IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n5878047.html

Financial Risk Early‐Warning of Neusoft Group Based on Support Vector Machine

Author

Listed:
  • Yuxuan Dai
  • Chenhui Yu

Abstract

As an emerging development industry, the information technology industry faces the most internal and external problems without effective risk prevention measures, which requires an effective financial risk early‐warning system to be established to control risks. Nowadays, the advantages of support vector machine (SVM) have gradually appeared. The research on financial risk early‐warning using SVM mostly stays in dichotomy. However, the financial risk of an enterprise will not only exist in absolute risk and no risk. There are many other levels of risk categories. Therefore, this paper proposes a new financial warning idea, which extends the support vector machine dichotomous to multidivision. This article focuses on the data modeling based on the financial data of listed companies in China’s A‐share information technology industry and applied to the case company Neusoft Group. First, the principal component analysis method is applied to assign the weights of financial indicators, and then the efficacy coefficient method is applied to comprehensively evaluate risk classification. Finally, the classified data were input into SVM for training and testing, and the model was applied to the financial risk early warning of Neusoft Group. The research results show that the model can better predict the financial risk of Neusoft Group.

Suggested Citation

  • Yuxuan Dai & Chenhui Yu, 2022. "Financial Risk Early‐Warning of Neusoft Group Based on Support Vector Machine," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:5878047
    DOI: 10.1155/2022/5878047
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/5878047
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/5878047?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. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    2. Billio, Monica & Casarin, Roberto & Costola, Michele & Pasqualini, Andrea, 2016. "An entropy-based early warning indicator for systemic risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 45(C), pages 42-59.
    3. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
    4. 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.
    5. 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.
    6. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    7. Pham Vo Ninh, Binh & Do Thanh, Trung & Vo Hong, Duc, 2018. "Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam," Economic Systems, Elsevier, vol. 42(4), pages 616-624.
    8. Mehdi Vafakhah & Saeid Khosrobeigi Bozchaloei, 2020. "Regional Analysis of Flow Duration Curves through Support Vector Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 283-294, January.
    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. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    2. Vo, D.H. & Pham, B.V.-N. & Pham, T.V.-T. & McAleer, M.J., 2019. "Corporate Financial Distress of Industry Level Listings in an Emerging Market," Econometric Institute Research Papers EI2019-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. 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).
    4. 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.
    5. Christian Lohmann & Steffen Möllenhoff & Thorsten Ohliger, 2023. "Nonlinear relationships in bankruptcy prediction and their effect on the profitability of bankruptcy prediction models," Journal of Business Economics, Springer, vol. 93(9), pages 1661-1690, November.
    6. Hui Li & Ting Sun & Jinquan Zhang, 2024. "Prediction of corporate financial distress based on corporate social responsibility: New evidence from DANP, VWP and MEOWA weights methodologies," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(5), pages 4537-4565, December.
    7. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    8. Frieda Rikkers & Andre E. Thibeault, 2009. "A Structural form Default Prediction Model for SMEs, Evidence from the Dutch Market," Multinational Finance Journal, Multinational Finance Journal, vol. 13(3-4), pages 229-264, September.
    9. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    10. Mahfuzur Rahman & Cheong Li Sa & Md. Abdul Kaium Masud, 2021. "Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model," JRFM, MDPI, vol. 14(5), pages 1-16, May.
    11. WILLIAM HOPWOOD & JAMES C. McKEOWN & JANE F. MUTCHLER, 1994. "A Reexamination of Auditor versus Model Accuracy within the Context of the Going†Concern Opinion Decision," Contemporary Accounting Research, John Wiley & Sons, vol. 10(2), pages 409-431, March.
    12. Kerstin Lopatta & Mario Albert Gloger & Reemda Jaeschke, 2017. "Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings," Accounting Perspectives, John Wiley & Sons, vol. 16(4), pages 315-343, December.
    13. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    14. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    15. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2025. "Bankruptcy prediction using machine learning and Shapley additive explanations," Review of Quantitative Finance and Accounting, Springer, vol. 65(1), pages 107-148, July.
    16. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    17. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    18. 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.
    19. 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.
    20. Thao Tran & Ngoc Hong Nguyen & Binh Thien Le & Nam Thanh Vu & Duc Hong Vo, 2023. "Examining financial distress of the Vietnamese listed firms using accounting-based models," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-17, May.

    More about this item

    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:wly:complx:v:2022:y:2022:i:1:n:5878047. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    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.