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A Hybrid Model for Portfolio Optimization: Integrating the Machine Learning and the Cardinality Constrained Mean-Variance Approaches | ||
| Communications in Combinatorics and Optimization | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 07 خرداد 1405 اصل مقاله (1.08 M) | ||
| نوع مقاله: Original paper | ||
| شناسه دیجیتال (DOI): 10.22049/cco.2026.31036.2723 | ||
| نویسندگان | ||
| Panumart Sawangtong* 1، 2، 3؛ Alireza Najafi4 | ||
| 1Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand | ||
| 2Research group for fractional calculus theory and applications, Science and Technology Research Institute, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand | ||
| 3Centre of Excellence in Mathematics, MHESI, Bangkok, 10400, Thailand | ||
| 4Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Guilan, Iran | ||
| چکیده | ||
| This paper focuses on developing an optimal investment portfolio that combines stocks and related options to minimize unsystematic risk. To achieve this goal, we utilize a deep learning algorithm known as the Deep Galerkin Method (DGM), which accurately prices European call and put options within a long-memory stochastic local volatility framework, thereby enhancing pricing accuracy. Using historical data from 79 stocks in the Russell 2000 Index between 2017 and 2022, we apply various machine learning methods—including Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) to identify optimal portfolios suitable for both risk-seeking and risk-averse investors in 2023. To further refine our investment strategy, we integrate the most effective machine learning methods with a Cardinality-Constrained Mean Variance (CCMV) portfolio optimization model. This integration enables us to construct portfolios tailored to different levels of risk tolerance among investors. | ||
| کلیدواژهها | ||
| Deep learning؛ Partial differential equation؛ Option pricing؛ Stochastic local volatility model؛ portfolio optimization | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 17 تعداد دریافت فایل اصل مقاله: 20 |
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