Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis

Bateni, Leila; Asghari, Farshid

doi:10.1007/s10614-016-9590-3

Cited by 27 publications

(12 citation statements)

References 44 publications

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“…By comparing our logistic regression results obtained by the stepwise selection technique, we can say that they are well above the average obtained by other studies on the topic of prediction of financial distress (Bateni and Asghari 2020;Cohen et al 2017;Vu et al 2019;Guan et al 2020;Ogachi et al 2020;Tong and Serrasqueiro 2021;Rahman et al 2021;Park et al 2021). On a sample of 64 listed companies in the Nairobi Securities Exchange, Ogachi et al (2020) correctly classified 83% of the companies through logistic regression with the following significant ratios: working capital ratio, current ratio, debt ratio, total asset, debtors turnover, debt-equity ratio, asset turnover, and inventory turnover.…”

Section: Discussionsupporting

confidence: 73%

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: Discussionsupporting

confidence: 73%

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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“…Although bankruptcy is one possible way of resolving a bad situation, businesses are advised to strive to remain competitive and viable in the market. In order for a business to avoid bankruptcy, there are many models capable of predicting the risk of bankruptcy and assessing the health of the company from a financial and economic point of view (Bateni et al, 2020;Hafiz et al, 2018;Hu et al, 2020;Kitowski et al, 2022;Kovacova et al, 2020;Shetty et al, 2022;Voda et al, 2021). It is also necessary to consider the main reasons for bankruptcy, including insufficient sales revenues (Pasternak-Malicka et al, 2021), unqualified management and poor business-economic competencies, external bankruptcy causes (Mitter et al, 2021), the size of the firms and the years of experience of its managers also have an impact on financial failure (Bozkurt & Kaya, 2022).…”

Section: Discussionmentioning

confidence: 99%

Development of Initiated Bankruptcies in Slovakia and the Czech Republic

Bič

2022

IJEK

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Nowadays, entrepreneurs can very easily find themselves in a bad financial situation. That is why it is essential to look for ways to help them effectively. However, this cannot be done without knowing the real situation in each country, which is the basis for creating the appropriate conditions. The study's main objective was to identify and compare the development of insolvency indicators representing the bankruptcy of entrepreneurs in Slovakia and the Czech Republic from the first quarter of 2017 to the second quarter of 2021. Descriptive analysis and primary research methods were used to achieve this objective. The analytical processing dealt with the indicator of initiated bankruptcies. A fluctuating decline was observed in both countries. In 2020, a slight increase was recorded, and a further increase can be expected due to the coronavirus disease 2019 (COVID-19) pandemic. 2018 and 2017 were the least positive years for Slovakia and the Czech Republic, respectively. The results indicated the need for interventions in the business environment of both countries to help entrepreneurs in bad situations.

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“…This model was able to detect 96.15% of failures, therefore overcoming the traditional models of bankruptcy prediction. Bateni and Asghari [69] predicted bankruptcy using techniques for predicting logit and genetic algorithms. The study compared the performance of predictive models on the basis of the data obtained from 174 bankrupt and non-bankrupt Iranian companies listed on the Tehran Stock Exchange in the years 2006-2014.…”

Section: Discussionmentioning

confidence: 99%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

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There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis

Cited by 27 publications

References 44 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

Development of Initiated Bankruptcies in Slovakia and the Czech Republic

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

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