The Stock Index Time Series Analysis Based On Hybrid Genetic Algorithm

Forecast data occupies an important position in the field of financial investment. And one of the most complex financial data is the stock price data which is characterized with wide range of change, huge number of change factors and instability of change and so on. Because of these characteristics, it has attracted the interest of many economists, who have been dedicated to the study of price changes in stock market with an aim to find some disciplines, avoiding large fluctuations of the stock market, and thereby maintaining economic prosperity and stability.The analysis time series is the typical of Data Mining and applied statistics. And GEP is a new adaptive evolutionary algorithm, a method which has been applied to many areas, with very good results achieved. However, because of its defects, namely, easy premature convergence and a local optimal solution, simulation annealing is introduced to make the two integrated with each other. GEPSAT-STOCK algorithm is used to establish model of stock index time series. It's necessary to choose suitable GEPSAT-STOCK model, including GSAT-G coding model, fitness function and GSAT-CT solution according to the characteristics of stock data.In the course of the study, three new operators are designed-the preservation operator, replaced operator, and the adaptive operator. What's more, the multi-thread technology of VC is used to provide visual interface systems dynamically demonstrating its results. The closing price of the 000002 (Wanke A) of stock from the first trading day in 2007 to May 12th in 2008 is chosen as the experimental data, which is used to compare the results from traditional method GEP's in order to analyze GEPSAT-STOCK algorithm in the application on the merits of the issue. The result shows that the use of the model established by GEPSAT-STOCK algorithm to forecast achieved better results with higher precision forecast. And the average forecast error is in around 1.4% when 4d is used as embedded dimension to forecast. Although the average error of forecast is very small, it is impossible to forecast the stock data accurately since it is under the influence of many factors. But it does not mean that the forecast of the stock time series is insignificant. Though the results of stock data cannot be accurately forecast, it's possible to estimate the general scope of the stock data so as to provide favorable conditions for predicting the trend of stocks. Therefore, it is likely to provide a reliable theoretical guarantee for stock transaction, and this can reduce the financial risks as well as the losses. By experimental verification, the program using 4d as the embedded dimension for forecasting could result in a 85% degree of accuracy in the judging through continuous optimization.