Are you a Zombie Firm? An Early Warning System Based on Machine Learning Methods

35 Pages Posted: 7 Jul 2020 Last revised: 1 Dec 2023

See all articles by Angela De Martiis

Angela De Martiis

University of Bern, Institute for Financial Management

Thomas Heil

Zeppelin Universität

Franziska J. Peter

Zeppelin University

Date Written: August 31, 2023

Abstract

In this paper, we propose an empirical approach that finds the features that matter to categorize zombie firms, separate them from the non-zombies and the recovered, and ultimately predict tomorrow’s zombies and recovered zombie firms. We apply our approach to listed US and European firms. Using machine learning models for feature selection and logistic regressions, we show that an ensemble of firm-level variables on the firm capital, financial, and industry structure lead to
the prediction of zombies and recovered zombie firms. The final model, our early warning system, produces variables that could be of use in monitoring firms’ zombie status versus recovered.

Keywords: machine learning, zombie firms, financial distress

JEL Classification: C55, C63, D22, G32, G33

Suggested Citation

De Martiis, Angela and Heil, Thomas and Peter, Franziska, Are you a Zombie Firm? An Early Warning System Based on Machine Learning Methods (August 31, 2023). Available at SSRN: https://ssrn.com/abstract=3625473 or http://dx.doi.org/10.2139/ssrn.3625473

Angela De Martiis (Contact Author)

University of Bern, Institute for Financial Management ( email )

Engehaldenstrasse 4
Bern, 3012
Switzerland

Thomas Heil

Zeppelin Universität ( email )

Am Seemooser Horn 20
DE-88045 Friedrichshafen
Germany

Franziska Peter

Zeppelin University ( email )

Am Seemooser Horn 20
Friedrichshafen, Lake Constance 88045
Germany

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