Determinants and Pricing Effects of Short-term Herd Behavior:An Empirical Test Based on High-Frequency Data
ZHU Feifei, LI Huixuan, XU Jianguo, LI Hongtai
Guanghua School of Management, Peking University; School of Economics, Beijing Technology and Business University; National School of Development, Peking University; Zhangzhou Municipal Government Office, Zhangzhou
Summary:
At the 19th National Congress of the Communist Party of China, the authorities emphasized the importance of financial sector institutional reform in China, particularly increasing the proportion of direct financing and promoting the healthy development of a multilevel capital market. As a vital part of the multilevel capital market, China's A-share market plays an important role in optimizing information transfer and allocating capital resources. However, a large body of literature argues that herding behaviors may negatively affect the information transparency and pricing efficiency of the stock market. Such behaviors can even cause financial turmoil in severe cases. Short-term speculative herding behaviors also lead to a high turnover rate of capital flow and impede the formation of long-term capital, which is harmful to economic growth. Thus, understanding herding behaviors in China's A-share stock market is critical to improve pricing efficiency, increase the proportion of direct financing, and support the healthy development of the real economy. Many theoretical studies find that herding behaviors are short-lived and fragile. However, research on herding behaviors in China is mainly based on the quarterly holdings data of institutional investors. First, short-lived herding behavior and its pricing effect are highly likely to be missed when using quarterly data. Second, correlated trades at the quarterly level tend to reflect changes in the fundamental values of stocks rather than herding behaviors. In a word, the limitations of using quarterly holdings data may result in significant deviations in measuring herding behaviors. For the above reasons, this paper improves the LSV method developed by Lakonishok, Schleifer, and Vishny (1992) and creatively uses daily trading data to obtain a more precise measure of short-term herding behaviors in China's A-share stock market. Based on this measure, we further investigate stock-specific characteristics that affect herding behaviors and the pricing effects of herding behaviors. We have four major findings. (1) The degree of herding monotonically increases with trading frequency. It is 3.92%, 2.48%, and 1.64% over the daily, weekly and monthly horizons, respectively. This result is consistent with the theoretical prediction that herding behaviors are short-lived and fragile. (2) Asymmetric information, the proportion of institutional investors, and stock size significantly affect the degree of herding behaviors. Herding behaviors are more severe in stocks with higher levels of asymmetric information or higher proportions of institutional investors, and there is a U-shaped relationship between the degree of herding and firm size. (3) There is a price reversal after the herding: a positive (negative) abnormal return is gained after the sell-side (buy-side) herding behaviors, and the price reversal is more significant following a higher degree of herding. (4) The price reversal effect after the herding behaviors is asymmetric: it is more pronounced for large and liquid stocks after buy-side herding than after sell-side herding. This paper makes three major contributions: First, we are the first to use daily trading data to obtain a more precise measure of short-term herding behaviors in China's A-share stock market, which overcomes the limitations of using quarterly data in previous studies. Second, based on this more accurate measure of herding behaviors, we deeply examine the determinants of herding behaviors and the effects of herding on future prices. Our research serves as an important supplement to the literature on herding behaviors in China's A-share market. Third, contrary to findings based on quarterly holdings data of institutional investors, we find that short-term herding is subsequently followed by price reversals, which supports the argument that herding behaviors negatively affect the price discovery function of the stock market. These findings have great value in deepening our understanding of investor behavior and improving the price discovery function of China's A-share stock market.
朱菲菲, 李惠璇, 徐建国, 李宏泰. 短期羊群行为的影响因素与价格效应——基于高频数据的实证检验[J]. 金融研究, 2019, 469(7): 191-206.
ZHU Feifei, LI Huixuan, XU Jianguo, LI Hongtai. Determinants and Pricing Effects of Short-term Herd Behavior:An Empirical Test Based on High-Frequency Data. Journal of Financial Research, 2019, 469(7): 191-206.
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