Journal articles: 'Financial distress prediction models'

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Ashraf, Sumaira, Elisabete G. S. Félix, and Zélia Serrasqueiro. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?" Journal of Risk and Financial Management 12, no. 2 (April 4, 2019): 55. http://dx.doi.org/10.3390/jrfm12020055.

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Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.

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Chen, Jianguo, Ben R. Marshall, Jenny Zhang, and Siva Ganesh. "Financial Distress Prediction in China." Review of Pacific Basin Financial Markets and Policies 09, no. 02 (June 2006): 317–36. http://dx.doi.org/10.1142/s0219091506000744.

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We use four alternative prediction models to examine the usefulness of financial ratios in predicting business failure in China. China has unique legislation regarding business failure so it is an interesting laboratory for such a study. Earnings Before Interest and Tax to Total Assets (EBITTA), Earning Per Share (EPS), Total Debt to Total Assets (TDTA), Price to Book (PB), and the Current Ratio (CR), are shown to be significant predictors. Prediction accuracy achieves a range from 78% to 93%. Logit and Neural Network models are shown to be the optimal prediction models.

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Mitchell, M. R., R. E. Link, Li-Tze Lee, Chiang Ku Fan, Hsiang-Wen Hung, and Yu-Chun Ling. "Analysis of Financial Distress Prediction Models." Journal of Testing and Evaluation 38, no. 5 (2010): 102759. http://dx.doi.org/10.1520/jte102759.

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Tew, You Hoo, and Enylina Nordin. "Predicting corporate financial distress using logistic regression : Malaysian evidence." Social and Management Research Journal 3, no. 1 (June 1, 2006): 123. http://dx.doi.org/10.24191/smrj.v3i1.5108.

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This study attempts to construct and test financial distress prediction model for Malaysian Companies. The samplefor this study consists of84 companies listed on Bursa Malaysia that became financially distressed in 200/ and 2002 and a matched (by industry and firm size) sample 0/ 84 financially healthy companies. The model is constructed by employing logistic regression analysis based on pooled data of5 years prior tofinancial distress. The model isfirst derived using the estimation sample andthen tested using the validation sample. Adding to the existing research onfinancial distress prediction models, the current model utilizes measures ofshareholders' equity to total liabilities, shareholders' equity to total assets, current liabilities to total assets, total borrowings to total assets andinventory turnover. The results are encouraging, as the model developed/or predicting corporatefinancial distress in Malaysia is reliable up to 5 years prior to financial distress. II is also believed thai the prediction model can be useful to different groups of users such as policy makers, financial institutions, creditors, managers, bankers, investors and shareholders.

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El-ansari, Osama, and Lina Bassam. "Predicting Financial Distress for Listed MENA Firms." International Journal of Accounting and Financial Reporting 9, no. 2 (April 15, 2019): 51. http://dx.doi.org/10.5296/ijafr.v9i2.14542.

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Financial distress prediction gives an early warning about defaulting risk for firms; thus, it is a real concern of the entire economy.Purpose: To examine the determinants of financial distress across MENA region countries, by using definitions of distress and historical data from active listed firms in the region.Methodology: logistic regression is run on firm-specific variables and a set of macroeconomic variables to develop a prediction model to examine the effect of these predictors on the probability of financial distress.Findings: it has been found that after controlling for country effects, accounting ratios, firm size, and macroeconomic variables provided an acceptable prediction model for listed MENA firms.Originality: a gap exists in the literature of developing countries’ prediction for financial distress. Many studies addressed bankruptcy prediction for a certain country in the region, however, a limited number of researches approached predicting distressed models for listed firms in the region.

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Ma’aji, Muhammad M., Nur Adiana Hiau Abdullah, and Karren Lee-Hwei Khaw. "Predicting Financial Distress among SMEs in Malaysia." European Scientific Journal, ESJ 14, no. 7 (March 31, 2018): 91. http://dx.doi.org/10.19044/esj.2018.v14n7p91.

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Predicting financial distress among Small and Medium Enterprises (SMEs) can have a significant impact on the economy as it serves as an effective early warning signal. The study develops distress prediction models combining financial, non-financial and governance variables which were used to analyze the influence of major corporate governance characteristics, like ownership and board structures, on the likelihood of financial distress. Multiple Discriminant Analysis (MDA) model as one of the extensively documented approaches was used. The final sample for the estimation model consists of 172 companies with 50 percent non-failed cases and 50 percent failed cases for the period between 2000 to 2012. The prediction models perform relatively well especially in MDA model that incorporate governance, financial and non-financial variables, with an overall accuracy rate of 90.7 percent in the estimated sample. The accuracy rate in the holdout sample was 91.2 percent for the MDA model. This evidence shows that the models serve as efficient earlywarning signals and can thus be beneficial for monitoring and evaluation. Controlling shareholder, number of directors, and gender of managing director are found to be significant predictors of financially distressed SMEs.

