Reasoning on Time-Series for Financial Technical Analysis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908859795243008 |
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| author | Koa, Kelvin J. L. Chen, Jan Ma, Yunshan Zheng, Huanhuan Chua, Tat-Seng |
| author_facet | Koa, Kelvin J. L. Chen, Jan Ma, Yunshan Zheng, Huanhuan Chua, Tat-Seng |
| contents | While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and optimize the reasoning trace using an inverse Mean Squared Error (MSE) reward objective. To produce time-series outputs from textual reasoning, we condition the outputs of a time-series backbone model on the reasoning-based attributes. Experiments on stock datasets across U.S., Chinese, and European markets show that VTA achieves state-of-the-art forecasting accuracy, while the reasoning traces also perform well on evaluation by industry experts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reasoning on Time-Series for Financial Technical Analysis Koa, Kelvin J. L. Chen, Jan Ma, Yunshan Zheng, Huanhuan Chua, Tat-Seng Statistical Finance Artificial Intelligence Machine Learning Computational Finance While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and optimize the reasoning trace using an inverse Mean Squared Error (MSE) reward objective. To produce time-series outputs from textual reasoning, we condition the outputs of a time-series backbone model on the reasoning-based attributes. Experiments on stock datasets across U.S., Chinese, and European markets show that VTA achieves state-of-the-art forecasting accuracy, while the reasoning traces also perform well on evaluation by industry experts. |
| title | Reasoning on Time-Series for Financial Technical Analysis |
| topic | Statistical Finance Artificial Intelligence Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2511.08616 |