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본 연구는 2002년부터 2013년 기간 동안 한국거래소에 상장된 기업을 대상으로 한 실증분석을 통하여 변동성의 각 요소 즉, 기업고유 변동성과 체계적 변동성이 유동성에 미치는 효과를 고찰한다. 특히 시장조성자의 입장에서 시장 전체에 미치는 정보수집비용이 기업 개별정보 수집 비용보다 저렴할 것이라는 가정 하에 총변동성 가운데 체계적 변동성의 크기가 클수록 또는 주식 가격의 동조화 정도가 클수록 역선택 비용이 작아 유동성이 크게 형성된다는 Chan, Hameed, and Kang(2013)의 연구결과에 주목하고, 시장조성자가 존재하지 않고 지정가주문을 통해 집합적으로 유동성이 공급되는 우리나라 시장 제도 하에서도 동일한 가설이 적용되는지를 검증한다. 실증분석의 결과, 기업고유 변동성의 크기가 큰 종목일수록 유동성은 떨어지며 체계적 변동성의 절대적인 크기가 큰 종목일수록 또 시장과의 동조화가 큰 종목일수록 유동성이 크다는 결과를 발견하였다. 특히, 각 변동성 요소와 유동성 사이의 관계는 매우 강하게 나타나는데, 다양한 종류의 실증분석을 적용함에도 일관된 결과를 보였다. 시장조성자가 존재하지 않는 한국의 주식시장에서도 시장조성자가 존재하는 미국 시장에서와 같이 유동성 공급을 담당하는 투자자들이 신중하게 시장의 상황을 탐색하고 시장 전체의 주문흐름에 대한 정보를 활발히 활용한다는 본 논문의 발견점은 지정가주문형 시장의 실효성에 대한 의미 있는 단서를 제공한다.
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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본 연구는 자산운용상 중요한 정책적 과정인 자산배분의 수익률 기여도를 분석하기 위해 한국, 미국, 영국 및 호주의 주식형펀드와 혼합형펀드를 대상으로 시계열 및 횡단면 회귀분석을 수행하고 결정계수를 산출, 비교하였다. 회귀분석 시 수익률 분해방식을 달리하여, 회귀분석의 종속변수로 총 수익률과 초과수익률을 이용한 두 가지 회귀식을 구성하였고, 각각의 회귀식에 대해 시계열 및 횡단면 분석을 수행하였다. 본 연구의 결과를 요약하면 다음과 같다. 총 수익률을 이용한 시계열 회귀분석 결과, 4개국의 주식형 및 혼합형펀드 모두 자산배분효과인 패시브 운용이 운용성과에 대한 설명력이 가장 높은 것으로 나타났고, 시장초과 수익률을 이용한 시계열 회귀분석에서는 운용성과의 대부분이 시장 움직임(market movement)에 의해 설명되는 것으로 분석되었다. 그리고 시장 움직임이 통제된 초과수익률을 이용한 횡단면 회귀분석의 결과, 4개국에서 공통적으로 주식형펀드는 액티브 운용의 설명력이 패시브 운용보다 높거나 유사한 수준이었고, 혼합형펀드는 패시브 운용의 설명력이 더 높았다.
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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