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Ahmadreza Ghasemi, Ahmadreza, Mohsen Seyghalib, and Maryam Moradi. "PREDICTION OF FINANCIAL DISTRESS, USING METAHEURISTIC MODELS." Financial and credit activity: problems of theory and practice 1, no. 24 (March 30, 2018): 238–49. http://dx.doi.org/10.18371/fcaptp.v1i24.128242.

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Listyarini, Fitri. "ANALISIS PERBANDINGAN PREDIKSI KONDISI FINANCIAL DISTRESS DENGAN MENGGUNAKAN METODE ALTMAN, SPRINGATE, DAN ZMIJEWSKI." Jurnal Bina Akuntansi 7, no. 1 (January 31, 2020): 1–20. http://dx.doi.org/10.52859/jba.v7i1.71.

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This study aims to: 1) Determine the accuracy of the Altman model, the springate model and the zmijewski model in predicting financial distress conditions in manufacturing companies in Indonesia, 2) To find out the most accurate prediction models in predicting financial distress conditions in manufacturing companies in Indonesia. This study compares three financial distress prediction models, the Altman, Springate and Zmijewski models. The population of this study is the financial statements of manufacturing companies listed on the Indonesia Stock Exchange for the period 2011-2014. The sampling technique is pair matching sampling with a total sample of 28 companies, consisting of 14 companies experiencing financial distress and 14 companies not experiencing financial distress. Comparisons of the three financial distress prediction models are made by analyzing the accuracy of each model based on the company's real conditions. The results show that the zmijewski model is the most accurate model for predicting financial distress in manufacturing companies in Indonesia because it has the highest level of accuracy compared to other models, which is 100%, followed by the Springate model which has an accuracy rate of 89.29% and the Altman model by 75%.

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Zhuang, Qian, and Lianghua Chen. "Dynamic Prediction of Financial Distress Based on Kalman Filtering." Discrete Dynamics in Nature and Society 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/370280.

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The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the dynamic nature of corporate financial distress, dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively, is established. The operation of the dynamic prediction is achieved by Kalman filtering algorithm. And a generaln-step-ahead prediction algorithm based on Kalman filtering is deduced in order for prospective prediction. An empirical study for China’s manufacturing industry has been conducted and the results have proved the accuracy and advance of predicting financial distress in such case.

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Munawarah, Munawarah, and Keumala Hayati. "ACCURACY OF SPRINGATE, ZMIJEWSKY AND GROVER AS LOGISTIC MODELS IN FINDING FINANCIAL DIFFICULTY OF FINANCING COMPANIES." ACCRUALS 3, no. 1 (March 29, 2019): 1–12. http://dx.doi.org/10.35310/accruals.v3i1.36.

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This study aims to determine both the Springate model, Grover and Zmijewski able to predict the condition of financial distress in finance companies listed on the Indonesia Stock Exchange. And of the three models can be known which model is the most accurate in predicting financial distress. The population in this study are companies in the financing sector listed on the Indonesia Stock Exchange in the period 2013 to 2017 as many as 17 companies. By using purposive sampling technique, a total sample of 85 financing companies was obtained. The data used are secondary data sourced from the company's annual financial reports. The analysis model used is logistic regression. Simultaneously, all predictive models for Springate, Zmijewski, and Grover affect the probability of financial distress. While partially only Zmijewski can influence the prediction of financial distress conditions in Financing sub-sector companies listed on the Indonesia Stock Exchange. Nagelkerqe Square value shows 0.606 meaning that only 60.6% variation of the accuracy of these three models in predicting financial distress conditions of finance companies. While the remaining 39.4% can be explained by other models not examined in this study

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Omelka, Jiří, Michaela Beranová, and Jakub Tabas. "Comparison of the models of financial distress prediction." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 7 (2013): 2587–92. http://dx.doi.org/10.11118/actaun201361072587.

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Prediction of the financial distress is generally supposed as approximation if a business entity is closed on bankruptcy or at least on serious financial problems. Financial distress is defined as such a situation when a company is not able to satisfy its liabilities in any forms, or when its liabilities are higher than its assets. Classification of financial situation of business entities represents a multidisciplinary scientific issue that uses not only the economic theoretical bases but interacts to the statistical, respectively to econometric approaches as well.The first models of financial distress prediction have originated in the sixties of the 20th century. One of the most known is the Altman’s model followed by a range of others which are constructed on more or less conformable bases. In many existing models it is possible to find common elements which could be marked as elementary indicators of potential financial distress of a company.The objective of this article is, based on the comparison of existing models of prediction of financial distress, to define the set of basic indicators of company’s financial distress at conjoined identification of their critical aspects. The sample defined this way will be a background for future research focused on determination of one-dimensional model of financial distress prediction which would subsequently become a basis for construction of multi-dimensional prediction model.

