Distilling Analysis from Generative Models for Investment Decisions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913539928621056 |
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| author | Chen, Chung-Chi Takamura, Hiroya Kobayashi, Ichiro Miyao, Yusuke |
| author_facet | Chen, Chung-Chi Takamura, Hiroya Kobayashi, Ichiro Miyao, Yusuke |
| contents | Professionals' decisions are the focus of every field. For example, politicians' decisions will influence the future of the country, and stock analysts' decisions will impact the market. Recognizing the influential role of professionals' perspectives, inclinations, and actions in shaping decision-making processes and future trends across multiple fields, we propose three tasks for modeling these decisions in the financial market. To facilitate this, we introduce a novel dataset, A3, designed to simulate professionals' decision-making processes. While we find current models present challenges in forecasting professionals' behaviors, particularly in making trading decisions, the proposed Chain-of-Decision approach demonstrates promising improvements. It integrates an opinion-generator-in-the-loop to provide subjective analysis based on each news item, further enhancing the proposed tasks' performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07225 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distilling Analysis from Generative Models for Investment Decisions Chen, Chung-Chi Takamura, Hiroya Kobayashi, Ichiro Miyao, Yusuke Statistical Finance Computation and Language Machine Learning Professionals' decisions are the focus of every field. For example, politicians' decisions will influence the future of the country, and stock analysts' decisions will impact the market. Recognizing the influential role of professionals' perspectives, inclinations, and actions in shaping decision-making processes and future trends across multiple fields, we propose three tasks for modeling these decisions in the financial market. To facilitate this, we introduce a novel dataset, A3, designed to simulate professionals' decision-making processes. While we find current models present challenges in forecasting professionals' behaviors, particularly in making trading decisions, the proposed Chain-of-Decision approach demonstrates promising improvements. It integrates an opinion-generator-in-the-loop to provide subjective analysis based on each news item, further enhancing the proposed tasks' performance. |
| title | Distilling Analysis from Generative Models for Investment Decisions |
| topic | Statistical Finance Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.07225 |