基于多尺度核自适应滤波的股票收益预测  

Stock return prediction via multi-scale kernel adaptive filtering

作　　者：汤兴恒 郭强[1,2] 徐天慧[1,2] 张彩明 TANG Xingheng;GUO Qiang;XU Tianhui;ZHANG Caiming(School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan Shandong 250014,China;Shandong Provincial Key Laboratory of Digital Media Technology(Shandong University of Finance and Economics),Jinan Shandong 250014,China;School of Software,Shandong University,Jinan Shandong 250101,China;Shandong Provincial Laboratory of Future Intelligence and Financial Engineering(Shandong Technology and Business University),Yantai Shandong 264005,China)

机构地区：[1]山东财经大学计算机科学与技术学院,济南250014 [2]山东省数字媒体技术重点实验室(山东财经大学),济南250014 [3]山东大学软件学院,济南250101 [4]山东省未来智能金融工程实验室(山东工商学院),山东烟台264005

出　　处：《计算机应用》2023年第5期1385-1393,共9页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61873145);山东省高等学校青创科技支持计划项目(2019KJN045)。

摘　　要：在股票市场中,投资者可通过捕捉历史数据中潜在的交易模式实现对股票未来收益的预测,股票收益预测问题的关键在于如何准确地捕捉交易模式,但受公司业绩、金融政策以及国家经济增长等不确定性因素的影响,交易模式往往难以捕捉。针对该问题,提出一种多尺度核自适应滤波(MSKAF)方法,从过去的市场数据中捕捉多尺度交易模式。为刻画股票的多尺度特征,该方法采用平稳小波变换(SWT)得到不同尺度的数据分量,不同尺度的数据分量蕴含着股票价格波动背后潜在的不同交易模式,然后采用核自适应滤波(KAF)方法捕捉不同尺度的交易模式,以预测股票未来收益。实验结果表明,相较于基于两阶段核自适应滤波(TSKAF)的预测模型,所提方法的预测结果的平均绝对误差(MAE)减小了10%,夏普比率增加了8.79%,可见所提方法实现了更好的股票收益预测性能。In stock market,investors can predict the future stock return by capturing the potential trading patterns in historical data.The key issue for predicting stock return is how to find out the trading patterns accurately.However,it is generally difficult to capture them due to the influence of uncertain factors such as corporate performance,financial policies,and national economic growth.To solve this problem,a Multi-Scale Kernel Adaptive Filtering(MSKAF)method was proposed to capture the multi-scale trading patterns from past market data.In this method,in order to describe the multiscale features of stocks,Stationary Wavelet Transform(SWT)was employed to obtain data components with different scales.The different trading patterns hidden in stock price fluctuations were contained in these data components.Then,the Kernel Adaptive Filtering(KAF)was used to capture the trading patterns with different scales to predict the future stock return.Experimental results show that compared with those of the prediction model based on Two-Stage KAF(TSKAF),the Mean Absolute Error(MAE)of the results generated by the proposed method is reduced by 10%,and the Sharpe Ratio(SR)of the results generated by the proposed method is increased by 8.79%,verifying that the proposed method achieves better stock return prediction performance.

关 键 词：股票收益预测 核自适应滤波 交易模式 多元数据依赖 序列学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]