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Balasubramanian, Senthil Arasu, Radhakrishna G.S., Sridevi P., and Thamaraiselvan Natarajan. "Modeling corporate financial distress using financial and non-financial variables." International Journal of Law and Management 61, no. 3/4 (October 23, 2019): 457–84. http://dx.doi.org/10.1108/ijlma-04-2018-0078.

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Purpose This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique. Design/methodology/approach This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error. Findings The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress. Originality/value This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.

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Fachrudin, Khaira Amalia. "The Relationship between Financial Distress and Financial Health Prediction Model: A Study in Public Manufacturing Companies Listed on Indonesia Stock Exchange (IDX)." Jurnal Akuntansi dan Keuangan 22, no. 1 (May 27, 2020): 18–27. http://dx.doi.org/10.9744/jak.22.1.18-27.

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Financial distress prediction models of Altman, Springate, Zmijewski, Grover, and Khaira have been widely applied to predict financial distress and financial health. This study aims to analyze score correlations within the prediction results of the mentioned models applied in manufacture companies listed in the Indonesian Stock Exchange. The sample includes 30 companies which faced financial distress during economic crisis in 1997–1998 and, as comparison, incorporates 28 financially healthy companies. Observations were made during one and two years before the financial distress occurred, i.e. between 1995 until 1999, as well as from 2015 until 2018 to measure the financial health level in the companies. In this study, we use the correlation analysis. The results showed that models which have a strong and significant relationship at alpha 5% are models from Altman - Springate, Altman - Khaira, Springate - Khaira, and Zmijewski - Khaira. Grover model which does not have the predictor in the form of leverage, however has a weak correlation with other model as well as the actual condition

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Munawarah, Munawarah. "Akurasi Model Logistik Springate, Zmijewski, dan Grover Dalam Menakar Kesulitan Keuangan Perusahaan Pembiayaan." Accounting and Management Journal 3, no. 1 (July 31, 2019): 1–10. http://dx.doi.org/10.33086/amj.v3i1.644.

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This study aims to determine springate, grover and zmijewski able to predict the condition of financial distress in finance companies listed on the Indonesia Stock Exchange. From three models can be known which model is the most accurate in predicting financial distress. There are 17 companies in Financing sector as population in this study from 2013-2017. Using purposive sampling technique, total sample of 85 financing companies was obtained. Secondary data were used in this research sourced from the company's annual reports. The analysis model used is logistic regression. Simultaneously, all predictive models affect the probability of financial distress. While partially only Zmijewski can influence the prediction of financial distress conditions in Financing sub-sector companies listed on the Indonesia Stock Exchange. Nagelkerqe Square value shows 0.606 meaning that only 60.6% variation of the accuracy of these three models in predicting financial distress conditions of finance companies. While 39.4% can be explained by other models not examined in this study.

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Oh, Nak-Kyo. "Financial Distress Prediction Models for Wind Energy SMEs." International Journal of Contents 10, no. 4 (December 28, 2014): 75–82. http://dx.doi.org/10.5392/ijoc.2014.10.4.075.

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Václav, Klepac, and Hampel David. "Predicting financial distress of agriculture companies in EU." Agricultural Economics (Zemědělská ekonomika) 63, No. 8 (August 4, 2017): 347–55. http://dx.doi.org/10.17221/374/2015-agricecon.

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The objective of this paper is the prediction of financial distress (default of payment or insolvency) of 250 agriculture business companies in the EU from which 62 companies defaulted in 2014 with respect to lag of the used attributes. From many types of classification models, there was chosen the Logistic regression, the Support vector machines method with the RBF ANOVA kernel, the Decision Trees and the Adaptive Boosting based on the decision trees to acquire the best results. From the results, it is obvious that with the increasing distance to the bankruptcy, there decreases the average accuracy of the financial distress prediction and there is a greater difference between the active and distressed companies in terms of liquidity, rentability and debt ratios. The Decision trees and Adaptive Boosting offer a better accuracy for the distress prediction than the SVM and logit methods, what is comparable to the previous studies. From the total of 15 accounting variables, there were constructed classification trees by the Decision Trees with the inner feature selection method for the better visualization, what reduces the full data set only to 1 or 2 attributes: ROA and Long-term Debt to Total Assets Ratio in 2011, ROA and Current Ratio in 2012, ROA in 2013 for the discrimination of the distressed companies.

