Assessing the Bankruptcy Risks of China's Emerging Port Industries: Modeling and Early Warning

Wang Ying, Igor A. Mayburov, Yulia V. Leontyeva

Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russia

Abstract

Vigorously developing emerging marine industries are an important way for China to implement the strategy of “Sea Power Nation”, and improving the ability of port enterprises to prevent financial and tax risks is a key link in accelerating the high-quality development of the marine economy. The research objective of this paper is to construct a reasonable early warning financial model for emerging port industries in Guangdong, Hong Kong, and Macao Greater Bay Area. The research hypothesis is that the original Z-SCORE model and F-SCORE model are not able to accurately predict the financial risk of emerging marine industries. The data of typical port enterprises are utilized to compare the financial risk; after that risk assessment and early warning are carried out. This paper adopts the Delphi method to assign weights to different indicators and utilizes the Analytic Hierarchy Process method to derive a financial and tax early warning model applicable to Guangdong, Hong Kong, and Macao Greater Bay Area. The results of the study found that the traditional “Z-score model” and “F-score model” are less applicable to the emerging industries in the ports of Guangdong, Hong Kong, and Macao Greater Bay Area. This paper will construct a financial and tax risk control model corresponding to the development of port emerging industries and provide early warning when exceeding a certain threshold to help enterprises develop better. In addition, this paper also puts forward policy suggestions for risk management of emerging port industries from the aspects of system improvement and government-enterprise linkage.

Keywords

port emerging industry; financial early warning model; Y-score model; F-score model; Delphi method; Analytic Hierarchy Process method

JEL classification

References

1. Tian, J. Deng, W., Yuan, J. (2023). Evaluation of sports tourism competitiveness of urban agglomerations in Guangdong-Hong Kong-Macao Greater Bay Area. Heliyon, Vol. 9, Issue 12, e22780. https://doi.org/10.1016/j.heliyon.2023.e22780

   2. Koycungil, A.S., Ozgulbas, N. (2012). Financial early warning system model and data minging application for risk detection. Expert Systems with Applications, Vol. 39, Issue 6, 6238–6253. https://doi.org/10.1016/j.eswa.2011.12.021

   3. Zhu, R., Chen, F. (2024). Tax and financial credit risks – Empirical evidence from Chinese investment enterprises. Finance Research Letters, Vol. 61, 104917. https://doi.org/10.1016/j.frl.2023.104917

   4. Altman, E.L. (1968). Financial Rations, Discriminant Analys is and Prediction of Corporate Bankruptcy. Journal of Finance, Vol. 9, Issue 4, 589–609. https://doi.org/10.2307/2978933

   5. Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, Vol. 18, No. 1, 109–131. http://dx.doi.org/10.2307/2490395

   6. Jing, A. (2023). The technology and digital financial risk management model using intelligent data processing. Optik, Vol. 273, 170410. https://doi.org/10.1016/j.ijleo.2022.170410

   7. Yang, B., Li, L.X., Ji, H., Xu, J. (2001). An early warning system for loan risk assessment using artificial neural networks. Knowledge-Based Systems, Vol. 14, Issue 5-6, 303–306. https://doi.org/10.1016/S0950-7051(01)00110-1

   8. Pendharkar, P.C. (2005). A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers & Operations Research, Vol. 32, Issue 10, 2561–2582. https://doi.org/10.1016/j.cor.2004.06.023

   9. Lepetit, L., Strobel, F. (2015). Bank insolvency risk and Z-score measures: A refinement. Finance Research Letters, Vol. 13, 214–224. https://doi.org/10.1016/j.frl.2015.01.001

   10. Ko, Y.-Ch., Hamido, F., Tianrui, Li. (2017). An evidential analysis of Altman Z-score for financial predictions: Case study on solar energy companies. Applied Soft Computing, Vol. 52, 748–759. https://doi.org/10.1016/j.asoc.2016.09.050

   11. Elliott, R.J., Siu, T.K., Fung, E.S. (2014). A Double HMM approach to Altman Z-scores and credit ratings. Expert Systems with Applications, Vol. 41, Issue 4, Part 2, 1553–1560. https://doi.org/10.1016/j.eswa.2013.08.052

   12. Chiaramonte, L., Croci, E., Poli, F. (2015). Should we trust the Z-score? Evidence from the European Banking Industry. Global Finance Journal, Vol. 28, 111–131. https://doi.org/10.1016/j.gfj.2015.02.002

   13. Li, X., Tripe, D., Malone, C., Smith, D. (2020). Measuring systemic risk contribution: The leave-one-out z-score method. Finance Research Letters, Vol. 36, 101316. https://doi.org/10.1016/j.frl.2019.101316

