TY  - RPRT
UR  - http://eprints.imtlucca.it/4077/
KW  - Keywords: machine learning; Bayesian statistical learning; financial constraints; bankruptcy;
zombie firms
JEL Codes: C53; C55; G32; G33; L21; L25
Y1  - 2020/06//
AV  - public
TI  - Machine Learning for Zombie Hunting.
Firms? Failures and Financial Constraints.
CY  - Lucca
M1  - imt_eic_working_paper
N2  - 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.
SN  - 2279-6894
A1  - Bargagli-Stoffi, Falco J.
A1  - Riccaboni, Massimo
A1  - Rungi, Armando
EP  - 40
ID  - eprints4077
ER  -