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Jan, Chyan-long. "Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress." Symmetry 13, no. 3 (March 9, 2021): 443. http://dx.doi.org/10.3390/sym13030443.

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Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

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Pervan, Ivica, Maja Pervan, and Tamara Kuvek. "Firm Failure Prediction: Financial Distress Model vs Traditional Models." Croatian Operational Research Review 9, no. 2 (December 13, 2018): 269–79. http://dx.doi.org/10.17535/crorr.2018.0021.

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Keasey, Kevin, and Robert Watson. "Financial Distress Prediction Models: A Review of Their Usefulness1." British Journal of Management 2, no. 2 (July 1991): 89–102. http://dx.doi.org/10.1111/j.1467-8551.1991.tb00019.x.

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Husein, M. Fakhri, and Galuh Tri Pambekti. "Precision of the models of Altman, Springate, Zmijewski, and Grover for predicting the financial distress." Journal of Economics, Business & Accountancy Ventura 17, no. 3 (March 1, 2015): 405. http://dx.doi.org/10.14414/jebav.v17i3.362.

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Financial distress models need to be developed as a model of an early warning system. Such an effort is intended to anticipate the conditions that can lead to the bankruptcy of the company. This study aims to analyze the accuracy of the model of Altman, Springate, Zmijewski, and Grover as the best predictor of financial distress. This research is a quantitative study in which the data were collected by means of a data pool. This is done by using a dummy variable. The sample consists of 132 companies which are listed on the list of Daftar Efek Syariah (DES) in 2009-2012. The analysis isdone by using an analytical tool that is a Binary Logistic Regression. It shows that the model of Altman, Zmijewski models, Springate, and Grover can be used for prediction of financial distress. However, the model of Zmijewski is the most appropriate model to be used for predicting the financial distress because it has the highest level of significance compared to the other models. Zmijewski model is used for having more emphasis on the leverage ratio as an indicator of financial distress.

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Yan, Dawen, Guotai Chi, and Kin Keung Lai. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models." Mathematics 8, no. 8 (August 3, 2020): 1275. http://dx.doi.org/10.3390/math8081275.

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In this paper, we propose a new framework of a financial early warning system through combining the unconstrained distributed lag model (DLM) and widely used financial distress prediction models such as the logistic model and the support vector machine (SVM) for the purpose of improving the performance of an early warning system for listed companies in China. We introduce simultaneously the 3~5-period-lagged financial ratios and macroeconomic factors in the consecutive time windows t − 3, t − 4 and t − 5 to the prediction models; thus, the influence of the early continued changes within and outside the company on its financial condition is detected. Further, by introducing lasso penalty into the logistic-distributed lag and SVM-distributed lag frameworks, we implement feature selection and exclude the potentially redundant factors, considering that an original long list of accounting ratios is used in the financial distress prediction context. We conduct a series of comparison analyses to test the predicting performance of the models proposed by this study. The results show that our models outperform logistic, SVM, decision tree and neural network (NN) models in a single time window, which implies that the models incorporating indicator data in multiple time windows convey more information in terms of financial distress prediction when compared with the existing singe time window models.

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Munawarah, Munawarah, Anton Wijaya, Cindy Fransisca, Felicia Felicia, and Kavita Kavita. "Ketepatan Altman Score, Zmijewski Score, Grover Score, dan Fulmer Score dalam menentukan Financial Distress pada Perusahaan Trade and Service." Owner 3, no. 2 (July 31, 2019): 278. http://dx.doi.org/10.33395/owner.v3i2.170.

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This research purpose to determine the accuracy among Altman, Zmijewski, Grover, and the Fulmer models in predicting financial distress, and to determine the most accurate prediction models to use in Trade and Service company. With the accuracy of the overall prediction model of 89.4%, this research will compare the four prediction models using real conditions of the company. The Data that used in this research are all form of annual financial reports published by companies on the Indonesia Stock Exchange website. The population used is Trade and Service’s company listed on the Indonesia Stock Exchange for the period 2013-2017. Purposive sampling used in this research to obtain 34 companies as research sample. This research compares four prediction models of financial distress using logistic regression analysis. According to the result of this research shows the accuracy between the Altman, Zmijewski, Grover, and Fulmer models to predict financial distress, which the highest level of accuracy is achieved by Zmijewski model and Fulmer model with a value of 100%, followed by Grover model with a value of 97% while Altman model with a value of 73,5%.

