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Investor Attention And Small-cap Market Performance

Posted on:2019-07-01Degree:MasterType:Thesis
Country:ChinaCandidate:Y Y ZhangFull Text:PDF
GTID:2429330596451867Subject:Master of Finance
Abstract/Summary:PDF Full Text Request
Since the birth of behavioral finance,people have gradually realized that people's attention is limited,and driven by limited attention,people cannot always make completely rational decisions.The scarcity of attention is ultimately the result of price deviations.Since the birth of behavioral finance,people have gradually realized that people's attention is limited,and driven by limited attention,people cannot always make completely rational decisions.The scarcity of attention is ultimately the result of price deviations.How to quantify attention is a very important issue.Scholars have made many attempts in this area.In the past,scholars usually used agency advertising fees,news media reports,and major announcements as proxy variables for investor attention.With the development of the Internet,more and more people interact with each other through the Internet as a medium,leaving traces on the Internet.Scholars have turned to the use of Internet-related data to try to quantify investor attention.In particular,after Google introduced Google Trends,people can use Google Trends to easily find the weekly search volume data of keywords.Scholars began to try to use Google Trends as a proxy variable for investors to study their relationship with the US stock market.After the Baidu Index was launched,Baidu Index could provide more accurate data,considering Baidu's extremely high market share in China and the Baidu Index's data in more detail.Based on the previous studies,this paper uses the CSI 500 stocks as an example to study the performance of the small-cap stock market and its relationship with the Baidu index.The reptile technology grabbed the stock abbreviation of the 500 constituent stocks of the China Securities 500 Index and the Baidu index data with a total of 1171 data for each trading day from January 1,2013 to October 31,2017.The31 stocks blocked by the suspension and Baidu Index eventually included 469 stocks.A total of 1,171 trading days Baidu index data,totaling 549,668 data.Using the Fama three-factor model,the company scale,the yield of the China SSE 500 Index,and the book value of the company's book value are used as control variables.Panel data are used to study the relationship between the daily rise and fall of the stock price and the daily rise and fall of the Baidu Index.This paper puts forward four hypotheses:(1)Baidu Index is related to the returns of small-cap stocks over the same period;(2)Baidu Index is positively correlated with the returns of small-cap stocks over the same period;(3)Baidu Index and previous small-cap stocks The stock yield is negatively correlated;(4)Baidu Index has asymmetrical effect on the yield of small-cap stocks.The main conclusions of the study are as follows:(1)There is a significant positive correlation between the stock returns of small-cap stocks and the degree of investor attention measured by Baidu's search data.This correlation is significant in different years.(2)The rise of attention brings about the reversal of the premium effect of rising share prices in the short term.The degree of attention of investors behind the 1-6 period is significantly negatively correlated with the current stock yield,and with the increase of the lag order,The influence of investor attention on the current stock returns is also weakening.(3)The influence of investor attention on stock returns is asymmetric.Specifically,Baidu Index has a stronger influence on individual stock returns as public attention on stocks rises;At that time,the impact of Baidu Index on the yield of individual stocks will be weakened.This may be due to herd mentality,when stocks attention is rising,people are vulnerable to market sentiment when making decisions and the limited nature of domestic short-selling means.
Keywords/Search Tags:Baidu index, Limited attention, small-cap stocks
PDF Full Text Request
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