Abstract
This paper aims to replicate 148 anomalies and to examine whether the performance of the Korean market anomalies is statistically and economically significant. First, the authors observe that only 37.8% anomalies in the universe of the KOSPI and the KOSDAQ and value-weighted portfolios have t-statistics that exceed 1.96. When the authors impose a higher threshold (an absolute value of t-statistics of 2.78), only 27.7% of the 148 anomalies survive. Second, microcaps have large impacts. The results vary significantly depending on whether the sample included stocks in the KOSDAQ and whether value-weighted or equal-weighted portfolios are used. The results suggest that data mining explains large portion of abnormal returns. Any tactical asset allocation strategies based on market anomalies should be applied very cautiously.
Highlights
The current asset management market is shifting from asset-based investments to factorbased investments, focusing on large institutional investors such as pension funds and insurance companies
We examine whether the return of each anomaly is statistically, as well as economically significant
2.1 Data and categories In this research, we replicate some of the anomalies found in existing academic papers using data for all companies listed on the KOSPI and KOSDAQ market
Summary
The current asset management market is shifting from asset-based investments to factorbased investments, focusing on large institutional investors such as pension funds and insurance companies. Smart beta-based ETFs in Korea are gradually expanding their shares, such that their market capitalization exceeded Korean â,©1.2tn as of March 2019 Both academically and practically, it has become more important to study which factor and strategy based on market anomalies provide premiums in the long-term. Our results suggest that a significant number of anomalies identified by most studies might reflect “inevitable” data mining This problem is linked to the problem described in recent studies (Harvey et al, 2016; Hou et al, 2018). By constructing the EW portfolio that allocates more weight to microcaps, anomalies tend to earn higher returns and be statistically significant than the VW portfolio These results suggest a need to control for effects from microcaps in studies on anomalies. Institutions engaged in fund evaluation could use this factor-based performance evaluation model derived in our study
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