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让另类数据发挥作用：用于金融预测的上下文增强型大语言模型
该 arXiv 预印本研究 LLM 能否通过 in-context learning 将另类数据（消费者交易、网络流量、预测市场等非传统来源）与传统财务信息结合来预测公司未来业绩。作者提出两 agent 框架：先判断每个另类数据渠道对哪些公司具有信息量，再基于公司级与渠道级上下文预测营收。在四个商业另类数据渠道上的实验显示，上下文中同时加入另类数据与其他财务信息，预测效果优于单用任一来源，也优于标准预测基线。作者认为 LLM 为整合异构另类数据提供了灵活且实用的路径。
AI推荐理由 arXiv 预印本，作者团队含多位研究者，摘要明确给出方法（两 agent 框架：先识别每个另类数据渠道对哪些公司有信息量，再用公司/渠道特定上下文预测营收）与实验结论（在四个商业另类数据渠道上，上下文加入另类数据后预测优于单用任一来源，也优于标准基线）。新信息在于把消费者交易、网络流量、预测市场等异构另类数据通过 in-context learning 整合进 LLM 财务预测，且无需任务特定参数更新。局限：仅摘要层面，未提供具体误差指标、样本区间与统计显著性，效果需看全文验证；属预印本，未经同行评审。
72
正文
Computer Science > Artificial Intelligence
arXiv:2609.11607 (cs)
[Submitted on 10 Sep 2026]
Title:Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting
Authors:Jihoon Kwon, Lawrence Liu, Daekyung Park, Sumin Kim, Haverty Jack, Hoyoung Lee, Katherine Bjorkman, Josh McKenney, Peter Laurelli, Nicole Kagan, Zach Golkhou, Thorsten Neumann, Edward Tong, Pete Petersen, Yoon Kim, Alejandro Lopez-Lira, Yongjae Lee, Chanyeol Choi
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Abstract: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.
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Artificial Intelligence (cs.AI)
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arXiv:2609.11607 [cs.AI]
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arXiv:2609.11607v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.11607
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From: Jihoon Kwon [view email]
[v1]
Thu, 10 Sep 2026 14:27:25 UTC (1,166 KB)
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