【作者】 张建军；

【导师】 朱连章；

【作者基本信息】 中国石油大学 ， 计算机技术， 2010， 硕士

【摘要】 股票市场是国家市场经济的真实反映,但由于股市其内部规律的复杂性,如:股价(股指)变化的非线性性?股价数据具有高噪声等特点,传统的数理统计技术对股市的预测效果并不理想?神经网络具有可以充分逼近任意复杂的非线性关系和有很强的鲁棒性和容错性的特点,所以非常适合用于对股票数据的分析?本文利用数据挖掘技术对我国股票行情波动趋势进行研究,其目的是为了预测未来股市的行情走势及其波动情况?本文指出了目前传统的数理统计在股票分析上的不足,使用BP神经网络算法对股市进行预测,通过建立一个三层结构的神经网络,即输入层?隐含层和输出层?对数据预处理后得到的数据进行挖掘,将市场普遍采用的技术指标,如科威特第纳尔指数,相对指标的差异等引入模型,得到了较好的预测模型,提高了预测的精度?更多还原

【Abstract】 The stock market reflects the fluctuation of the market economy, and receives ten million investors’attention since its initial development. The stock market is characterized by high-risk, high-yield, so investors are concerned about the analysis of the stock market and trying to calculate the trend of the stock market. However, stock market is affected by the politics, economy and many other factors, coupled with the complexity of its internal law, such as price (stock index) changes in the non-linear, and shares data with high noise characteristics, therefore the traditional mathematical statistical techniques to forecast the stock market has not yielded satisfactory results. Neural networks can approximate any complex non-linear relations and has robustness and fault-tolerant features. Therefore, it is very suitable for the analysis of stock data.In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. After building the data pre-processing set before data mining, lots of widely used stock market technical indicators such as the KD indicators, similarities and differences between exponential smoothing moving average MACD, Relative Strength Index RSI, will be introduced into the model. Finally, we get a better predictive model to improve forecast accuracy.更多还原