粒子群优化的支持向量机在股票预测中的研究与应用

发布时间：2018-04-10 07:52

本文选题：支持向量机　切入点：粒子群　出处：《广东工业大学》2012年硕士论文

【摘要】：股票在市场经济中占有重要地位,发行股票的公司可以通过从社会上融资来扩大公司规模,个体也可以通过控股的方式来影响公司的经营,可以说股票对推动市场经济发展起到了重要的作用。因此,对股市进行分析和预测,从小的方面看,有利于市场中的个体获益,从大的方面看,有利于决策者对宏观经济进行调整,保持国民经济的平稳发展。随着统计机器学习领域的发展,各种智能算法也不断涌现。从股票的特点来看,它在短期投资上会产生很大的不确定性,但是在长期趋势上则符合统计学规律。因此,在有限样本的情况下,通过机器学习算法来预测股票,是股票预测研究中的一个重要发展方向。本文在研究了各项股票预测技术的基础上,将统计机器学习的思想作为算法的基础,首先分析使用统计机器学习原理进行股票预测的可行性,然后提出了一种新的股票预测方法,这种方法以支持向量机分类为核心,首先使用K均值聚类,对股票的历史数据从形态上进行分类,然后对每一类的历史数据,提取股票预测中的经典时态指标作为特征,用支持向量机进行训练,在训练过程中,使用优化的粒子群算法对支持向量机的关键参数进行调整,从而得到分类更准确的支持向量机模型。在预测时,首先使用最近邻分类将待预测样本归到某一个聚类中,再使用该类相关的支持向量机进行预测。通过这种多级预测的算法,提高了分类的准确率。为了真实全面地评估该算法的有效性,本文通过实际的上证大盘股票的历史数据作为训练集和预测集进行分析,在实验的内容上,也包括了本算法与其他预测算法的结果比较。实验结果表明,本算法在预测精度和适应性上都比其他算法有了明显进步,预测率得到了明显提升。
[Abstract]:Stocks play an important role in the market economy. Companies that issue stocks can expand the size of the company through financing from the society, and individuals can also influence the operation of the company through holding shares.It can be said that the stock market economy to promote the development of an important role.Therefore, the analysis and prediction of the stock market is beneficial to the individual benefit in the market from the small aspect, and to the policy makers to adjust the macro economy and maintain the steady development of the national economy.With the development of statistical machine learning, a variety of intelligent algorithms are emerging.According to the characteristics of stock, it will produce a lot of uncertainty in short-term investment, but accord with the statistical law in long-term trend.Therefore, in the case of limited samples, it is an important development direction in stock forecasting research to predict stocks by machine learning algorithm.On the basis of studying the stock forecasting technology, this paper takes the idea of statistical machine learning as the basis of the algorithm, and analyzes the feasibility of using the statistical machine learning principle to predict the stock.Then, a new stock forecasting method is proposed, which takes support vector machine classification as the core. Firstly, K-means clustering is used to classify the historical data of stocks, and then the historical data of each class are classified.The classical temporal index of stock prediction is extracted as the feature, and the support vector machine is used to train. In the process of training, the key parameters of the support vector machine are adjusted by using the optimized particle swarm optimization algorithm.Thus, a more accurate support vector machine model is obtained.In prediction, the nearest neighbor classification is first used to classify the samples to a certain cluster, and then the support vector machine associated with this class is used to predict the prediction.The accuracy of classification is improved by using this multilevel prediction algorithm.In order to evaluate the validity of the algorithm, this paper analyzes it by using the actual historical data of Shanghai stock market as the training set and prediction set. In the content of the experiment, it also includes the comparison of the results of the algorithm and other prediction algorithms.The experimental results show that the prediction accuracy and adaptability of this algorithm are better than those of other algorithms, and the prediction rate is improved obviously.
【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP18;F832.51

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