“A volatility interruption (VI) is a sophisticated microstructure mechanism providing cooling-off periods and effective price discovery functionalities in brief periods of abnormal volatility for an individual stock” (Eom, Ra, Park, and Ahn, 2015). VIs consist of two types: dynamic and static. The dynamic VI is invoked when a price fluctuation due to a single order exceeds a predetermined range, e.g., ±2% in the Korea Exchange (KRX). The static VI is activated when a cumulative price fluctuation due to multiple orders and transactions exceeds a predetermined range, e.g., ±10%. On September 1, 2014, KRX preemptively adopted the dynamic VI. Then, on June 15, 2015, the exchange additionally introduced the static VI while simultaneously expanding the price limit to ±30% to alleviate a wider range of price changes. In this paper, focusing on the adoption that occurred on June 15, 2015, we examine whether the newly introduced static VI, in addition to the existing dynamic VI, produces economic benefits consistent with the purpose of introducing VIs, such as price stabilization and discovery. In this process, we also identify the characteristics involved in dynamic and static VI invocations and discuss what type of VIs or price stabilization mechanisms are best suited to the Korean stock market. The related domestic and foreign papers are limited in number. The unique feature of this paper is the direct comparative analysis of the economic effectiveness of both dynamic and static VIs. We were able to perform this analysis because KRX adopted the dynamic and static VIs consecutively. In addition, the price-limit system, which is unique to the Korean stock market, allows us to discuss the economic relationship between the static VI and price limit. We analyze 1,937 stocks listed on the KOSPI and KOSDAQ markets in KRX over 2 periods of 48 trading days each before and after June 15, 2015, when the static VI was introduced. The previous period denotes the period when only the dynamic VI was implemented, and the latter period refers to the period when both the dynamic and static VIs were implemented in tandem with an increase of the price limit to ±30%. The results of our analyses using trade and quote data are as follows. First, the stocks for which the static VI is invoked are mainly small and medium-sized, low-priced, and highly volatile, as are those for which the dynamic VI is invoked. Unlike the dynamic VI, however, the static VI tends to be invoked more frequently as trading volume increases, suggesting that the static VI invocation is a phenomenon that occurs when investors’ opinions vary. The static VI is invoked more often than the dynamic VI in terms of the number of stocks or frequencies. Second, there is little relation between dynamic VI invocation and the hit occurrence of upper and lower limits. Meanwhile, the static VI has a significant influence on the hit occurrence of upper and lower limits in direction and magnitude. This suggests that the static VI plays a role in enforcing narrow upper and lower limits. However, assuming that the intrinsic volatility of the KOSPI and KOSDAQ markets remained constant, the expansion of the price limit to ±30% appears to have increased the realized volatility of these markets by about 14~15%. Third, the price stabilization and price discovery effects of the dynamic VI are fairly solid, and the effects themselves are consistent regardless of the introduction of the static VI or the expansion of the price limit. These effects are also almost the same quantitatively and qualitatively as those of the dynamic VI that was adopted in September 2014. The static VI, in contrast, appears to contribute to some degree to price discovery, but greatly impairs price stability. This is attributable to the static VI and price limit, which function fairly similarly, being implemented at the same time. The VI system in Korea was adopted to eventually replace the existing price-limit system, which has been evaluated as ineffective in preventing temporarily abnormal fluctuations in prices that may occur in high-frequency trading, manipulative trading, etc. Our results show that the dynamic VI is serving its intended purposes relatively well, while the static VI has a significant negative impact on price stability. This seems to be due to a conflict between the designed functionality of the static VI and its practical role as a narrow upper and lower price limit within the existing price-limit system. In addition, the dynamic VI has a limited effect on the establishment of an equilibrium price immediately after the VI invocation period in cases where the stock price overshoots to a remarkable level just before the dynamic VI invocation. Therefore, in-depth verification of the various parameters (e.g., ±10% variation in the static VI) that constitute the specific activating requirements of the dynamic and static VIs adopted by KRX is advised.

