Spatial autocorrelation and clusters in modelling corporate bankruptcy of manufacturing firms

Andreano, M. Simona; Benedetti, Roberto; Mazzitelli, Andrea; Piersimoni, Federica

doi:10.1007/s40812-018-0097-x

Cited by 3 publications

(4 citation statements)

References 58 publications

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“…On the other hand, southern provinces show specialization in the primary sector (agriculture) and present higher liquidation rates. These results are in line with previous investigations that show a spatial dependence on the bankruptcy solution among countries in [26] or countries with similar systems, such as Spain and Italy in [27][28][29]. At this point, the question is what factors can explain the regional disparities in the success rate?…”

Section: Industrial Specialization By Province and The Bankruptcy Outcomesupporting

confidence: 92%

“…Prior studies such as [28] analyzed the spatial autocorrelation to predict bankruptcy likelihood through companies' interconnection (economic environment) by employing decision trees. Other studies such as [29], assessed the spatial relationship between companies' financial health and the judicial system's efficiency (institutional environment) through logistic regression.…”

Section: Spatial Autocorrelation and Bankruptcymentioning

confidence: 99%

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Regional Disparities and Spatial Dependence of Bankruptcy in Spain

2021

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Firm survival, bankruptcy, and turnaround are of great interest nowadays. Bankruptcy is the ultimate resource for a company to survive when it is affected by a severe decline. Thus, determinants of firm turnaround and survival in the context of bankruptcy are of interest to researchers, managers, and policy-makers. Prior turnaround literature has broadly studied firm-specific factors for turnaround success. However, location-specific factors remain relatively unstudied despite their increasing relevance. Thus, this paper aims to evaluate the existence of spatial dependence on the outcome of the bankruptcy procedure. Economic geography and business literature suggest that location matters and closer companies behave similarly to further ones. For this purpose, we designed a longitudinal analysis employing spatial correlation techniques. The analyses were conducted on a sample of 862 Spanish bankrupt firms (2004–2017) at a regional level (province). For overcoming the limitations of the broadly usually logistic model employed for the turnaround context, the Moran’s Index and the Local Association Index (LISA) were applied with gvSIG and GeoDa software. The empirical results show that the predictors GDP per capita and manufacturing specialization are related to higher bankruptcy survival rates. Both characteristics tend to be present in the identified cluster of provinces with better outcomes located in the North of Spain. We suggest that location broadly impacts the likelihood of the survival of a bankrupt firm, which can condition the strategic decision of locating in one region or another. Our findings provide policy-makers, managers, and researchers with relevant contributions and future investigation lines.

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Section: Industrial Specialization By Province and The Bankruptcy Outcomesupporting

confidence: 92%

Section: Spatial Autocorrelation and Bankruptcymentioning

confidence: 99%

Regional Disparities and Spatial Dependence of Bankruptcy in Spain

2021

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“…The relevance of the location and its influence on the failure rates of restaurants could be documented by Parsa et al (2005), Parsa et al (2011), andParsa et al (2015). In this context, Andreano et al (2018) and Platt & Platt (2008) generally state that the location of a company influences the probability of default. In the study by Andreano et al (2018), the probability of solvency is three times higher when similar solvent companies are located in the neighborhood.…”

Section: Network-based-view (Nbv)mentioning

confidence: 99%

“…In this context, Andreano et al (2018) and Platt & Platt (2008) generally state that the location of a company influences the probability of default. In the study by Andreano et al (2018), the probability of solvency is three times higher when similar solvent companies are located in the neighborhood. Regions with high growth tend to be home to firms with a high stock of network capital, which provides access to economically beneficial knowledge and innovation (Audretsch & Dohse, 2007;Huggins & Thompson, 2015, 2014.…”

Section: Network-based-view (Nbv)mentioning

confidence: 99%

Financial distress in the Austrian tourism industry: hotels and restaurants analysis

Situm¹

2023

EJTR

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Considering the limited number of studies on the early detection of crises and insolvencies in the tourism industry, there are several research gaps. Therefore, this study analyzes which financial and non-financial variables significantly influence the financial distress of the tourism businesses (hotels and restaurants) in Austria. The resource-based and network-based views were used as theoretical foundations to determine which variables influence the probability of financial distress, with a total of 776 observations from 2005 to 2015 inclusive. The results show that variables describing the endogenous unsystematic risk of tourism businesses (firm-specific level and destination level) make the greatest contribution to explaining financial distress, while exogenous unsystematic and exogenous systematic risk variables show little or no relevance.

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Machine learning techniques in bankruptcy prediction: A systematic literature review

Dasilas,

Rigani

2024

Expert Systems with Applications

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Spatial autocorrelation and clusters in modelling corporate bankruptcy of manufacturing firms

Cited by 3 publications

References 58 publications

Regional Disparities and Spatial Dependence of Bankruptcy in Spain

Regional Disparities and Spatial Dependence of Bankruptcy in Spain

Financial distress in the Austrian tourism industry: hotels and restaurants analysis

Machine learning techniques in bankruptcy prediction: A systematic literature review

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