RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data
This addresses financial risk prediction for investors and analysts, but it is incremental as it applies existing LLM techniques to an underexplored task.
The paper tackles financial risk prediction by introducing RiskLabs, a framework that uses large language models to analyze multimodal data like earnings calls and market time series, showing effectiveness in forecasting market volatility and variance.
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.