Silva, A.F.; Brito, J.H.; Lourenço, M.; Pereira, J.M. Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence. Sustainability2023, 15, 16482.
Silva, A.F.; Brito, J.H.; Lourenço, M.; Pereira, J.M. Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence. Sustainability 2023, 15, 16482.
Silva, A.F.; Brito, J.H.; Lourenço, M.; Pereira, J.M. Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence. Sustainability2023, 15, 16482.
Silva, A.F.; Brito, J.H.; Lourenço, M.; Pereira, J.M. Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence. Sustainability 2023, 15, 16482.
Abstract
The transport sector is pivotal and indispensable in our daily existence, being the exclusive conduit for the intercontinental conveyance of commodities and individuals. Hence, comprehending the phenomenon of business failure within the transport industry assumes paramount significance in delineating an effective competitive policy for this sector. The primary objective of this research paper is to execute a comparative investigation between statistical models forecasting business failure and models founded on artificial intelligence, within the context of the transport sector. This analysis spans the temporal expanse from 2014 to 2021 and encompasses the nations of Portugal, Spain, France, and Italy, aiming to ascertain superior efficacy among these models. The dataset employed for this endeavor encompassed a final cohort of 4866 companies, comprising 2881 that endured as going concerns and 1985 that succumbed to business failure. The models developed for this study exhibited the capacity to accurately categorize a proportion of companies ranging from 71% to 73%. Nonetheless, upon comparative scrutiny of these outcomes with those derived from the statistical models dedicated to business failure prediction, it becomes evident that the latter demonstrate an enhanced predictive prowess, manifesting fewer errors in the classification of the scrutinized companies.
Keywords
Artificial Intelligence; Forecasting; Business Failure; Financial Sustainability; Financial Indicators; Transport Sector
Subject
Business, Economics and Management, Accounting and Taxation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
