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

TL;DR

FinRipple introduces a novel framework that enhances large language models with financial theory and dynamic market structures to better predict ripple effects caused by localized events in financial markets.

Contribution

This work is the first to define and address ripple effect prediction in finance using LLMs guided by financial theory and a time-varying knowledge graph.

Findings

FinRipple outperforms baseline models in ripple effect prediction tasks.

The framework effectively incorporates market structure into LLM reasoning.

Experimental results validate the importance of financial theory guidance.

Abstract

Financial markets exhibit complex dynamics where localized events trigger ripple effects across entities. Previous event studies, constrained by static single-company 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.