Empirical Study Between Expected Beta Coeffieient And Accounting Variables In Chinese Stock Market

Beta coefficient, as the key parameter of correctly understanding the theory of capital market related the relationship between the returns and risk, is important to the capital market research. Beacauseβcoefficient is more difficult to estimate, although a number of estimation methods have been universally accepted - for example, historical transaction data based on estimates, the data needed to calculate under this method is relatively large, and the new listing stocks often lack sufficient historical transaction data. In fact, most investors don't need to estimateβcoefficient, exactly the reverse, to find out: Which factors affect theβcoefficient in the end,what extent.In the basis of summarizing the accounting variable and the beta coefficient, I selected the stock listing in Shanghai and Shenzhen stock markets before 1 January, 1998, altogether 501 shares. The sample time selected in thie article is from 1998 to 2008, altogether 11 years. Taking into account the actual situation of China's securities market, the choice of the yield of time period used in this article is the week yield of securities data.The aim is to increase the amount of data. The "single index model": Ri =αi +βi Rm +εi,is used to compute the beta coefficient, which stock price yield is to consider the resumption of dividend price. The logarithmic differential is used to compute the rate of market return: Rt = Ln ( Pt ) ? Ln ( Pt?1), which the logarithm of the arithmetic mean of the SSE Composite Index and Shenzhen Composite Index taken on the numbers are to represent the market index.In the selection of accounting variables, the paper selected the 21 initial variables, in theory, these variables are closely related to the company's risks, and the correlation coefficient analysis, cluster analysis, principal component analysis and so on in the software of SPSS15.0 are utilized to screen the variables, and ultimately I receive 13 accounting variables, then make assumptions to the selected accounting variables and expectedβcoefficient.In the process of the Empirical Study, first of all based on total assets (on behalf of firm size), I utilize the traditional method to divide the 501 selected companies into five groups, that is, 20% of the largest enterprises, 20% of major enterprises, up to 20% of the smallest enterprises. According this classification, followed by code for the A (top 100 enterprise portfolio), B (position 101-200 of the business combination), C (position 201-300 of the business combination), D (rank 301-400 business combination), E (top of the business combination 401-501). After this classification, the similar scale of the enterprises will be placed in the same category. It can weaken the ultimate analysis deviation caused by the firm size in a certain extent. Then this article analyzes the relationship between accounting variables withβcoefficient in each group.`From the analysis results after this classification, compareing to the study of previous research scholars, the goodness of fit of each year significantly is improved. It shows that the degree of linear relationship of the selected accounting variables and theβcoefficient in this article is very close, and it can provide a scientific and theoretical basis and analysis methods to forecastβcoefficient on some extent.