Application of Neural Networks in Financial Data Mining

� Abstract—This paper deals with the application of a well-known neural network technique, multi-layer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining sys-tem is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it. Keywords—data mining, neural network, stock forecasting. Though the efficient market hypothesis (13), which is currently the most popular view on market behavior, states that no mining system can achieve consistently average returns exceeding the average returns of the market indices as a whole. However, many research results have shown that the mining system based on neural networks can outperform market if it is properly designed (14). In this paper, the theory of neural networks is briefly discussed, and a neural network model to forecast future trends of stock time series is researched. Some improved strategies are presented. At last, an intelligent mining system is developed. The simulation results to Shanghai Composite Index show that neural networks can be applied to make profit for different investors.

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