F in R ipple: Aligning Large Language Models with Financial Market for Event Ripple Effect Awareness (2025)

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.

## introduction financial markets are naturally complex, and sudden events can often impact the value of companies @xcite . a recent example underscores the impact of market reactions: on august 13, 2024, starbucks announced chipotle ceo brian niccol as its new ceo, triggering a 24.5% surge in starbucks' stock-the largest single-day gain in its history-while chipotle's stock dropped over 10%. the ripple effect extended to starbucks' supply chain, with jones soda co. rising 9.52%, brc inc. gaining 6.25%, and celsius holdings inc. up 3.81%. this example demonstrates the ripple effect that a single market event can have, not just on the company involved, but on other relevant companies @xcite . understanding and predicting these market ripple effects is crucial for informed financial decision-making, risk management, and strategic portfolio optimization. investors and risk managers rely on such insights into how company announcements @xcite , external news @xcite , or macroeconomic shocks @xcite may cascade through the market to anticipate broader impacts, enabling proactive strategies in volatile conditions @xcite . however, capturing these ripple effects remains a complex and underexplored challenge due to intricate, evolving, and interconnected factors at play. event studies have followed two main directions: case-by-case analysis and unified modeling based on learning theory. the former focuses on how specific market events affect the stock performance of a company or industry, which is a rather simplified assumption. for example, @xcite analyzed patent innovations in biotechnology, @xcite studied m&as in banking, and @xcite assessed stock reactions to green policies. while useful for direct impact assessment, these studies struggle to capture ripple effects across industries or the broader market. on the other hand, learning-based approaches primarily use news sentiment to predict stock movements, acknowledging that a company's stock price is influenced by its related entities @xcite . recent innovations integrate multisource information @xcite to enhance the prediction of emotions. @xcite demonstrated that transformer-based models outperform lexicon-based and statistical approaches in event-driven word representation. however, relying solely on text sentiment can overlook critical r e la te d c o ff e e c o m p a n y soft drinks h e a lt h d r in k s n on -a lc oh ol ic d rin ks h ire d fo rm er ce o beverage sector catering sector auguest starbucks starbucks has ceo laxam narasimhan and chipotle' s former ceo brian niccol. fired hired +24.5% july auguest . . . dynamics-for instance, positive news for one company may negatively impact its associates. thus, a more comprehensive framework is needed to model ever-changing market dynamics and explain complex intercompany relationships. recently, large language models (llms) have been widely used across various domains due to their advanced reasoning abilities @xcite . they excel in structured information extraction @xcite , analogical reasoning @xcite , and question answering, making them promising candidates for analyzing event-driven ripple effects. given their ability to model complex interactions, leveraging llms for financial market predictions is a natural step. however, financial markets, characterized by interconnected companies and dynamic relationships, evolve in response to various events, making the direct application of llms insufficient and potentially misleading @xcite . to accurately model ripple effects, llms must be complemented with the latest market state. a viable solution to address this challenge lies in integrating a time-varying financial knowledge graph (kg), which provides a structured view of the market by capturing up-to-date company relationships. continuously updating the kg ensures a reliable snapshot of the evolving market @xcite , enabling the modeling of dynamic corporate interactions @xcite . to effectively incorporate this knowledge into the llm, we employ an adapter-based approach, injecting structured information without retraining the model from scratch. this method avoids potential information loss from retrieval-based methods and offers an extendable framework. by aligning llms with the financial market's unique characteristics, our approach significantly enhances their ability to analyze event-driven ripple effects. we validate its effectiveness in asset pricing and portfolio management through extensive experiments. the contributions of this work can be summarized as follows: ## methodology in this section, we commence by formalizing the mathematical framework for the ripple prediction task. we initially delineate the necessary inputs and outputs for the task, as well as the evaluation metrics. subsequently, in section 3.2, we present the overall architecture of finripple. this architecture primarily comprises two pivotal components: knowledge injection and market alignment. the theoretical underpinnings of the optimization objectives can be found in appendix a. ## conclusion in conclusion, we present finripple, a novel training framework that empowers llms to analyze and predict the ripple effects of sudden events in financial markets. by constructing a time-varying financial kg and integrating it into the llm using adapters, we align the model with the dynamic market structure without retraining from scratch. our rigorous validation showcases finripple's strong potential in real-world applications like asset pricing and portfolio construction.