基于混沌优化的多尺度小波核v-支持向量机及其在股票市场中的应用
发布时间：2018-11-11 13:13
【摘要】：股票市场是现代金融市场中的重要组成部分。它对国家经济的发展,股份制企业和股票投资者都具有无法替代的作用。同时,股票市场的波动对经济建设也有不小的副作用。所以,对股票市场走势的分析和预测具有重要的意义。由于股票价格的影响因素非常多而且非常复杂,研究者很难对股票市场进行精确的预测。时间序列方法对股价的预测是一个较好的选择。然而股价具有非线性,高噪音和异方差的特点,这样传统的序列模型并不能很好地分析与预测股价。本文的主要工作是建立基于混沌优化方法的多尺度小波核v-支持向量机的回归预测模型,达到更准确预测股价的目的。与单尺度小波核v-支持向量机,小波神经网络和径向基函数ε-支持向量机等模型相比,这种模型可以更准确的预测股票价格的未来走势。股票市场的参与者借助这种模型可以在降低投资风险的同时获得更高的投资收益。 本文首先总结了股票价格时间序列预测方法的研究进展,特别是介绍了人工智能算法与小波理论的混合模型在股票价格时间序列预测中的应用；然后阐述了支持向量机的理论基础和小波理论的基础知识。根据支持向量机核函数构造的方法,本文着重分析了单尺度小波核和多尺度小波核作为v-支持向量机的核函数的合理性。接着,本文分析了以多尺度小波核作为核函数的v-支持向量机模型处理具有非线性,高噪音特点的时间序列的优势。多尺度小波核v-支持向量机模型的参数具有个数较多,每个参数的取值范围不一的特点,使得支持向量机的常用参数选择方法--交叉验证方法不适合这一模型。针对这一问题,本文提出了混沌优化方法作为多尺度小波核v-支持向量机模型的参数选择方法。 本文具体的研究方法和成果是：首先利用上海证券交易所发布的建筑指数研究了混沌优化方法对多尺度小波核v-支持向量机的优化效果。本文通过多次试验比较了粒子群优化方法,混沌优化方法和混沌粒子群优化方法对模型的优化效果,证明了混沌优化方法对模型的优化效果的有效性和稳定性。混沌粒子群优化方法的优化效果曲线显示了粒子群优化方法对从混沌优化方法中得到的优化粒子没有进一步地优化效果。接着,本文比较了多尺度小波核v-支持向量机,单尺度小波核v-支持向量机,小波神经网络和径向基核函数ε-支持向量机对上证指数的预测效果。在这次试验中,本文将上证指数分为了牛市期,熊市期和震荡期三个阶段。在每一个阶段中,基于混沌优化方法的多尺度小波核v-支持向量机取得了比另外三个模型更好的预测效果。
[Abstract]:Stock market is an important part of modern financial market. It plays an irreplaceable role in the development of national economy, stock-holding enterprises and stock investors. At the same time, the volatility of the stock market has no small side effects on economic construction. Therefore, it is of great significance to analyze and forecast the trend of stock market. Because there are many and complicated factors affecting stock price, it is difficult for researchers to predict the stock market accurately. Time series method is a good choice for stock price prediction. However, the stock price has the characteristics of nonlinear, high noise and heteroscedasticity, so the traditional sequential model can not well analyze and predict the stock price. The main work of this paper is to establish the regression prediction model of multi-scale wavelet kernel v-support vector machine based on chaos optimization method, so as to predict the stock price more accurately. Compared with single scale wavelet kernel v- support vector machine, wavelet neural network and radial basis function 蔚-support vector machine, this model can predict the future trend of stock price more accurately. With the help of this model, participants in the stock market can achieve higher investment returns while reducing investment risk. This paper first summarizes the research progress of stock price time series prediction, especially introduces the application of hybrid model of artificial intelligence algorithm and wavelet theory in stock price time series prediction. Then, the theoretical basis of support vector machine and the basic knowledge of wavelet theory are expounded. According to the method of constructing kernel function of support vector machine, the rationality of single-scale wavelet kernel and multi-scale wavelet kernel as kernel function of v-support vector machine is analyzed in this paper. Then, this paper analyzes the advantage of the v-support vector machine model which uses multi-scale wavelet kernel as kernel function to deal with nonlinear and high-noise time series. The multi-scale wavelet kernel v-support vector machine model has a large number of parameters and a different range of values for each parameter, which makes the commonly used parameter selection method of support vector machine, the cross-validation method, not suitable for this model. To solve this problem, a chaotic optimization method is proposed as a parameter selection method for multi-scale wavelet kernel v-support vector machine model. The specific research methods and results are as follows: firstly, the optimization effect of chaos optimization method on multi-scale wavelet kernel v-support vector machine is studied by using the building index published by Shanghai Stock Exchange. In this paper, the effects of particle swarm optimization method, chaos optimization method and chaotic particle swarm optimization method on model optimization are compared, and the effectiveness and stability of chaotic optimization method for model optimization are proved. The optimization effect curve of chaotic particle swarm optimization method shows that the particle swarm optimization method has no further optimization effect on the optimization particles obtained from chaos optimization method. Then, this paper compares the prediction effect of multi-scale wavelet kernel v-support vector machine, single-scale wavelet kernel v-support vector machine, wavelet neural network and radial basis function 蔚 -support vector machine on Shanghai stock index. In this experiment, the index is divided into three stages: bull period, bear period and shock period. In each stage, the multi-scale wavelet kernel v-SVM based on chaotic optimization method achieves better prediction results than the other three models.