   14. Zhu, L., Li, M., Metawa, N. (2021). Financial risk evaluation Z-score model for intelligent IoT-based enterprises. Information Processing & Management, Vol. 58, Issue 6, 102692. https://doi.org/10.1016/j.ipm.2021.102692

   15. Li, H. (2024). Research on financial risk early warning system model based on second-order blockchain differential equation. Intelligent Decision Technologies, Vol. 18, No. 1, 327–342. https://doi.org/10.3233/IDT-230318

   16. Yi, W. (2012). Z-score model on financial crisis early-warning of listed real estate companies in China: a financial engineering perspective. Systems Engineering Procedia, Vol. 3, 153–157. https://doi.org/10.1016/j.sepro.2011.11.021

   17. Bouvatier, V., Lepetit, L., Rehault, P.-N., Strobel, F. (2023). Time-varying Z-score measures for bank insolvency risk: Best practice. Journal of Empirical Finance, Vol. 73, 170–179. https://doi.org/10.1016/j.jempfin.2023.06.002

   18. Tang, P., Tang, T., Lu, C. (2024). Predicting systemic financial risk with interpretable machine learning. The North American Journal of Economics and Finance, Vol. 71, 102088. https://doi.org/10.1016/j.najef.2024.102088

   19. Wang, J., Zeng, C., Han, X., Ma, Z., Zheng, B. (2023). Detecting early warning signals of financial crisis in spatial endogenous credit model using patch-size distribution. Physica A: Statistical Mechanics and its Applications, Vol. 625, 128925. https://doi.org/10.1016/j.physa.2023.128925

   20. Ouyang, Z.-S., Yang, X.-Y., Lai, Y. (2021). Systemic financial risk early warning of financial market in China using Attention-LSTM model. The North American Journal of Economics and Finance, Vol. 56, 101383. https://doi.org/10.1016/j.najef.2021.101383

   21. Zhu, K., Liu, D., Wu, J., Sun, L. (2012). The research of the regional financial risk early-warning model integrating the regression of lagging factors. AASRI Procedia, Vol. 1, 428–434. https://doi.org/10.1016/j.aasri.2012.06.067

   22. Tarkocin, C., Donduran, M. (2024). Constructing early warning indicators for banks using machine learning models. The North American Journal of Economics and Finance, Vol. 69, 102018. https://doi.org/10.1016/j.najef.2023.102018

   23. Allaj, E., Sanfelici, S. (2023). Early Warning Systems for identifying financial instability. International Journal of Forecasting, Vol. 39, Issue 4, 1777–1803. https://doi.org/10.1016/j.ijforecast.2022.08.004

   24. Wu, D., Ma, X., Olson, D.L. (2022). Financial distress prediction using integrated Z-score and multilayer perceptron neural networks. Decision Support Systems, Vol. 159, 113814. https://doi.org/10.1016/j.dss.2022.113814

   25. Xiao, J., Wen, Z., Jiang, X., Yu, L., Wang, S. (2024). Three-stage research framework to assess and predict the financial risk of SMEs based on hybrid method. Decision Support Systems, Vol. 177, 114090. https://doi.org/10.1016/j.dss.2023.114090

   26. Meziani, A.S., Rezvani, F. (1990). Using the analytical hierarchy process to select a financing instrument for a foreign investment. Mathematical and Computer Modelling, Vol. 13, Issue 7, 77–82. https://doi.org/10.1016/0895-7177(90)90130-F

   27. Gonzalez-Urango, H., Mu, E., Ujwary-Gil, A., Florek-Paszkowska, A. (2024). Analytic network process in economics, finance and management: Contingency factors, current trends and further research. Expert Systems with Applications, Vol. 237, 121415. https://doi.org/10.1016/j.eswa.2023.121415

   28. Nguyen, M.-T., Vu, Q.-H., Truong, H.-H., Nguyen, H.-H. (2023). A comprehensive evaluation of private sector investment decisions for sustainable water supply systems using a fuzzy-analytic hierarchy process: A case study of Ha Nam province in Vietnam. Heliyon, Vol. 9, Issue 9, e19727. https://doi.org/10.1016/j.heliyon.2023.e19727

   29. Murugan, S., Kala, T.S. (2023). Large-scale data-driven financial risk management & analysis using machine learning strategies. Measurement: Sensors, Vol. 27, 100756. https://doi.org/10.1016/j.measen.2023.100756

   30. Conte, D., Bussoli, C., Hemmings, D. (2024). Responsible risk-taking and the CSP-financial performance relation in the banking sector: A mediation analysis. Research in International Business and Finance, Vol. 69, 102293. https://doi.org/10.1016/j.ribaf.2024.102293

