Article: Deep learning for financial forecasting and strategic business optimisation in enterprises Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.19 pp.79 - 101 Abstract: In modern enterprises, financial forecasting is critical in determining investment strategies, risk management, and the basis for decision-making. Both ARIMA and GARCH models are plagued by the inability to tackle the nonlinearity and volatility of markets. An LSTM, transformers, and hybrid CNN-LSTM-based deep learning framework is proposed to increase this study's predictive performance. The models are developed with historical stock and macroeconomic data and are evaluated using MAE, RMSE, and directional accuracy. The hybrid CNN-LSTM model achieved a 34% lower RMSE than ARIMA and a 74.3% directional accuracy. Results prove that deep learning surpasses the traditional methods, with the CNN-LSTM model proving to be more accurate and robust than others. However, being interpretable and computationally intensive are still important issues for enterprise adoption. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning for financial forecasting and strategic business optimisation in enterprises

Authors: Zhaoming Dong; Li Xu

Addresses: Department of Economic Management, Shanghai Communications Polytechnic, Shanghai, 200431, China ' Personnel Division, Shanghai Communications Polytechnic, Shanghai, 200431, China

Abstract: In modern enterprises, financial forecasting is critical in determining investment strategies, risk management, and the basis for decision-making. Both ARIMA and GARCH models are plagued by the inability to tackle the nonlinearity and volatility of markets. An LSTM, transformers, and hybrid CNN-LSTM-based deep learning framework is proposed to increase this study's predictive performance. The models are developed with historical stock and macroeconomic data and are evaluated using MAE, RMSE, and directional accuracy. The hybrid CNN-LSTM model achieved a 34% lower RMSE than ARIMA and a 74.3% directional accuracy. Results prove that deep learning surpasses the traditional methods, with the CNN-LSTM model proving to be more accurate and robust than others. However, being interpretable and computationally intensive are still important issues for enterprise adoption.

Keywords: financial forecasting; deep learning; LSTM; transformer; business strategy; time-series prediction.

DOI: 10.1504/IJICT.2025.146700

International Journal of Information and Communication Technology, 2025 Vol.26 No.19, pp.79 - 101

Received: 19 Mar 2025
Accepted: 23 Apr 2025
Published online: 13 Jun 2025 *

Title: Deep learning for financial forecasting and strategic business optimisation in enterprises

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