A Unified Framework for Fast Large-Scale Portfolio Optimization
We introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques. This framework addresses multiple objectives, including minimum variance, mean-variance, and the maximum Sharpe ratio, and also adapts to various portfolio w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Data Science in Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26941899.2023.2295539 |
| _version_ | 1826946594749022208 |
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| author | Weichuan Deng Paweł Polak Abolfazl Safikhani Ronakdilip Shah |
| author_facet | Weichuan Deng Paweł Polak Abolfazl Safikhani Ronakdilip Shah |
| author_sort | Weichuan Deng |
| collection | DOAJ |
| description | We introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques. This framework addresses multiple objectives, including minimum variance, mean-variance, and the maximum Sharpe ratio, and also adapts to various portfolio weight constraints. For each optimization scenario, we detail the translation into the corresponding quadratic programming (QP) problem and then integrate these solutions into a new open-source Python library. Using 50 years of return data from US mid to large-sized companies, and 33 distinct firm-specific characteristics, we utilize our framework to assess the out-of-sample monthly rebalanced portfolio performance of widely-adopted covariance matrix estimators and factor models, examining both daily and monthly returns. These estimators include the sample covariance matrix, linear and nonlinear shrinkage estimators, and factor portfolios based on Asset Pricing (AP) Trees, Principal Component Analysis (PCA), Risk Premium PCA (RP-PCA), and Instrumented PCA (IPCA). Our findings emphasize that AP-Trees and PCA-based factor models consistently outperform all other approaches in out-of-sample portfolio performance. Finally, we develop new [Formula: see text] and [Formula: see text] regularizations of factor portfolio norms which not only elevate the portfolio performance of AP-Trees and PCA-based factor models but they have a potential to reduce an excessive turnover and transaction costs often associated with these models. |
| first_indexed | 2024-03-08T00:32:02Z |
| format | Article |
| id | doaj.art-d49888f44423408d8245fdacb1811ecb |
| institution | Directory Open Access Journal |
| issn | 2694-1899 |
| language | English |
| last_indexed | 2025-02-17T21:15:24Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Data Science in Science |
| spelling | doaj.art-d49888f44423408d8245fdacb1811ecb2024-12-07T17:25:00ZengTaylor & Francis GroupData Science in Science2694-18992024-12-013110.1080/26941899.2023.2295539A Unified Framework for Fast Large-Scale Portfolio OptimizationWeichuan Deng0Paweł Polak1Abolfazl Safikhani2Ronakdilip Shah3Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USADepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USADepartment of Statistics, George Mason University, Fairfax, VA, USADepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USAWe introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques. This framework addresses multiple objectives, including minimum variance, mean-variance, and the maximum Sharpe ratio, and also adapts to various portfolio weight constraints. For each optimization scenario, we detail the translation into the corresponding quadratic programming (QP) problem and then integrate these solutions into a new open-source Python library. Using 50 years of return data from US mid to large-sized companies, and 33 distinct firm-specific characteristics, we utilize our framework to assess the out-of-sample monthly rebalanced portfolio performance of widely-adopted covariance matrix estimators and factor models, examining both daily and monthly returns. These estimators include the sample covariance matrix, linear and nonlinear shrinkage estimators, and factor portfolios based on Asset Pricing (AP) Trees, Principal Component Analysis (PCA), Risk Premium PCA (RP-PCA), and Instrumented PCA (IPCA). Our findings emphasize that AP-Trees and PCA-based factor models consistently outperform all other approaches in out-of-sample portfolio performance. Finally, we develop new [Formula: see text] and [Formula: see text] regularizations of factor portfolio norms which not only elevate the portfolio performance of AP-Trees and PCA-based factor models but they have a potential to reduce an excessive turnover and transaction costs often associated with these models.https://www.tandfonline.com/doi/10.1080/26941899.2023.2295539AP-treesIPCAand regularizationRP-PCAmean and covariance matrixshrinkage estimators |
| spellingShingle | Weichuan Deng Paweł Polak Abolfazl Safikhani Ronakdilip Shah A Unified Framework for Fast Large-Scale Portfolio Optimization Data Science in Science AP-trees IPCA and regularization RP-PCA mean and covariance matrix shrinkage estimators |
| title | A Unified Framework for Fast Large-Scale Portfolio Optimization |
| title_full | A Unified Framework for Fast Large-Scale Portfolio Optimization |
| title_fullStr | A Unified Framework for Fast Large-Scale Portfolio Optimization |
| title_full_unstemmed | A Unified Framework for Fast Large-Scale Portfolio Optimization |
| title_short | A Unified Framework for Fast Large-Scale Portfolio Optimization |
| title_sort | unified framework for fast large scale portfolio optimization |
| topic | AP-trees IPCA and regularization RP-PCA mean and covariance matrix shrinkage estimators |
| url | https://www.tandfonline.com/doi/10.1080/26941899.2023.2295539 |
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