An Improved Neural Manufacturing Corporate Credit Rating Model Based on LSTM
Keywords:
Manufacturing, Corporate Credit Rating, Multi-head Self-attention, Long Short-Term Memory, Neural NetworkAbstract
This paper proposes an improved neural manufacturing corporate credit rating model based on Multi-head Self-attention (MSA) mechanism and Long Short-Term Memory (LSTM) network. The proposed model leverages MSA to simulate the market dynamics and generate dynamic weights for each indicator based on the financial data of all manufacturing companies. Meanwhile, LSTM is utilized to extract sequential features from long-term financial and operational data to capture the long-term financial status and reduce the risk of deviation. The experimental results show that the proposed model provides more objective and reliable credit ratings for manufacturing companies. In the comparison experiment with the baseline model, it was proven that the model proposed in this paper outperforms other baseline models. In the comparison experiment with SMAGRU, it was proven that the proposed model has better prediction ability than SMAGRU on both datasets, and it also demonstrates that the GRU simplifies the internal computation of LSTM. The ablation experiment verified the feasibility of the two modules of the proposed model separately, which further proved the effectiveness of the proposed model.
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B. Chen and S. Long, "A Novel End-to-End Corporate Credit Rating Model Based on Self-Attention Mechanism," IEEE Access, vol. 8, pp. 203876-203889, 2020 2020, doi: 10.1109/ACCESS.2020.3036469.
A. Vaswani et al., "Attention is all you need," 2017, pp. 5998-6008.
S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
W. H. Beaver, "Financial ratios as predictors of failure," Journal of accounting research, pp. 71-111, 1966.
D. Chepkoech et al., "The Effect of Credit Assessment Process on Repayment of Bank Loans in Commercial Banks in Kenya," MBA Project, University of Nairobi, 2014.
E. I. Altman and A. Saunders, "Credit risk measurement: Developments over the last 20 years," Journal of banking & finance, vol. 21, no. 11-12, pp. 1721-1742, 1997.
E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The journal of finance, vol. 23, no. 4, pp. 589-609, 1968.
R. C. West, "A factor-analytic approach to bank condition," Journal of Banking & Finance, vol. 9, no. 2, pp. 253-266, 1985.
R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of eugenics, vol. 7, no. 2, pp. 179-188, 1936.
Daniel and Martin, "Early warning of bank failure: A logit regression approach," Journal of Banking & Finance, 1977.
J. A. Ohlson, "Financial ratios and the probabilistic prediction of bankruptcy," Journal of accounting research, pp. 109-131, 1980.
M. E. Zmijewski, "Methodological issues related to the estimation of financial distress prediction models," Journal of Accounting research, pp. 59-82, 1984.
M. D. Odom and R. Sharda, "A neural network model for bankruptcy prediction," in 1990 IJCNN International Joint Conference on neural networks, 1990: IEEE, pp. 163-168.
K. Y. Tam and M. Y. Kiang, "Managerial applications of neural networks: the case of bank failure predictions," Management science, vol. 38, no. 7, pp. 926-947, 1992.
R. R. Trippi and E. Turban, Neural networks in finance and investing: Using artificial intelligence to improve real world performance. McGraw-Hill, Inc., 1992.
P. K. Coats and L. F. Fant, "Recognizing financial distress patterns using a neural network tool," Financial management, pp. 142-155, 1993.
G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: a new learning scheme of feedforward neural networks," 2004 2004, vol. 2: Ieee, pp. 985- 990,
doi:10.1109/IJCNN.2004.1380068.[Online].
Available:https://ieeexplore.ieee.org/abstract/document/1380068
M. Lundy, "Cluster analysis in credit scoring," Credit Scoring and credit control, pp. 91-107, 1992.
D. B. EDELMAN, "An application of cluster analysis in credit control," IMA Journal of Management Mathematics, vol. 4, no. 1, pp. 81-87, 1992.
G. Chi, J. Hao, Z. Xu, and Z. Zhu, "Cluster analysis for weight of credit risk evaluation index," Systems Engineering-Theory Methodology Application, vol. 10, no. 1, pp. 64-67, 2001.
Y.-Z. Luo, S.-L. Pang, and S.-S. Qiu, "Fuzzy cluster in credit scoring," in Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693), 2003, vol. 5: IEEE, pp. 2731-2736.
W. Henley, "Construction of a k-nearest-neighbour credit-scoring system," IMA Journal of Management Mathematics, vol. 8, no. 4, pp. 305-321, 1997.
F.-C. Li, "The hybrid credit scoring strategies based on knn classifier," in 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009, vol. 1: IEEE, pp. 330-334.
R. Babu and A. R. Satish, "Improved of k-nearest neighbor techniques in credit scoring," International Journal For Development of Computer Science and Technology, vol. 1, no. 2, 2013.
