K近邻及其集成模型的股票价格预测  

Stock price predication based on KNN and its ensemble model

作　　者：张伟楠 鲁统宇 孙建明 Zhang Weinan;Lu Tongyu;Sun Jianming(College of Economics and Management,China Jiliang University,Hangzhou 310018,China)

机构地区：[1]中国计量大学经济与管理学院,浙江杭州310018

出　　处：《电子技术应用》2019年第5期9-13,22,共6页Application of Electronic Technique

基　　金：国家哲学社会科学基金项目(15BTJ016)

摘　　要：为了验证股票的价格运动与过去应该是相似的这一假设,运用K近邻算法,将价格运动简单划分为涨跌两类进行预测,进行假设验证。使用滑窗方法比较现在的价格运动与何时的历史价格更为相似,将多个K近邻模型组合成集成模型,实现模型的泛化和策略收益的调整。使用中证500指数的历史价格数据进行预测实证,2017年～2018年9月的预测结果显示单个K近邻模型策略获得76. 72%的收益,现在的价格运动与遥远的过去更为相似,集成模型能更好地控制风险。该模型利用K近邻模型的含义验证了股票价格运动具有相似性,可以作为证券交易的择时策略。In order to verify the assume that stock price movement is similar to the past, pricing movement is simply dividend into up and down by K-Nearest Neighbor algorithm for forecasting. Sliding window method is used for comparing which historical period is more similar to the current in data feature. Multiple KNN models construct ensemble models for the strategy generalization and return adjustment. The CSI500 price is used for verification. With the predication, single KNN model wins 76. 72 % return with fee return from 2017 to Sep. 2018, remote historical period is more similar to the current in data feature, and ensemble models are better in risk control. This model verifies the stock price is similar with K-Nearest Neighbor character, which could be used as an in-vestment timing strategy.

关 键 词：K近邻 滑窗 集成模型 择时 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]