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An AutoML application to forecasting bank failures

Author

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  • Anna Agrapetidou
  • Paulos Charonyktakis
  • Periklis Gogas
  • Theophilos Papadimitriou
  • Ioannis Tsamardinos

Abstract

We investigate the performance of an automated machine learning (AutoML) methodology in forecasting bank failures, called Just Add Data (JAD). We include all failed U.S. banks for 2007–2013 and twice as many healthy ones. An automated feature selection procedure in JAD identifies the most significant forecasters and a bootstrapping methodology provides conservative estimates of performance generalization and confidence intervals. The best performing model yields an AUC 0.985. The current work provides evidence that JAD, and AutoML tools in general, could increase the productivity of financial data analysts, shield against methodological statistical errors, and provide models at par with state-of-the-art manual analysis.

Suggested Citation

  • Anna Agrapetidou & Paulos Charonyktakis & Periklis Gogas & Theophilos Papadimitriou & Ioannis Tsamardinos, 2021. "An AutoML application to forecasting bank failures," Applied Economics Letters, Taylor & Francis Journals, vol. 28(1), pages 5-9, January.
    • Handle: RePEc:taf:apeclt:v:28:y:2021:i:1:p:5-9
    DOI: 10.1080/13504851.2020.1725230
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    Cited by:

    1. Teddy Lazebnik & Tzach Fleischer & Amit Yaniv-Rosenfeld, 2023. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks," Sustainability, MDPI, vol. 15(14), pages 1-9, July.

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