EcoSta 2022: Start Registration
View Submission - EcoSta2022
A0463
Title: Can unlisted firms benefit from market information? A data-driven approach Authors:  Michele Modina - University of Molise (Italy) [presenting]
Alessandro Bitetto - University of Pavia (Italy)
Stefano Filomeni - University of Essex - Essex Business School (United Kingdom)
Abstract: A sample of 10,136 Italian micro-, small-, and mid-sized enterprises (MSMEs) that borrow from 113 cooperative banks is employed to examine whether market pricing of public firms adds additional information to accounting measures in predicting default of private firms. Specifically, we first match the asset prices of listed firms following a data-driven clustering by means of Neural Networks Autoencoder so to evaluate the firm-wise probability of default (PD) of MSMEs. Then, we adopt three statistical techniques, namely linear models, multivariate adaptive regression spline, and random forest to assess the performance of the models and to explain the relevance of each predictor. We find a significant improvement in model performance when including the estimated PD in the predictive specifications.