FinRipple: Aligning Large Language Models with Financial Market for Event Ripple Effect Awareness

ACL ARR 2025 February Submission2169 Authors

Abstract:

Financial markets exhibit complex dynamics where localized events trigger ripple effects across entities.
Previous event studies, constrained by static single-companies analyses and simplistic assumptions, fail to capture these ripple effects.
While large language models (LLMs) offer emergent reasoning capabilities, their direct application falters due to structural market unawareness and limited capacity to analyze ripple effects.
We propose FinRipple, an elegant framework that empowers LLMs with the ability to analyze ripple effects through financial theory-guided large-scale reinforcement learning.
We begin by relaxing the assumptions of previous methods, incorporating a time-varying knowledge graph to accurately represent market structure.
By seamlessly integrating classical asset pricing theory, we align the LLM with the market, enabling it to predict ripple effects.
To the best of our knowledge, we are the first to provide a standardized definition of ripple effect prediction, a task that is extremely important yet unexplored in the financial domain.
Extensive experiments demonstrate that FinRipple provides a promising solution to this task.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: NLP Applications, knowledge graphs, financial/business NLP

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English

Submission Number: 2169