An Empirical Research On The Impact Of Investor Sentiment To Chinese Stock Market Return

The efficient market hypothesis (EMH). which is proposed by Fama (1970) is one of the core hypothesis in the traditional financial research areas. This hypothesis think that in the efficient capital market, the price of financial assets such as stocks already contains all information, so investors just need to take a "buy to hold" or other passive trading strategies and do not need to waste time and energy on active investment. However, there are so many arbitrage opportunities and anomalies in the real market that making the EMH theory was widely questioned. Reasonably, the EMH is mainly based on the premise that the investors are rational and when they face the same information, all investors’ interpreting to this information are consistent, but ignoring the heterogeneity between individual investors. A large number of experiments in cognitive psychology have confirmed that there exist differences among human individuals and they are not always completely rational, so some scholars gradually relaxed the prerequisite of investors’ rational and try to research investors’ mental activity how to influence the investment decision. Thus, the behavior finance which merging the theory of cognitive psychology and finance has been risen and gotten considerable development. Unlike traditional finance, the behavior finance emphasized the important role of sentiment factor and investment trading in the process of securities analysis and return forecasting, and think that the returns of securities is not only influenced by securities’ intrinsic value, but also largely affected by investor sentiment.Compared with thedeveloped countries’ capital markets, China’s capital market is unique, which the most prominent manifestation is individual investorsoccupying an absolute advantagein the investor structures. Due to limitations of energy and skills, individual investors are generally considered as irrational traders. Therefore, mainly to the individual investors in China’ smarket environment, mutual heterogeneity among individual investors will undoubtedly make the investor sentiment playing a very important influence on changes of market returns. How reasonable and effective measure of investor sentiment, meanwhile, analyzing the impact of investor sentiment to changes in China’s stock market, It was very high practical significancefor an accurate understanding of the transaction behavior and grasping the variation rule of stock market. Based on this, for clarifyingthe role of investor sentiment in the decision process ofChina’s stock returns, this thesis attempts to provide solid evidence by means of empirical analysis.Specifically, around the topic that investor sentiment how to influence China’s stock market return, this thesis is divided into eight portions for argumentation. All eight portions of content structure and conclusions are as follows:Chapter 1, Introduction. This chapter firstly describes the background and the significance of the thesis from the academic value and practical value terms. Secondly, pointed out the main contents of the thesis and a variety of research methods and means used in the process of research. Lastly, point to the innovations in the research.Chapter 2, Literature Review. This chapter mainly base on two aspects of the topic to summarize and review the relevant existing research at home and abroad. These two aspects arethe relationship between investor sentiment and market returns and asset pricing model based on investor sentiment. Among them, the relationship between investor sentiment and market returns are divided into three dimensions:investor sentiment how to impact returns volatility, investor sentiment how to impact stock market returns and investor sentiment how to interpret the market anomalies.Chapter 3, The Construction of Investor Sentiment Index.This chapter firstly introduces a new method- the partial least squares (abbreviated as PLS)-to construct investor sentiment index, and then chooseZhigao Yi and Ning Mao’s(2009) sentiment proxies as standard to compare the composite index of investor sentiment, which constructed by PLS or principal component analysis, according to the accuracy when they fit the changing of market returns, and find that PLS method is superior to principal component analysis. Finally, re-select more appropriate individual investor sentiment proxies, including three objectiveproxies which are the closed fund discount rate, the monthly market turnover, the ETF trading volume and two subjective proxies which are the investor confidence indicators,new open accounts, to construct a composite index of investor sentiment which based on the PLS for empirical analysis, and by means of Granger causality test between newly composite index of investor sentiment and market returns confirms that investor sentiment Granger cause changes in market returns.Chapter 4, The Indirect Impact of Investor Sentiment to Market Returns. This chapter based on the angle of the relationship between risks-returns that existed in the market, firstly usedfour volatility forecasting models which including the Rolling Window model (RW), the Mixed Data Sampling Approach (MIDAS), GARCH (1,1) and Asymmetric GARCH(1,1) to