Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”

Iotti, Mattia; Bonazzi, Giuseppe

doi:10.3390/su10040947

Cited by 13 publications

(14 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prediction of financial distress or bankruptcy risks by employing the appropriate model with high accuracy has become an important research issue in the area of business analytics [1,2,66]. In this study, we have suggested a new kind of hybrid SVM and GA model, termed GOSVM, in order to improve the performance of the typical SVM algorithm for financial distress prediction.…”

Section: Discussionmentioning

confidence: 99%

Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018

View full text Add to dashboard Cite

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018

View full text Add to dashboard Cite

show abstract

“…Every firm is part of a network of relations, such as; labor relations, supplier relations, sales relations, customer relations, financial institutions relations, tax payments, social relations and many more. Therefore, any financial trouble within a firm will directly impact its associated socio-economic system and subsequently sustainability in the long-run [4,5]. Li et al [6] states that high financial risk has serious implications for a firm's financial sustainability, while studies such as [7,8] point out that financial distress threatens the corporate financial health.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the establishment of an early warning mechanism for financial distress can improve a firm's financial sustainability. Consequently, recent studies [4,9] have employed different measures of financial distress to gauge the financial sustainability of firms.…”

Section: Introductionmentioning

confidence: 99%

Is Bankruptcy Risk Tied to Corporate Life-Cycle? Evidence from Pakistan

Akbar

Tang

et al. 2019

Sustainability

View full text Add to dashboard Cite

In this paper we analyze the relationship between bankruptcy risk and the corporate life cycle in Pakistan from 2005 to 2014. For this purpose, we run a Hierarchical Linear Mixed Model (HLM) for a sample of 301 non-financial listed firms in 12 different sectors. The empirical outcomes reveal that firms during introduction, growth and, decline stages (mature stage) of life-cycle experience higher (lower) bankruptcy risk. Moreover, in juxtaposition with growth stage, bankruptcy risk is higher at the introduction stage of life-cycle. These findings suggest that financial managers should be cautious about the financial fragility of the firm at each stage of corporate life-cycle. The results also entail that Pakistani firms do not follow a sequential pattern in their life-cycle, rather they have the tendency to revert to a previous stage or jump to the next stage of life-cycle. This is the first study that empirically examines the association between firm life-cycle stage and corresponding bankruptcy risk and asserts that managers must incorporate the life-cycle effects into their financial planning and decision making for the sustainable working of an enterprise.

show abstract

“…Tomatoes are vulnerable to postharvest losses due to their perishable nature [8]. In recent years, tomato processing firms have encountered increasing difficulties, due both to an increase in raw material costs and market difficulties [9].…”

Section: Introductionmentioning

confidence: 99%

Marketability Probability Study of Cherry Tomato Cultivars Based on Logistic Regression Models

Díaz-Pérez¹,

Carreño-Ortega²,

Gómez-Galán³

et al. 2018

Agronomy

View full text Add to dashboard Cite

The purpose of this study was to demonstrate interest in applying simple and multiple logistic regression analyses to the marketability probability of commercial tomato (Solanum lycopersicum L.) cultivars when the tomatoes are harvested as loose fruit. A fruit’s firmness and commercial quality (softening or over-ripe fruit, cracking, cold damage, and rotting) were determined at 0, 7, 14, and 21 days of storage. The storage test simulated typical conditions from harvest to purchase-consumption by the consumer. The combined simple and multiple analyses of the primary continuous and categorical variables with the greatest influence on the commercial quality of postharvest fruit allowed for a more detailed understanding of the behavior of different tomato cultivars and identified the cultivars with greater marketability probability. The odds ratios allowed us to determine the increase or decrease in the marketability probability when we substituted one cultivar with a reference one. Thus, for example, the marketability probability was approximately 2.59 times greater for ‘Santyplum’ than for ‘Angelle’. Overall, of the studied cultivars, ‘Santyplum’, followed by ‘Dolchettini’, showed greater marketability probability than ‘Angelle’ and ‘Genio’. In conclusion, the logistic regression model is useful for studying and identifying tomato cultivars with good postharvest marketability characteristics.

show abstract

Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”

Cited by 13 publications

References 45 publications

Predicting Corporate Financial Sustainability Using Novel Business Analytics

Predicting Corporate Financial Sustainability Using Novel Business Analytics

Is Bankruptcy Risk Tied to Corporate Life-Cycle? Evidence from Pakistan

Marketability Probability Study of Cherry Tomato Cultivars Based on Logistic Regression Models

Contact Info

Product

Resources

About