[Paper Review] RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data

Yupeng Cao, Zhi Chen+8 more|arXiv (Cornell University)|Apr 11, 2024

Stock Market Forecasting Methods4 citations

TL;DR

RiskLabs proposes a multimodal, multi-source framework that leverages large language models (LLMs) to predict financial risk by integrating earnings conference call transcripts and audio, time-series market data, and contextual news. By fusing these diverse data streams through specialized encoders and multimodal attention mechanisms, the framework achieves significant improvements in forecasting market volatility and Value-at-Risk (VaR), especially in medium- to long-term horizons (7, 15, 30 days).

ABSTRACT

The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering, and stock movement prediction (binary classification), the application of LLMs to financial risk prediction remains underexplored. Addressing this gap, in this paper, we introduce RiskLabs, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely integrates multimodal financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data to improve financial risk prediction. Empirical results demonstrate RiskLabs' effectiveness in forecasting both market volatility and variance. Through comparative experiments, we examine the contributions of different data sources to financial risk assessment and highlight the crucial role of LLMs in this process. We also discuss the challenges associated with using LLMs for financial risk prediction and explore the potential of combining them with multimodal data for this purpose.

Motivation & Objective

• Address the gap in applying large language models (LLMs) to financial risk prediction, as existing work focuses primarily on summarization, Q&A, or stock direction prediction.
• Integrate multimodal and multi-source financial data—textual and vocal earnings conference calls (ECCs), time-series market data, and contextual news—into a unified risk prediction framework.
• Develop a modular, multi-stage framework that enables comprehensive, multi-task financial risk forecasting with enhanced interpretability and adaptability.
• Demonstrate the incremental value of each data source and component in improving prediction performance, particularly for medium- and long-term risk assessment.
• Overcome limitations of traditional supervised models by leveraging LLMs’ zero-shot generalization and contextual reasoning for improved domain adaptability and robustness.

Proposed method

• Employ a specialized Earnings Conference Call (ECC) Encoder to extract and analyze textual and vocal features from ECCs using LLMs, including tone, speech rate, and sentiment.
• Implement a Time-Series Encoder to process historical market data prior to ECC dates, modeling risk dynamics across different timeframes (short-, medium-, long-term).
• Use a News-Market Reactions Encoder to collect and analyze daily financial news via LLMs, capturing market sentiment and event-driven reactions.
• Apply multimodal fusion techniques—specifically, multi-head self-attention mechanisms—to integrate features from the three encoders into a unified representation for risk prediction.
• Utilize a dynamic moving time window and time decay hyper-parameter to enable flexible, daily retraining and improve forecast timeliness and accuracy.
• Train and evaluate the RiskLabs framework using multi-task learning objectives focused on predicting market volatility and Value-at-Risk (VaR) across multiple horizons (1, 7, 15, 30 days).

Experimental results

Research questions

• RQ1 To what extent can LLMs enhance financial risk prediction when integrated with multimodal and multi-source data?
• RQ2 How do different data sources—ECC transcripts, audio, time-series data, and news—contribute individually and collectively to risk prediction performance?
• RQ3 What is the impact of LLM-based processing on the accuracy of volatility and VaR forecasts across short-, medium-, and long-term horizons?
• RQ4 How does the integration of audio and textual features from earnings calls improve risk prediction compared to text-only models?
• RQ5 What challenges arise when scaling news data integration, and how can data quality and model robustness be maintained in real-world deployment?

Key findings

• RiskLabs significantly improves financial risk prediction by combining LLM-powered analysis of earnings conference calls, time-series data, and news, outperforming models using isolated data sources.
• The integration of audio and textual features from earnings calls leads to a measurable performance boost, particularly in short-term risk forecasting, indicating that vocal cues carry predictive value.
• Incorporating time-series data before ECC dates enhances model performance, especially for medium- to long-term forecasts (7, 15, 30 days), highlighting the importance of historical market context.
• The News-Market Reactions Encoder, when properly filtered, contributes to performance gains, though data quality remains a challenge requiring advanced filtering and dynamic data management.
• Ablation studies confirm that each component of RiskLabs—ECC, time-series, and news encoders—makes a meaningful and independent contribution to final prediction accuracy.
• The use of dynamic time windows and time decay hyper-parameters enables more adaptive and timely risk forecasts, improving the model’s practical utility for investors.