estimate China’s A-share market volatility of the monthly rate of return, and then according to the composite index of investor sentiment divided the whole sampleperiod into pessimistic period and optimistic period, and analyzed the changes of relationship between risks and returns under differentperiods. It was found that no matter what volatility forecasting model selected, during the pessimistic period China’s A-share market has a positive relationship between risks and returns, but during the optimistic period the positive relationship has been weakened, even appeared negative relationship.In order to test the robustness of the conclusions, the thesis also constructed a macroeconomic cycle index by PLS method to divide the whole sampleperiod into boom period and recession period and tested the relationship between risks and returns under different economic cycles. It was found that there will be a big difference with volatility forecasting models, so we could ignore the macroeconomic factors bringing impact on the relationship between risks and returns, and confirmed that the investor sentiment is the main factor to affect this relationship. Thus, it could be studied investor sentiment how to exert indirectly impact on market returns through the path of "Investor sentiment-Risk-Return."Chapter 5, The Direct Impact of Investor Sentiment to Market Returns. This chapter from the angle of cross-sectional analyzed the direct impact of changes in investor sentiment to different types of portfolio returns. In the division of the stock type, the thesis mainly based onthree dimensional features, which are listedcompany’s own characteristics, stock tradingcharacteristics on market and industry characteristics. It was found that all types ofportfolio average returns in optimistic period are greater than in pessimistic period, indicating that the investor optimistic sentiment enhanced stock returns. Furthermore, the returns spread between optimistic period and pessimistic period is significantly higher than the average returns in the whole sample period, indicating that the changes in investor sentiment have great impact on stock returns.Chapter 6, The Impact of Investor Sentiment on the Market Anomalies- a Case of the Momentum Effect. This chapter made momentum effect as the main representative of the market anomalies, firstly according to the returns performance that momentumstrategy under optimistic period and pessimistic period examinedwhether different investor sentiment influenced the appearance of momentum effect. It was found that the momentum effect is more obvious in optimistic period, but not obvious even appeared reversal effect in pessimistic period. Through there appeared momentum effect, the extent in pessimistic period is also less than in optimistic period. Then in order to test the robustness of the conclusions, the thesis by means of comparing the momentum portfolio construction methods and some traditional factors, such asthe listed company’s own characteristics, market state, in explaining the effect of the momentum effect, the results confirmed that even chose different sampling methods and controlledthe tradition factors, the thesis’s conclusion still holds.Chapter7, The Asset Pricing Model-Based on Investor Sentiment Factors. This chapter attempts to take investor sentiment factors into the traditional asset pricing models to improve the accuracy of models in predicting stock returns, especially the market anomalies. Among them, the approach that included investor sentiment into the models was divided three ways:the one way was to treatinvestor sentiment as information variables to set the expression of conditionbeta and constructed the conditional asset pricing model which included investor sentiment; the second way was to design a sentiment pricing factor which according to the sensitivity of sample stocks to investor sentiment and built an multi-factor asset pricing model which included sentiment pricing factor; the third way was combine the first two ways and found the asset pricing model which included both sentiment conditional information variable and sentiment pricing factor. Then according to the accuracy that all founded pricing models interpreted return anomalies under the Avramov and Chordia (2006) two-step regression analysis framework chose the best sentimental asset pricing model. The empirical resultsfound that the effect of the pricing model in predicting market returns anomalies had changed greatly when added investor sentiment to the pricing models. At the same time, according to the effect that sentimental pricing models explainedreturn anomalies such as the size, value, liquidity and momentum, etc., ultimately proved the models that contained scale factor, value factor, liquidity factor, momentum facor and sentiment factor was almost best in predicting the effect of stock return anomalies.Chapter8, Conclusion.This chapter firstly summarizedthe main conclusions andof the thesis, and then pointed out the deficiencies which existed in the research process and the needforimprovements in the future. Finally, pointed out how to use the Internet to provide massive data which was related toinvestor behavior to measure investor sentiment andthe relatedissues would be thenew academic hot point.