Stock Market Price Behavior Research Based On Time Series Data Mining

The research on stock market price behaviors is important in macro-scope and microcosmic aspect. In the view of macro-scope, realizing the stock market behavior character deeply is a foundation of government to establish stock market police. In the view of microcosmic, realizing the stock market behavior character deeply has impact on the investment strategy of the stock market participator. Model analysis is a traditional method on stock market research. By analysis the time series data, it could accomplish the forecasting, establishing model and controlling of the time series system. Following the strict logic, it use induction research method. The problem is it can not validate the precondition and it try to establish a universal model. There is little probability to establish a universal and precise model for stock market as a complexity system and it impossible to make whole forecasting. Use data mining method to analysis stock market behavior can fetch up the model method shortage. This work will research following problem based on data mining method.First, research on stock market multi-scale technical index induced. According to low I/N rate, multi-scale, nonlinear, non-stationary and long memory and so on characters of stock market, we establish a stock market multi-scale technical index induced method. We use wavelet transform to get multi-scale time series data of stock market. For the multi-scale time series, we use correlation analysis to get the long memory character. We use unit root and moving average methods to get the non-stationary character.Second,research on establishment of stock market multi-scale technical index system. For the complexity of stock market time series neighbor, we use attribute reduction which based on classification complexity as stock market technical index system establish method. To compared with the result of multi-scale technical index system, we also establish traditional technical index system by the above four methods.Third, research on stock market price behavior trend forecasting and the forecasting mechanism. Based on the forecasting feasibility, we forecast stock market price behavior trend with multi-scale technical index system and traditional technical index system respectively. Compared with the result of traditional technical index system, the correct forecasting rate of multi-scale technical index system is almost twenty percent better. To interpret the mechanism of the better forecasting, we use fuzzy rough set method to analysis the forecasting ability of the multi-scale technical index and traditional technical index.Finally,research on stock market price behavior technical trade rule of multi-scale technical index system. Based on the introducing stock market traditional technical trade rule, we propose stock market price behavior technical trade rule on data mining. We induce multi-scale technical index trade rule with rough set and decision tree methods. As the example of decision tree methods, combined with the experience of technical analysis investment, we interpret the mechanism of stock market multi-scale technical index trade rule with fuzzy rough set.