Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis

TL;DR

This paper enhances financial risk prediction by optimizing LSTM models, demonstrating superior performance over traditional methods in handling complex time series data, and validating its practical effectiveness.

Contribution

It introduces an optimized LSTM model with hyperparameter tuning for improved financial risk prediction accuracy.

Findings

Optimized LSTM outperforms random forest, BP neural network, and XGBoost in AUC.

The model effectively handles complex time series data.

Experimental results confirm the model's efficiency and practicality.

Abstract

This paper focuses on the application and optimization of LSTM model in financial risk prediction. The study starts with an overview of the architecture and algorithm foundation of LSTM, and then details the model training process and hyperparameter tuning strategy, and adjusts network parameters through experiments to improve performance. Comparative experiments show that the optimized LSTM model shows significant advantages in AUC index compared with random forest, BP neural network and XGBoost, which verifies its efficiency and practicability in the field of financial risk prediction, especially its ability to deal with complex time series data, which lays a solid foundation for the application of the model in the actual production environment.