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

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.

Supervised Learning for the Prediction of Firm Dynamics

“…Against this background, we want to stress that applying SL algorithms and economic intuition regarding the research question at hand should ideally com-plement each other. Economic intuition can aid the choice of the algorithm and the selection of relevant attributes, thus leading to better predictive performance [12]. Furthermore, it requires a deep knowledge of the studied research question to properly interpret SL results and to direct their purpose so that intelligent machines are driven by expert human beings.…”

“…The former stream of literature (bankruptcy prediction)which has its foundations in the seminal works of Udo [96], Lee et al [63], Shin et al [88], and Chandra et al [23]-compares the binary predictions obtained with SL algorithms with the actual realized failure outcomes and uses this information to calibrate the predictive models. The latter stream of literature (financial distress prediction)-pioneered by Fantazzini and Figini [36]-deals with the problem of predicting default probabilities (DPs) [77,12] or financial constraint scores [66]. Even if these streams of literature approach the issue of firms' viability from slightly different perspectives, they train their models on dependent variables that range from firms' bankruptcy (see all the "bankruptcy" papers in Table 4) to firms' insolvency [12], default [36,14,77], liquidation [17], dissolvency [12] and financial constraint [71,92].…”

“…As, on the one side, informed choices of the predictors can boost the performance of the SL model, on the other side, economic intuition can guide researchers in the choice of the best SL algorithm to be used with the disposable data sources. Bargagli-Stoffi et al [12] show that an SL methodology that incorporates the information on missing data into its predictive model-i.e., the BART-mia algorithm by Kapelner and Bleich [53]-can lead to staggering increases in predictive performances when the predictors are missing not at random (MNAR) and their missingness patterns are correlated with the outcome. 3 As different attributes can have different predictive powers with respect to the chosen output variable, it may be the case that researchers are interested in providing to policy-makers interpretable results in terms of which are the most important variables or the marginal effects of a certain variable on the predictions.…”

“…Decision-tree-based algorithms, such as random forest [19], survival random forests [50], gradient boosted trees [39], and Bayesian additive regression trees [24], provide useful tools to investigate the aforementioned dimensions (i.e., variables importance, partial dependency plots, etc.). Hence, most of the economics papers dealing with bankruptcy or financial distress predictions implement such techniques [14,66,77,12] in service of policy-relevant implications. On the other side, papers in the fields of computer science and business, which are mostly interested in the quality of predictions, de-emphasizing the interpretability of the methods, are built on black box methodologies such as artificial neural networks [2,18,48,91,94,95,99,63,96].…”

“…Using data which emerges as a by-product of economic activity has a positive impact on firms' growth [37], and strong data analytic capabilities leverage corporate performance [75]. Simultaneously, publicly accessible data sources that cover information across firms, industries, and countries open the door for analysts and policy-makers to study firm dynamics on a broader scale such as the fate of start-ups [43], product success [79], firm growth [100], and bankruptcy [12].…”