Leveraging LLMS for Top-Down Sector Allocation In Automated Trading
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912319055855616 |
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| author | Heng, Ryan Quek Wei Vittori, Edoardo Ong, Keane Mao, Rui Cambria, Erik Mengaldo, Gianmarco |
| author_facet | Heng, Ryan Quek Wei Vittori, Edoardo Ong, Keane Mao, Rui Cambria, Erik Mengaldo, Gianmarco |
| contents | This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector allocation by processing multiple data streams simultaneously, including policy documents, economic indicators, and sentiment patterns. Empirical results demonstrate superior risk-adjusted returns compared to traditional cross momentum strategies, achieving a Sharpe ratio of 2.51 and portfolio return of 8.79% versus -0.61 and -1.39% respectively. These results suggest that LLM-based systematic macro analysis presents a viable approach for enhancing automated portfolio allocation decisions at the sector level. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging LLMS for Top-Down Sector Allocation In Automated Trading Heng, Ryan Quek Wei Vittori, Edoardo Ong, Keane Mao, Rui Cambria, Erik Mengaldo, Gianmarco Computational Engineering, Finance, and Science Portfolio Management This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector allocation by processing multiple data streams simultaneously, including policy documents, economic indicators, and sentiment patterns. Empirical results demonstrate superior risk-adjusted returns compared to traditional cross momentum strategies, achieving a Sharpe ratio of 2.51 and portfolio return of 8.79% versus -0.61 and -1.39% respectively. These results suggest that LLM-based systematic macro analysis presents a viable approach for enhancing automated portfolio allocation decisions at the sector level. |
| title | Leveraging LLMS for Top-Down Sector Allocation In Automated Trading |
| topic | Computational Engineering, Finance, and Science Portfolio Management |
| url | https://arxiv.org/abs/2503.09647 |