Does size matter in predicting SMEs failure?

Abstract

This study acknowledges the diversity between micro, small, and medium‐sized firms while predicting bankruptcy and financial distress of the United States small and medium‐sized enterprises. Empirical findings suggest that survival (failure) probability increases (decreases) with increasing firm size and firms in different size categories have varying determinants of bankruptcy, whereas factors affecting their financial distress are mostly invariant. Magnitude of significant covariates changes across the size categories of both bankrupt and financially distressed firms. Further, operating cash flow information does not add any marginal increment in prediction performance of multivariate hazard models above baseline models developed using information from income statements and balance sheets. This result holds for failure likelihood of small and medium‐sized enterprises and their respective size categories.

To EMU or not to EMU: Can TFP “provoke” the capital structure puzzle of SMEs?

“…Motivated by the recent works of Gupta, Barzotto, and Khorasgani (2018) and Zhang and Liu (2017) on the role of SMEs' size in access to finance and bankruptcy and on the TFP component respectively, this article investigates the dynamic causal relationship between the three subcategories (micro, small, and medium) of SMEs' capital structure and macroeconomic variables for European countries both in and outside the EMU, focusing primarily on the role of the country TFP component in financial leverage. As TFP is relatively stable over both the short and the long term, it reflects the efficiency of the production process, not depending exclusively on managers' incentives (Zhang & Liu, 2017).…”

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

“…Another common finding of this group of papers is that prediction models which have been developed for specific industries perform better than generic models (Rikkers and Thibeault 2011 ) and that using models, specifically estimated on SME samples, increases the default prediction accuracy rates (Altman et al 2020 ; Gupta et al 2015 ). For example, several authors (El Kalak and Hudson 2016 ; Gupta et al 2015 , 2018a , b ) develop different prediction models for different-sized categories of SMEs (micro, small and medium-sized companies) and find that analysing small and micro firms separately, resulted in an improved default prediction accuracy.…”

“…A significant number of papers in this cluster (Calabrese et al 2016 ; Ciampi et al 2018 ; Gupta, Gregoriou, et al 2018a , b ; Yoshino and Taghizadeh-Hesary 2015 ) also investigate the potential of non-conventional methodologies for default prediction modelling, such as neural networks (Ciampi and Gordini 2013 ; Mittal et al 2011 ; Wu and Wang 2000 ), Grabit model (Sigrist and Hirnschall 2019 ) and other operational research methodologies (Calabrese et al 2016 ). These studies find that non-parametric models such as neural networks, Kohonen maps, Binary Generalized Extreme Value Additive and Grabit models perform better than traditional logistic regression and Cox proportional hazard, especially at longer horizons.…”

Peru's emerging craft‐brewing industry and its implications for tourism

“…Arguably, however, and as depicted in Figure 1, these two strategies can make a substantial contribution towards cost efficiencies. With the numerous challenges faced by micro firms that also include their vulnerability to financial distress (Gupta, Barzotto, & Khorasgani, 2018), focusing on their key strengths is paramount to their long-term sustainability. In the case of the present research, a key partner was identified in Peru's emerging gastronomy, which presents huge potential for food-beer pairings, and undoubtedly can be utilised to enhance even further the already established image of the country as a tourist destination.…”