Application of Neural Networks in Financial Data Analysis for Enhanced Corporate Performance Evaluation

This study explores the application of neural networks in financial data analysis and corporate performance evaluation, addressing challenges of nonlinearity and data noise. It evaluates multiple architectures (feedforward, recurrent, and convolutional), supported by real-world Python implementations and performance metrics such as accuracy and AUC. Key limitations like interpretability and overfitting are discussed, with proposed remedies and future research directions.