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Research on financial early warning of mining listed companies based on BP neural network model

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  • Sun, Xiaojun
  • Lei, Yalin

Abstract

Mining industry is the basic industry of the national economy. However, in recent years, listed mining companies have suffered serious financial risks due to special reasons such as poor spot market liquidity of their products, strong policy dependence, and long investment payback periods. In the previous studies, most of the financial crisis prediction focused on the whole industry and manufacturing industry. The research on the financial risk of mining enterprises focuses more on how to adjust R&D activities, environmental performance to improve the financial performance of enterprises. There is still a lot of room for in-depth research on the systematic prevention and early warning of financial risks of listed mining companies. At the same time, in terms of research methods, many scholars used multivariate discriminant model, logistic regression model and support vector machine model. Compared with the Back-Propagation (BP) neural network model, these model methods have more or less defects. Therefore, we take mining listed companies as the research object, select the financial data of China's A-share mining listed companies in 2018, and construct the BP neural network financial early warning model, trying to provide more practical means for the financial risk early warning of mining companies. The research conclusions of this paper are as follows: (1) The BP neural network financial early warning model constructed in this paper has high prediction accuracy, which can be well used in the practice of financial early warning of mining listed companies; (2) The financial situation of China's A-share mining listed companies in 2018 is generally in a good state. The companies with good financial status can effectively control the cost and have good debt paying ability while earning income; (3) For companies with financial status that require early warning, the root cause is mainly that they do not pay attention to the risk of bad debt losses, which makes current credit sales income and accounts receivable are at high levels, and they also do not have good profitability.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jrpoli:v:73:y:2021:i:c:s0301420721002348
    DOI: 10.1016/j.resourpol.2021.102223
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    as
    1. Liu, Bai & Ju, Tao & Bai, Min & Yu, Chia-Feng (Jeffrey), 2021. "Imitative innovation and financial distress risk: The moderating role of executive foreign experience," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 526-548.
    2. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    3. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    4. Nel, A.J.H. & Vosloo, J.C. & Mathews, M.J., 2018. "Financial model for energy efficiency projects in the mining industry," Energy, Elsevier, vol. 163(C), pages 546-554.
    5. 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.
    6. Rafiq, Shuddhasattwa & Salim, Ruhul & Smyth, Russell, 2016. "The moderating role of firm age in the relationship between R&D expenditure and financial performance: Evidence from Chinese and US mining firms," Economic Modelling, Elsevier, vol. 56(C), pages 122-132.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    8. Scalzer, Rodrigo S. & Rodrigues, Adriano & Macedo, Marcelo Álvaro da S. & Wanke, Peter, 2019. "Financial distress in electricity distributors from the perspective of Brazilian regulation," Energy Policy, Elsevier, vol. 125(C), pages 250-259.
    9. Saes, Beatriz Macchione & Muradian, Roldan, 2021. "What misguides environmental risk perceptions in corporations? Explaining the failure of Vale to prevent the two largest mining disasters in Brazil," Resources Policy, Elsevier, vol. 72(C).
    10. Ben Jabeur, Sami, 2017. "Bankruptcy prediction using Partial Least Squares Logistic Regression," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 197-202.
    11. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    12. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    13. Kosmidou, K. & Pasiouras, F. & Zopounidis, C. & Doumpos, M., 2006. "A multivariate analysis of the financial characteristics of foreign and domestic banks in the UK," Omega, Elsevier, vol. 34(2), pages 189-195, April.
    14. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    15. Matthew Himley, 2010. "Global Mining and the Uneasy Neoliberalization of Sustainable Development," Sustainability, MDPI, vol. 2(10), pages 1-21, October.
    16. Fan, Songmei & Yan, Jingjing & Sha, Jinghua, 2017. "Innovation and economic growth in the mining industry: Evidence from China's listed companies," Resources Policy, Elsevier, vol. 54(C), pages 25-42.
    17. Jonek-Kowalska, Izabela, 2019. "Consolidation as a risk management method in the lifecycle of a mining company: A novel methodological approach and evidence from the coal industry in Poland," Resources Policy, Elsevier, vol. 60(C), pages 169-177.
    18. 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.
    19. Sun, Wei & Xu, Yanfeng, 2016. "Financial security evaluation of the electric power industry in China based on a back propagation neural network optimized by genetic algorithm," Energy, Elsevier, vol. 101(C), pages 366-379.
    20. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    21. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    22. Chand, Pushpendu & Thakkar, Jitesh J. & Ghosh, Kunal Kanti, 2020. "Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model," Resources Policy, Elsevier, vol. 68(C).
    23. Wang, Delu & Tong, Xian & Wang, Yadong, 2020. "An early risk warning system for Outward Foreign Direct Investment in Mineral Resource-based enterprises using multi-classifiers fusion," Resources Policy, Elsevier, vol. 66(C).
    24. Mahtani, Umesh S. & Garg, Chandra Prakash, 2018. "An analysis of key factors of financial distress in airline companies in India using fuzzy AHP framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 87-102.
    25. Jonek-Kowalska, Izabela & Nawrocki, Tomasz L., 2019. "Holistic fuzzy evaluation of operational risk in polish mining enterprises in a long-term and sectoral research perspective," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    26. Modak, Mousumi & Pathak, Khanindra & Ghosh, Kunal Kanti, 2017. "Performance evaluation of outsourcing decision using a BSC and Fuzzy AHP approach: A case of the Indian coal mining organization," Resources Policy, Elsevier, vol. 52(C), pages 181-191.
    27. Dinh, Dung V. & Powell, Robert J. & Vo, Duc H., 2021. "Forecasting corporate financial distress in the Southeast Asian countries: A market-based approach," Journal of Asian Economics, Elsevier, vol. 74(C).
    28. Sun, Yongping & Yang, Ying & Huang, Nan & Zou, Xin, 2020. "The impacts of climate change risks on financial performance of mining industry: Evidence from listed companies in China," Resources Policy, Elsevier, vol. 69(C).
    29. Zhou, Zhifang & Xiao, Tian & Chen, Xiaohong & Wang, Chang, 2016. "A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 304-315.
    30. Abban, Abdul Rashid & Hasan, Mohammad Zahid, 2021. "The causality direction between environmental performance and financial performance in Australian mining companies - A panel data analysis," Resources Policy, Elsevier, vol. 70(C).
    Full references (including those not matched with items on IDEAS)

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    6. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
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