Robust Portfolio Selection Under Model Ambiguity Using Deep Learning
In this study, we address the ambiguity in portfolio optimization, particularly focusing on the uncertainty related to the statistical parameters governing asset returns. We propose a novel method that combines robust optimization with artificial neural networks (ANNs). Our approach effectively hand...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | International Journal of Financial Studies |
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| Online Access: | https://www.mdpi.com/2227-7072/13/1/38 |
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| author | Sadegh Miri Erfan Salavati Mostafa Shamsi |
| author_facet | Sadegh Miri Erfan Salavati Mostafa Shamsi |
| author_sort | Sadegh Miri |
| collection | DOAJ |
| description | In this study, we address the ambiguity in portfolio optimization, particularly focusing on the uncertainty related to the statistical parameters governing asset returns. We propose a novel method that combines robust optimization with artificial neural networks (ANNs). Our approach effectively handles both the randomness inherent in asset prices and the ambiguity in their governing parameters. Through our method, we consider both simulated data, using the Exponential Ornstein–Uhlenbeck process, and real-world stock price data. The results showcase that our ANN-based method outperforms traditional benchmark methods such as equally weighted portfolio and adaptive mean–variance portfolio selection. |
| format | Article |
| id | doaj-art-3662b3bfcd6d4da39267ef8cecc3b4fc |
| institution | OA Journals |
| issn | 2227-7072 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Financial Studies |
| spelling | doaj-art-3662b3bfcd6d4da39267ef8cecc3b4fc2025-08-20T02:11:14ZengMDPI AGInternational Journal of Financial Studies2227-70722025-03-011313810.3390/ijfs13010038Robust Portfolio Selection Under Model Ambiguity Using Deep LearningSadegh Miri0Erfan Salavati1Mostafa Shamsi2Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, IranDepartment of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, IranDepartment of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, IranIn this study, we address the ambiguity in portfolio optimization, particularly focusing on the uncertainty related to the statistical parameters governing asset returns. We propose a novel method that combines robust optimization with artificial neural networks (ANNs). Our approach effectively handles both the randomness inherent in asset prices and the ambiguity in their governing parameters. Through our method, we consider both simulated data, using the Exponential Ornstein–Uhlenbeck process, and real-world stock price data. The results showcase that our ANN-based method outperforms traditional benchmark methods such as equally weighted portfolio and adaptive mean–variance portfolio selection.https://www.mdpi.com/2227-7072/13/1/38portfolio optimizationrobust optimizationartificial neural networksambiguity |
| spellingShingle | Sadegh Miri Erfan Salavati Mostafa Shamsi Robust Portfolio Selection Under Model Ambiguity Using Deep Learning International Journal of Financial Studies portfolio optimization robust optimization artificial neural networks ambiguity |
| title | Robust Portfolio Selection Under Model Ambiguity Using Deep Learning |
| title_full | Robust Portfolio Selection Under Model Ambiguity Using Deep Learning |
| title_fullStr | Robust Portfolio Selection Under Model Ambiguity Using Deep Learning |
| title_full_unstemmed | Robust Portfolio Selection Under Model Ambiguity Using Deep Learning |
| title_short | Robust Portfolio Selection Under Model Ambiguity Using Deep Learning |
| title_sort | robust portfolio selection under model ambiguity using deep learning |
| topic | portfolio optimization robust optimization artificial neural networks ambiguity |
| url | https://www.mdpi.com/2227-7072/13/1/38 |
| work_keys_str_mv | AT sadeghmiri robustportfolioselectionundermodelambiguityusingdeeplearning AT erfansalavati robustportfolioselectionundermodelambiguityusingdeeplearning AT mostafashamsi robustportfolioselectionundermodelambiguityusingdeeplearning |