A Novel Hybrid Regression Model for Banking Loss Estimation

Pınar Karadayı Ataş ()
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Pınar Karadayı Ataş: İstanbul Arel University

Bingol University Journal of Economics and Administrative Sciences, 2024, vol. 8, issue 1, 91-105

Abstract: Given the critical need to identify financial risks in the banking sector early, this study presents a novel approach that uses historical financial ratios from the FDIC database to predict bank failures in the United States. Accurate estimation of potential losses is essential for risk management and decision-making procedures. We present a novel hybrid approach to loss estimation in the context of bank failures in this study. ElasticNet regression and relevant data extraction techniques are combined in our method to improve prediction accuracy. We conducted thorough experiments and evaluated our hybrid approach's performance against that of conventional regression techniques. With a remarkably low Mean Squared Error (MSE) of 0.001, a significantly high R-squared value of 0.98, and an Explained Variance Score of 0.95, our proposed model demonstrates superior performance compared to existing methodologies. The accuracy of our method is further demonstrated by the Mean Absolute Error (MAE) of 1200 units. Our results highlight the potential of our hybrid approach to transform loss estimation in the banking and finance domain, offering superior predictive capabilities and more accurate loss estimations.

Keywords: Financial risk analysis; financial stability assessment; bank risk management; machine learning (search for similar items in EconPapers)
JEL-codes: C13 C53 C58 G21 G28 G32 G38 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bgo:journl:v:8:y:2024:i:1:p:91-105

DOI: 10.33399/biibfad.1391666

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