中国管理科学 ›› 2021, Vol. 29 ›› Issue (11): 23-32.doi: 10.16381/j.cnki.issn1003-207x.2020.0076
• 论文 • 上一篇
李爱忠1
收稿日期:
2020-01-15
修回日期:
2020-05-13
发布日期:
2021-11-22
通讯作者:
李爱忠(1972-),男(汉族),山西人,山西财经大学财政与公共经济学院,副教授,博士,硕士生导师,研究方向:数量经济、投资组合分析、金融工程与风险管理,Email:lazshp@sina.com.
E-mail:lazshp@sina.com
基金资助:
LI Ai-zhong1, REN Ruo-en2, LI Ze-kai3, YU Le-an4
Received:
2020-01-15
Revised:
2020-05-13
Published:
2021-11-22
Contact:
李爱忠
E-mail:lazshp@sina.com
摘要: 面对金融市场的大量不确定性因素,如何合理选择有效的定价因子并构建科学的资产定价体系,一直是金融理论研究的核心问题之一。本文利用图嵌入的方法,基于稀疏表示和低秩表示策略,深度挖掘潜含在数据集中的内在结构,构建了能够同时揭示数据局部结构信息和全局结构信息的集成学习策略,以实现不同维度的多源数据融合。从CAPM和APT理论出发,通过集成预测的方法构建量化多因子资产选择模型,代表性地选择了卷积神经网络、梯度提升决策树、时间序列及支持向量机等模型进行单一预测,并通过稀疏低秩的图近似最小二乘回归集成策略进行优化。实证结果表明基于集成预测的稀疏低秩策略其资产选择能力更强,超额收益率更高。采用机器学习的非线性预测方法更有利于揭示金融系统的复杂特性。实证结论对投资组合管理具有重要指导意义。
中图分类号:
李爱忠. 图嵌入下稀疏低秩集成预测的多因子资产选择策略[J]. 中国管理科学, 2021, 29(11): 23-32.
LI Ai-zhong, REN Ruo-en, LI Ze-kai, YU Le-an. Multi-factor Asset Selection Strategy Based on Sparse Low-rank Ensemble Prediction under Graph Embedding[J]. Chinese Journal of Management Science, 2021, 29(11): 23-32.
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