A Financial Risk Prediction Method Based on Conditional Random Fields
Guishan Xie · International Journal of High Speed Electronics and Systems · 2025
Given the volatility and complexity of financial markets, accurate risk prediction is paramount for effective risk management. Traditional methods like Support Vector Machines (SVM) and Back Propagation Neural Networks (BPNNs) have shown limitations, particularly in capturing the dependencies in sequential data inherent to financial time series. This paper proposes a novel approach to financial risk prediction using Conditional Random Fields (CRFs), leveraging their potential in sequence data modeling to address these challenges. We first elaborate on the construction of a comprehensive feature set, incorporating market data, macroeconomic indicators, and sentiment analysis from financial news, tailored to enhance the predictive power of the CRFs model. The model itself is customized with carefully designed state and observation sequences, along with feature functions specifically engineered for financial risk prediction. Experiments conducted across multiple financial datasets demonstrate the superiority of our CRF-based model over traditional and machine learning benchmarks in terms of accuracy and stability. Our findings not only contribute to the literature by filling the gap left by traditional financial risk prediction methods but also offer a robust tool for practitioners in financial risk management. This work exemplifies the potential of applying advanced sequence modeling techniques to financial risk prediction, setting a new benchmark for future research in the field.