DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911990577889280 |
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| author | Choi, Donghee Kim, Jinkyu Gim, Mogan Lee, Jinho Kang, Jaewoo |
| author_facet | Choi, Donghee Kim, Jinkyu Gim, Mogan Lee, Jinho Kang, Jaewoo |
| contents | Utilizing market forecasts is pivotal in optimizing portfolio selection strategies. We introduce DeepClair, a novel framework for portfolio selection. DeepClair leverages a transformer-based time-series forecasting model to predict market trends, facilitating more informed and adaptable portfolio decisions. To integrate the forecasting model into a deep reinforcement learning-driven portfolio selection framework, we introduced a two-step strategy: first, pre-training the time-series model on market data, followed by fine-tuning the portfolio selection architecture using this model. Additionally, we investigated the optimization technique, Low-Rank Adaptation (LoRA), to enhance the pre-trained forecasting model for fine-tuning in investment scenarios. This work bridges market forecasting and portfolio selection, facilitating the advancement of investment strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13427 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection Choi, Donghee Kim, Jinkyu Gim, Mogan Lee, Jinho Kang, Jaewoo Computational Engineering, Finance, and Science Artificial Intelligence Utilizing market forecasts is pivotal in optimizing portfolio selection strategies. We introduce DeepClair, a novel framework for portfolio selection. DeepClair leverages a transformer-based time-series forecasting model to predict market trends, facilitating more informed and adaptable portfolio decisions. To integrate the forecasting model into a deep reinforcement learning-driven portfolio selection framework, we introduced a two-step strategy: first, pre-training the time-series model on market data, followed by fine-tuning the portfolio selection architecture using this model. Additionally, we investigated the optimization technique, Low-Rank Adaptation (LoRA), to enhance the pre-trained forecasting model for fine-tuning in investment scenarios. This work bridges market forecasting and portfolio selection, facilitating the advancement of investment strategies. |
| title | DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence |
| url | https://arxiv.org/abs/2407.13427 |