RiskLabs: Predicting Financial Risk Using Large Language Model Based on
  Multi-Sources Data
Yupeng Cao Zhi Chen Qingyun Pei Fabrizio Dimino Lorenzo Ausiello Prashant Kumar K. P. Subbalakshmi Papa Momar Ndiaye
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 (Q$\&$A), and stock movement
prediction (binary classification), with a notable gap in the application of
LLMs for financial risk prediction. Addressing this gap, in this paper, we
introduce \textbf{RiskLabs}, a novel framework that leverages LLMs to analyze
and predict financial risks. RiskLabs uniquely combines different types of
financial data, including textual and vocal information from Earnings
Conference Calls (ECCs), market-related time series data, and contextual news
data surrounding ECC release dates. Our approach involves a multi-stage
process: initially extracting and analyzing ECC data using LLMs, followed by
gathering and processing time-series data before the ECC dates to model and
understand risk over different timeframes. Using multimodal fusion techniques,
RiskLabs amalgamates these varied data features for comprehensive multi-task
financial risk prediction. Empirical experiment results demonstrate RiskLab's
effectiveness in forecasting both volatility and variance in financial markets.
Through comparative experiments, we demonstrate how different data sources
contribute to financial risk assessment and discuss the critical role of LLMs
in this context. Our findings not only contribute to the AI in finance
application but also open new avenues for applying LLMs in financial risk
assessment.