A territorial perspective of SME’s default prediction models

Abstract

Purpose The purpose of this paper is to test whether the qualitative variables regarding the territory and the firm–territory relationship can improve the accuracy rates of small business default prediction models. Design/methodology/approach The authors apply a logistic regression to a sample of 141 small Italian enterprises located in the Marche region, and the authors build two different default prediction models: one using only financial ratios and one using jointly financial ratios and variables related to the relationship between firm and territory. Findings Including variables regarding the relationships between firms and their territory, the accuracy rates of the default prediction model are significantly improved. Research limitations/implications The qualitative variables data collected are affected by subjective judgments of respondents of the firms studied. In addition, neither other qualitative variables (such as those regarding competitive strategies, or managerial skills) are included nor those variables regarding the relationships between firms and financial institutions are included. Practical implications The study suggests that financial institutions should include territory qualitative variables, and, above all, qualitative variables regarding the firm–territory relationship, when constructing business default prediction models. Including this type of variables, it could be able to reduce the tendency to place unnecessary restrictions on credit. Originality/value The field of business failure prediction modeling using variables regarding the relationship between firm–territory is a unexplored area as it count of a very few studies.

Important factors which predict entrepreneur’s perception in business risk

“…Fang He et al (2018) argued that partnership disputes lead to business failure. So, for SMEs it can be vital to have a network with other firms and interested parties (Gabbianelli, 2018). Moreover, cooperation with vertical partners reduces the effect of business failure in manufacturing sectors (Martinez et al, 2019).…”

Unravelling trends, patterns and intellectual structure of research on bankruptcy in SMEs: A bibliometric assessment and visualisation

“…The results demonstrate that neural networks and logistic regression outperform alternative strategies. Similarly, Gabbianelli [ 108 ] examined how certain default prediction models for SMEs are implemented by looking at the variables inherent to the territory and enterprise-territory interaction. Moreover, Tobback et al [ 2 ] demonstrated that linking businesses based on their managers and board members enhances the standard bankruptcy prediction by adding complementary predictive power in SMEs.…”

“…Future research might be fruitful and intriguing if it expands into a multi-period framework [ 149 ] . Further, a comprehensive understanding of bankruptcy research may be attained by combining individual subjectivity and broader social structure, making a mixed-method approach preferable to a single-method approach [ 108 ] .…”

Rethinking SME default prediction: a systematic literature review and future perspectives

“…Using the Kohonen map-based trajectories, they find that using company prior payment behaviour-related variables improves prediction accuracy, especially for smaller firms and for a horizon longer than one year. Another study (Gabbianelli 2018 ) suggests that variables related to the characteristics of the territory (degree of attractiveness, articulation of the financial system, orientation to innovation) in which the SMEs operate and the firm–territory relationship (degree of territorial rooting, resources of the territory) exert a significant effect on SME default risk.…”