   31. Rahman, M.J., Zhu, H. (2024). Predicting financial distress using machine learning approaches: Evidence China. Journal of Contemporary Accounting & Economics, Vol. 20, Issue 1, 100403. https://doi.org/10.1016/j.jcae.2024.100403

   32. Yu, H., Su, T. (2024). ESG performance and corporate solvency. Finance Research Letters, Vol. 59, 104799. https://doi.org/10.1016/j.frl.2023.104799

   33. Prol, J.L., Paul, A. (2024). Profitability landscapes for competitive photovoltaic self-consumption. Energy Policy, Vol. 188, 114084. https://doi.org/10.1016/j.enpol.2024.114084

   34. Chiaramonte, L., Dreassi, A., Pisera, S., Khan, A. (2023). Mergers and acquisitions in the financial industry: A bibliometric review and future research directions. Research in International Business and Finance, Vol. 64, 101837. https://doi.org/10.1016/j.ribaf.2022.101837

   35. Zhu, W., Zhang, T., Wu, Y., Li, S., Li, Z. (2022). Research on optimization of an enterprise financial risk early warning method based on the DS-RF model. International Review of Financial Analysis, Vol. 81, 102140. https://doi.org/10.1016/j.irfa.2022.102140

   36. Chen, L., Zhang, C. (2023). The impact of financial agglomeration on corporate financialization: The moderating role of financial risk in Chinese listed manufacturing enterprises. Finance Research Letters, Vol. 58, 104655. https://doi.org/10.1016/j.frl.2023.104655

   37. Afshan, S., Leong, K.Y., Najmi, A., Razi, U., Lelchumanan, B., Cheong, C.W.H. (2024). Fintech advancements for financial resilience: Analysing exchange rates and digital currencies during oil and financial risk. Resources Policy, Vol. 88, 104432. https://doi.org/10.1016/j.resourpol.2023.104432

   38. Saputra, W.H., Prastyo, D.D., Kuswanto, H. (2024). Machine Learning Modeling on Mixed-frequency Data for Financial Growth at Risk. Procedia Computer Science, Vol. 234, 397–403. https://doi.org/10.1016/j.procs.2024.03.020

   39. Tian, S., Li, S., Gu, Q. (2023). Measurement and contagion modelling of systemic risk in China's financial sectors: Evidence for functional data analysis and complex network. International Review of Financial Analysis, Vol. 90, 102913. https://doi.org/10.1016/j.irfa.2023.102913

   40. Kandpal, B., Backe, S., del Granado, P.S. (2024). Power purchase agreements for plus energy neighbourhoods: Financial risk mitigation through predictive modelling and bargaining theory. Applied Energy, Vol. 358, 122589. https://doi.org/10.1016/j.apenergy.2023.122589

   41. Cafferata, A., Casellina, S., Landini, S., Uberti, M. (2023). Financial fragility and credit risk: A simulation model. Communications in Nonlinear Science and Numerical Simulation, Vol. 116, 106879. https://doi.org/10.1016/j.cnsns.2022.106879

  42. Yang, L., Cui, X., Hamori. S., Cai, X. (2023). Risk spillover from international financial markets and China's macro-economy: A MIDAS-CoVaR-QR model. International Review of Economics & Finance, Vol. 84, 55–69. doi.org/10.1016/j.iref.2022.11.006

About Authors

Wang Ying

Post-Graduate Student, Department of Financial and Tax Management, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19); ORCID https://orcid.org/0009-0007-8225-028X e-mail: 1127486294@qq.com

Igor Anatolyevich Mayburov

Doctor of Economics, Professor, Head of the Department of Financial and Tax Management, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19); ORCID https://orcid.org/0000-0001-8791-665X e-mail: mayburov.home@gmail.com

Yulia Vladimirovna Leontyeva

Candidate of Economic Sciences,, Associate Professor, the Department of Financial and Tax Management, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19); ORCID https://orcid.org/0000-0003-4676-9926 e-mail: uv.leonteva@mail.ru

For citation

Ying, W., Mayburov, I.A., Leontyeva, Yu.V. (2024). Assessing the Bankruptcy Risks of China's Emerging Port Industries: Modeling and Early Warning. Journal of Applied Economic Research, Vol. 23, No. 3, 776-800. https://doi.org/10.15826/vestnik.2024.23.3.031

Article info

Received April 10, 2024; Revised May 9, 2024; Accepted June 4, 2024.

DOI: https://doi.org/10.15826/vestnik.2024.23.3.031

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