The prediction of failure of a firm is a challenging topic in business research. In this paper, we consider a machine learning approach to detect the state of asset shortfall in the Italian small and medium-sized enterprises’ context. More precisely, we use the recurrent neural networks to predict the insolvency of firms. The huge dataset we study allows us to overcome problems of distortions given by smaller sample sizes. The observed sample comes from AIDA database, and consider thirty variables replicated for five years. The main result is that recurrent neural networks outperform the multi-layer perceptron architecture used as benchmark. The obtained accuracy scores are in line with those found in the literature, and this suggests that the use of new techniques such as those tried out in this study could produce even better results.

Recurrent ANNs for Failure Predictions on Large Datasets of Italian SMEs

Leonardo Nadali;Marco Corazza
;
Francesca Parpinel
;
Claudio Pizzi
2020

Abstract

The prediction of failure of a firm is a challenging topic in business research. In this paper, we consider a machine learning approach to detect the state of asset shortfall in the Italian small and medium-sized enterprises’ context. More precisely, we use the recurrent neural networks to predict the insolvency of firms. The huge dataset we study allows us to overcome problems of distortions given by smaller sample sizes. The observed sample comes from AIDA database, and consider thirty variables replicated for five years. The main result is that recurrent neural networks outperform the multi-layer perceptron architecture used as benchmark. The obtained accuracy scores are in line with those found in the literature, and this suggests that the use of new techniques such as those tried out in this study could produce even better results.
Neural Approaches to Dynamics of Signal Exchanges
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3722056
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