한국증권시장에서의 장기성과 측정모형의 검정력과 통계적 오류

본 연구는 합병, 유상증자 및 자사주 취득 등과 같은 개별기업의 주요 의사결정 이후의 장기성과를 측정하는 다양한 모형들의 검정력과 설정 오류의 정도(the power and misspecification)를 한국증권시장의 실제 월별수익률 자료를 이용한 시뮬레이션 분석을 통해 살펴보았다. 또한, 이러한 장기성과 측정모형들의 검정력과 설정 오류에 관한 시뮬레이션 분석 결과를 바탕으로 한국증권시장에서 통계적으로 가장 신뢰할 수 있는 장기성과 측정모형의 도출을 시도하였다. 시뮬레이션 분석 결과에 의하면, 한국증권시장에서 장기성과 측정모형의 설정 오류가 없어 통계적 신뢰성을 확보할 수 있는 모형은 매입보유초과수익률(BHAR)로 장기성과를 측정하면서 시가비율과 기업규모 순서로 개별 표본기업과 대응시 킨 통제기업 (book-to-market/size matched control firm)을 수익률 벤치마크로 사용한 모형이 유일한 것으로 나타났다. 특히, 이 모형은 한국증권시장을 대상으로 한 장기성과 연구에서 통상적으로 많이 사용되고 있는 여타 모형에 비해 표본평균 초과수익률의 편의(biasedness)와 검정력 함수의 비대칭도(asymmetry)가 가장 작게 나타났다. 따라서, 이 모형이 한국증권시장에서의 장기성과 연구에 가장 적합한 성과측정 모형으로 판단된다.

This paper investigates the empirical power and misspecification of test statistics based on a variety of models to measure long-run abnormal stock returns following firm-specific events by using the observed monthly returns of randomly selected Korea Exchange(KRX) securities. Based on the simulation analysis, this paper also attempts to find the long-run performance model that is well-specified and the most statistically reliable in the Korea stock market. To detect long-run abnormal returns, this paper employs three asset pricing models (two market adjusted models and market model), four reference portfolios (size, book-to-market, size/book-to-market, book-to-market/size based portfolios), four control firm methods (size, book-to-market, size/book-to-market, book-to-market/size matched control firms), and Fama-French three-factor model(value weights and equal weights).<BR> In order to assess the empirical power and specification of test statistics using these models, one thousand samples of 200 securities are constructed. The securities are selected at random and with replacement from the pool of 820 securities for which monthly return data of at least 3 years are available on the Stock DB of Korea Securities Research Institute (KRSI). For each security, a hypothetical event month is randomly selected with replacement using a uniform probability distribution from January, 1980 through December, 2004. Long-run abnormal returns are estimated over the following three fixed periods: 12-, 36-, and 60-month period. To analyze the power of the thirteen methods of calculating long-term performance, a particular level of excess return is artificially introduced into a given sample by adding a constant level of abnormal return, which ranges from -30% to +30% in increments of 10%, to the calculated 36-month CAR and BHAR of each sample. The results of this paper can be summarized as follows:<BR> (1) For all the models measuring long-run abnormal returns, sample-wide mean CARs and BHARs are different from zero at the 5% significance level, which means they are positively or negatively biased depending on the performance measures. The stronger the magnitude of abnormal performance are the longer the horizon is. However, the control firm methods yield much less biased sample mean abnormal returns than any other methods, regardless of the horizon.<BR> (2) Most of the test statistics based on long-run performance measures to calculate 12-, 36-, and 60-month CARs, and BHARs are severely misspecified. For example, in 1,000 randomly selected samples of 200 securities with no systematic abnormal performance, the Fama-French three-factor model using value-weighted portfolio returns shows abnormal performance over a 36-month interval for 46.9% of the samples. The misspecification of these models can be attributed to the significantly positive or negative mean abnormal CARs and BHARs. However, one exception is the book-to-market/size matched control firm method.