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Prediction of Corporate Failures for Small and Medium-Sized Enterprises in Europe: A Comparison of Statistical and Machine Learning Approaches
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Author(s): Marianna Eskantar (Technical University of Crete, Greece), Michalis Doumpos (Technical University of Crete, Greece), Evangelos Grigoroudis (Technical University of Crete, Greece)and Constantin Zopounidis (School of Production Engineering and Management, Technical University of Crete, Greece & Audencia Business School, France)
Copyright: 2021
Pages: 13
Source title:
Machine Learning Applications for Accounting Disclosure and Fraud Detection
Source Author(s)/Editor(s): Stylianos Papadakis (Hellenic Mediterranean University, Greece), Alexandros Garefalakis (Hellenic Mediterranean University, Greece), Christos Lemonakis (Hellenic Mediterranean University, Greece), Christiana Chimonaki (University οf Portsmouth, UK)and Constantin Zopounidis (School of Production Engineering and Management, Technical University of Crete, Greece & Audencia Business School, France)
DOI: 10.4018/978-1-7998-4805-9.ch015
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Abstract
The risk of bankruptcy is naturally faced by all corporate organizations, and there are various factors that may lead an organization to bankruptcy, including microeconomic and macroeconomic ones. Many researchers have studied the prediction of business bankruptcy risk in recent decades. However, the research on better tools continues to evolve, utilizing new methodologies from various scientific fields of management science and computer science. This chapter deals with the development of statistical and artificial intelligence methodologies for predicting failures for small and medium-sized enterprises, considering financial and macroeconomic data. Empirical results are presented for a large sample of European firms.
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