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By empirically testing short selling activities that occur before changes in analysts’ recommendations, this paper examines the “informed front-running hypothesis” that predicts an abnormal decrease (increase) of short selling before analyst upgrades (downgrades). According to Blau and Wade’s (2012) conceptual framework, this study divides analyst recommendation changes into analyst upgrades and downgrades and uses four measures of abnormal short turnover used in Christophe et al. (2010) and Blau and Wade (2012). This study shows that abnormal shorting increases significantly before both downgrades and upgrades. After dividing the sample into KOSPI and KOSDAQ categories based on market type, domestic and foreign analysts’ recommendation changes, and investor type (institutions, foreigners, and individuals), the study finds no evidence that short sellers have the ability to acquire information about upcoming recommendation changes before the information becomes publicly available. The study also finds that pre-recommendation short selling does not predict upcoming events. These findings disprove the informed front-running hypothesis and are generally consistent with Blau and Wade’s (2012) argument that short selling before recommendation changes is more likely to be speculative than informed.

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This study investigates the effect of external shocks on stock market volatility, focusing on improving the explanatory power and prediction ability of the heterogeneous autoregressive model of realized volatility (HAR-RV) using intraday high-frequency 5-minute return data from the KOSPI market index over the period from January 2004 to June 2016. Based on previous studies, we use improved methods in our empirical design to enhance the reliability of the empirical results: a method extracting jump components from measurements of realized volatility based on statistical significance evaluation; a method incorporating nighttime market information (without trading) into the measurements of realized volatility; a method assessing whether to improve the prediction ability of a proposed model through statistical significance evaluation; and a robustness test comparing the proposed model with models containing well-known explanatory variables of the volatility leverage effect and realized skewness and kurtosis. This study creates time series data on the external shock variable (ES) using principal components analysis by combining the selected 10-type variables that have the property of external shocks in the international financial markets, raw material markets, and commodity markets. It then uses the ES variable in empirical tests. The main results are as follows. First, the ES variable created from the principal components analysis does well at reflecting large changes in international markets. Second, the ES variable has a significant Granger causality relationship to the realized volatility over the whole period, while each of the selected 10-type variables has a significant causality relationship only for the specific periods of interest. Third, from the perspective of in-sample analysis, the HAR-RV model with the ES variable significantly improves the explanatory power of changes in the future realized volatility. In the out-of-sample analysis, the ES variable has the significant information value of enhancing the predictive power of the model in future periods. Finally, these are robust results regardless of whether the volatility leverage effect and realized skewness/kurtosis variables are included in the HAR-RV model. Our results show robust evidence that the external shocks have meaningful information value for explaining and predicting changes in future volatility in the HAR-RV model, and imply that the methodology of constructing the ES variable may provide new directions for future research using the HAR-RV model.

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Aït-Sahalia, Parker, and Yogo (2004) suggest using luxury goods retail sales data as an alternative measure of consumption to obtain more reasonable estimates for the coefficient of relative risk aversion that better match the observed equity premium. We apply their novel idea of using data that reflect discretionary consumption by the wealthy to the Korean context by using sales revenue of three largest sellers of high-end whisky and two major airlines as proxies for discretionary consumption that are more likely to respond to movements in the stock market. When theses proxies for discretionary consumption are used in place of standard consumption, the estimates for relative risk aversion are an order of magnitude smaller and economically plausible, similar to the findings reported by Aït-Sahalia et al. (2004) for the United States.

