Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization

Shiguo Huang, Linyu Cao, Ruili Sun (), Tiefeng Ma and Shuangzhe Liu
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Shiguo Huang: School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China
Linyu Cao: College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou 450001, China
Ruili Sun: College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou 450001, China
Tiefeng Ma: School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
Shuangzhe Liu: Faculty of Science & Technology, University of Canberra, Canberra 2617, Australia

Mathematics, 2024, vol. 12, issue 21, 1-21

Abstract: The portfolio selection problem has been a central focus in financial research. A complete portfolio selection process includes two stages: stock pre-selection and portfolio optimization. However, most existing studies focus on portfolio optimization, often overlooking stock pre-selection. To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). Specifically, we utilize a CNN to capture individual stock information and a GCN to capture relationships among stocks. Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. We empirically validate the proposed two-stage approach using real-world data through numerical studies, achieving a roughly 35% increase in Cumulative Returns compared to portfolio optimization models without stock pre-selection, demonstrating its robust performance in the Average Return, Sharp Ratio, Turnover-adjusted Sharp Ratio, and Sortino Ratio.

Keywords: portfolio selection; deep learning; self-attention; k-reciprocal NN; GMV (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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