【作者】 王宏杰；

【导师】 王力；

【作者基本信息】 贵州大学 ， 计算机应用技术， 2009， 硕士

【摘要】 随着经济的发展和人们投资意识的转变,股市已成为现代人生活中的一个重要组成部分,股市投资已成为社会公众谈论的中心之一,而股市的健康发展和繁荣也成为管理者和投资者关心和研究的重点。股市投资的收益与风险往往是成正比的,即投资收益越高,可能冒的风险越大。因此,股市预测方法的研究具有极其重要的应用价值和理论意义。但是股价系统内部结构的复杂性、外部因素的多变性决定了这项任务的艰巨性,而传统的预测工具已不能满足这种需要。本文在深入分析股市投资理论和股价预测方法的基础上,使用BP神经网络作为股市预测的网络模型。股市市场是一个极其复杂的非线性动力学系统,而神经网络具有很强的非线性逼近能力和自学习、白适应等特性,实验证明,利用神经网络对股市建模可以取得较好的预测效果。因为股市市场的走势看起来杂乱无章,但实际上有其内在的变化规律,而这正是神经网络预测股市的基础。BP网络通过对以往历史数据的学习,找出股市运行的内在规律,并将其存储在网络具体的权值、阈值中,用以预测未来的走势,尤其对于短期的预测效果更为明显。然而,BP神经网络存在学习收敛速度缓慢、易陷入局部极小点等缺点,使其对股市预测的效果不能令人满意。鉴于此,本文采用遗传算法与BP神经网络的融合策略,来达到克服其缺点的目的。遗传算法与BP神经网络相结合运用于股市预测的研究早已不是什么新鲜事了,但对于换手率作为因子能否用于股市预测?如何在预测网络模型中加入换手率因子?到目前为止还没有学者作过研究。作为首次研究,本文提出了两种将换手率作为因子加入到预测网络模型的方案:一,换手率和收盘价同时作为网络的输入数据;二,平均换手率作为隐含层阈值的附加值。对每个方案开发一个预测系统,通过系统的实际运行来判断换手率作为因子能否用于股市预测以及预测效果如何,从而达到研究遗传算法与BP神经网络融合策略在时间序列预测中应用的目的。更多还原

【Abstract】 With the economic growth and the conversion of people’s investment consciousness, stock has become an important part of people’s life in modern time. Investment in stock has greatly become one of focuses of public topic. How to keep the development and boost stock market is becoming the emphasis of concern and research of manager and investor. The proceeds of stock investment always equal the risk. That means the good proceeds is based on the high risk of failure. Therefore, the study of stock prediction method has great application value and theoretical significance. The complexity of inside structure and levity of exterior complication in system of stock market make stock market predication a complex problem. The traditional methods and tools have not met its challenge, the thesis presents a method of modeling stock market using neural network based on thorough study of stock investment theories and stock prediction methods.On base of analysis of investment theory and the stock market price prediction method, BP neural network was utilized as a network model for stock market prediction. The stock market is an extremely complex non-linear dynamical system, and neural network has strong nonlinear approximation ability and self-learning, adaptive and other features, experiments show that the use of neural network modeling can achieve good forecast results on the stock market. Because the trend of the stock market looks chaotic, but in fact the changes are in its internal law of neural network which is the basis for forecasting the stock market. BP network makes use of the historical data of previous study to identify the inherent law of development of the stock market, and store it in the network as weighting value and the threshold value to predict the future trend, especially with more visible effect for short-term prediction.However, BP neural network learning convergence is slow, vulnerable to the shortcomings of local minimum points, which results in the unsatisfactory prediction effect on the stock market. In view of this, this thesis adopts genetic algorithm and BP neural network integration strategy to overcome its shortcomings.Genetic algorithm and BP neural network used in a combination of stock market forecasts are not new study, but can the turnover rate be used as a factor to forecast the stock market? How the turnover rate factor can be added into the forecasting network model? So far scholars have not done research in this aspect. As the first study, this thesis presents two kinds of turnover rate as a factor to be added to the proposal of forecasting network model: First, turnover rate and the closing price at the same time as the network input data; Second, the average turnover rate as the threshold of hidden layer value-added value. For each proposal a forecasting system is developed, running through the system to determine the actual turnover rate as a factor could be used to predict the effect of the stock market, for the purpose of researching on genetic algorithm and BP neural network convergence strategy in the application of time series prediction.更多还原