让大模型同时懂股价数据和自然语言，生成可解释的股票预测
Reasoning on Time-Series for Financial Technical Analysis
- 将股价数据转为文本注释，用反向均方误差优化推理过程
- 在中美欧三地股市上预测精度达顶尖水平
- 专家评估显示推理过程清晰可信，适合金融从业者使用
尽管大型语言模型已用于生成可解释的股票预测，但主要聚焦于文本报告分析，而非历史价格数据（即技术分析）。该任务具有挑战性，因需在时序数据与自然语言之间切换：股价输入输出属时序域，推理步骤则需自然语言。本文提出新型框架Verbal Technical Analysis（VTA），结合语言与隐空间推理，生成既准确又可解释的股票时序预测。通过将股价数据转换为文本注释，并以逆向均方误差（MSE）为奖励目标优化推理路径；再将时序主干模型的输出条件化于基于推理的属性。在涵盖美国、中国和欧洲市场的多个股票数据集上实验表明，VTA在预测精度上达到当前最优，且专家评估显示其推理轨迹表现良好。
原文摘要 · Abstract (English)
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.
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