Bankruptcy Prediction: The Case of the Greek Market

Papana, Angeliki; Spyridou, Anastasia

doi:10.3390/forecast2040027

Cited by 19 publications

(11 citation statements)

References 36 publications

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“…For instance, using neural networks with 42 nodes in the hidden layer, Kim et al (2018) found an accuracy of 71.9% through 41 financial ratios selected from 1548 Korean heavy industry companies. To predict bankruptcy in the Greek market, Papana and Spyridou (2020) achieved by neural networks a good classification rate of 65.7% two years before bankruptcy and 70% one year before bankruptcy; however, our results are lower than those of Islek and Oguducu (2017) and Paule-Vianez et al (2020). We take as an example the Paule-Vianez et al (2020) model that achieved an overall success of 97.3% in predicting the financial distress of Spanish credit institutions.…”

Section: Discussionmentioning

confidence: 81%

“…For neural networks, our best results outperform those of Kim et al (2018), Lukason and Andresson (2019), Papana and Spyridou (2020), and Malakauskas and Lakštutien ė (2021). For instance, using neural networks with 42 nodes in the hidden layer, Kim et al (2018) found an accuracy of 71.9% through 41 financial ratios selected from 1548 Korean heavy industry companies.…”

Section: Discussionmentioning

confidence: 88%

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An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

Zizi

Alaoui

Goumi

et al. 2021

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In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 88%

An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

Zizi

Alaoui

Goumi

et al. 2021

Risks

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“…After making predictions for a single financial ratio and assuming a uniform dataset structure, the advantage of this method is an indication of the direction and strength of independent variables that affect the dependent variable [35,76]. In addition, some methods develop a linear model, such as Logit and Probit application modeling [77,78] or artificial neural networks [79,80], which improve the overall prediction accuracy. On the other hand, the disadvantage of this approach is the assumption that the variables from which the explanatory variable values are estimated are independent.…”

Section: Discussionmentioning

confidence: 99%

Application of Canonical Variate Analysis to Compare Different Groups of Food Industry Companies in Terms of Financial Liquidity and Profitability

et al. 2021

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Financial liquidity and profitability are two critical phenomena present in the financial economy of a company, whose relations depend on each other and may course in different directions. At the same time, they are an example of the complexity of the problem, which demands a proper approach, allowing one to reconcile two opposing objectives of any enterprise, i.e., maximizing the benefits for the owners and minimizing the risk of losing financial liquidity. Until now, the relationship between liquidity and profitability has not been examined explicitly, using multidimensional methods in particular. Nevertheless, the links between profitability and financial liquidity maintenance ensure the sustainable development of enterprises in different branches. This paper formulates two aims: scientific and practical. The scientific one concerns adopting the canonical variate analysis method to visualize the differences and relationships between food industry companies regarding financial liquidity and profitability. The practical one relates to indicating the relationship between financial liquidity and profitability in different groups of food industry companies. To study the relationships between the selected groups of enterprises and describe them, the liquidity and profitability ratios were utilized, involving canonical variate analysis based on transformation by linear combination and singular value decomposition. The analysis found that the most important feature highlighting the group of the examined entities regarding financial liquidity was the cash conversion cycle. The research results showed the existence of multidirectional relationships between liquidity and profitability. The research indicates that they depend on indicators describing financial dependencies and the industries in which they operate. This led to a much deeper and broader interpretation of the assessment of the financial situation of companies to support their sustainable development.

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“…Interactive verification is an important discriminative effect verification technology that has gradually developed in recent years [10]. The specific method is to remove one case from each category when establishing the discriminant function.…”

Section: Interactive Verificationmentioning

confidence: 99%

Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis

Li¹,

Alalkawi

2022

Applied Mathematics and Nonlinear Sciences

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This article first uses a new method of nonlinear combination forecasting based on neural networks to construct a financial crisis early warning model and conduct an empirical study. The drafting article uses Fisher's second-class linear discriminant analysis and binary logistic regression to establish a three-year early warning model for listed companies before the financial crisis. Empirical research shows that this early warning model applies to various industries. It can play a certain role in predicting and preventing the financial crisis of Chinese companies.

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Bankruptcy Prediction: The Case of the Greek Market

Cited by 19 publications

References 36 publications

An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

Application of Canonical Variate Analysis to Compare Different Groups of Food Industry Companies in Terms of Financial Liquidity and Profitability

Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis

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