Bankruptcy Prediction of Privately Held SMEs Using Feature Selection Methods

Item Type Monograph (Working Paper)
Abstract In this paper, we test alternative feature selection methods for bankruptcy prediction and illustrate their superiority versus popular models used in the literature. We test these methods using a comprehensive dataset of more than one million financial statements covering the entire universe of privately held Norwegian SMEs in 2006-2017. Our methods can choose among 155 accounting-based input variables derived from prior literature. We find that the input variables chosen by an embedded least absolute shrinkage and selection operator (LASSO) method yield the best in-sample fit and out-of-sample performance. We show in a simulation, which mimics a real-world competitive credit market, that using LASSO to choose bankruptcy predictors improves credit risk pricing and decision making, resulting in significantly higher bank profits. Finally, we show that model performance can be further improved by running feature selection methods on sub-sets of the company universe, such as for example within-industry.
Authors Paraschiv, Florentina; Schmid, Markus & Wahlstrom, Ranik Raaen
Language English
Subjects finance
HSG Classification contribution to scientific community
HSG Profile Area SOF - System-wide Risk in the Financial System
Refereed Yes
Date 27 August 2021
Series Name School of Finance Working Paper Series
Number of Pages 64
Contact Email Address markus.schmid@unisg.ch
Depositing User Beatrix Kobelt-Glock
Date Deposited 04 Jul 2022 10:23
Last Modified 17 Aug 2022 13:17
URI: https://www.alexandria.unisg.ch/publications/266620

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Paraschiv, Florentina; Schmid, Markus & Wahlstrom, Ranik Raaen: Bankruptcy Prediction of Privately Held SMEs Using Feature Selection Methods. School of Finance Working Paper Series, 2021,

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https://www.alexandria.unisg.ch/id/eprint/266620
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