Enterprise Cash Flow and Liability Management Forecast Optimization Implemented by Temporal Fusion Transformer
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Abstract
To improve the interpretability of enterprise cash-flow and liability-management forecasting, this study applies a Temporal Fusion Transformer model incorporating financial rule constraints. Static and dynamic feature embedding and bidirectional LSTM encoding are first used to extract long-term evolutionary representations of enterprise financial status. A Gated Residual Network then screens key variables, and prior financial rules, including “debt ratio > 70%” and “ cash flow < 3 months of operating expenses,” are embedded into the multi-head attention layer. A learnable gain coefficient adjusts attention weights for high-risk historical periods, thereby improving the financial rationality and traceability of model decisions. A sequence-to-sequence decoding structure is finally used to jointly forecast multi-period cash-flow and liability indicators. Experimental results show that the proposed method achieves strong predictive accuracy, with an average cash-flow MAPE of 6.3%, an average debt-structure Theil’s U of 0.20, and an average rulecompliance rate of 89.7%. The model can be integrated into enterprise financial decision-support systems to support dynamic debt management, liquidity-risk monitoring, and explainable engineering-oriented financial forecasting.
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References
L. Wei, “Optimizing cash flow management for ship financing leasing: Risk identification, risk assessment, and management strategies,” Cogent Business & Management, vol. 12, no. 1, Art. no. 2464941, 2025, doi: 10.1080/23311975.2025.2464941.
A. E. Nasimiyu, “Cashflow management practices and financial performance of small and medium business enterprises in Kenya,” African Journal of Commercial Studies, vol. 4, no. 3, pp. 252-263, 2023, [Online]. Available: https://ijcsacademia.com/index.php/journal/article/view/182.
B. H. Subhani, S. Zunhuan, and M. A. Khan, “Finance for a Greener future: Evolving the financial sector for ESG and sustainable corporate debt management,” Borsa Istanbul Review, vol. 25, no. 2, pp. 337-349, 2025, doi: 10.1016/j.bir.2025.01.011.
F. Fasano, C. Adornetto, I. Zahid, M. La Rocca, L. Montaleone, G. Greco, et al., “The dilemma of accuracy in bankruptcy prediction: A new approach using explainable AI techniques to predict corporate crises,” European Journal of Innovation Management, vol. 28, no. 11, pp. 1-22, 2025, doi: 10.1108/EJIM-06-2024-0633.
C. P. Vijay, “A Deep Learning Approach for Financial Risk Prediction in Enterprise Management Systems,” EKSPLORIUM-BULETIN PUSAT TEKNOLOGI BAHAN GALIAN NUKLIR, vol. 46, no. 1, pp. 1579-1597, 2025, [Online]. Available: https://eksplorium.com/index.php/journal/article/view/184.
H. Wei and X. Wang, “Financial Risk Management Early-Warning Model for Chinese Enterprises,” Journal of Risk and Financial Management, vol. 17, no. 7, pp. 255, 2024, doi: 10.3390/jrfm17070255.
H. Chu, “An Empirical analysis of corporate financial management risk prediction based on associative memory neural network,” Computational Intelligence and Neuroscience, vol. 2021, no. 1, Art. no. 4383742, 2021, doi: 10.1155/2021/4383742.
N. R. Mazouei, “Investigating the Effect of Cash Flow Management on Firm Performance: The Mediating Role of the Business Cycle in Companies Listed in the Tehran Stock Exchange,” Accounting and Auditing with Applications, vol. 1, no. 2, pp. 103-114, 2024, doi: 10.22105/aaa.v1i2.39.
Y. Luo and C. Jiang, “The impact of corporate capital structure on financial performance based on convolutional neural network,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, Art. no. 5895560, 2022, doi: 10.1155/2022/5895560.
D. Septyanto, N. F. Welandasari, and N. Sayidah, “An empirical test of the financial ratio effect on financial distress in Indonesia (study in garment and textile industry),” Economics and Business Quarterly Reviews, vol. 5, no. 2, pp. 222-230, 2022, [Online]. Available: https://ssrn.com/abstract=4146069.
M. Akhtar, K. Yusheng, M. Haris, Q. U. Ain, and H. M. Javaid, “Impact of financial leverage on sustainable growth, market performance, and profitability,” Economic Change and Restructuring, vol. 55, no. 2, pp. 737-774, 2022, doi: 10.1007/s10644-021-09321-z.
H. H. Yousef and H. K. Ojah, “The Effect of Free Cash Flow on the Efficiency of Investment Decisions in a Sample of Iraqi Companies Listed on the Iraqi Stock Exchange,” American Journal of Economics and Business Management, vol. 5, no. 11, pp. 176-191, 2022, [Online]. Available: https://www.researchgate.net/profile/Hashim-Hayder-2/publication/365823160_The_Effect_of_Free_Cash_Flow_on_the_Efficiency_of_Investment_Decisions_in_a_Sample_of_Iraqi_Companies_Listed_on_the_Iraqi_Stock_Exchange.
T. Ling and Y. Cai, “Financial Crisis Prediction Based on Long-Term and Short-Term Memory Neural Network,” Wireless Communications and Mobile Computing, vol. 2022, no. 1, Art. no. 5728470, 2022, doi: 10.1155/2022/5728470.
H. Yang, E. Li, Y. F. Cai, J. Li, and G. X. Yuan, “The extraction of early warning features for predicting financial distress based on XGBoost model and shap framework,” International Journal of Financial Engineering, vol. 8, no. 03, Art. no. 2141004, 2021, doi: 10.1142/S2424786321410048.
