CNN-DRL with Shuffled Features in Finance
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916116133052416 |
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| author | Montazeri, Sina Mirzaeinia, Akram Mirzaeinia, Amir |
| author_facet | Montazeri, Sina Mirzaeinia, Akram Mirzaeinia, Amir |
| contents | In prior methods, it was observed that the application of Convolutional Neural Networks agent in Deep Reinforcement Learning to financial data resulted in an enhanced reward. In this study, a specific permutation was applied to the feature vector, thereby generating a CNN matrix that strategically positions more pertinent features in close proximity. Our comprehensive experimental evaluations unequivocally demonstrate a substantial enhancement in reward attainment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03338 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CNN-DRL with Shuffled Features in Finance Montazeri, Sina Mirzaeinia, Akram Mirzaeinia, Amir Computational Finance Machine Learning In prior methods, it was observed that the application of Convolutional Neural Networks agent in Deep Reinforcement Learning to financial data resulted in an enhanced reward. In this study, a specific permutation was applied to the feature vector, thereby generating a CNN matrix that strategically positions more pertinent features in close proximity. Our comprehensive experimental evaluations unequivocally demonstrate a substantial enhancement in reward attainment. |
| title | CNN-DRL with Shuffled Features in Finance |
| topic | Computational Finance Machine Learning |
| url | https://arxiv.org/abs/2402.03338 |