Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting

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.

Authors 18

Alejandro Lopez-Lira, Chanyeol Choi, Daekyung Park, Edward Tong, Haverty Jack, Hoyoung Lee, Jihoon Kwon, Josh McKenney, Katherine Bjorkman, Lawrence Liu, Nicole Kagan, Pete Petersen, Peter Laurelli, Sumin Kim, Thorsten Neumann, Yongjae Lee, Yoon Kim, Zach Golkhou

Specification

Official page

arxiv.org/abs/2609.11607

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Arxiv announce type

new

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

arXiv id

2609.11607

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Categories

cs.AI

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

PDF

arxiv.org/pdf/2609.11607

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Primary category

cs.AI

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Published

12 Sept 2026

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

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