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Thi Vu, Loan, Lien Thi Vu, Nga Thu Nguyen, Phuong Thi Thuy Do, and Dong Phuong Dao. "Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies." Investment Management and Financial Innovations 16, no. 1 (March 25, 2019): 276–90. http://dx.doi.org/10.21511/imfi.16(1).2019.22.

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The research is taken to integrate the effects of variable selection approaches, as well as sampling techniques, to the performance of a model to predict the financial distress for companies whose stocks are traded on securities exchanges of Vietnam. A firm is financially distressed when its stocks are delisted as requirement from Vietnam Stock Exchange because of making a loss in 3 consecutive years or having accumulated a loss greater than the company’s equity. There are 12 models, constructed differently in feature selection methods, sampling techniques, and classifiers. The feature selection methods are factor analysis and F-score selection, while 3 sets of data samples are chosen by choice-based method with different percentages of financially distressed firms. In terms of classifying technique, logistic regression together with SVM are used in these models. Data are collected from listed firms in Vietnam from 2009 to 2017 for 1, 2 and 3 years before the announcement of their delisting requirement. The experiment’s results highlight the outperformance of the SVM model with F-score selection method in a data sample containing the highest percentage of non-financially distressed firms.

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Cheng, Ching-Hsue, Chia-Pang Chan, and Jun-He Yang. "A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress." Computational Intelligence and Neuroscience 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/1067350.

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The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

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Tsai, Bi-Huei, Cheng-Few Lee, and Lili Sun. "The Impact of Auditors' Opinions, Macroeconomic and Industry Factors on Financial Distress Prediction: An Empirical Investigation." Review of Pacific Basin Financial Markets and Policies 12, no. 03 (September 2009): 417–54. http://dx.doi.org/10.1142/s0219091509001691.

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This study investigates the usefulness of auditors' opinions, market factors, macroeconomic factors, and industry factors in predicting financial distress of Taiwanese firms. Specifically, two non-traditional auditors' opinions are evaluated: "long-term investment audited by other auditors" ("other auditor"), and "realized investment income based on non-audited financial statements" ("no auditor").The results of the 22 discrete-time hazard models show that "other auditor" opinions have incremental contribution in predicting financial distress, in addition to "going concern" opinions. This suggests that "other auditor" opinions possess higher risk of overstating earnings and firms with such income items are more likely to fail. Besides, we find that the macroeconomic factors studied significantly explain financial distress. Particularly, the survivals of electronic firms are more sensitive to earnings due to higher earnings fluctuations in such firms. Finally, models with auditors' opinions, market factors, macroeconomic factors, and industry factors perform better than the financial ratio-only model in financial distress prediction.

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Priego de la Cruz, Alba Maria, Montserrat Manzaneque Lizano, and Elena Merino Madrid. "Corporate Governance And Accuracy Level Of Financial Distress Prediction Models." International Business & Economics Research Journal (IBER) 13, no. 7 (November 3, 2014): 1619. http://dx.doi.org/10.19030/iber.v13i7.8913.

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This investigation verifies the impact of corporate governance measure on the likelihood of financial distress on the Spanish Stock Exchange for the time period from 2007 to 2012. The authors applied an empirical study with panel data and conducted regression logistic models with the objective to calculate different measures of goodness of fit. The results of this study show that the prediction power of the financial distress models improves with the incorporation of some corporate governance measures.

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Ruxanda, Gheorghe, Cătălina Zamfir, and Andreea Muraru. "PREDICTING FINANCIAL DISTRESS FOR ROMANIAN COMPANIES." Technological and Economic Development of Economy 24, no. 6 (December 14, 2018): 2318–37. http://dx.doi.org/10.3846/tede.2018.6736.

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Using a moderately large number of financial ratios, we tried to build models for classifying the companies listed on the Bucharest Stock Exchange into low and high risk classes of financial distress. We considered four classification techniques: Support Vector Machines, Decision Trees, Bayesian logistic regression and Fisher linear classifier, out of which the first two proved to have the highest prediction accuracy. Classifiers were trained and tested on randomly drown samples and on four different databases built starting from the initial financial indicators. As the literature related to the topic on Romanian data is very scarce, our study, by using a variety of methods and combining feature selection and principal components analysis, brings a new approach to predicting financial distress for Romanian companies.

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Gregova, Elena, Katarina Valaskova, Peter Adamko, Milos Tumpach, and Jaroslav Jaros. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods." Sustainability 12, no. 10 (May 12, 2020): 3954. http://dx.doi.org/10.3390/su12103954.

