Financial distress prediction using integrated Z-score and multilayer perceptron neural networks

doi:10.1016/j.dss.2022.113814

Decision Support Systems

Volume 159, August 2022, 113814

https://doi.org/10.1016/j.dss.2022.113814 Get rights and content

Highlights

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A stock market forecasting model combining a multi-layer perceptron artificial neural network with Altman Z-Score model.
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A new hybrid enterprise crisis warning model combining Z-score and MLP-ANN models.
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Demonstrated using Chinese data.
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Model can provide early warning signals of a company's deteriorating financial situation.

Abstract

The COVID-19 pandemic led to a great deal of financial uncertainty in the stock market. An initial drop in March 2020 was followed by unexpected rapid growth over 2021. Therefore, financial risk forecasting continues to be a central issue in financial planning, dealing with new types of uncertainty. This paper presents a stock market forecasting model combining a multi-layer perceptron artificial neural network (MLP-ANN) with the traditional Altman Z-Score model. The contribution of the paper is presentation of a new hybrid enterprise crisis warning model combining Z-score and MLP-ANN models. The new hybrid default prediction model is demonstrated using Chinese data. The results of empirical analysis show that the average correct classification rate of thew hybrid neural network model (99.40%) is higher than that of the Altman Z-score model (86.54%) and of the pure neural network method (98.26%). Our model can provide early warning signals of a company's deteriorating financial situation to managers and other related personnel, investors and creditors, government regulators, financial institutions and analysts and others so that they can take timely measures to avoid losses.

Keywords

Financial risk

Chinese banking

Artificial neural networks

Z-score model

Cited by (0)

David L. Olson is the James & H.K. Stuart Professor and Chancellor’s Professor at the University of Nebraska. He has published research in over 200 refereed journal articles, primarily on the topic of multiple objective decision-making, information technology, supply chain risk management, and data mining. He teaches in the management information systems, management science, and operations management areas. He has authored over 40 books, to include Decision Aids for Selection Problems, Introduction to Information Systems Project Management, Managerial Issues of Enterprise Resource Planning Systems, Supply Chain Risk Management, and Supply Chain Information Technology. Additionally, he has co-authored the books Introduction to Business Data Mining, Enterprise Risk Management, Advanced Data Mining Techniques, Enterprise Information Systems, Enterprise Risk Management Models, and Financial Enterprise Risk Management. He has served as associate editor of Service Business, Decision Support Systems, and Decision Sciences and co-editor in chief of International Journal of Services Sciences. He has made over 200 presentations at international and national conferences on research topics. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001. He was named the Raymond E. Miles Distinguished Scholar award for 2002, and was a James C. and Rhonda Seacrest Fellow from 2005 to 2006. He was named Best Enterprise Information Systems Educator by IFIP in 2006. He is a Fellow of the Decision Sciences Institute.

Desheng Wu is a Distinguished Professor with the Economics and Management School, University of Chinese Academy of Sciences, Beijing, China, and Professor with the Stockholm Business School, Stockholm University, Sweden. He has published over 150 ISI-indexed papers in refereed journals, such as Production and Operations Management, Decision Sciences, Risk Analysis, and the IEEE Transactions on Systems, Man, and Cybernetics, and 7 books at Springer etc. He has been invited to give plenary lectures and keynote talks in various international conferences more than 20 times. His current research interests include mathematical modeling of systems containing uncertain and risky situations, with special interests in the finance-economics operations interface, maximizing operational and financial goals using the methodologies for game theory, and large-scale optimization. He is Elected Member of Academia Europaea (The Academy of Europe), and Elected Member of European Academy of Sciences and Arts, and Elected Member of International Eurasian Academy of Sciences. Prof. Wu was a recipient of the ten big impact articles in the Journal of the Operational Research Society, 2019 Elsevier Most Cited Researcher, the Top 25 Hottest Article in Elsevier Journals, and the Best Paper Award Most Cited Articles in Human and Ecological Risk Assessment. He has served as an Associate Editor and a Guest Editor for several journals, such as Risk Analysis, IEEE Transactions on Systems, Man, and Cybernetics, the Annals of Operations Research, Computers and Operations Research, the International Journal of Production Economics, and Omega. He serves as the book series editor on computational risk management at Springer.

Xiyuan Ma is a graduate student in the University of Chinese Academy of Sciences, Beijing, China.

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