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

2024-04-11

AI Summary

Existing research predominantly focuses on financial text summarization, question answering, and binary market trend prediction, leaving fine-grained financial risk modeling—such as volatility and variance forecasting—unexplored for large language models (LLMs). This work introduces RiskLabs, the first systematic framework leveraging LLMs for multimodal financial risk prediction. It integrates earnings call transcripts and speech (text + audio), market time-series data, and contextualized financial news, innovatively unifying cross-modal alignment encoding, time-series feature enhancement, and finance-informed prompting into a synergistic modeling paradigm. Experiments on S&P 500 volatility and VIX variance forecasting demonstrate that RiskLabs significantly outperforms conventional statistical models and unimodal LLM baselines, achieving a 12.6% improvement in AUC and a 19.3% reduction in prediction error. Moreover, the framework exhibits promising causal interpretability capabilities.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Predicting financial risk using large language models
• Integrating multimodal data for improved risk assessment
• Exploring LLMs' role in market volatility forecasting

Innovation

• Leverages LLMs for financial risk prediction
• Integrates multimodal financial data sources
• Combines textual, vocal, and time-series data