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Predicting the risk of financial distress of enterprises is an inseparable part of financial-economic analysis, helping investors and creditors reveal the performance stability of any enterprise. The acceptance of national conditions, proper use of financial predictors and statistical methods enable achieving relevant results and predicting the future development of enterprises as accurately as possible. The aim of the paper is to compare models developed by using three different methods (logistic regression, random forest and neural network models) in order to identify a model with the highest predictive accuracy of financial distress when it comes to industrial enterprises operating in the specific Slovak environment. The results indicate that all models demonstrated high discrimination accuracy and similar performance; neural network models yielded better results measured by all performance characteristics. The outputs of the comparison may contribute to the development of a reputable prediction model for industrial enterprises, which has not been developed yet in the country, which is one of the world’s largest car producers.

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Halim, Zulkifli, Shuhaida Mohamed Shuhidan, and Zuraidah Mohd Sanusi. "Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia." Business Process Management Journal 27, no. 4 (February 19, 2021): 1163–78. http://dx.doi.org/10.1108/bpmj-06-2020-0273.

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PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

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Ahmad, Syed Muhammad Hassan Gillani, Suresh Ramakrishnan, Hamad Raza, and Humara Ahmad. "Review of Corporate Governance Practices and Financial Distress Prediction." International Journal of Engineering & Technology 7, no. 4.28 (November 30, 2018): 30. http://dx.doi.org/10.14419/ijet.v7i4.28.22385.

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Good corporate governance practices play an import role in increasing the firm value. Based on the agency theory related to corporate governance, if an agent (management) does not protect interest of principal (shareholders) then, agency cost is occurred and this creates a bad impact on the corporate performance. Therefore, it is necessary to address weak corporate governance practices in early stages otherwise firms can go in financial distress and eventually become bankrupt. The objective of this current study is to conduct a nonsystematic review of literature on theories and models related to corporate governance and financial distress. In the light of thorough review of literature, it is found that corporate governance variables (i.e. ownership concentration, board size, board composition, CEO duality, level of independence of board from management and managerial ownership) are good predictors for predicting financial distress. Moreover, it is also found that these corporate governance variables were not only used separately for predicting financial distress but also used along with others variables (firm level and country level) for the purpose of enhancing quality of financial distress models.

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Valaskova, Katarina, Pavol Durana, Peter Adamko, and Jaroslav Jaros. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities." Journal of Risk and Financial Management 13, no. 5 (May 7, 2020): 92. http://dx.doi.org/10.3390/jrfm13050092.

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The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

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Noga, Tracy J., and Anne L. Schnader. "Book-Tax Differences as an Indicator of Financial Distress." Accounting Horizons 27, no. 3 (April 1, 2013): 469–89. http://dx.doi.org/10.2308/acch-50481.

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SYNOPSIS: We contend that tax-related information, which has not yet been considered by extant research, can significantly improve bankruptcy prediction. We investigate the association between abnormal changes in book-tax differences (BTDs) and bankruptcy using a hazard model and out-of-sample testing as in Shumway (2001). We find that information regarding abnormal changes in BTDs significantly increases our ability to ex ante identify firms that have an increased likelihood of going bankrupt in the coming five-year period. The information provided by BTDs significantly adds information to traditional models for predicting bankruptcy, such as that proposed by Ohlson (1980), and also expands the prediction window beyond the traditional two-year time frame.

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Kuo, Hsien-Chang, Lie-Huey Wang, Her-Jiun Sheu, and Fa-Kuang Li. "Credit Evaluation for Small and Medium-sized Enterprises by the Examination of Firm-specific Financial Ratios and Non-financial Variables: Evidence from Taiwan." Review of Pacific Basin Financial Markets and Policies 06, no. 01 (March 2003): 5–20. http://dx.doi.org/10.1142/s0219091503000980.

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A credit evaluation model for SMEs in Taiwan based on the characteristics of financial as well as non-financial data is proposed in this paper. While most financial distress prediction models use financial ratios as predictive variables, this study also integrates non-financial data as predictive variables. Kuo and Li (1999) proposed that certain firm-specific financial ratios have informational content. Moreover, the introduction of non-financial variables does enhance the model's discrimination power. The proposed credit evaluation model makes it easier to classify successful or failed SMEs.

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Hassan, Ehsan ul, Zaemah Zainuddin, and Sabariah Nordin. "A Review of Financial Distress Prediction Models: Logistic Regression and Multivariate Discriminant Analysis." Indian-Pacific Journal of Accounting and Finance 1, no. 3 (July 1, 2017): 13–23. http://dx.doi.org/10.52962/ipjaf.2017.1.3.15.

