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A Comparative Machine Learning Survival Models Analysis for Predicting Time to Bank Failure in the US (2001-2023)

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  • Diego Vallarino

    (Independent Researcher, Spain)

Abstract

This study investigates the likelihood of time to bank failures in the US between 2001 and April 2023, based on data collected from the Federal Deposit Insurance Corporation's report on "Bank Failures in Brief - Summary 2001 through 2023". The dataset includes 564 instances of bank failures and several variables that may be related to the likelihood of such events, such as asset amount, deposit amount, ADR, deposit level, asset level, inflation rate, short-term interest rates, bank reserves, and GDP growth rate. We explore the efficacy of machine learning survival models in predicting bank failures and compare the performance of different models. Our findings shed light on the factors that may influence the probability of bank failures with a time perspective and provide insights for improving risk management practices in the banking industry.

Suggested Citation

  • Diego Vallarino, 2024. "A Comparative Machine Learning Survival Models Analysis for Predicting Time to Bank Failure in the US (2001-2023)," Journal of Economic Analysis, Anser Press, vol. 3(1), pages 129-144, March.
    • Handle: RePEc:bba:j00001:v:3:y:2024:i:1:p:129-144:d:169
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    References listed on IDEAS

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    Cited by:

    1. Diego Vallarino, 2024. "Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency," Papers 2410.01864, arXiv.org.

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