让另类数据发挥作用：用于金融预测的上下文增强大语言模型
Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting
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中文总结 AI 辅助
本研究提出一个双智能体框架，利用上下文学习将另类数据与财务信息整合进大语言模型，以提升企业收入预测的准确性，实验证明该方法优于单一数据源和标准基线。
中文摘要 AI 辅助
在预测企业未来财务表现时，另类数据——即从消费交易、网络流量和预测市场等非传统来源收集的数据——能够提供关于企业运营活动和更广泛市场状况的及时信号。这些信号可能揭示传统公开来源未涵盖的信息，因此可以为预测企业未来财务表现提供补充信息。然而，企业层面的另类数据通常历史覆盖有限，仅与特定预测目标或企业子集相关，且分布在众多异构渠道中，使其难以灵活地纳入传统预测方法。与此同时，大语言模型（LLMs）能够理解指令、从上下文示例中学习，并通过结合异构信息生成预测，而无需针对特定任务进行参数更新。受这种潜在灵活性的启发，我们研究了大语言模型是否能够通过上下文学习，将另类数据与其他财务信息相结合来预测企业表现。我们提出了一个双智能体框架，该框架首先识别每个另类数据渠道可能对其具有信息价值的企业，然后利用企业和渠道特定的上下文预测收入。我们在四个商业另类数据渠道上评估了该框架。在我们的实验中，在上下文中将另类数据与其他财务信息一起添加，相比单独使用任一来源，提高了大语言模型的预测准确性，并且这些预测比标准预测基线的预测更准确。这些发现表明，大语言模型为将另类数据与异构财务信息相结合提供了一种灵活且实用的方法。
英文摘要
When forecasting a firm's future financial performance, alternative data - data collected from non-traditional sources such as consumer transactions, web traffic, and prediction markets - can provide timely signals about firms' operating activities and broader market conditions. These signals may reveal information that is not captured by traditional public sources and can therefore provide complementary information for forecasting firms' future financial performance. However, firm-level alternative data often have limited historical coverage, are relevant only to specific prediction targets or subsets of firms, and are distributed across numerous heterogeneous channels, making them difficult to incorporate flexibly into conventional forecasting approaches. Meanwhile, large language models (LLMs) can interpret instructions, learn from in-context examples, and generate predictions by combining heterogeneous information without task-specific parameter updates. Motivated by this potential flexibility, we investigate whether an LLM can forecast firm performance by integrating alternative data with other financial information through in-context learning. We propose a two-agent framework that first identifies the firms for which each alternative data channel is likely to be informative and then predicts revenue using firm- and channel-specific context. We evaluate the framework across four commercial alternative data channels. In our experiments, adding alternative data in context alongside other financial information improves the LLM's forecasting relative to either source alone, and these forecasts are more accurate than those of standard forecasting baselines. These findings suggest that LLMs provide a flexible and practical approach to integrating alternative data with heterogeneous financial information.
发表机构
- LinqAlpha
- Carbon Arc
- Kalshi
- J.P. Morgan（摩根大通）
- Standard Chartered（渣打银行）
- Google（谷歌）
- Causeway Capital
- MIT（麻省理工学院）
- University of Florida（佛罗里达大学）
- UNIST（蔚山科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
