An Empirical Study Of Noise Theory On China's Security Market

In the standard finance theory, noise trading can not influence the price in security market. Because the noise in the Efficient Market Hypothesis is a random error term with a mean of zero, and the security price will incline to its value as time goes on, then the noise will disappear, at the same time the noise traders will retreat form the security market in the alternative market mechanisms, at last it is the rational traders dominant the security market. But in resent years, both the anomalies in security market and the irrational tests on the investors all confirm that the trading of irrational investor can influence the price in security market, so the noise theory which use the investor bounded rationality as its hypothesis is introduced into the finance market.The thesis is a formal investigation on the noise trading in security market. On the basis of an overall citation and review the related finance literature, the noise trading is categorized as information-biased noise trading, cognitive-biases noise trading and manipulation-based noise trading. The thesis not only elaborates the information-biased noise trading and manipulation-based noise trading model, also uses the game theory to analysis the survival mechanisms of noise traders. The noise phenomenon in China's security market is not different form those in the finance theory, so the thesis aims at three kinds of noise trading to analysis China's current situations of noise securities trading, and does a empirical investigation about the noise trading status and the influence of noise trading on the securities' price.By the empirical investigation, we draw two conclusions: The first one is about the existence of noise trading. We can find that the noise trading in China's security market is more serious than developed market in the duration, range and degree, and too much of noise trading lowers the efficiency of security market. The second one is about the influence of the noise trading on security market. By the test of the Behavioral Assets Pricing Model we can find that in a long time the securities that has higher liquidity all have noise trader risk.But the construction of the Behavioral Portfolio is complicated, and due to limitation of the author, the ShenZhen component index is used as a substitute to the Behavioral Portfolio, so the precision of the model will be influenced. So the construction of the Mean-Variance Portfolio of real market will be studied deep-seated in the future.