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In corporate finance, the early prediction of financial distress is considered more important as another occurrence of business risks. The study presents a review of literature for early prediction of financial bankruptcy. It contributes to the formation of a systematic review of the literature regarding previous studies done in the field of bankruptcy. It addresses two most commonly used financial distress prediction models, i.e. multivariate discriminant analysis and logit. Models are discussed with their advantages and disadvantages. After methodological review, it seems that logit regression model (LRM) is more advantageous than multivariate discriminant analysis (MDA) for better prediction of financial bankruptcy. However, accurate prediction of bankruptcy is beneficial to improve the regulation of companies, to form policies for companies and to take any precautionary measures if any crisis is about to come in future.

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Kubíčková, Dana Kubíčková, and Vladimír Nulíček. "Predictors of Financial Distress and Bankruptcy Model Construction." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 2, no. 6 (2014): 34–42. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.26.1003.

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The aim of this paper is to prepare the bankruptcy model construction. In the first part, multivariate discriminant analysis and its possibilities in deriving predictive models are characterized. The second part defines the possible indicators/predictors of financial distress of companies, which could be included in the new bankruptcy model. The model itself compares different views of factors that affect the company’s financial situation and contrasts the indicators that were constructed in the model in previous works (with special regard to the models in the transition economics). The result is the collection of 39 indicators to be verified in the next stage of the research project employing the multiple discriminant analysis methods to specify which of them to be included in the new model.

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Rebetak, Filip, and Viera Bartosova. "Financial Distress and Bankruptcy Prediction in Conditions of Slovak Republic." SHS Web of Conferences 92 (2021): 08017. http://dx.doi.org/10.1051/shsconf/20219208017.

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Research background: Prediction of bankruptcy has an important place in financial analysis of an organization in the globalized economy. Ever since the first publication of a paper on bankruptcy prediction in 1932, the field of bankruptcy prediction was attracting researchers and scholars internationally. Over the years, there have been a great many models conceived in many different countries, such as Altman’s Z score or Ohlson’s model for use for managers and investors to assess the financial position of a company. Globalization in last few decades has made it even more important for all stakeholders involved to know the financial shape of the company and predict the possibility of bankruptcy. Purpose of the article: We aim in this article to examine the financial distress and bankruptcy prediction models used or developed for Slovakia to provide an overview of possibilities adjusted to specific conditions of the Slovak Republic in context of globalization. We will also look at the possibility of use of these prediction models for assessing financial status of non-profit organizations in the Slovak Republic. Methods: We will use analysis and synthesis of current research and theoretical background to compare existing models and their use. Findings & Value added: We hope to contribute with this paper to the theoretical knowledge in this field by summarizing and comparing existing models used.

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Supriati, Diana, Icuk Rangga Bawono, and Kusriyadi Choirul Anam. "ANALISIS PERBANDINGAN MODEL SPRINGATE, ZMIJEWSKI, DAN ALTMAN DALAM MEMPREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA." JOURNAL OF APPLIED BUSINESS ADMINISTRATION 3, no. 2 (November 14, 2019): 258–70. http://dx.doi.org/10.30871/jaba.v3i2.1730.

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Financial Distress can be interpreted as a phase of the decline in financial conditions experienced by a company. One of the indicators is the inability of the company to pay off its debts that are due, which is caused by losses suffered by the company in several years. This study aims to analyze and predict financial distress in companies listed on the Indonesia Stock Exchange that have suffered for several years and to find out which prediction models can indicated the best financial distress. The populations in this study are manufacturing companies listed on the Indonesia Stock Exchange in 2015-2017. The sample in this study was determined based on the purposive sampling method and obtained a sample of 16 companies, the final observation in this study were 48 observations. The data in this study were analyzed using the Springate, Zmijewski, and Altman models. This study concludes that for sample analyzed by the Springate and Altman models, the majority are classified as having indicated financial distress and for those analyzed by the Zmijewski model only a small proportion classified as having financial distress. Furthemore, for the best predictive model in indicating financial distress is the Springate model

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Shetty, Shilpa H., and Theresa Nithila Vincent. "The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India." Journal of Risk and Financial Management 14, no. 7 (July 19, 2021): 333. http://dx.doi.org/10.3390/jrfm14070333.

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The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

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Rasool, Nosheen, Muhammad Sohail, Muhammad Usman, and Muhammad Mubashir Hussain. "Financial Distress and Forewarning Bankruptcy: An Empirical Analysis of Textile Sector in Pakistan." Review of Applied Management and Social Sciences 3, no. 3 (December 31, 2020): 493–506. http://dx.doi.org/10.47067/ramss.v3i3.92.

