산업별 주가 변동성과 거래량의 관계구조:행태경제학적 접근
The Structure of Volatility-Volume Relationship in KOSPI Industrial Indices: A Behavioral Economics Approach
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초록
본 연구는 투자자의 거래행태(tradingbehavior)에 닻내림효과(anchoringeffect)(TverskyandKahneman,1974)와 같은 인지편향이 존재한다고 가정한다.즉, 투자자는 시장 수익률 정보를 특정한 기준점(anchor)으로 간주하여 산업별 주가지수 수익률이 시장 주가지수 수익률보다 높은 경우에 거래량을 늘리는 거래행태를 보임으로써 산업별 주가지수 수익률의 변동성과 거래량의 관계구조가 달라지는 닻내림 효과가 발생할 수 있다. 이 가설을 입증하기 위해 변수들의 비선형 관계구조를 분석할 수 있을 뿐만 아니라 분포 꼬리 부분의 의존관계와 비대칭성을 상세히 판단할 수 있는 코퓰러 회귀분위수(copularegressionquantile,CRQ)접근법을 제안한다. 그리고 2019년부터 2021년까지 KOSPI시장의 산업별 주가지수 중 대표적인 8개 산업의 일별 자료를 이용하여 실증분석을 수행한다.실증분석 결과에 따르면 거의 모든 산업에서 변동성과 거래량 변수가 정(+)의 비선형 의존관계를 보여주고 있으며, 이런 비선형 의존성은 높은 변동성으로 인한 불확실 상황을 나타내는 변동성 분포의상단 꼬리부분에서 더 강하게 나타난다. 또한 가설검정 수행결과에 의하면 화학, 금융업, 운수장비, 기계, 종이목재 산업에서 두 변수의 의존관계에 대한 닻내림 효과가통계적으로 유의미하게 발생하며,불확실 상황(0.95th분위수)에서 화학, 금융업, 운수장비 산업의 닻내림 효과는 더 강하고 높은 통계적 유의성을 나타낸다. 결론적으로 닻내림 효과로 인한 인지편향은 대표적 산업의 과반에서 현저하게 존재하며,불확실성이 커지면 이 닻내림 효과는 더 강해진다는 흥미로운 사실을 발견하였다.
Abstract
This study assumes that there exist cognitive biases such as the anchoring effect(TverskyandKahneman,1974)in the investors' trading behavior. In other words, investors may consider market return information as a particular reference point like an anchor and increase trading volume if stock returns on a specific industry is higher than market returns,resulting in increasing returns volatility. Furthermore, this investors' trading behaviorgives rise to changing the relationship structure between trading volume and volatility.To prove this hypothesis empirically, this study proposes a copula regression quantile(CRQ) approach that can analyze the nonlinear relationship structure of variables, and also determine the asymmetric dependence in distribution tails. In addition,empirical analyses are conducted using daily indices data from 2019 to 2021 for eight industries representative of the KOSPI market. Empirical results show that trading volume and volatility variables in almost all industries have a positive nonlinear dependence,which is stronger at the upper tail of the volatility distribution indicating high uncertainty. Hypothesis testing also supports statistically significant anchoring effects on the dependence of the variables in the chemical, financial, transportation equipment, machinery and paper·wood industries,with stronger and higher statistical significance in uncertain situations. In conclusion, from a behavioral economics perspective, we find interesting facts that the anchoring effect occurs significantly in more than the half of representative industries, and itbecomes stronger as uncertainty increases.
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