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Predicting Bank Insolvency with Random Forest Classification

Vuono, Michael LU (2019) NEKN02 20191
Department of Economics
Abstract
The aim of this paper is to evaluate a machine learning technique, Random Forest, to predict default rates for banks in the United States. This study extends the findings of a Random Forest model first introduced by Petropoulos et al. (2017) by extending their model by evaluating a longer sample period and adding macroeconomic variables to analyze how current market conditions impact the prediction of default rates of U.S. Banks from 1994-2016. Petropoulous et al. (2017) evaluated multiple traditional and artificial intelligence models to find that Random Forest produced the best results utilizing quarterly data from the FDIC from 2008-2014. Numerous studies have suggested that the financial condition of banks is purely determined by bank... (More)
The aim of this paper is to evaluate a machine learning technique, Random Forest, to predict default rates for banks in the United States. This study extends the findings of a Random Forest model first introduced by Petropoulos et al. (2017) by extending their model by evaluating a longer sample period and adding macroeconomic variables to analyze how current market conditions impact the prediction of default rates of U.S. Banks from 1994-2016. Petropoulous et al. (2017) evaluated multiple traditional and artificial intelligence models to find that Random Forest produced the best results utilizing quarterly data from the FDIC from 2008-2014. Numerous studies have suggested that the financial condition of banks is purely determined by bank specific variables. Our empirical results confirm that theory as a bank default prediction model utilizing Random Forest classification performed worse with the addition of macroeconomic variables when compared to a model based purely on bank specific variables. (Less)
Please use this url to cite or link to this publication:
author
Vuono, Michael LU
supervisor
organization
course
NEKN02 20191
year
type
H1 - Master's Degree (One Year)
subject
keywords
Random Forest, Forecasting Defaults, CAMELS, Macroeconomic Variables
language
English
id
8982037
date added to LUP
2019-08-08 10:28:40
date last changed
2019-08-08 10:28:40
@misc{8982037,
  abstract     = {{The aim of this paper is to evaluate a machine learning technique, Random Forest, to predict default rates for banks in the United States. This study extends the findings of a Random Forest model first introduced by Petropoulos et al. (2017) by extending their model by evaluating a longer sample period and adding macroeconomic variables to analyze how current market conditions impact the prediction of default rates of U.S. Banks from 1994-2016. Petropoulous et al. (2017) evaluated multiple traditional and artificial intelligence models to find that Random Forest produced the best results utilizing quarterly data from the FDIC from 2008-2014. Numerous studies have suggested that the financial condition of banks is purely determined by bank specific variables. Our empirical results confirm that theory as a bank default prediction model utilizing Random Forest classification performed worse with the addition of macroeconomic variables when compared to a model based purely on bank specific variables.}},
  author       = {{Vuono, Michael}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Predicting Bank Insolvency with Random Forest Classification}},
  year         = {{2019}},
}