Machine Learning for Zombie Hunting. Firms' Failures and Financial Constraints

40 Pages Posted: 30 Apr 2020 Last revised: 26 May 2020

See all articles by Falco Bargagli Stoffi

Falco Bargagli Stoffi

Harvard University

Massimo Riccaboni

KU Leuven - Department of Managerial Economics, Strategy, and Innovation; IMT Institute for Advanced Studies

Armando Rungi

IMT School for Advanced Studies - Lucca

Date Written: April 28, 2020

Abstract

In this contribution, we exploit machine learning techniques to predict the risk of failure of firms. Then, we propose an empirical definition of zombies as firms that persist in a status of high risk, beyond the highest decile, after which we observe that the chances to transit to lower risk are minimal. We implement a Bayesian Additive Regression Tree with Missing Incorporated in Attributes (BART-MIA), which is specifically useful in our setting as we provide evidence that patterns of undisclosed accounts correlate with firms' failures. After training our algorithm on 304,906 firms in Italy in the period 2008-2017, we show how it outperforms proxy models like the Z-scores and the Distance-to-Default, traditional econometric methods, and other widely used machine learning techniques. We document that zombies are on average 21% less productive, 76% smaller, and they increased in times of financial crisis. In general, we argue that our application helps in the design of evidence-based policies in the presence of market failures, for example optimal bankruptcy laws. We believe our framework can help to inform the design of support programs for highly distressed firms after the recent pandemic crisis.

Keywords: machine learning, Bayesian statistical learning, financial constraints, bankruptcy, zombie firms

JEL Classification: C53, C55, G32, G33, L21, L25

Suggested Citation

Bargagli Stoffi, Falco and Riccaboni, Massimo and Rungi, Armando, Machine Learning for Zombie Hunting. Firms' Failures and Financial Constraints (April 28, 2020). Available at SSRN: https://ssrn.com/abstract=3588410 or http://dx.doi.org/10.2139/ssrn.3588410

Massimo Riccaboni

KU Leuven - Department of Managerial Economics, Strategy, and Innovation ( email )

Naamsestraat 69 bus 3500
Leuven, 3000
Belgium

IMT Institute for Advanced Studies ( email )

Complesso San Micheletto
Lucca, 55100
Italy

Armando Rungi (Contact Author)

IMT School for Advanced Studies - Lucca ( email )

Piazza S. Francesco 19
Lucca, IT-55100
Italy

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