Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization
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 addres...
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MDPI AG
2024-10-01
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| author | Shiguo Huang Linyu Cao Ruili Sun Tiefeng Ma Shuangzhe Liu |
| author_facet | Shiguo Huang Linyu Cao Ruili Sun Tiefeng Ma Shuangzhe Liu |
| author_sort | Shiguo Huang |
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| description | 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. |
| format | Article |
| id | doaj-art-389f32b79b57436fbc7ec218939625c6 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-389f32b79b57436fbc7ec218939625c62025-08-20T02:14:16ZengMDPI AGMathematics2227-73902024-10-011221337610.3390/math12213376Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio OptimizationShiguo Huang0Linyu Cao1Ruili Sun2Tiefeng Ma3Shuangzhe Liu4School of Mathematics and Statistics, Guizhou University, Guiyang 550025, ChinaCollege of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaCollege of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaSchool of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, ChinaFaculty of Science & Technology, University of Canberra, Canberra 2617, AustraliaThe 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.https://www.mdpi.com/2227-7390/12/21/3376portfolio selectiondeep learningself-attentionk-reciprocal NNGMV |
| spellingShingle | Shiguo Huang Linyu Cao Ruili Sun Tiefeng Ma Shuangzhe Liu Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization Mathematics portfolio selection deep learning self-attention k-reciprocal NN GMV |
| title | Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization |
| title_full | Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization |
| title_fullStr | Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization |
| title_full_unstemmed | Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization |
| title_short | Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization |
| title_sort | enhancing portfolio optimization a two stage approach with deep learning and portfolio optimization |
| topic | portfolio selection deep learning self-attention k-reciprocal NN GMV |
| url | https://www.mdpi.com/2227-7390/12/21/3376 |
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