Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection
Stochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study introduces a novel family of generalized Exponentiated Gradient updates (EGAB) derived from the alpha-beta (AB) divergence regularization. The EGAB f...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807168/ |
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| author | Andrzej Cichocki Sergio Cruces Auxiliadora Sarmiento Toshihisa Tanaka |
| author_facet | Andrzej Cichocki Sergio Cruces Auxiliadora Sarmiento Toshihisa Tanaka |
| author_sort | Andrzej Cichocki |
| collection | DOAJ |
| description | Stochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study introduces a novel family of generalized Exponentiated Gradient updates (EGAB) derived from the alpha-beta (AB) divergence regularization. The EGAB framework provides enhanced flexibility for processing data with varying distributions, thanks to the tunable hyperparameters of the AB divergence. We explore the applicability of these updates in online portfolio selection (OLPS) for financial markets with the goal of developing algorithms that achieve high risk-adjusted returns, even under relatively high transaction costs. The proposed EGAB algorithms are developed using constrained gradient optimization with regularization terms, demonstrating their versatility in OLPS by unifying the directional search of various algorithms and enabling interpolation between them. Our analysis and extensive computer simulations reveal that EGAB updates outperform existing OLPS algorithms, delivering good results on several performance metrics, such as cumulative return, average excess return, Sharpe ratio, and Calmar ratio, especially when transaction costs are significant. In conclusion, this study introduces a new family of exponentiated gradient updates and demonstrates their flexibility and effectiveness through extensive simulations across a wide range of real-world financial datasets. |
| format | Article |
| id | doaj-art-07c770f19e6f4d1d8de43b9071d30e78 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-07c770f19e6f4d1d8de43b9071d30e782024-12-31T00:01:02ZengIEEEIEEE Access2169-35362024-01-011219700019702010.1109/ACCESS.2024.352038910807168Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio SelectionAndrzej Cichocki0Sergio Cruces1https://orcid.org/0000-0003-4121-7137Auxiliadora Sarmiento2https://orcid.org/0000-0003-2587-1382Toshihisa Tanaka3https://orcid.org/0000-0002-5056-9508Polish Academy of Science, Systems Research Institute, Warszawa, PolandDepartamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Seville, SpainDepartamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Seville, SpainDepartment of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, JapanStochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study introduces a novel family of generalized Exponentiated Gradient updates (EGAB) derived from the alpha-beta (AB) divergence regularization. The EGAB framework provides enhanced flexibility for processing data with varying distributions, thanks to the tunable hyperparameters of the AB divergence. We explore the applicability of these updates in online portfolio selection (OLPS) for financial markets with the goal of developing algorithms that achieve high risk-adjusted returns, even under relatively high transaction costs. The proposed EGAB algorithms are developed using constrained gradient optimization with regularization terms, demonstrating their versatility in OLPS by unifying the directional search of various algorithms and enabling interpolation between them. Our analysis and extensive computer simulations reveal that EGAB updates outperform existing OLPS algorithms, delivering good results on several performance metrics, such as cumulative return, average excess return, Sharpe ratio, and Calmar ratio, especially when transaction costs are significant. In conclusion, this study introduces a new family of exponentiated gradient updates and demonstrates their flexibility and effectiveness through extensive simulations across a wide range of real-world financial datasets.https://ieeexplore.ieee.org/document/10807168/Alpha-beta divergencesexponentiated gradient algorithmson-line portfolio selection |
| spellingShingle | Andrzej Cichocki Sergio Cruces Auxiliadora Sarmiento Toshihisa Tanaka Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection IEEE Access Alpha-beta divergences exponentiated gradient algorithms on-line portfolio selection |
| title | Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection |
| title_full | Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection |
| title_fullStr | Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection |
| title_full_unstemmed | Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection |
| title_short | Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection |
| title_sort | generalized exponentiated gradient algorithms and their application to on line portfolio selection |
| topic | Alpha-beta divergences exponentiated gradient algorithms on-line portfolio selection |
| url | https://ieeexplore.ieee.org/document/10807168/ |
| work_keys_str_mv | AT andrzejcichocki generalizedexponentiatedgradientalgorithmsandtheirapplicationtoonlineportfolioselection AT sergiocruces generalizedexponentiatedgradientalgorithmsandtheirapplicationtoonlineportfolioselection AT auxiliadorasarmiento generalizedexponentiatedgradientalgorithmsandtheirapplicationtoonlineportfolioselection AT toshihisatanaka generalizedexponentiatedgradientalgorithmsandtheirapplicationtoonlineportfolioselection |