混沌理论在股票市场走势预测中的应用研究
发布时间：2018-11-28 12:40
【摘要】：混沌是自相对论和量子力学之后人类科学的又一伟大发现,是对人类整个知识体系的又一次巨大冲击。它改变了自牛顿体系确定以来人们对于整个知识体系的认知,扩展了人们对于自然界的认识。混沌是一种低阶确定性的非线性动力系统所表现出来的非常复杂的行为,动力系统长期演化中任一变量的演化过程都包含了系统所有变量的信息,通过单变量时间序列反向构造出原系统相空间结构。 中国股票市场自20世纪90年代成立,经过20多年的发展,曾经历了几次暴涨暴跌。国内外研究表明,股票市场存在混沌现象,而中国股票市场是一个具有分形维结构的混沌系统。而与股票市场有着重要关系的国际原油市场也被研究证明了是一个存在混沌现象的复杂系统。本文在对股票市场研究预测模型进行了简单概述后,使用BP神经网络训练预测模型对中国股票市场上证指数收盘价进行训练预测,同时对国际原油期货价格市场WTI即美国西德克萨斯轻质原油价格使用了同样的BP神经网络模型进行了训练预测分析。 本文对混沌现象发现过程进行了梳理,从图灵模式到别洛乌索夫反应,从《确定性的非周期流》到逻辑斯蒂方程,从费根鲍姆常数到曼德勃罗集,从自组织到自相似。对混沌学理论基础做了理论分析。之后简述了中国股票市场发展历史,对股票指数的计算方法和对中国股票市场预测研究现状做了简述。最后使用混沌理论方法对上证指数收盘价和WTI原油价格进行混沌分析,互信息函数方法求取延迟时间,CAO方法求取嵌入维数,对其进行了相空间重构。并对上证指数收盘价根据G-P算法对上证指数收盘价计算其关联维为非整数,使用最小数据量方法计算对这两个经济数据计算最大Lyapunov指数都大于零,由此可以得出我国股票市场和国际原油市场是一个存在混沌现象的复杂系统,并对其建立了基于最大Lyapunov指数混沌预测模型。通过和BP神经网络训练模型进行对比分析,发现基于最大Lyapunov指数混沌预测模型具有较好的预测效果。
[Abstract]:Chaos is another great discovery of human science since relativity and quantum mechanics, and it is another great impact on the whole human knowledge system. It has changed people's cognition of the whole knowledge system since Newton's system was determined, and expanded people's understanding of nature. Chaos is a very complex behavior of a low-order deterministic nonlinear dynamic system. The evolution process of any variable in the long-term evolution of the dynamical system contains the information of all the variables of the system. The phase space structure of the original system is inversely constructed by univariate time series. After more than 20 years of development, China's stock market has experienced several spikes and plunges since its establishment in the 1990 s. Studies at home and abroad show that there is chaos in the stock market, while the Chinese stock market is a chaotic system with fractal dimension structure. The international crude oil market, which has an important relationship with the stock market, has also been proved to be a complex system with chaotic phenomena. In this paper, after a brief overview of the stock market research forecasting model, the BP neural network training forecasting model is used to forecast the closing price of the Shanghai Stock Exchange Index in China stock market. At the same time, the same BP neural network model is used to predict the international crude oil futures market WTI, that is, the West Texas light crude oil price. In this paper, the discovery process of chaotic phenomena from Turing model to Belousov reaction, from deterministic aperiodic flow to logical Stey equation, from Fegenbaum constant to Manderborough set, from self-organization to self-similarity is discussed. The theoretical basis of chaos is analyzed. Then, the history of Chinese stock market development, the calculation method of stock index and the present situation of stock market prediction in China are briefly described. Finally, chaos theory is used to analyze the closing price of Shanghai Stock Exchange Index and the price of WTI crude oil, the delay time is obtained by mutual information function method, and the embedding dimension is obtained by CAO method, and the phase space is reconstructed. On the basis of G-P algorithm, the correlation dimension of the closing price of Shanghai Stock Exchange Index is calculated as a non-integer, and the maximum Lyapunov index of the two economic data is calculated by the method of minimum data. It is concluded that the stock market in China and the international crude oil market are a complex system with chaotic phenomena, and a chaotic prediction model based on the largest Lyapunov exponent is established. By comparing with the BP neural network training model, it is found that the chaotic prediction model based on the maximum Lyapunov exponent has better prediction effect.
【学位授予单位】：东北林业大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F224;F832.51
本文编号：2362848
[Abstract]:Chaos is another great discovery of human science since relativity and quantum mechanics, and it is another great impact on the whole human knowledge system. It has changed people's cognition of the whole knowledge system since Newton's system was determined, and expanded people's understanding of nature. Chaos is a very complex behavior of a low-order deterministic nonlinear dynamic system. The evolution process of any variable in the long-term evolution of the dynamical system contains the information of all the variables of the system. The phase space structure of the original system is inversely constructed by univariate time series. After more than 20 years of development, China's stock market has experienced several spikes and plunges since its establishment in the 1990 s. Studies at home and abroad show that there is chaos in the stock market, while the Chinese stock market is a chaotic system with fractal dimension structure. The international crude oil market, which has an important relationship with the stock market, has also been proved to be a complex system with chaotic phenomena. In this paper, after a brief overview of the stock market research forecasting model, the BP neural network training forecasting model is used to forecast the closing price of the Shanghai Stock Exchange Index in China stock market. At the same time, the same BP neural network model is used to predict the international crude oil futures market WTI, that is, the West Texas light crude oil price. In this paper, the discovery process of chaotic phenomena from Turing model to Belousov reaction, from deterministic aperiodic flow to logical Stey equation, from Fegenbaum constant to Manderborough set, from self-organization to self-similarity is discussed. The theoretical basis of chaos is analyzed. Then, the history of Chinese stock market development, the calculation method of stock index and the present situation of stock market prediction in China are briefly described. Finally, chaos theory is used to analyze the closing price of Shanghai Stock Exchange Index and the price of WTI crude oil, the delay time is obtained by mutual information function method, and the embedding dimension is obtained by CAO method, and the phase space is reconstructed. On the basis of G-P algorithm, the correlation dimension of the closing price of Shanghai Stock Exchange Index is calculated as a non-integer, and the maximum Lyapunov index of the two economic data is calculated by the method of minimum data. It is concluded that the stock market in China and the international crude oil market are a complex system with chaotic phenomena, and a chaotic prediction model based on the largest Lyapunov exponent is established. By comparing with the BP neural network training model, it is found that the chaotic prediction model based on the maximum Lyapunov exponent has better prediction effect.
【学位授予单位】：东北林业大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F224;F832.51
【引证文献】
相关硕士学位论文 前1条
1 詹财鑫;基于SVM_AdaBoost模型的股票涨跌实证研究[D];华南理工大学;2013年
，本文编号：2362848
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