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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