conservative policiesto maintain current investments (obtain new investments) from financial institutions than these not near a rating change issue. To test our conjecture, we use data on credit ratings to Korean firms over the 2011-2022 period. Following Kisgen (2006), We define firms with a plus (+) or minus (-) rating as these closer to rating changes (upgrade or downgrade). Using the rating outlook data, we also define firms with a "Positive" or "Negative" ("Stable") outlook as these near (not near) rating changes. Moreover, we measure corporate financial policies using a firm’s leverage, dividend, and cash holdings. Our final sample includes 2,736 firm-year observations between 2011 and 2022. We find that firms with a plus or minus rating tend to use less debt, pay less dividend, and hold more cash than these without a plus or minus rating. We further find that firms with a "Positive" or "Negative"outlook are likely to borrow less, pay less dividend, and reserve more cash than these with a "Stable" outlook. The results suggest that firms more prone to rating changes are more likely to have conservative financial policies to reduce the cost of raising capital and meet the investment criteria of financial institutions. In addition, our findings remain consistent after mitigating endogeneity issues subject to reverse causality, omitted variable bias, and measurement errors. Specifically, our results persist when we re-estimate the likelihood of credit rating changes using the rating outlook data and perform propensity score matching analysis and system generalized method of moments (GMM) estimations. Unlike the previous literature that hasexamined on a linear relationship between credit ratings and corporate decisions, this paper contributes to the corporate finance literature by suggesting a non-linear relationship between the likelihood of credit rating changes and corporate financial policies. Furthermore, while existing studies on credit rating changes have focused on capital structure (Kisgen, 2006; Kim, Seol, and Kim, 2007; Kim and Yoon, 2013), earnings management (Kim, 2016), cost behavior (Kim and Chung, 2017), corporate governance (Hong and Kim, 2019), and voluntary disclosure incentive (Kim, 2022), this paper adds to the literature by exploring various corporate financial policies and enriching the understanding of corporate decisions with respect to credit rating concerns.

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This study is a study to empirically confirm the insolvency risk caused by the induction of large-sized securities companies after the introduction of the comprehensive financial investment business (extra-large securities companies) policy in the Korean capital market. Since the introduction of the polcy, there have been concerns that the expansion of the business scope of securities firms may expose them to various risks, such as high-risk investments due to leverage expansion, concentration of real estate PF assets deepening, and credit risk and interest rate risk due to trading of derivatives-linked securities. In this study, the impact of equity capital on insolvency risk was analyzed using a panel regression model for 39 domestic securities firms from the first quarter of 2009 to the fourth quarter of 2016, before and after the introduction of the policy in October 2013. Among previous studies, studies that presented positive results regarding the enlargement of financial companies focused on individual financial companies, while studies that presented negative results focused on the perspective of the financial system as a whole. The introduction of comprehensive financial investment businesses, which was the focus of this study, is a topic from the financial system perspective, so it is appropriate to conservatively examine the impact of policy introduction. Therefore, this study established the hypothesis that “the enlargement of securities companies following the introduction of the comprehensive financial investment business policy increases their insolvency risk.” The dependent variable of the analysis model is the natural logarithm of the default risk variables (EDF and Z-Score), and the independent variable is the amount of equity capital (natural logarithm) of individual securities companies. Other control variables include asset size (total assets (natural logarithm)), capital adequacy (NCR), profitability (ROE), asset soundness (NPL), and macroeconomic variables (GDP growth rate, housing price fluctuation rate, KOSPI growth rate). As a result, first, although securities companies are becoming larger in line with the purpose of introducing the comprehensive financial investment business policy, it was confirmed that their insolvency risk increased after the introduction of the policy. In other words, in the case of large securities companies, EDF increased and Z-Score decreased. Second, especially for super-large securities companies with equity capital of KRW 3 trillion or more, their insolvency risk increased significantly in proportion to the size of equity capital after the introduction of the policy, which can be attributed to the risk exposure due to the expansion of the scope of business. These results suggests the following: First, it needs to be intended to promote stability in the capital market by actively supervising the risk-seeking business of large securities companies following deregulation, and establishing risk-monitoring system in terms of macroprudentiality that can immediately intervene in their insolvency. Second, It can urge the awakening of all securities industry. In particular, in the process of reorganizing the sales and profit structures of securities industry due to the policy, it can be suggested that policies such as specialization strategies for small and medium-sized securities companies in order to minimize the negative impact of the relative weakening of their competitiveness. Finally, companies seeking to access the capital market can be expected to actively monitor the securities firms they deal with. This perspective is especially useful for small and medium-sized businesses. The insolvency risk of financial companies is inversely proportional to the financial inclusion of small and medium-sized businesses. Above all, without undermining financial stability, coordination and cooperation of market participants such as companies, investors, intermediaries, and advisors, as well as regulatory policies of policy authorities are of utmost importance.

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This study investigates the anti-manipulation effects of average futures within a generalized framework. Our primary contributions include revising and extending the anti-manipulation features proposed by Yoo (2015) and providing an in-depth comparison of existing anti-manipulation settlement mechanisms and average futures in terms of their efficacy as manipulation deterrents. We find that the anti-manipulation effects are weaker than in Yoo (2015) if a manipulator engages in an average futures contract with a smaller contract multiplier or more reference dates, manipulates it not only on its expiration date but also on other reference dates, or if the price momentum of the underlying asset due to the manipulation is stronger. Additionally, average futures effectively deter manipulation and help preserve market stability, unlike traditional anti-manipulation mechanisms that often distort prices. This comprehensive comparison underscores the advantages of using average futures to maintain market equilibrium and mitigate manipulation.

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