基于神经网络和模式匹配的股票预测研究

Stock Market Forecasting Research Based on Neural Network and Pattern Matching

BP神经网络是分析股票数据最流行的工具之一。近期对模式匹配算法的研究表明模式匹配简化了股票趋势预测的复杂度并为股票市场预测提供了一种简单有效的方法。文中分别阐述了BP神经网络和模式匹配识别的原理,并提出将两种算法相结合,建立一个基于BP神经网络和模式匹配识别的股票市场分析和预测系统。这个系统克服了神经网络预测系统目标函数存在局部最小和模式匹配识别预测系统缺少股票价格自身变化特性的缺点,具有两种算法在股票预测应用方面的优势。通过对泰山石油的股价进行分析来测试这个系统。实验结果表明此方法不仅收敛速度快、预测精度高,而且易于操作,具有一定应用价值。

BP Neural Networks is one of the most popular tools in the analysis of stock data.Recent research activities in Pattern Matching indicate that Pattern Matching just simplify the complexity of stock trend prediction and provide a simple but effective way for the stock market prediction.This paper analyses the theory of BP Neural Networks and Pattern Matching,proposes a method for combining these two algorithms to establish a stock market forecasting system based on BP Neural Networks and Pattern Matching.This system overcomes the shortcomings of the local least in the Neural Networks forecasting system＇s objective function and Pattern Matching System＇s lack of stock changing probabilities,takes advantage of the unique strength in stock price forecasting of these two algorithms.Finally,test this system by analyzing and forecasting the Titan Oil＇s stock price.The experimental results show that not only this method has a quick convergent rate and a precise forecast,but also that it is easy to use and has much application value.