An AutoML application to forecasting bank failures

Anna Agrapetidoua Department of Economics, Democritus University of Thrace, Komotini, GreeceCorrespondence[email protected]

https://orcid.org/0000-0002-8407-7552

Paulos Charonyktakisb Gnosis Data Analysis PC, Heraklion, Greece

Periklis Gogasa Department of Economics, Democritus University of Thrace, Komotini, Greece

Theophilos Papadimitrioua Department of Economics, Democritus University of Thrace, Komotini, Greece

Ioannis Tsamardinosb Gnosis Data Analysis PC, Heraklion, Greece;c Computer Science Department, University of Crete, Heraklion, Greece

References

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Gogas, P., T. Papadimitriou, and A. Agrapetidou. 2018. “Forecasting Bank Failures and Stress Testing: A Machine Learning Approach.” International Journal of Forecasting 34 (3): 440–455. doi:10.1016/j.ijforecast.2018.01.009.
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Lagani, V., G. Athineou, A. Farcomeni, M. Tsagris, and I. Tsamardinos. 2017. Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets, Journal of Statistical Software, 80(7). doi:10.18637/jss.v080.i07.
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Tsamardinos, I., E. Greasidou, and G. Borboudakis. 2018. “Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-validation.” Machine Learning 107: 1895–1922. doi:10.1007/s10994-018-5714-4.
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An AutoML application to forecasting bank failures

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