图嵌入下稀疏低秩集成预测的多因子资产选择策略

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。

Multi-factor Asset Selection Strategy Based on Sparse Low-rank Ensemble Prediction under Graph Embedding

Faced with a large number of uncertainties in the financial market, how to rationally choose effective pricing factors and construct a scientific asset pricing system has always been one of the core issues in financial theory research. The method of graph embedding, based on the sparse representation and low rank representation strategies, is used to deeply mine the inherent structure hidden in the data set, and an integrated learning strategy is constructed that can simultaneously reveal the local structure information and global structure information of the data in order to achieve different dimensions of Multi-source data fusion.Based on the theory of CAPM and APT, a quantitative multi-factor portfolio selection model is constructed by integrating learning methods，and the gradient boosting decision Tree method, convolutional neural network, time series and support vector machine are representatively selected to perform combined prediction.It is optimized by the sparse low-rank graph approximation least squares vector regression integration strategy.At the same time, a sparse low-rank model is constructed that can reveal both local and global structural information of the data, thereby learning to obtain a more accurate representation of multi-source data and high-dimensional data in the feature subspace.The empirical results show that the sparse low-rank strategy based on integrated prediction has stronger securities selection ability and higher excess return rate.The non-linear prediction method using machine learning is more conducive to revealing the complex characteristics of financial system. The empirical conclusion has important guiding significance for portfolio management.