IDEAS home Printed from https://ideas.repec.org/a/aiy/jnjaer/v24y2025i2p555-583.html
   My bibliography  Save this article

Scenario-Based Forecasting of Bankruptcy Risks for Woodworking Industry Enterprises in the Sverdlovsk Region

Author

Listed:
  • Ilya V. Naumov
  • Anna A. Bychkova
  • Natalia L. Nikulina

Abstract

The woodworking industry in the Sverdlovsk region is a significant sector of the regional economy, contributing to the development of small and medium-sized businesses, attracting investments, and creating jobs. However, the high dependence on timber prices, transportation costs, and macroeconomic factors makes enterprises in industry vulnerable and increases their risk of bankruptcy. This article explores the application of regression and autoregressive ARIMA/ARMA models to develop predictive scenarios for changes in the probability of bankruptcy among enterprises in the industry. The study uses annual financial statements of woodworking enterprises in the Sverdlovsk region for the period 1999–2023. Key internal factors (such as turnover of current assets, quick liquidity ratio, availability of own working capital, profitability of current assets, inventory turnover, etc.) and external factors (such as bank loan interest rates, import of technologies, and technical services) influencing the probability of bankruptcy were identified. Calculations were carried out for three development scenarios: an inertia scenario (assuming the continuation of current trends), an extremely optimistic scenario, and a pessimistic scenario. The study revealed that the levels of financial stability among woodworking enterprises vary significantly, which is attributed to both their size and the impact of macroeconomic factors. The modeling results showed that the risk of bankruptcy remains moderate for large enterprises, while medium-sized enterprises face increased risks due to the volatility of financial indicators. Small enterprises, on the other hand, demonstrate relatively stable financial indicators, are less prone to bankruptcy risks, but face challenges related to asset liquidity and working capital availability. The practical significance of the study lies in the potential use of the obtained forecasts to develop measures aimed at reducing the risks of financial instability. The results can be useful for government agencies, creditors, and entrepreneurs to enhance the financial stability of enterprises in the industry. Further research is planned to delve deeper into financial regulation mechanisms and develop strategies to improve business resilience in conditions of macroeconomic uncertainty.

Suggested Citation

  • Ilya V. Naumov & Anna A. Bychkova & Natalia L. Nikulina, 2025. "Scenario-Based Forecasting of Bankruptcy Risks for Woodworking Industry Enterprises in the Sverdlovsk Region," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 24(2), pages 555-583.
    • Handle: RePEc:aiy:jnjaer:v:24:y:2025:i:2:p:555-583
    DOI: https://doi.org/10.15826/vestnik.2025.24.2.019
    as

    Download full text from publisher

    File URL: https://journalaer.ru//fileadmin/user_upload/site_15934/2025/07_Naumov_Bychkova_Nikulina.pdf
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.15826/vestnik.2025.24.2.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. T. A. Burtseva & A. R. Akhmadov, 2022. "Method for Assessing the Impact of Creating a New Industrial Infrastructure on the Industrial and Socio-Economic Development of Regions," Economics of Science, Delo Publishing house, vol. 8(1).
    2. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    3. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    4. Edward I. Altman & Małgorzata Iwanicz-Drozdowska & Erkki K. Laitinen & Arto Suvas, 2020. "A Race for Long Horizon Bankruptcy Prediction," Applied Economics, Taylor & Francis Journals, vol. 52(37), pages 4092-4111, July.
    5. Xinlin Wang & Zs'ofia Kraussl & Mats Brorsson, 2024. "Datasets for Advanced Bankruptcy Prediction: A survey and Taxonomy," Papers 2411.01928, arXiv.org.
    6. Ilya V. Naumov & Anna A. Bychkova & Natalia L. Nikulina, 2024. "Theoretical and Methodological Approach to Assessing the Probability of Bankruptcy of Enterprises in Economic Sectors," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 24-41, December.
    7. Oliver Lukason & Tiia Vissak, 2024. "Exporters’ failure predictors and processes: a multi-country analysis based on the theoretical concept of firms’ financial crisis types," Cogent Economics & Finance, Taylor & Francis Journals, vol. 12(1), pages 2399957-239, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    2. Dariusz Sala & Kostiantyn Pavlov & Olena Pavlova & Anton Demchuk & Liubomur Matiichuk & Dariusz Cichoń, 2023. "Determining of the Bankrupt Contingency as the Level Estimation Method of Western Ukraine Gas Distribution Enterprises’ Competence Capacity," Energies, MDPI, vol. 16(4), pages 1-13, February.
    3. Oliver Lukason & Germo Valgenberg, 2021. "Failure Prediction in the Condition of Information Asymmetry: Tax Arrears as a Substitute When Financial Ratios Are Outdated," JRFM, MDPI, vol. 14(10), pages 1-13, October.
    4. Marco Botta & Luca Colombo, 2016. "Macroeconomic and Institutional Determinants of Capital Structure Decisions," DISCE - Working Papers del Dipartimento di Economia e Finanza def038, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    5. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    6. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    7. Philippe Jardin, 2025. "Designing Ensemble-Based Models Using Neural Networks and Temporal Financial Profiles to Forecast Firms’ Financial Failure," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 149-209, January.
    8. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    9. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    10. Christophe Godlewski, 2004. "Modélisation de la Prévision de Défaillance Bancaire Une Application aux Banques des Pays Emergents," Finance 0409026, University Library of Munich, Germany.
    11. Denis Fougère & Golfier, C. & Guillaume Horny & Elisabeth Kremp, 2013. "What has been the impact of the 2008 crisis on firms default? (in French)," Working papers 463, Banque de France.
    12. Maurice Peat, 2007. "Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 303-324, September.
    13. Shekar Shetty & Mohamed Musa & Xavier Brédart, 2022. "Bankruptcy Prediction Using Machine Learning Techniques," JRFM, MDPI, vol. 15(1), pages 1-10, January.
    14. Silvia Mariela Méndez-Prado & Ariel Flores Ulloa, 2022. "The Impact Analysis of Psychological Issues and Pandemic-Related Variables on Ecuadorian University Students during COVID-19," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    15. Guido Max Mantovani & Gregory Gadzinski, 2022. "How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts," JRFM, MDPI, vol. 15(11), pages 1-18, October.
    16. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    17. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    18. Quader, Syed Manzur, 2017. "Differential effect of liquidity constraints on firm growth," Review of Financial Economics, Elsevier, vol. 32(C), pages 20-29.
    19. Ali Uyar & Simone Pizzi & Fabio Caputo & Cemil Kuzey & Abdullah S. Karaman, 2022. "Do shareholders reward or punish risky firms due to CSR reporting and assurance?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(5), pages 1596-1620, July.
    20. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • L73 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Forest Products

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aiy:jnjaer:v:24:y:2025:i:2:p:555-583. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Natalia Starodubets (email available below). General contact details of provider: https://edirc.repec.org/data/seurfru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.