Advancing Corporate Financial Forecasting: The Role of LSTM and AI in Modern Accounting

Abstract

This study aims to explore the trend prediction method of corporate financial performance based on long short-term memory network (LSTM), focusing on analyzing the application potential of deep learning technology in the accounting field. By comparing traditional statistical methods (such as ARIMA), machine learning methods (such as SVR and random forest), and other deep learning models (such as GRU and MLP), the experimental results show that the LSTM model has significant advantages in both prediction accuracy and generalization ability. Through its unique gating mechanism, the LSTM model can effectively capture the time dependence and nonlinear dynamic characteristics in financial data, greatly improving the accuracy and reliability of prediction. In the model design, this study uses the sliding window method to segment the data and uses the Dropout mechanism to prevent overfitting. At the same time, the Adam optimizer is combined to accelerate the convergence of the model, further optimizing the experimental results. The data set uses the real corporate financial data of Compustat Fundamentals Annual, which is widely used in financial research, to provide a reliable data basis for the experiment. The experimental results not only verify the wide applicability of the LSTM model in corporate financial management but also provide theoretical support and practical guidance for the deep integration of AI technology and accounting. In the future, AI technology will play a more important role in multimodal data analysis, real-time forecasting, and intelligent financial management and will help companies achieve intelligent decision-making and efficient management in a dynamic market.