K. Li and Y. Zhou, “Improved financial predicting method based on time series long short-term memory algorithm,” Mathematics, vol. 12, no. 7, pp. 1074, 2024, doi: 10.3390/math12071074.
L. C. de Jesus Jr, F. Fernández-Navarro, and M. Carbonero-Ruz, “Enhancing financial time series forecasting through topological data analysis,” Neural Computing and Applications, vol. 37, no. 9, pp. 6527-6545, 2025, doi: 10.1007/s00521-024-10787-x.
H. Yu, “An Adaptive Sharpe Ratio-Based Temporal Fusion Transformer for Financial Forecasting,” Frontiers in Humanities and Social Research, vol. 2, no. 1, pp. 54-65, 2025, doi: 10.71465/fhsr23.
J. Yang, P. Li, Y. Cui, X. Han, and M. Zhou, “Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach,” Sensors, vol. 25, no. 3, pp. 976, 2025, doi: 10.3390/s25030976.
A. Farooq, M. I. Uddin, M. Adnan, A. A. Alarood, E. Alsolami, and S. Habibullah, “Interpretable multi-horizon time series forecasting of cryptocurrencies by leverage temporal fusion transformer,” Heliyon, vol. 10, no. 22, Art. no. e40142, 2024, [Online]. Available: https://www.cell.com/heliyon/fulltext/S2405-8440(24)16173-7.
J. Liu, S. Wu, and V. T. Pham, “Design of Accounting Earnings Forecasting Model Based on Artificial Intelligence,” Scientific Programming, vol. 2022, no. 1, Art. no. 8793596, 2022, doi: 10.1155/2022/8793596.
C. Zhang, H. Zhang, T. Pu, and J. Pan, “Supply Chain Demand Forecasting Based on Data Mining Algorithm and Seq2Seq,” International Journal of Control, Automation and Systems, vol. 23, no. 1, pp. 89-104, 2025, doi: 10.1007/s12555-024-0141-8.
H. Niu, Q. Pan, and K. Xu, “Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China’s leading stocks,” Plos One, vol. 18, no. 11, Art. no. e0294460, 2023, doi: 10.1371/journal.pone.0294460.
X. Zhang, Y. Ma, and M. Wang, “An attention-based Logistic-CNN-BiLSTM hybrid neural network for credit risk prediction of listed real estate enterprises,” Expert Systems, vol. 41, no. 2, Art. no. e13299, 2024, doi: 10.1111/exsy.13299.
E. Balat, C. T. Ekinci, H. ¸S. Arlı, C. Ulus, and M. F. Akay, “Deep Learning Based Financial Forecasting Models,” AINTELIA Science Notes Journal, vol. 1, no. 2, pp. 6-12, 2022, doi: 10.5281/zenodo.8071617.
J. Li and C. Wang, “A deep learning approach of financial distress recognition combining text,” Electronic Research Archive, vol. 31, no. 8, pp. 4683-4707. https://www.aimspress.com/article/doi/10.3934/era.2023240, 2023.
H. Lin and Q. Sun, “Financial volatility forecasting: A sparse multi-head attention neural network,” Information, vol. 12, no. 10, pp. 419, 2021, doi: 10.3390/info12100419.
S. Knifo and A. Alzubi, “DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction,” Complex & Intelligent Systems, vol. 11, no. 1, pp. 53, 2025, doi: 10.1007/s40747-024-01613-x.
W. Xu, Y. Liu, W. Liu, H. Li, and G. Sun, “HDML: Hybrid data-driven multi-task learning for China’s stock price forecast,” Applied Intelligence, vol. 54, no. 23, pp. 12420-12438, 2024, doi: 10.1007/s10489-024-05838-8.
Z. Li, Z. Jiang, and X. Pan, “Default risk prediction of enterprises based on convolutional neural network in the age of big data: Analysis from the viewpoint of different balance ratios,” Complexity, vol. 2022, no. 1, Art. no. 5139562, 2022, doi: 10.1155/2022/5139562.
X. Deng, “Research on Enterprise Financial Information Risk Identification and Dynamic Control Management Model Based on Genetic Algorithm,” International Journal of High Speed Electronics and Systems, vol. 34, no. 03, Art. no. 2540019, 2025, doi: 10.1142/S0129156425400191.
J. Laborda, S. Ruano, and I. Zamanillo, “Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers,” Mathematics, vol. 11, no. 12, pp. 2625, 2023, doi: 10.3390/math11122625.
Y. Pang, S. Shi, Y. Shi, and Y. Zhao, “A nonlinear dynamic approach to cash flow forecasting,” Review of Quantitative Finance and Accounting, vol. 59, no. 1, pp. 205-237, 2022, doi: 10.1007/s11156-022-01066-8.
B. Talebi, R. Abdi, Z. Hajiha, and N. Rezaei, “Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms),” Advances in Mathematical Finance and Applications, vol. 4, no. 3, pp. 1007, 2023, doi: 10.22034/AMFA.2021.1910900.1493.
S. J. Delgado, N. J. Panganiban, K. A. Robles, A. D. Buenaventura, M. J. Vergara, and C. N. Evangelista, “Advancements in Deep Learning: A Comprehensive Survey on Architectures, Optimization Techniques, and Applications,” International Journal of Technology and Modeling, vol. 3, no. 2, pp. 57-68, 2024, doi: 10.63876/ijtm.v3i2.141.
Y. Shao, J. Yang, W. Zhou, H. Sun, L. Xing, Q. Zhao, et al., “An improvement of Adam based on a cyclic exponential decay learning rate and gradient norm constraints,” Electronics, vol. 13, no. 9, pp. 1778, 2024, doi: 10.3390/electronics13091778.