【学位授予单位】：兰州大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：O212.1;O211.61;F830.91
本文编号：2324943
[Abstract]:Stock market is an important part of modern financial market. It plays an irreplaceable role in the development of national economy, stock-holding enterprises and stock investors. At the same time, the volatility of the stock market has no small side effects on economic construction. Therefore, it is of great significance to analyze and forecast the trend of stock market. Because there are many and complicated factors affecting stock price, it is difficult for researchers to predict the stock market accurately. Time series method is a good choice for stock price prediction. However, the stock price has the characteristics of nonlinear, high noise and heteroscedasticity, so the traditional sequential model can not well analyze and predict the stock price. The main work of this paper is to establish the regression prediction model of multi-scale wavelet kernel v-support vector machine based on chaos optimization method, so as to predict the stock price more accurately. Compared with single scale wavelet kernel v- support vector machine, wavelet neural network and radial basis function 蔚-support vector machine, this model can predict the future trend of stock price more accurately. With the help of this model, participants in the stock market can achieve higher investment returns while reducing investment risk. This paper first summarizes the research progress of stock price time series prediction, especially introduces the application of hybrid model of artificial intelligence algorithm and wavelet theory in stock price time series prediction. Then, the theoretical basis of support vector machine and the basic knowledge of wavelet theory are expounded. According to the method of constructing kernel function of support vector machine, the rationality of single-scale wavelet kernel and multi-scale wavelet kernel as kernel function of v-support vector machine is analyzed in this paper. Then, this paper analyzes the advantage of the v-support vector machine model which uses multi-scale wavelet kernel as kernel function to deal with nonlinear and high-noise time series. The multi-scale wavelet kernel v-support vector machine model has a large number of parameters and a different range of values for each parameter, which makes the commonly used parameter selection method of support vector machine, the cross-validation method, not suitable for this model. To solve this problem, a chaotic optimization method is proposed as a parameter selection method for multi-scale wavelet kernel v-support vector machine model. The specific research methods and results are as follows: firstly, the optimization effect of chaos optimization method on multi-scale wavelet kernel v-support vector machine is studied by using the building index published by Shanghai Stock Exchange. In this paper, the effects of particle swarm optimization method, chaos optimization method and chaotic particle swarm optimization method on model optimization are compared, and the effectiveness and stability of chaotic optimization method for model optimization are proved. The optimization effect curve of chaotic particle swarm optimization method shows that the particle swarm optimization method has no further optimization effect on the optimization particles obtained from chaos optimization method. Then, this paper compares the prediction effect of multi-scale wavelet kernel v-support vector machine, single-scale wavelet kernel v-support vector machine, wavelet neural network and radial basis function 蔚 -support vector machine on Shanghai stock index. In this experiment, the index is divided into three stages: bull period, bear period and shock period. In each stage, the multi-scale wavelet kernel v-SVM based on chaotic optimization method achieves better prediction results than the other three models.
【学位授予单位】：兰州大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：O212.1;O211.61;F830.91
【参考文献】
相关期刊论文 前5条
1 任世锦;吴铁军;;基于径向基小波核的多尺度小波支持向量机[J];电路与系统学报;2008年04期
2 王彦峰;高风;;基于支持向量机的股市预测[J];计算机仿真;2006年11期
3 崔艳;程跃华;;小波支持向量机在交通流量预测中的应用[J];计算机仿真;2011年07期
4 李元诚;方廷健;;小波支持向量机[J];模式识别与人工智能;2004年02期
5 施燕杰;基于支持向量机(SVM)的股市预测方法[J];统计与决策;2005年04期
相关硕士学位论文 前4条
1 郝博乾;基于时间序列分析的股票预测模型研究[D];电子科技大学;2011年
2 张海珍;小波神经网络在股价预测中的应用[D];西安科技大学;2008年
3 夏冬;小波神经网络在我国股本权证市场中的应用[D];华东师范大学;2009年
4 张清华;小波神经网络参数优化及其应用[D];东北农业大学;2009年
，本文编号：2324943
本文链接：https://www.wllwen.com/guanlilunwen/zhqtouz/2324943.html
最近更新
教材专著
