eprintid: 4077
rev_number: 13
eprint_status: archive
userid: 69
dir: disk0/00/00/40/77
datestamp: 2020-06-15 11:52:24
lastmod: 2020-06-15 12:06:00
status_changed: 2020-06-15 11:52:24
type: monograph
metadata_visibility: show
creators_name: Bargagli-Stoffi, Falco J.
creators_name: Riccaboni, Massimo
creators_name: Rungi, Armando
creators_id: falco.bargaglistoffi@imtlucca.it
creators_id: massimo.riccaboni@imtlucca.it
creators_id: armando.rungi@imtlucca.it
title: Machine Learning for Zombie Hunting.
Firms’ Failures and Financial Constraints.
ispublished: pub
subjects: HA
subjects: HG
divisions: EIC
full_text_status: public
monograph_type: imt_eic_working_paper
keywords: Keywords: machine learning; Bayesian statistical learning; financial constraints; bankruptcy;
zombie firms
JEL Codes: C53; C55; G32; G33; L21; L25
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.
date: 2020-06
date_type: published
number: 1/2020
place_of_pub: Lucca
pages: 40
institution: IMT Institute for Advanced Studies Lucca
issn: 2279-6894
citation:   Bargagli-Stoffi, Falco J. and Riccaboni, Massimo and Rungi, Armando  Machine Learning for Zombie Hunting. Firms’ Failures and Financial Constraints.  EIC working paper series  #1/2020/2020     ISSN 2279-6894.      
document_url: http://eprints.imtlucca.it/4077/2/File%20per%20upload.pdf