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

2025-05-28

AI Summary

Financial market local events often trigger cross-entity cascading effects, yet conventional event studies rely on static, single-firm analyses and fail to model dynamic propagation mechanisms. To address this, we propose the novel task of “ripple effect prediction” and develop a market-structure-aware LLM reasoning framework. Our method comprises three key innovations: (i) a financial-theory-guided large-scale reinforcement learning alignment paradigm to enhance LLMs’ causal reasoning under asset pricing logic; (ii) a time-varying knowledge graph that explicitly encodes evolving inter-entity dependencies; and (iii) integration of asset pricing theory constraints into the reasoning process. Evaluated on a multi-market event dataset, our approach significantly outperforms existing baselines, demonstrating both the predictability of cascading effects and strong cross-market generalization. The framework delivers interpretable, structure-aware insights for event-driven investment decision-making.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Analyzing ripple effects in financial markets using LLMs
• Overcoming limitations of static single-company event studies
• Aligning LLMs with market structure for ripple effect prediction

Innovation

• Financial theory-guided reinforcement learning
• Time-varying knowledge graph representation
• Standardized ripple effect prediction definition