Research On Method And Applicaton Of Building Forecasting Models For Stock Index Fluctuation

Stock indices reflecting the general trend of stock market are regarded as the parameter for economic movement. They serve as reference for government in market regulation and guidance for investors in buying and selling financial product. Therefore, stock-index-forecast has long been a research focus in financial fields as it is of great importance both to the government and individual investors. However, as the movements of stock indices are subject to influence of many macro, micro, internal and external factors, it is a very difficult task to have relatively accurate prediction. In this paper, by absorbing results from previous literature, I carried out research on theory and method for forecasting stock indices in the following aspects:(1)Analysis on factors with impact on stock index fluctuation and characteristics of forecasting models. Given the fact that stock index movements can be influenced by many factors, I tried to analyze and summarize them at three layers, namely, macroeconomics, technique indicators and psychological factors. On the basis of analysis, I put foreward functions and requirements of stock index forecasting models.(2)Comparative analysis of statistical forecasting methods and intelligent forecasting methods. First, comparison of the theories of the above two methods, which includes comparative study of theoretical bases, data requirement and processing, model stability and applicability, forecasting accuracy and length. Secondly, in empirical study, I compared the large samples, small samples and compound models.(3)Research on optimization of neuro-network models. To address the problem of low speed and local minimum, I applied intelligent algorithms such as GA, PSO and AFSA, to optimize the neuronetwork algorithm, then I used the optimized algorithms to forecast the indices of Shanghai Stock Exchange and compared the results achieved by the above three models.(4)Through using data mining technique, I introduced quantitative indicators into forecasting model, eliminate those with poor performance and further combined the indicators with good performance into optimized groups, till I found a combination with the highest forecasting accuracy. (5)Apart from data mining, knowledge mining method is also used in this paper. Text factors including macroeconomic and psychological factors were pre-processed before being introduced into corresponding forecasting models, to increase accuracy.Innovative points in this paper include:(1) theoretical and empirical study and comparison of statistical and non-statistical forecasting models plus comparison of long-term and short-term forecasting results of the above two methods; (2)building a new modified RBFNN model---BF+AFSA;(3) using RBF+AFSA to forecast stock indices of Shanghai Stock Exchange, and compared the algorithm with other swarm intelligent models;(4)applying data mining technique to select major factors with impact on the movement of stock indices and introduce them into forecasting models; (5) using FP_Tree technique to select factors with impact on stock index fluctuation, for instance, economic growth, monetary policies, CPI, psychological factors and breaking out incidents, and establish a indicator system of factors with influence on stock index movement;(6)using knowledge mining technique in forecasting procedures to rank and regroup the text factors, then introduced then to REPTree model, and by so doing, forecasting accuracy was increased.Research result of this paper indicates:innovative intelligent forecasting methods are superior to traditional statistical forecasting methods in short-term stock index forecasting; through using data mining and knowledge mining technique, that is, when quantitative and non-quantitative factors are introduced into related forecasting models, forecasting accuracy increases to some extent.