This paper introduces RiskLabs, a novel framework that applies Large Language Models (LLMs) to financial risk prediction, addressing a significant gap in existing research where LLMs have been primarily used for tasks like text summarization and binary stock movement classification. The framework 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 enhance prediction accuracy. By leveraging LLMs' natural language processing capabilities, RiskLabs analyzes these diverse data sources to forecast financial risks, particularly market volatility and variance.

The research employs a comparative experimental design to evaluate the contributions of different data modalities to risk assessment, highlighting the crucial role of LLMs in processing and synthesizing complex financial information. Empirical results demonstrate the framework's effectiveness in improving risk prediction through multimodal integration, while also discussing challenges associated with applying LLMs in financial contexts. The study advances the application of AI in finance by showing how LLMs can be combined with diverse data sources for sophisticated risk analysis, potentially informing better investment decisions and risk management strategies.