Machine Learning for Zombie Hunting: Predicting Distress from Firms’ Accounts and Missing Values

The Institute of Economics will hold a seminar meeting as part of its Seminar Series on Tuesday, March 7, 2023: Massimo Riccaboni from IMT, Lucca will present the paper "Machine Learning for Zombie Hunting: Predicting Distress from Firms’ Accounts and Missing Values" (with: Falco J. Bargagli-Stoffi and Armando Rungi).

Abstract:

In this contribution, we propose machine learning techniques to predict zombie firms. First, we derive the risk of failure by training and testing our algorithm on disclosed financial information and non-random missing values by 304,906 firms active in Italy in the period 2008-2017. Then, we spot the highest distress conditional on the predicted risk being in the last decile of the risk of failure distribution as this is the threshold after which the observed chances of firms transiting to a lower risk of failure are negligible. We identify zombies as firms that persist in a status of high risk of failure based on their permanence in above-the-last-decile predicted risk status. For our predictive purpose, we implement a rework of the Bayesian Additive Regression Tree with Missingness 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. We show that BART-MIA outperforms (i) proxy models like the Z-scores and the Distance-to-Default, (ii) traditional econometric methods, and (iii) other widely used machine learning techniques. Eventually, we document that zombies are on average less productive, smaller, and they tend to increase in times of crisis. In general, we argue that our application can be of help to financial institutions and public authorities in the design of evidence-based policies – e.g., optimal bankruptcy laws.

The seminar will be held in blended mode. In person participation is possible in Aula 3 and available seats are allocated on a first come first served basis. For online participation please use the following link.