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Research on Financial Early Warning Based on Combination Forecasting Model

Author

Listed:
  • Jin Kuang

    (School of International Business, Zhejiang International Studies University, Hangzhou 310023, China)

  • Tse-Chen Chang

    (School of Tourism Management, Sun Yat-sen University, Zhuhai 528406, China)

  • Chia-Wei Chu

    (Faculty of Data Science, City University, Macau SAR, China)

Abstract

Since entering the 21st century, “economic globalization” has become a hot topic. Under the impact of “economic globalization”, the competition of the Chinese domestic market continues to intensify, and Chinese enterprises are facing enormous pressure for survival and development. Among them, there are many cases of poor business operation caused by financial crisis which have directly put these companies in trouble, even causing them to go bankrupt. Therefore, it is very practical to establish a scientific data model to analyze and predict the financial data of enterprises. It can not only monitor the financial status of the enterprise in real time, but also play an effective financial early warning role. This research focuses on using the combined forecasting method to establish a more comprehensive financial early warning model to solve the related financial crisis forecasting problem. Specifically, two different forecasting methods are first adopted in this study to conduct financial early warning research. The first is time series forecasting. It is a dynamic data processing statistical method, which is often used in forecasting research in the business field. The second is the BP neural network algorithm (referred to as BP), which is an error back-propagation learning algorithm, which is often used in the field of artificial intelligence. Then, the prediction error values of the two methods are compared and they are applied to the combined prediction method. Finally, a new error prediction formula is obtained. The result shows that the BP method provides the best performance over others, while the combinational forecasting method offers better performance than any single method.

Suggested Citation

  • Jin Kuang & Tse-Chen Chang & Chia-Wei Chu, 2022. "Research on Financial Early Warning Based on Combination Forecasting Model," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12046-:d:923472
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    References listed on IDEAS

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    1. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    2. Sergey Dzuba & Denis Krylov, 2021. "Cluster Analysis of Financial Strategies of Companies," Mathematics, MDPI, vol. 9(24), pages 1-21, December.
    3. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
    4. Samer Khouri & Lubos Elexa & Michal Istok & Andrea Rosova, 2019. "A Study from Slovakia on the Transfer of Slovak Companies to Tax Havens and Their Impact on the Sustainability of the Status of a Business Entity," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    5. Robert Luther, 1998. "The development of fixed asset accounting in South African gold mining companies: confronting the issues of prudence, matching, periodicity and capital maintenance," Accounting and Business Research, Taylor & Francis Journals, vol. 28(4), pages 281-295.
    6. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    7. Qiutong Xue & Sixian Feng & Kairan Chen & Muchen Li, 2022. "Impact of Digital Finance on Regional Carbon Emissions: An Empirical Study of Sustainable Development in China," Sustainability, MDPI, vol. 14(14), pages 1-26, July.
    8. Bigerna, Simona & D’Errico, Maria Chiara & Polinori, Paolo, 2021. "Energy security and RES penetration in a growing decarbonized economy in the era of the 4th industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    10. Serin, Faruk & Alisan, Yigit & Kece, Adnan, 2021. "Hybrid time series forecasting methods for travel time prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    11. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
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    1. Ronghua Xu & Yiran Liu & Meng Liu & Chengang Ye, 2023. "Sustainability of Shipping Logistics: A Warning Model," Sustainability, MDPI, vol. 15(14), pages 1-15, July.

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