A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction

TL;DR

This paper introduces a deep learning model combining CNN and BiLSTM to analyze and predict financial systemic risk, demonstrating superior accuracy and robustness on real data compared to traditional models.

Contribution

The study presents a novel CNN-BiLSTM framework that captures complex market patterns and long-term dependencies for systemic risk analysis in finance.

Findings

F1-score of 0.88 indicating high discriminant ability

Outperforms traditional models like BiLSTM, CNN, Transformer, TCN

Shows robustness to data noise and high-dimensional data

Abstract

This study proposes a deep learning model based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) for discriminant analysis of financial systemic risk. The model first uses CNN to extract local patterns of multidimensional features of financial markets, and then models the bidirectional dependency of time series through BiLSTM, to comprehensively characterize the changing laws of systemic risk in spatial features and temporal dynamics. The experiment is based on real financial data sets. The results show that the model is significantly superior to traditional single models (such as BiLSTM, CNN, Transformer, and TCN) in terms of accuracy, recall, and F1 score. The F1-score reaches 0.88, showing extremely high discriminant ability. This shows that the joint strategy of combining CNN and BiLSTM can not only fully capture the complex patterns of market data but also effectively deal with the long-term dependency problem in time series data. In addition, this study also explores the robustness of the model in dealing with data noise and processing high-dimensional data, providing strong support for intelligent financial risk management. In the future, the research will further optimize the model structure, introduce methods such as reinforcement learning and multimodal data analysis, and improve the efficiency and generalization ability of the model to cope with a more complex financial environment.