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.