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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23988 |
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| _version_ | 1866914509604519936 |
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| author | Cui, Yanwei Wang, Guanghui Zhang, Xing He, Peiyang Li, Ziyuan Zhu, Bing Qiu, Wei Wang, Xusheng Yu, Zheng Xin, Anqi |
| author_facet | Cui, Yanwei Wang, Guanghui Zhang, Xing He, Peiyang Li, Ziyuan Zhu, Bing Qiu, Wei Wang, Xusheng Yu, Zheng Xin, Anqi |
| contents | Time series models predict numbers; decision-makers need advisory -- directional signals with reasoning, actionable suggestions, and risk management. Training language models for such predictive advisory faces a fundamental challenge: quality depends on outcomes unknown at prediction time. We bridge two ideas from reinforcement learning -- using information unavailable during execution to retrospectively generate training signal, and preference alignment -- and propose Hindsight Preference Optimization: observed outcomes let an LLM judge rank candidate advisories on dimensions that scalar metrics cannot capture, producing preference pairs for DPO without human annotation. We apply this to Vision-Language-Model-based predictive advisories on S&P 500 equity time series, demonstrated by a 4B model outperforming its 235B teacher on both accuracy and advisory quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23988 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hindsight Preference Optimization for Financial Time Series Advisory Cui, Yanwei Wang, Guanghui Zhang, Xing He, Peiyang Li, Ziyuan Zhu, Bing Qiu, Wei Wang, Xusheng Yu, Zheng Xin, Anqi Machine Learning Artificial Intelligence Time series models predict numbers; decision-makers need advisory -- directional signals with reasoning, actionable suggestions, and risk management. Training language models for such predictive advisory faces a fundamental challenge: quality depends on outcomes unknown at prediction time. We bridge two ideas from reinforcement learning -- using information unavailable during execution to retrospectively generate training signal, and preference alignment -- and propose Hindsight Preference Optimization: observed outcomes let an LLM judge rank candidate advisories on dimensions that scalar metrics cannot capture, producing preference pairs for DPO without human annotation. We apply this to Vision-Language-Model-based predictive advisories on S&P 500 equity time series, demonstrated by a 4B model outperforming its 235B teacher on both accuracy and advisory quality. |
| title | Hindsight Preference Optimization for Financial Time Series Advisory |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.23988 |