IDEAS home Printed from https://ideas.repec.org/a/hin/complx/2067065.html
   My bibliography  Save this article

Empirical Study on Indicators Selection Model Based on Nonparametric -Nearest Neighbor Identification and R Clustering Analysis

Author

Listed:
  • Yan Liu
  • Zhan-jiang Li
  • Xue-jun Zhen

Abstract

The combination of the nonparametric -nearest neighbor discriminant method and R cluster analysis is used to construct a double-combination index screening model. The characteristics of the article are as follows: firstly, the nonparametric -nearest neighbor discriminant method is used to select the indicators which have significant ability to discriminate the default loss rate, which makes up the shortcomings of the previous research that only focuses on the indicators with significant ability to discriminate default state. Additionally, the R cluster analysis applied in this paper sorts the indicators by criterion class, rather than sorting the indicator by the whole index system. This approach ensures that indicators which are clustered in one class have the same economic implications and data characteristics. This approach avoids the situation where indicators that are clustered in one class only have the same data characteristics but have different economic implications.

Suggested Citation

  • Yan Liu & Zhan-jiang Li & Xue-jun Zhen, 2018. "Empirical Study on Indicators Selection Model Based on Nonparametric -Nearest Neighbor Identification and R Clustering Analysis," Complexity, Hindawi, vol. 2018, pages 1-9, April.
    • Handle: RePEc:hin:complx:2067065
    DOI: 10.1155/2018/2067065
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/2067065.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2018/2067065.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/2067065?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. Reusens, Peter & Croux, Christophe, 2017. "Sovereign credit rating determinants: A comparison before and after the European debt crisis," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 108-121.
    2. Siavash Fakhimi Derakhshan & Alireza Fatehi, 2015. "Non-monotonic robust H fuzzy observer-based control for discrete time nonlinear systems with parametric uncertainties," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(12), pages 2134-2149, September.
    3. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    4. Katarzyna Bijak & Lyn C Thomas, 2015. "Modelling LGD for unsecured retail loans using Bayesian methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 342-352, February.
    5. Doumpos, Michael & Niklis, Dimitrios & Zopounidis, Constantin & Andriosopoulos, Kostas, 2015. "Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 599-607.
    6. Vivake Anand & Kamran Ahmed Soomro & Suneel Kumar Solanki, 2016. "Determinants of Credit Rating and Optimal Capital Structure among Pakistani Banks," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 19(60), pages 169-182, June.
    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. Carbone, Sante & Giuzio, Margherita & Kapadia, Sujit & Krämer, Johannes Sebastian & Nyholm, Ken & Vozian, Katia, 2021. "The low-carbon transition, climate commitments and firm credit risk," Working Paper Series 2631, European Central Bank.
    2. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    3. Doumpos, Michalis & Figueira, José Rui, 2019. "A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method," Omega, Elsevier, vol. 82(C), pages 166-180.
    4. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    5. 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.
    6. He, Ni & Yongqiao, Wang & Tao, Jiang & Zhaoyu, Chen, 2022. "Self-Adaptive bagging approach to credit rating," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    8. Sami Ben Mim & Ridha Nouira & Fatma Mabrouk, 2023. "Non-Linear Determinants of Developing Countries’ Sovereign Ratings: Evidence from a Panel Threshold Regression (PTR) Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    9. Boumparis, Periklis & Milas, Costas & Panagiotidis, Theodore, 2019. "Non-performing loans and sovereign credit ratings," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 301-314.
    10. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.
    11. Stewart Jones & Nurul Alam, 2019. "A machine learning analysis of citation impact among selected Pacific Basin journals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(4), pages 2509-2552, December.
    12. Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.
    13. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    14. Zhiyong Li & Xinyi Hu & Ke Li & Fanyin Zhou & Feng Shen, 2020. "Inferring the outcomes of rejected loans: an application of semisupervised clustering," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 631-654, February.
    15. Jaspreet Kaur & Madhu Vij & Ajay Kumar Chauhan, 2023. "Signals influencing corporate credit ratings—a systematic literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 91-114, March.
    16. Aggarwal, Nidhi & Singh, Manish K. & Thomas, Susan, 2023. "Do decreases in Distance-to-Default predict rating downgrades?," Economic Modelling, Elsevier, vol. 129(C).
    17. Bart H. L. Overes & Michel van der Wel, 2021. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Papers 2101.12684, arXiv.org, revised Jul 2021.
    18. Ambrocio, Gene & Hasan, Iftekhar, 2019. "Friends for the benefits: The effects of political ties on sovereign borrowing conditions," Bank of Finland Research Discussion Papers 13/2019, Bank of Finland.
    19. Fahmida E. Moula & Chi Guotai & Mohammad Zoynul Abedin, 2017. "Credit default prediction modeling: an application of support vector machine," Risk Management, Palgrave Macmillan, vol. 19(2), pages 158-187, May.
    20. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.

    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:hin:complx:2067065. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.