M. Mukid, T. Widiharih, A. Rusgiyono, and A. Prahutama, "Credit scoring analysis using weighted k nearest neighbor," in Journal of Physics: Conference Series, 2018, vol. 1025, no. 1: IOP Publishing, p. 012114.
K.-j. Kim and H. Ahn, "A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach," Computers & Operations Research, vol. 39, no. 8, pp. 1800-1811, 2012.
Y.-C. Lee, "Application of support vector machines to corporate credit rating prediction," Expert Systems with Applications, vol. 33, no. 1, pp. 67-74, 2007.
Z. Huang, H. Chen, C.-J. Hsu, W.-H. Chen, and S. Wu, "Credit rating analysis with support vector machines and neural networks: a market comparative study," Decision support systems, vol. 37, no. 4, pp. 543-558, 2004.
C.-L. Huang, M.-C. Chen, and C.-J. Wang, "Credit scoring with a data mining approach based on support vector machines," Expert systems with applications, vol. 33, no. 4, pp. 847-856, 2007.
L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and regression trees. CRC press, 1984.
T. Lim, W. Loh, and Y. Shih, "An empirical comparison of decision trees and other classification methods (Technical Report No. 979)," Madison, WI: Department of Statistics, University of Wisconsin, Madison, 1997.
T. Gu and S. Yang, "Duration prediction for truck crashes based on the XGBoost algorithm," in CICTP 2019, 2019, pp. 5021-5031.
H. Tisshaw, "Evaluation of downside risk using financial ratios," University Business School, London, 1976.
M. Lincoln, "An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk," Journal of banking & finance, vol. 8, no. 2, pp. 321- 340, 1984.
E. I. Altman, J. Hartzell, and M. Peck, "Emerging market corporate bonds—a scoring system," in Emerging Market Capital Flows: Proceedings of a Conference held at the Stern School of Business, New York University on May 23–24, 1996, 1998: Springer, pp. 391-400.
E. I. Altman, "An emerging market credit scoring system for corporate bonds," Emerging markets review, vol. 6, no. 4, pp. 311-323, 2005.
L. Becchetti and J. Sierra, "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of banking & finance, vol. 27, no. 11, pp. 2099-2120, 2003.
S. Tomczak, "Comparative analysis of liquidity ratios of bankrupt manufacturing companies," Business and Economic Horizons, vol. 10, no. 3, pp. 151- 164, 2014.
S. K. Tomczak and E. Radosiński, "The effectiveness of discriminant models based on the example of the manufacturing sector," Operations Research and Decisions, vol. 27, 2017.
B. Tongli, H. Tono, and S. Tanasal, "Financial Performance Analysis in Manufacturing Companies of The Cement Sectors Listing On Indonesia Stock Exchange (IDX) Period 2013-2017," Available at SSRN 3306516, 2018.
S. Tomczak, "Multi-class Models for assessing the financial condition of manufacturing enterprises," Contemporary Economics, vol. 14, no. 2, pp. 219-235, 2020.
Y. Xia, T. Xu, M.-X. Wei, Z.-K. Wei, and L.-J. Tang, "Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods," Sustainability, vol. 15, no. 2, p. 1087, 2023.
M.-T. Luong, H. Pham, and C. D. Manning, "Effective approaches to attention-based neural machine translation," arXiv preprint arXiv:1508.04025, 2015.
D. Duffie and K. J. Singleton, "Credit risk: pricing, measurement, and management," ed: Princeton University Press, 2004.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling," arXiv preprint arXiv:1412.3555, 2014.
S. Li, X. Li, D. Zhang, and L. Zhou, "Joint Optimization of Distribution Network Design and Two-Echelon Inventory Control with Stochastic Demand and CO2 Emission Tax Charges," PloS one, vol. 12, no. 1, pp. 168-526, 2017, doi: 10.1371/journal.pone.0168526.
P. s. B. o. China, "People's Republic of China Financial Industry Standard: Specification for Credit Rating in the Credit Market and Interbank Bond Market, Part 2: Specification for Credit Rating Business," Chinese Law & Government, vol. 43, no. 3, pp. 67-85, 2010.
J. J. Liang, H. Song, B. Qu, W. Liu, and A. K. Qin, "Neural network based on dynamic multi-swarm particle Swarm optimizer for ultra-short-term load forecasting," in International Conference in Swarm Intelligence, 2014: Springer, pp. 384-391.
Pathak, D. G. ., Angurala, D. M. ., & Bala, D. M. . (2020). Nervous System Based Gliomas Detection Based on Deep Learning Architecture in Segmentation. Research Journal of Computer Systems and Engineering, 1(2), 01:06. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/3
George, N. ., & B. K., A. . (2023). Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 16–27. https://doi.org/10.17762/ijritcc.v11i4s.6303
Dhingra, M., Dhabliya, D., Dubey, M.K., Gupta, A., Reddy, D.H. A Review on Comparison of Machine Learning Algorithms for Text Classification (2022) Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, pp. 1818-1823.
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