用大模型把券商报告里的定性信息转成可预测的量化分数
Quantifying Qualitative Insights: Leveraging LLMs to Market Predict
- 用券商报告提取关键因素，与价格数据融合形成动态上下文
- 通过设计提示词给定性内容打分，再缩放为真实预测值
- 在市场预测上超越传统时序模型，适合金融量化研究者
大型语言模型（LLMs）有望通过融合数值与文本数据革新金融分析。然而，多模态信息融合时上下文不足，且难以衡量模型生成的定性输出价值，限制了其在金融预测中的应用。本研究利用券商每日报告，将文本提炼为关键因素，并与价格等数值数据结合形成上下文集。通过基于查询时间动态更新少样本示例，确保信息时效性，使上下文高度贴近预测点。进一步设计特定提示词对关键因素赋分，将定性洞察转化为定量结果。经过归一化处理后，得分用于市场预测。实验表明，该方法在市场预测中优于传统时序模型，但仍存在可复现性不足与解释性有限的问题。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have the potential to transform financial analytics by integrating numerical and textual data. However, challenges such as insufficient context when fusing multimodal information and the difficulty in measuring the utility of qualitative outputs, which LLMs generate as text, have limited their effectiveness in tasks such as financial forecasting. This study addresses these challenges by leveraging daily reports from securities firms to create high-quality contextual information. The reports are segmented into text-based key factors and combined with numerical data, such as price information, to form context sets. By dynamically updating few-shot examples based on the query time, the sets incorporate the latest information, forming a highly relevant set closely aligned with the query point. Additionally, a crafted prompt is designed to assign scores to the key factors, converting qualitative insights into quantitative results. The derived scores undergo a scaling process, transforming them into real-world values that are used for prediction. Our experiments demonstrate that LLMs outperform time-series models in market forecasting, though challenges such as imperfect reproducibility and limited explainability remain.
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