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Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title The effect of retail investor attention on stock returns after major price shocks - The case of China's stock market
Organization Unit
Authors
  • Timo Büchler
Supervisors
  • Ming Deng
Language
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Number of Pages 64
Date 2020
Zusammenfassung In the age of the internet, we are exposed to more and more information. Choosing what is valuable and what is worthless information can thus be time-consuming. As most people are restricted towards their resources, they favor information gathering processes that require the least amount of time and money spent. As Online search engines are both free to use and incredibly fast in filtering valuable from worthless information, they form an ideal tool for our need for information. Online search engines are undoubtedly a powerful method in the investment world, where time is often key to making decisions. Especially for retail investors, which often do not have access to sophisticated sources of news, online search engines provide them with access to financial information. In this study I use aggregate Baidu Search Engine data to identify investor attention. This method was first proposed by Da, Engelberg & Gao (2011) and has since been a popular measure for investor attention in many asset pricing studies. Search engine data is especially powerful when trying to identify retail investor attention, as these often do not have access to costly financial databases like Bloomberg (Ben-Rephael, Da, & Israelsen, 2017). The Chinese stock market provides an ideal setting to explore the impact of investor’s attention on stock returns, since this market has historically been dominated by retail investor participation. As individual investors are often subject to behavioral biases like overconfidence and herding (Daniel, Hirshleifer & Subrahmanyam, 2018), price reaction patterns like over- or underreaction to news are expected to be more pronounced and thus easier to identify in the Chinese stock market. In this paper, I employ an event study approach, where extreme abnormal daily returns are used to identify information shocks on the Shanghai and Shenzhen stock exchange. The dataset ranges from October 2011 up until March 2019. I extract Baidu search frequency data using a web scraping program on the Baidu index data base. In a second step, a portfolio sorting analysis as proposed in Fama and French (1992) is used to identify patterns in the post-jump returns for multiple event windows. Finally, I run several Fama-MacBeth regressions to analyze the cross section of stock returns after major price shocks. The results of the empirical analysis show strong signs of overreaction followed by reversal after positive price shocks for observations that are accompanied by high retail investor attention. For the negative jumps, I find mixed results, due to heavy clustering around the market downfalls. Further research is needed to confirm the negative price jump findings in the environment of the Chinese market. Furthermore, I find evidence for the hypothesis that retail investors are net buyers of attention grabbing stocks in the Chinese market, and that the abnormal online search frequency can identify investor attention even when other proxies like extreme events, Turnover or return volatility fail to do so.
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