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We examine the effects of idiosyncratic and systematic volatility and stock return synchronicity on stock liquidity using a sample of firms listed on the Korea Exchange (KRX) from 2002 to 2013. The association between volatility and liquidity is extensively studied in the literature, with a typical focus on how “total” volatility affects liquidity. Few studies, however, divide volatility into idiosyncratic and systematic components to see how these individually influence liquidity. Distinguishing between the two components is important to studies examining the relationship between volatility and liquidity because they may influence liquidity in significantly different ways. Market makers face two sources of risk in providing liquidity to the market: inventory risk and adverse selection risk. Greater inventory risk, greater adverse selection risk or both lead to greater spreads posted by market makers to cover their potential losses, resulting in reduced liquidity. To understand how this mechanism works, we need to understand how idiosyncratic and systemic volatility each pose inventory concerns and adverse selection risk to market makers. Let us first consider how market makers’ inventory risk is affected by the two volatility components. Predictions are possible in either direction. Since systematic volatility can be hedged away by market makers, it may not pose much inventory concern. Idiosyncratic volatility, however, may have a direct effect on inventory costs because it cannot be removed by hedging. Portfolio diversification has a completely opposite implication. Although idiosyncratic volatility can be diversified away easily, systematic volatility cannot. Hence, the former may not have as significant an effect on inventory costs as the latter. The two volatility components may also have different influences on adverse selection risk. The costs related to information asymmetry tend to be greater with idiosyncratic volatility than with systematic volatility because it is easier for a market maker to access and interpret common signals to the market. As a result, firms with greater idiosyncratic volatility are more likely to have greater adverse selection risk and thus lower liquidity. Studies investigating the association between the individual volatility components and liquidity fall into two groups: those that examine the link between systematic volatility and liquidity (e.g., Baruch et al., 2007; Baruch and Saar, 2009) and those that explore the effect of idiosyncratic volatility on liquidity (Bali et al., 2005; Spiegel and Wang, 2005). Chan et al. (2013) merge these two strands by examining how both idiosyncratic and systematic volatility affect liquidity at the same time. They propose that collecting market-wide information costs uninformed market makers less than collecting firm-specific information, and thus firms with greater systematic risk have smaller adverse selection costs and greater liquidity. Based on their empirical analysis of U.S. stocks, Chan et al. show that stocks with greater systematic volatility and greater return synchronicity with the market have greater liquidity. We investigate whether the same relationship holds in the Korean stock market, where there are no market makers and liquidity is provided collectively by investors placing limit orders. We measure idiosyncratic volatility using residuals from the market model. Systematic volatility is then obtained by subtracting the idiosyncratic variance from the total variance. Return synchronicity, meanwhile, is measured as R2 from the market model. For liquidity measures, we use the Amihud liquidity measure and Roll’s spread, the two most popular liquidity proxies available with daily data. We regress the two liquidity proxies on the volatility components and return synchronicity measures together with a group of control variables that are known to affect liquidity in the literature. The latter includes stock price, equity market capitalization, turnover, and institutional ownership. We find that idiosyncratic volatility decreases liquidity, whereas systematic volatility and stock return synchronicity increase liquidity. Our results are consistent with the explanation that the collective liquidity providers in the Korean stock market also find it cheaper to gather market-wide information than firm-specific information, which leads to lower adverse selection costs and greater liquidity for stocks with greater systematic volatility. Our empirical findings are robust to alternative variable definitions and model specifications. Market makers play a central role in providing liquidity on the New York Stock Exchange (NYSE). However, on the KRX, there are no market makers. Limit orders submitted by the investing public serve as the primary repository of liquidity in stock trading. Despite this apparent difference in market structure, our empirical results generally support the findings of Chan et al. (2013) on the NYSE, which have an interesting implication for the efficacy of the limit-order trading system. Although individual limit-order traders do not possess the same set of advantages that market makers have in terms of their monopolistic access to order flow information, it appears that “collectively” the investing public substitutes market makers and plays the same role in providing liquidity to the market.

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The literature on tax-expense momentum is unclear as to whether its effect is due to anomaly or risk. We apply the approach developed by Ohlson and Bilinski (2015) to assess whether the positive relationship between tax expense momentum (surprise) and future stock returns is explained by anomaly or risk. We find that tax expense momentum increases the probability of a high return and decreases that of a low return. This supports an anomaly-based explanation for the tax expense momentum.

