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Application Of The Noise Trading Theory In Chinese Stock Market

Posted on:2011-02-24Degree:MasterType:Thesis
Country:ChinaCandidate:N N ChenFull Text:PDF
GTID:2189360305474556Subject:Applied Mathematics
Abstract/Summary:PDF Full Text Request
Since the 80s of the 20th century , the capital asset pricing model (CAPM) and the efficient market hypothesis (EMH) as the representative of the standard theory of financial theory practice face a major challenge: in the standard financial theory, the noise trading on the stock price does not impact, because the noise in the efficient market hypothesis is a zero mean random disturbance term, over time, stock prices will be close to intrinsic value, the noise will gradually disappear; but in recent years in various financial market anomalies irrational investor behavior, and experiments confirmed the behavior of irrational traders can affect the securities market, noise and noise traders on stock prices is systemic, the impact will spread to most of the securities or the securities market . Therefore, a rational investor is limited to the noise trading hypothesis theory is introduced to the financial markets.China's stock market as a double feature both emerging and transitional capital market, has the typical characteristics of non-effective and non-rational, a lot of information not related to fundamentals, the company dominates the market. Fundamentals in the Chinese stock market has nothing to do with the proliferation of noise, noise trading dominated the market, resulting in distorted price formation mechanism, the stock market volatility increases, market inefficiencies, the stock market bubble, the stock market risk accumulation. Market development based on the contradiction between seeking the root causes of the functions of a deep understanding of stock positioning and operating mechanism, and prevent financial risks and financial crises, and promote market development and improvement purposes, this transaction the Chinese stock markets as the breakthrough point and the theme of noise, noise introduced the specific content of trade theory and empirical analysis and experience with verification tools, combined with the practice of Shanghai stock market, on the behavior of asset pricing model is validation.In order to quantify China's stock market noise trader risk, the paper made at the behavior of the current asset pricing model based on the results, combined with Chinese scholars on China's securities market quantitative indicators of emotional constructs containing investor sentiment Stock Market market index for China's securities market Noise trader risk situation improved test model, based on this model to empirical data for the Shanghai stock market study, obtained more than the capital asset pricing model fit the actual conclusion To further improve the reform of China's stock market need to provide direct empirical evidence. China's stock market trading and noise trading on the stock status and noise impact of asset prices, the empirical analysis.The empirical analysis of the main conclusions are: (1) the existence of noise trading in Shanghai stock market; (2) noise trading risk is very large, while the risk of noise trading may bring benefits to the investors can also cause losses to investors, but with more likely to damage; (3) in Shanghai stock market, BAPM more effective than the CAPM. Shows very low efficiency of China's securities market for the current situation, I made the stock market governance Suggestions noise.Shortcomings of this article is the use of behavioral asset pricing model verification noise trading on the impact of China's securities market can not be precisely quantified to find acts of portfolio turnover as an alternative to choose, so the accuracy of the model had some effect . Find the true market mean-variance efficient frontier of the portfolio will be the noise trading theory applied to asset pricing in the securities research, is also the focus of my research topic since.
Keywords/Search Tags:Noise Trader Risk, Behavioral Asset Pricing model, Turnover Rate
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