Exploring the Role of Global Value Chain Position in Economic Models for Bankruptcy Forecasting

Mélanie Croquet, Loredana Cultrera, Dimitri Laroutis, Laetitia Pozniak and Guillaume Vermeylen ()
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Mélanie Croquet: Belgium-Soci&ter and Risk Research Institutes, Warocqué School of Business and Economics, University of Mons, BE-7000 Mons, Belgium
Loredana Cultrera: Belgium-Soci&ter and Risk Research Institutes, Warocqué School of Business and Economics, University of Mons, BE-7000 Mons, Belgium
Dimitri Laroutis: ESC Amiens, Centre de Recherche en Risk Management and Soci&ter Research Institute, FR-80000 Amiens, France
Laetitia Pozniak: Belgium-Soci&ter and Risk Research Institutes, Warocqué School of Business and Economics, University of Mons, BE-7000 Mons, Belgium
Guillaume Vermeylen: CEBRIG and DULBEA Research Institutes, Warocqué School of Business and Economics, Belgium-Soci&ter, University of Mons, BE-7000 Mons, Belgium

Econometrics, 2024, vol. 12, issue 4, 1-19

Abstract: This study addresses a significant gap in the literature by comparing the effectiveness of traditional statistical methods with artificial intelligence (AI) techniques in predicting bankruptcy among small and medium-sized enterprises (SMEs). Traditional bankruptcy prediction models often fail to account for the unique characteristics of SMEs, such as their vulnerability due to lean structures and reliance on short-term credit. This research utilizes a comprehensive database of 7104 Belgian SMEs to evaluate these models. Belgium was selected due to its unique regulatory and economic environment, which presents specific challenges and opportunities for bankruptcy prediction in SMEs. Our findings reveal that AI techniques significantly outperform traditional statistical methods in predicting bankruptcy, demonstrating superior predictive accuracy. Furthermore, our analysis highlights that a firm’s position within the Global Value Chain (GVC) impacts prediction accuracy. Specifically, firms operating upstream in the production process show lower prediction performance, suggesting that bankruptcy risk may propagate upward along the value chain. This effect was measured by analyzing the firm’s GVC position as a variable in the prediction models, with upstream firms exhibiting greater vulnerability to the financial distress of downstream partners. These insights are valuable for practitioners, emphasizing the need to consider specific performance factors based on the firm’s position within the GVC when assessing bankruptcy risk. By integrating both AI techniques and GVC positioning into bankruptcy prediction models, this study provides a more nuanced understanding of bankruptcy risks for SMEs and offers practical guidance for managing and mitigating these risks.

Keywords: financial econometrics; artificial intelligence; forecasting; GVC (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2024
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