Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms

Ciampi, Francesco

doi:10.1016/j.jbusres.2014.10.003

Cited by 147 publications

(183 citation statements)

References 72 publications

(154 reference statements)

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“…The validity test on the SE discriminant function gives a global accuracy level of 80.58% with 18.74% of Type 1 errors and 20,10% Type 2 errors, while the same test on the SMLE discriminant function gives 20,80% Type 1 errors, 22,20%% Type 2 errors and an overall accuracy of 78.5%. These results confirm H1: small firms have their own specific structural and strategic characteristics, which are unlike those of large firms (Ciampi, 1994;Ciampi, 2015;Pompe & Bilderbeek, 2005) and consequently their credit risk profiles are significantly different from larger companies. Financial institutions should therefore use credit rating models specifically built for SEs in order to maximize their capacity to create value for their shareholders (Altman & Sabato, 2007).…”

Section: Methodssupporting

confidence: 77%

“…Consequently, it is essential that credit rating takes the business sector in which a firm operate into account; b) More importantly, the increases in prediction accuracy are higher for the SE discriminant functions (the SE model gives a mean increase of 14,55%) compared to the SMLE discriminant functions (the SMLE model gives a mean increase of 10,61%), thus confirming H2B. Small firms are more subject than larger firms to adapt their structures, strategic behaviors and financial profiles to the different industrial contexts in which they operate (Ciampi, 2015); as a consequence, pooling (separating) different business sectors reduces (improves) a model's prediction accuracy especially in the case of SEs.…”

Section: Resultssupporting

confidence: 55%

“…Apart from the contribution by Edmister (1972), only a few and relatively recent studies have focused on small and medium sized enterprises default prediction modelling (Altman & Sabato, 2007;Ciampi, 2015;Ciampi & Gordini, 2013). Edmister (1972) analysing a sample of 562 SMEs during the period 1956-1969 and using the multivariate discriminant analysis, built a SME default prediction model based on 18 financial ratios.…”

Section: Literature Reviewmentioning

confidence: 99%

“…SEs are normally quicker and more flexible when reacting to environmental (competitive, technological, social, etc. ) changes and have a notably "personal" character, with owners and managers who are often one and the same people (Ciampi 2015;Burke& Jarrat 2004). The SE growth is a result of clear, positively motivated business intentions and actions on the part of the owner-manager, driven by the belief that he can produce the desired outcomes (Morrison, Breen, & Ali 2003).…”

Section: Hypothesesmentioning

confidence: 99%

“…For example, SEs are normally quicker and more flexible when reacting to environmental (competitive, technological, social, etc. ) changes and have a notably "personal" character, with owners and managers who are often one and the same people (Burke & Jarrat 2004;Ciampi, 2015). From this it follows that building and testing instruments specifically developed in order to capture, interpret and asses these particular characteristics represent promising research exercises (Ciampi & Gordini, 2013).…”

Section: Introductionmentioning

confidence: 99%

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The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

Ciampi¹

2017

IJBM

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The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms. In fact, a multivariate discriminant analysis was applied to a sample of 2,200 small manufacturing firms located in Central Italy and a SE default prediction model was developed based on a selected group of financial ratios and specifically constructed to capture the specificities of SEs' risk profiles. Subsequently, the prediction accuracy rates obtained by this model were compared with those obtained from a second model based on a sample of 3,200 manufacturing firms situated in Central Italy which belong to all dimensional classes. The findings are the following: 1) evaluating the probability of default of SEs separately from that of larger firms improves prediction performance; 2) the predictive power of the discriminant function improves if it takes into account the different profiles of firms operating in different industry sectors; 3) this improvement is much greater for SEs compared to larger firms.

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Section: Methodssupporting

confidence: 77%

Section: Resultssupporting

confidence: 55%

Section: Literature Reviewmentioning

confidence: 99%

Section: Hypothesesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

Ciampi¹

2017

IJBM

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Lost in a black‐box? Interpretable machine learning for assessing Italian SMEs default

Crosato,

Liberati,

Repetto

2023

Appl Stoch Models Bus & Ind

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Academic research and the financial industry have recently shown great interest in Machine Learning algorithms capable of solving complex learning tasks, although in the field of firms' default prediction the lack of interpretability has prevented an extensive adoption of the black‐box type of models. In order to overcome this drawback and maintain the high performances of black‐boxes, this paper has chosen a model‐agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors' impact on the likelihood of default and rank them according to their contribution to the model outcome. Prediction is achieved by two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Networks) compared with three standard discriminant models. Results show that our analysis of the Italian Small and Medium Enterprises manufacturing industry benefits from the overall highest classification power by the eXtreme Gradient Boosting algorithm still maintaining a rich interpretation framework to support decisions.

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How can banks and finance companies incorporate value chain factors in their risk management strategy? The case of agro‐food firms

Wasan

Kumar

Luthra³

2022

Bus Strat Env

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A value chain framework for guiding the financial firms in their credit decisions is urgent, as the current COVID-19 pandemic has highlighted, but missing in the extant literature, particularly for those that lend to industries sensitive to value and supply chain bottlenecks. This study creates knowledge in value chain finance, a big untapped and un-researched market. It constructs, confirms, and validates a value chain framework for assessing risks in lending to Agro and Food Processing firms in which value chain risks are major business concerns globally. To pursue the objectives of the study, we use a novel methodology that integrates the Modified Delphi technique, exploratory factor analysis, confirmatory factor analysis, and discriminant analysis. Based on testing and analysis of primary data, including loan data, a framework comprising six factors is proposed for use in conjunction with existing risk assessment models of finance companies to improve the quality of their credit decisions, contributing to their performance sustainability.agro-food, bankruptcy, default risk, risk management, supply chain financing, value chain | INTRODUCTIONIncreasing modernization is causing higher integration and interdependence among firms across the value chain. Technology has connected value chains globally, providing new opportunities for small companies and market players from developing countries and emerging-market economies to partake in the world economy as they are now not required to hold expertise in all the stages of production and distribution (OECD, 2021). Technology-enabled changes over the last many years have also magnified the swiftness and size of disruptions in an unprecedented manner, which conjoined with increasing trends of globalization, urbanization, environmental performance concerns, and shifting demographics are amplifying and intensifying uncertainty in the marketplace (Lee & Trimi, 2021). The outbreak of the COVID-19 pandemic in the year 2020 is estimated to have caused combined revenue losses of up to $4 trillion in U.S. and European firms from supply chain disruptions (Mitchell, 2021). It has highlighted and advanced the need for firms to bring changes in their traditional business models for sustaining performance in periods of crisis (Breier et al., 2021;Khan et al., 2021). Sixty-four percent of U.S. and European firms had revenue losses between 6% and 20% in 2020, along with severe damage to their brand value (Mitchell, 2021). Although the COVID-19 in present times is a significant disruptor responsible for straining and breaking the value chains, it is only one among other disruptive forces such as cyberattacks, diverging regulations, and instability in commodity prices (Clark, 2021). Companies across industries can expect significant financial loss causing value chain disruptions every 3.7 years

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Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms

Cited by 147 publications

References 72 publications

The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

Lost in a black‐box? Interpretable machine learning for assessing Italian SMEs default

How can banks and finance companies incorporate value chain factors in their risk management strategy? The case of agro‐food firms

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