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Financial Distress Prediction in an Imbalanced Data Stream Environment

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Hybrid Artificial Intelligent Systems (HAIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14001))

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Abstract

Corporate bankruptcy predictions are crucial to companies, investors, and authorities. However, most bankruptcy prediction studies have been based on stationary models, and they tend to ignore important challenges of financial distress like data non-stationarity, concept drift and data imbalance. This study proposes methods for dealing with these challenges and uses data collected from financial statements quarterly provided by companies to the Securities and Exchange Commission of Brazil (CVM). It is composed of information from 10 years (2011 to 2020), with 905 different corporations and 23,834 records with 82 indicators each. The sample majority have no financial difficulties, and only 651 companies have financial distress. The empirical experiment uses a sliding window, a history and a forgetting mechanism to avoid the degradation of the predictive model due to concept drift. The characteristics of the problem, especially the data imbalance, the performance of the models is measured through AUC, Gmean, and F1-Score and achieved 0.95, 0.68, and 0.58, respectively.

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Notes

  1. 1.

    https://dados.cvm.gov.br/.

  2. 2.

    https://github.com/rubensmchaves/ml-fdp.

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Acknowledgment

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Author information

Authors and Affiliations

  1. University of Brasília, Brasília, DF, 70910-090, Brazil

    Rubens Marques Chaves & Luís Paulo Faina Garcia

  2. São Paulo State University, Itapeva, SP, 18409-010, Brazil

    André Luis Debiaso Rossi

Authors
  1. Rubens Marques Chaves
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  2. André Luis Debiaso Rossi
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  3. Luís Paulo Faina Garcia
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Corresponding authors

Correspondence to Rubens Marques Chaves or Luís Paulo Faina Garcia .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of Leon, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. University of A Coruña, Ferrol - Coruña, Spain

    José Luis Calvo Rolle

  8. University of A Coruña, Ferrol - Coruña, Spain

    Héctor Quintián

  9. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Chaves, R.M., Rossi, A.L.D., Garcia, L.P.F. (2023). Financial Distress Prediction in an Imbalanced Data Stream Environment. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_15

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  • DOI: https://doi.org/10.1007/978-3-031-40725-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40724-6

  • Online ISBN: 978-3-031-40725-3

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