IDEAS home Printed from https://ideas.repec.org/a/ami/journl/v10y2011i3p351-374.html
   My bibliography  Save this article

A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy

Author

Listed:
  • Javier DE ANDRES

    (University of Oviedo, Department of Accounting, Spain)

  • Fernando SÁNCHEZ-LASHERAS

    (University of Oviedo, Department of Construction and Manufacturing Engineering, Spain)

  • Pedro LORCA

    (University of Oviedo, Department of Accounting, Spain)

  • Francisco Javier DE COS JUEZ

    (University of Oviedo, Department of Exploitation and Exploration of Mines, Spain)

Abstract

This paper proposes a hybrid approach to the forecasting of firms’ bankruptcy of Spanish enterprises from the construction sector. Our proposal starts splitting the group of healthy companies into two subgroups: borderline and non-borderline companies. Borderline companies are healthy companies with marked financial similarities with bankrupt ones. Then, each subgroup is divided in clusters according to their financial similarities and then each cluster is replaced by a director vector which represents the companies included in the cluster. In order to do this, we use Self Organizing Maps (SOM). Once the companies in clusters have been replaced by director vectors, we estimate a classification model through Multivariate Adaptive Regression Splines (MARS). Our results show that the proposed hybrid approach is much more accurate for the identification of the companies that go bankrupt than other approaches such as a multi-layer perceptron neural network and a simple MARS model.

Suggested Citation

  • Javier DE ANDRES & Fernando SÁNCHEZ-LASHERAS & Pedro LORCA & Francisco Javier DE COS JUEZ, 2011. "A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 10(3), pages 351-374, September.
    • Handle: RePEc:ami:journl:v:10:y:2011:i:3:p:351-374
    as

    Download full text from publisher

    File URL: http://online-cig.ase.ro/RePEc/ami/articles/10_3_4.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aroa González Fuentes & Nélida M. Busto Serrano & Fernando Sánchez Lasheras & Gregorio Fidalgo Valverde & Ana Suárez Sánchez, 2020. "Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms," Energies, MDPI, vol. 13(10), pages 1-16, May.
    2. Sergio Luis Suárez Gómez & Francisco García Riesgo & Carlos González Gutiérrez & Luis Fernando Rodríguez Ramos & Jesús Daniel Santos, 2020. "Defocused Image Deep Learning Designed for Wavefront Reconstruction in Tomographic Pupil Image Sensors," Mathematics, MDPI, vol. 9(1), pages 1-15, December.
    3. Jose Ramón Rogada & Lourdes A. Barcia & Juan Angel Martinez & Mario Menendez & Francisco Javier De Cos Juez, 2017. "Comparative Modeling of a Parabolic Trough Collectors Solar Power Plant with MARS Models," Energies, MDPI, vol. 11(1), pages 1-15, December.
    4. Krzemień, Alicja, 2019. "Fire risk prevention in underground coal gasification (UCG) within active mines: Temperature forecast by means of MARS models," Energy, Elsevier, vol. 170(C), pages 777-790.
    5. Carlo Caserio & Delio Panaro & Sara Trucco, 2014. "A statistical analysis of reliability of audit opinions as bankruptcy predictors," Discussion Papers 2014/174, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

    More about this item

    Keywords

    Bankruptcy; Self Organized Maps (SOM); Multivariate Adaptive Regression Splines (MARS); Construction firms;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    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:ami:journl:v:10:y:2011:i:3:p:351-374. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Cristina Tartavulea (email available below). General contact details of provider: .

    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.