基于朴素贝叶斯分类算法的股指预测研究  

On stock index prediction based on native Bayes classification algorithm

作　　者：任民宏[1] 肖海蓉[1] 

机构地区：[1]陕西理工学院数学与计算机科学学院,陕西汉中723000

出　　处：《陕西理工学院学报（自然科学版）》2014年第3期68-73,共6页Journal of Shananxi University of Technology:Natural Science Edition

摘　　要：预测大盘指数的涨跌幅度在股票投资中具有重要的意义。大盘指数的涨跌既与国家的宏观经济政策有关,也与大盘指数自身运行状态有关。结合朴素贝叶斯分类算法和股票大盘指数涨跌的影响因素建立了大盘指数分类预测模型,以上证指数为例进行了实验,结果表明分类预测模型有效,准确性较高。Margin prediction for the broader market index is of great significance in stock investment . Broader market index is not only related to national macroeconomic policies , but also related to the broader index operation itself .Based on native Bayes classification algorithm and influence factors of stock market index , a classification prediction model is established about market index , and followed by an experiment of Shanghai stock index as a case study .The experiment results prove that the classification prediction model is effective with higher accuracy .

关 键 词：朴素贝叶斯分类算法 大盘指数 预测模型 

分 类 号：F830.91[经济管理—金融学] TP181[自动化与计算机技术—控制理论与控制工程]