Predicting corporate financial distress using data mining techniques

Salehi, Mahdi; Shiri, Mahmoud Mousavi; Pasikhani, Mohammad Bolandraftar

doi:10.1108/ijlma-06-2015-0028

Cited by 23 publications

(11 citation statements)

References 38 publications

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“…The results of the study by Purves et al showed that a well-functioning management team or board is part of the success factor that can protect companies from failure [52]. Salehi et al state that, given the growing number of companies in financial distress, which would lead to an increasing number of companies in bankruptcy, the need to predict the financial future of companies is becoming increasingly important [53]. In this context, they further state that forecasting models of financial distress are of particular importance for the business decision-making of various stakeholders in organizations, including auditors, creditors, and shareholders.…”

Section: Predicting Bankruptcy Using Altman's Z-score Model and Determining The Possibility Of Fraudulent Financial Reporting Using The Bmentioning

confidence: 99%

Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies

et al. 2021

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In recent decades, predicting company bankruptcies and financial troubles has become a major concern for various stakeholders. Furthermore, because financially sustainable businesses are affected by numerous highly complex factors, both internal and external, the situation is even more complex. This paper applies Altman’s Z-score models; more precisely, the paper applies the initial Z-score model (a model for manufacturing companies), the Z′-score model (for companies operating in emerging markets), and the Z-score bankruptcy probability calculation. Therefore, this paper offers the results of the application of different Z-score models and the calculation of bankruptcy probability on a sample of agricultural companies listed on the Belgrade Stock Exchange in the period 2015–2019. In addition, different Z-score models are used for the same sample so that the difference between their results and application can be determined. In addition, the validity of the data published in the financial statements of the respective companies was confirmed using the Beneish M-score model with five and eight variables. The results obtained by applying Altman’s Z-score model (initial and adapted to emerging markets) indicate that a certain number of companies had impaired financial stability during the observed period, i.e., that they were in danger of bankruptcy. In addition, based on the results obtained using the Beneish M-score model, it was identified that a number of companies showed signals that indicate possible fraudulent financial reporting. Further, it was found that less than half of the observed companies reported on environmental protection in their annual reports, and they did so by providing a modest amount of information. The originality and value of the paper lies in suggesting that policymakers in the Serbian emerging markets should pay more attention to the operations of companies from the observed sector, as well as to their financial and non-financial reporting. Future research should focus on comparisons with agricultural companies from the same sector whose securities are listed on stock exchanges in the region.

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Section: Predicting Bankruptcy Using Altman's Z-score Model and Determining The Possibility Of Fraudulent Financial Reporting Using The Bmentioning

confidence: 99%

Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies

et al. 2021

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“…To identify causes and influencing factors contributing to financial crisis, Andersen et al (2012) applied BN to four types of financial organisations and found that the industry widely failed to manage operational risks. Using accounting information, Salehi et al (2016) compared data mining techniques such as artificial neural networks and naive Bayesian classifier in predicting corporate financial distress. Some earlier studies can be found in Gestel et al (2006), Yin and Peng (2006) and Zhang et al (1999) while discussing BN in risk prediction in comparison with the use of liner statistical models of discriminant analysis and logistic regression.…”

Section: Analysing Tanker Shipping Bankruptcy Risksmentioning

confidence: 99%

Application of Bayesian networks in analysing tanker shipping bankruptcy risks

Wang

Yang

Zhang

et al. 2017

MABR

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Purpose-This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms. Design/methodology/approach-This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks. Findings-The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies' financial soundness under dynamic environments. Research limitations/implications-The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available. Practical implications-The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

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“…Finally the predicted values of stock have a testing accuracy of more than 85%.Some researches applied hybrid model to increase the prediction accuracy. Salehi, Mousavi Shiri, & Bolandraftar Pasikhani (2016) gave description of prediction of the financial distress of Iranian firms, with four techniques: support vector machines, artificial neural networks (ANN), k-nearest neighbor and naïve. Besides that it is also the first study in Iran which used such methods for analyzing the data.…”

Section: Literature Reviewmentioning

confidence: 99%

The prediction of stock returns with regression approaches and feature extraction

Liew¹,

Chou²

2016

J. adm. bus. stud.

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Value investing is one of the most popular investment strategies for investors to search for the undervalued stocks based on their financial reports and balance sheets. However, the numerous metrics derived from the financial statements are not easy for the investor to analyze and determine the financial health of a company. The main purpose of this study is to employ feature extraction to identify a smaller number of financial ratios for the prediction of stock return which reflects the quality of a company. Two regression approaches, including Multilayer Perceptron model and Meta Regression by discretization model, were incorporated with feature extraction to evaluate the forecast performance for two different industries in Taiwan. The results demonstrated that the prediction errors were improved for both models by the feature extraction strategy which reduced the original 16 variables to5 variables. Besides that, both models achieved better prediction result in concrete industry rather than rubber industry. Finally, this paper concluded that the overall performance of the Multilayer Perceptron model is better than the other model.

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Predicting corporate financial distress using data mining techniques

Cited by 23 publications

References 38 publications

Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies

Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies

Application of Bayesian networks in analysing tanker shipping bankruptcy risks

The prediction of stock returns with regression approaches and feature extraction

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