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This study documents that predictability of insider trading on future stock price crash varies according to the types of insiders and the timing of the sale. Using insider trading data from Korea between 2005 and 2014, we find that largest shareholders tend to sell far before a stock price crash, while other types of insiders, including other large shareholders and executives, are more likely to sell immediately prior to a crash. Such pattern is more pronounced in firms with low CSR scores and low R-squares, but not observed among firms with high CSR scores or high R-squares. We also find that our results are stronger amongst firms with higher litigation risk. These findings suggest that largest shareholders may be well aware of the potential legal or reputational risk associated with insider trading while the remaining insiders may be less concerned.

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Asset allocation is one of the most important decisions made in the asset management process to achieve a portfolio’s target return within the constraints of its risk tolerance level. Brinson et al. (1986, 1991) conclude that asset allocation policy explains 93.6% or 91.5% of performance. This greatly helps practitioners understand the importance of asset allocation in asset management. However, at the same time, it triggers the publication of other studies with different views on its empirical testing and different interpretations of its results. For example, Ibbotson et al. (2000) and Vardharaj et al. (2007) conduct empirical tests using regression models to measure the explanatory powers of asset allocation in fund performance and find that the dispersion of R2 in their models is wider than those of Brinson et al. Alternatively, Xiong et al. (2010) decompose the total return into three components: market movement, asset allocation policy return in excess of the market return, and active return. They then run time-series and cross-sectional regressions on total returns and excess market returns for both equity and blend funds, respectively. Based on their regression analyses, Xiong et al. (2010) conclude that market movement dominates the other two components. Additionally, they find that the explanatory powers of passive management (asset allocation policy) and active management are at similar levels. In sum, although multiple studies propose different conclusions from Brinson et al. (1986), they all agree that asset allocation is significant in the asset management process. Applying the methodologies of Brinson et al. (1986) and Xiong et al. (2010), this study performs time-series and cross-sectional analyses of equity and blend fund returns in Korea, the U.S., the U.K. and Australia, based on each country’s style benchmark and leading market index returns. A performance attribution analysis is conducted to determine the contribution of passive and active management to fund performance by comparing the coefficients of determination (R2) for each country’s regression models. The results indicate that in accordance with the time-series regression analysis of Brinson et al. (1986), the explanatory power of asset allocation reaches its highest level when the total fund return is explained by benchmark returns reflecting market movement. However, in all four countries, when market movement is separated, it’s the greatest contribution to the performance of both equity and blend funds. When a market-movement-excluded excess return is used, in three countries, excluding the U.K., active management has higher explanatory power for equity funds and passive management has higher explanatory power for blend funds. When a cross-sectional regression analysis is conducted using the methodology of Xiong et al. (2010) to measure the explanatory power of asset allocation performance without market movement, the explanatory power of active management is higher for equity funds in all four countries. In the case of blend funds, however, the explanatory power of active management is higher in the U.S. and U.K., where equities hold a larger portion of funds than bonds. Finally, passive management shows higher explanatory power in Korea, where its funds are composed of similar levels of equities and bonds. Comprehensively reviewing the results of this study from a mid- and long-term investment policy perspective, we conclude that the selected variables in strategic asset allocation consist of investors’ risk preferences and asset class risk premiums, not short-term market movements. Therefore, it is difficult to agree with Xiong et al. (2010) that it is reasonable to exclude market movements from returns because the contribution to investment performance for mid- and long-term market movements are already reflected in risk premiums. We add, however, that if market movements are under control, which can be explained as constant asset class risk premiums, then we support the finding of preceding studies that active management is as significant as passive management in terms of its contribution to performance.

