Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints

Falco J. Bargagli-Stoffi (), Massimo Riccaboni () and Armando Rungi
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Falco J. Bargagli-Stoffi: IMT School for Advanced Studies Lucca
Massimo Riccaboni: IMT School for Advanced Studies Lucca

No 01/2020, Working Papers from IMT School for Advanced Studies Lucca

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 active 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 (search for similar items in EconPapers)
JEL-codes: C53 C55 G32 G33 L21 L25 (search for similar items in EconPapers)
Pages: 40
Date: 2020-06, Revised 2020-06
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in EIC working paper series

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