计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 64-67.doi: 10.11896/jsjkx.200300086
张宁, 石鸿伟, 郑朗, 单子豪, 吴浩翔
ZHANG Ning, SHI Hong-wei, ZHENG Lang, SHAN Zi-hao, WU Hao-xiang
摘要: 作为量化投资程序中的重要组成部分,量化多因子选股模型是通过历史金融数据建模来预测股票收益,该模型中引入了包括深度学习在内的众多机器学习方法。文中则首次探究了PCANet这样一种深度架构在量化选股中的应用。具体来说,该框架一方面将金融时序数据转换为二维图像数据,从而将金融时间序列预测问题转变为图像分类问题;另一方面将PCA应用于深度架构,充分发挥其能力,同时提供了金融行业可以理解和反馈的可解释性。两年的实际数据回测表明,该方法获得了57.17%的夏普比率、16.84%的超额收益以及-18.14%的最大回撤。相比传统的线性回归模型和深度学习的CNN模型,所提基于PCANet的价值成长多因子选股模型获得了更高的超额收益和夏普比率,同时保持了继承于PCA的特征提取的解释性。
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