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This study examines the relationship between employee pay and the capital structure of publicly listed firms in Korea. Consistent with Hanka (1998) and Hovakimian and Li (2011), we find that higher leverage is associated with lower average employee pay. However, our findings contrast with the theory of Berk, Stanton, and Zechner (2010) and the theory of the compensating wage differential. When firms add more debt to their balance sheet, the probability of bankruptcy increases and their employees face a greater risk of unemployment. Studies have suggested that workers bear significant economic and psychological costs from involuntary unemployment. In this context, the economic theory of Berk et al. (2010) predicts that firms compensate for this risk by increasing employee pay to increase the leverage related to higher average wages. However, in contrast to Berk et al. (2010), several studies have documented a negative correlation between leverage and employee wages. Hanka (1998) and Hovakimian and Li (2011) find that increased leverage is associated with lower employee pay. They interpret this as being a result of the risk shifting from shareholders and managers to employees. In addition to the risk-shifting motive, Perotti and Spier (1993) argue that firms use leverage as a bargaining tool against labor, especially when in financial distress. Moreover, Benmelech, Bergman, and Enriquez (2012) find that firms tend to renegotiate employee wages downward when they are under financial distress. Thus, from this perspective, higher leverage is associated with lower average employee pay among firms that are in financial difficulty. In summary, studies that predict a negative relationship between leverage and wages have suggested that increased leverage imposes disciplinary effects on labor by reducing employee wages. In our empirical analysis, we estimate panel fixed effect models that control for the time-invariant heterogeneity among firms and general time trends. In firm fixed effect regressions controlling solely for leverage, we find that leverage is associated with lower average employee pay. Moreover, in regressions that control for other firm characteristics, we find that higher leverage is associated with lower average employee pay. The Fama and MacBeth (1973) regressions deliver the same results. These findings suggest that the disciplinary effect of leverage is more pronounced than the compensating effect of higher bankruptcy risk. As our sample period covers the periods before and after the 2008 global financial crisis, we test whether the global financial crisis had any significant effects on the relationship between leverage and wages. We conduct the same regression analyses for the pre- and post-financial crisis periods (2003~2007, 2010~2015), and our empirical results suggest that the negative relationship between leverage and wages significantly intensified after the financial crisis. Moreover, the negative relationship between leverage and wages was only significant for the post-crisis period. This result confirms that the disciplinary effect of leverage was more pronounced after the financial crisis. We also consider industry heterogeneity and the labor market conditions in our analysis of the variation in the leverage-wage relationship. We expect employees’ wage levels to vary significantly among different industries, especially technology firms and non-technology firms (Chemmanur, Cheng, and Zhang, 2013). We examine the variations across industries and markets by conducting comparative analyses of KOSPI and KOSDAQ firms, and firms in the technology industry and those in other industries classified according to the KSIC code. We find that the leverage-wage relationship is significantly more negative among non-technology firms. We also predict that the disciplinary effect of leverage is stronger in industries with lower unionization and shortage rates, as employees in these industries have less bargaining power against the firms. Consistent with our prediction, we find that the negative relationship between leverage and wages is strengthened in industries with lower unionization and shortage rates. In summary, among Korean public firms, we find that higher leverage is associated with lower employee pay. This result substantiates the view that debt disciplines labor. However, our findings do not support the theory of Berk et al. (2010) that firms compensate for increased unemployment risk by increasing wages to gain greater leverage. In addition, we find that after the financial crisis (2008~2009), the negative relationship between leverage and wages increased significantly. Our findings suggest that the disciplinary effect of debt was significantly pronounced after the financial crisis. We also examine the effects of the labor market conditions and industry heterogeneity on the leverage-wage relationship. Consistent with our prediction, the negative relationship between leverage and wages is significantly strengthened among firms in non-technology industries and those belonging to industries with lower unionization and shortage rates. One limitation of our study is that we cannot fully control for the endogeneity of leverage. We expect that future research will mitigate this problem. Our evidence contributes to the growing body of literature connecting labor economics and corporate finance and to the research on the relationship between leverage and wages by providing additional evidence to support the disciplinary effect of debt.

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This paper examines the mechanism of systemic risk propagation through system- wide latent leverage channel. We focus on the hidden leverage-induced asset value dynamics in the financial markets, intertwined with balance-sheet components of the banking system. We propose a latent leverage index by estimating smooth transition regression models based on the intrinsic element of the financial system, off-balance-sheet transaction, and cross-border activities of the Korean commercial banking system. We find that a shock to the latent leverage index impacts the macroeconomy with the lag of three quarters. This finding provides an important policy-oriented implication for macroprudential supervision of banking system.

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