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The Influence Of The Asymmetric Information On Asset Price Behavior From The Perspective Of Big Data

Posted on:2016-01-12Degree:DoctorType:Dissertation
Country:ChinaCandidate:J Y LiFull Text:PDF
GTID:1109330485955041Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
Big data becomes a hotspot in nowadays technology application and research, the essence of it is deep mining and analysis of the value information at the data level and the information is the important premise and basis in investments. With the development of technology, the quantity and accuracy of data which we can get has been improved continuously, from minute data to high-frequency data and even ultra-high frequency data. This process not only shortens the time interval of data, but also includes all the trading information in data for mining and analyzing, which can reflect correctly the integration of information into market and the interact of different investor. Thus, we can find a more accurate evidence of stock price.Despite of the value of information reflected by market trading, network data are also has great influence on investor in this Internet age while network is the main access to information for many investors. News covered by mass media can influence most of investment directly and the attention of internet information directly conveys investor expectation and business preference. According to this, the development of big data allows us to extract and purify valuable informations from large-scale data.In the view of big data, this dissertation study the information structures reflected by high-frequency data and analysis the relationship of investor behavior and asset pricing based on market microstructure and investors focus theory. The content is mainly divided into the following three aspects:First part is the impact of informed trading to asset price behavior. To begin with, on the basis of verification about month and calendar effect on mainland of China, we can combine informed trading probability PIN with returns and do research on PIN month effect to reveal the cause of month effect at information level. Then, taking the declaration dates of the changing in reserve requirement ratio as object, modeling balanced trading and unbalanced trading to examine the interaction effect between arrival rates of informed and uninformed trading in stock market. This method can turn the measuring accuracy of PIN to days. By this means, we can also study on the change process of informed and uninformed trader before and after the declaration dates. In the study, examines are done on firms of different scale.Second part is about the relationship between investor attention and IPO underpricing. Based on the underpricing of China’s new shares, the author measures investor attention though internet data before and after IPO. Additionally, study change about the return of stocks which is highly concerned before IPO to analysis the connection of them and determines the effect time of investor attention to the stock price, then verifying explanatory ability and degree of influence of Internet information on this account.Third part, research of influence from media information on stock returns. The author do survey on the impact of internet information to the returns and compare the effect of trading and non-trading time respectfully. In the first place, establish a complete Internet data mining system to grasp contents of stock information websites and establish structured memory of them for making sure the measurement methods of searching keywords and media coverage. Then, specific to firms of different scale, the author compares the relationship of daily media attention and the returns. On the basis of it, broke down the information right to the very minute and classify information according to trading and non-trading time to study the incidence of them to the returns. During the research, the author compares the coherence between these data and Baidu index to verify if the data reflect the attention of investors.Forth part is the relationship between analyst attention and investors’ trading behavior. At first, research classifies the analyst rating report, modeling the panel data, to determine the earning at next period which are involved in depth reports and general reports and research the choice of rating report from investor. At the same time, reseach analyses the different reaction after reports issued, and study the affect with investors’ trading behavior to market volatility, for which will make the better descion on trading and marker regulators.
Keywords/Search Tags:Market microstructure, Informed trading, Big Data, IPO underpricing, Investor attention
PDF Full Text Request
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