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基于机器学习和资产特征的投资组合选择研究

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随着可投资资产与资产信息的爆炸式增长,投资组合选择研究面临资产和特征双重高维挑战.为此,本文提出一个基于机器学习和资产特征的投资组合选择框架,该框架借助机器学习技术的天然优势,运用高维特征直接预测投资组合权重,避开了常规的两步投资组合管理范式中的收益预测过程,并用于中国股票市场的资产配置研究.结果显示:1)基于此框架提出的投资策略能够捕捉高维特征中的增量信息,并挖掘资产特征与投资权重之间线性与非线性关系,大幅提升了投资绩效;2)交易摩擦类特征是投资权重预测中最为重要的资产特征;3)策略在套利限制较为严重的股票上回报更高,而对宏观经济状态变化的敏感性较低;在其他经济约束下,策略表现依然稳健.本文拓展了现代投资组合理论的研究框架,促进了人工智能与量化投资领域的交叉融合发展.
Research on portfolio selection based on machine learning and asset characteristics
With the explosive growth of investable assets and asset information,portfolio se-lection faces the dual challenges of high dimensionality in both assets and characteristics.This paper proposes a portfolio selection framework based on machine learning and asset character-istics.Leveraging the inherent advantages of machine learning,the framework utilizes asset characteristics to directly predict portfolio weights,bypassing return distribution prediction in the conventional two-step portfolio management paradigm.The framework is applied to asset al-location research in the Chinese stock market.The research results show that:1)The proposed investment strategies capture incremental information within high-dimensional characteristics and uncover both linear and non-linear relationships between asset characteristics and portfolio weights,resulting in a significant enhancement of investment performance.2)Trading friction-related characteristics are the most important indicators for predicting portfolio weights.3)These strategies yield higher returns on stocks with stricter arbitrage restrictions while exhibit-ing lower sensitivity to changes in macroeconomic conditions.Under other economic constraints,these strategies remain robust.This paper expands the research framework of modern portfolio theory,contributing to the development of artificial intelligence and quantitative investment.

portfolio selectionartificial intelligenceasset characteristicslarge dimensional asset allocationquantitative investment

李斌、屠雪永

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武汉大学经济与管理学院,武汉 430072

武汉大学金融研究中心,武汉 430072

投资组合选择 人工智能 资产特征 大维资产配置 量化投资

国家自然科学基金国家自然科学基金科技创新2030——"新一代人工智能"重大项目课题国家社会科学基金重大项目

71971164723711912020AAA010850520&ZD105

2024

10.12011/SETP2023-1784

系统工程理论与实践
中国系统工程学会

系统工程理论与实践

CSTPCDCSSCI北大核心
影响因子：1.575
ISSN：1000-6788
年,卷(期)：2024.44(1)
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