神经网络技术在股票价格短期预测中的应用研究
发布时间：2018-08-11 15:04
【摘要】：随着我国经济快速增长和股票市场的不断扩大，股票市场产生了大量有价值的数据信息，这些数据成为投资者进行股票投资的重要分析主体。同时，股票价格的预测也成为投资者和相关学者的一个重要研究对象。日益增长的数据不仅难以处理，更给股票价格的预测者们带来了无从选择的难题。BP神经网络作为大数据预测方面的经典算法备受投资者和研究人员的青睐，但是，BP算法本身固有的一些缺点也制约着其预测的效率和效果，在股票价格短期预测方面依然存在着预测精度方面的缺陷。 本文在深入分析股票价格短期预测面临的问题和比较多种股票价格预测方法的基础上，探讨BP神经网络、主成分分析法和遗传算法对股票价格进行短期预测的可行性。BP神经网络能够利用对过往股票市场数据的学习，找出股票市场发展变化的内在规律，从而实现对未来一段时间内股票价格数据变动的预测。为此，本文所做的主要研究工作有： 针对股票价格数据影响因素多的问题，选用主成分分析法来解决了BP神经网络输入向量的维数约减问题，同时，为了建立影响因素和预测向量之间的相关性关系，提高预测的精度，引入了计量经济学中拟合优度的概念，结合传统的主成分分析法，创新性的提出了相关主成分分析法。 针对BP算法容易陷入局部极小点而影响预测精度的缺点，利用遗传算法对BP神经网络进行优化，构建了遗传算法优化的BP神经网络预测模型。在前面算法的基础上，建立了以相关主成分分析法和遗传神经网络模型相结合的综合预测模型，并在Matlab7.0中予以实现。 最后，，为了检验所提出算法的有效性。文章最后进行实验，利用上证指数数据，对文章提出的相关主成分分析法、遗传神经网络模型和综合预测模型分别进行了仿真实验并进行了误差分析，误差分析表明以相关主成分分析法为维数约减方法的遗传神经网络模型在股票价格短期预测精度上有一定程度的改进。
[Abstract]:With the rapid economic growth and the continuous expansion of the stock market, the stock market has produced a large number of valuable data information, which has become an important analysis of investors in stock investment. At the same time, stock price prediction has become an important research object for investors and related scholars. The growing data is not only difficult to deal with, but also a difficult problem for stock price forecasters. BP neural network is favored by investors and researchers as the classical algorithm of big data prediction. However, some inherent shortcomings of BP algorithm also restrict the efficiency and effect of its prediction, and there are still some defects in forecasting accuracy in the short-term forecasting of stock price. Based on the in-depth analysis of the problems faced by short-term forecasting of stock prices and the comparison of various methods of forecasting stock prices, this paper discusses BP neural network. Feasibility of short-term forecasting of stock price by principal component analysis and genetic algorithm. BP neural network can find out the inherent law of stock market development and change by learning from past stock market data. In order to achieve a period of future stock price data changes in the prediction. Therefore, the main research work in this paper is as follows: aiming at the problem that there are many factors affecting stock price data, principal component analysis (PCA) is used to solve the dimension reduction problem of BP neural network input vector, at the same time, In order to establish the correlation between influencing factors and prediction vectors and improve the accuracy of prediction, the concept of goodness of fit in econometrics is introduced. Combining with the traditional principal component analysis, the correlation principal component analysis is innovatively proposed. In view of the disadvantage that BP algorithm is prone to fall into local minima and affect the prediction accuracy, BP neural network is optimized by genetic algorithm, and a BP neural network prediction model optimized by genetic algorithm is constructed. Based on the previous algorithms, a comprehensive prediction model based on correlation principal component analysis (PCA) and genetic neural network (GNN) is established and implemented in Matlab7.0. Finally, in order to verify the effectiveness of the proposed algorithm. At the end of the paper, the experiment is carried out, and the related principal component analysis method, genetic neural network model and comprehensive prediction model are simulated and the error is analyzed by using the index data of Shanghai Stock Exchange. The error analysis shows that the genetic neural network model with correlation principal component analysis as dimension reduction method has a certain degree of improvement in the short-term forecasting accuracy of stock price.
【学位授予单位】：重庆交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F832.51;TP183
[Abstract]:With the rapid economic growth and the continuous expansion of the stock market, the stock market has produced a large number of valuable data information, which has become an important analysis of investors in stock investment. At the same time, stock price prediction has become an important research object for investors and related scholars. The growing data is not only difficult to deal with, but also a difficult problem for stock price forecasters. BP neural network is favored by investors and researchers as the classical algorithm of big data prediction. However, some inherent shortcomings of BP algorithm also restrict the efficiency and effect of its prediction, and there are still some defects in forecasting accuracy in the short-term forecasting of stock price. Based on the in-depth analysis of the problems faced by short-term forecasting of stock prices and the comparison of various methods of forecasting stock prices, this paper discusses BP neural network. Feasibility of short-term forecasting of stock price by principal component analysis and genetic algorithm. BP neural network can find out the inherent law of stock market development and change by learning from past stock market data. In order to achieve a period of future stock price data changes in the prediction. Therefore, the main research work in this paper is as follows: aiming at the problem that there are many factors affecting stock price data, principal component analysis (PCA) is used to solve the dimension reduction problem of BP neural network input vector, at the same time, In order to establish the correlation between influencing factors and prediction vectors and improve the accuracy of prediction, the concept of goodness of fit in econometrics is introduced. Combining with the traditional principal component analysis, the correlation principal component analysis is innovatively proposed. In view of the disadvantage that BP algorithm is prone to fall into local minima and affect the prediction accuracy, BP neural network is optimized by genetic algorithm, and a BP neural network prediction model optimized by genetic algorithm is constructed. Based on the previous algorithms, a comprehensive prediction model based on correlation principal component analysis (PCA) and genetic neural network (GNN) is established and implemented in Matlab7.0. Finally, in order to verify the effectiveness of the proposed algorithm. At the end of the paper, the experiment is carried out, and the related principal component analysis method, genetic neural network model and comprehensive prediction model are simulated and the error is analyzed by using the index data of Shanghai Stock Exchange. The error analysis shows that the genetic neural network model with correlation principal component analysis as dimension reduction method has a certain degree of improvement in the short-term forecasting accuracy of stock price.
【学位授予单位】：重庆交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F832.51;TP183
【参考文献】
相关期刊论文 前10条
1 夏莉,黄正洪;马尔可夫链在股票价格预测中的应用[J];商业研究;2003年10期
2 张U
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