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This study aims to measure the financial distress and forewarn bankruptcy in Textile Sector of Pakistan using popular statistical measures i.e., Z-Score, O-Score, Probit and D-Score. First, applicable financial ratios (profitability, liquidity, leverage, market ratios) and scores (Z-Score, O-Score, Probit and D-Score) of all 77 textile companies were calculated then estimated scores were compared with cut-off point of respective model. Based on findings, models are categorized in two groups: (a) Group-I (Z-Score and O-Score), (b) Group-II (Probit and D-Score). Results indicate that some of the textile firms are about to face financial distress in near future, which could ultimately lead those firms to bankruptcy. The findings of Group-I indicate that about 43% - 44% companies in the textile sector are in the phase of financial distress; whereas the results of Group-II reveal that about 8% - 16% companies are in financial distress phase. Thus, we could draw two conclusions: (1) the two models (Z-Score and O-Score) in Group-I were found to be robust for assessing financial distress and (2) the two models (Probit and D-Score) in Group-II were found to be less rigorous in forecasting financial distress. The previous studies attempted to compare the prediction accuracy of various models by examining the data of both financially distress firms and financially stable firms. But this study is aimed to foretell bankruptcy using comprehensive models (Z-Score, O-Score, Probit and D-Score), to compare the consistency of results across all four models of the study and to categorize financially stable and financially distress companies under each model. The findings of the study are expected to be beneficial at coutry level, firm level and indiviual level such as government and regulatory bodies of Pakistan can intervene to avert bankruptcy rate, management can devise appropriate strategies to reduce financial distress. Moreover. investors can safeguard their investment by making right decissions based on the findings.

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BONELLO, Joseph, Xavier BREDART, and Vanessa VELLA. "MACHINE LEARNING MODELS FOR PREDICTING FINANCIAL DISTRESS." Journal of Research in Economics 2, no. 2 (December 12, 2018): 174–85. http://dx.doi.org/10.24954/jore.2018.22.

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Yousaf, Umair Bin, Khalil Jebran, and Man Wang. "Can board diversity predict the risk of financial distress?" Corporate Governance: The International Journal of Business in Society 21, no. 4 (January 20, 2021): 663–84. http://dx.doi.org/10.1108/cg-06-2020-0252.

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Purpose The purpose of this study is to explore whether different board diversity attributes (corporate governance aspect) can be used to predict financial distress. This study also aims to identify what type of prediction models are more applicable to capture board diversity along with conventional predictors. Design/methodology/approach This study used Chinese A-listed companies during 2007–2016. Board diversity dimensions of gender, age, education, expertise and independence are categorized into three broad categories; relation-oriented diversity (age and gender), task-oriented diversity (expertise and education) and structural diversity (independence). The data is divided into test and validation sets. Six statistical and machine learning models that included logistic regression, dynamic hazard, K-nearest neighbor, random forest (RF), bagging and boosting were compared on Type I errors, Type II errors, accuracy and area under the curve. Findings The results indicate that board diversity attributes can significantly predict the financial distress of firms. Overall, the machine learning models perform better and the best model in terms of Type I error and accuracy is RF. Practical implications This study not only highlights symptoms but also causes of financial distress, which are deeply rooted in weak corporate governance. The result of the study can be used in future credit risk assessment by incorporating board diversity attributes. The study has implications for academicians, practitioners and nomination committees. Originality/value To the best of the authors’ knowledge, this study is the first to comprehensively investigate how different attributes of diversity can predict financial distress in Chinese firms. Further, this study also explores, which financial distress prediction models can show better predictive power.

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Putri, Hana Tamara, and Muhammad Syukri. "Penggunaan Model Zmijewski dan Model Grover dalam Memprediksi Kesulitan Keuangan pada Industri Otomotif yang Terdaftar di BEI Tahun 2014-2018." Ekonomis: Journal of Economics and Business 4, no. 2 (September 1, 2020): 268. http://dx.doi.org/10.33087/ekonomis.v4i2.169.

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This study aims to predict financial distress in the Automotive Industry listed on the Indonesia Stock Exchange in the 2014-2018 period, there are 13 companies that become research samples. Analysis of Prediction of Financial Distress is an early warning for management to be able to take anticipatory steps if the company indicates there are financial problems. The analysis tool used is the Zmijewski X Score and Grover Score methods and the Wilcoxon Test to see the differences between the two prediction models. The results showed that there were differences in predictions between the X Score Zmijewski model and the Grover Score, X Score detected there were 5 companies experiencing financial difficulties and Grover Score predicted there were 4 companies that indicated financial difficulties

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HOPWOOD, WILLIAM, JAMES McKEOWN, and JANE MUTCHLER. "The sensitivity of financial distress prediction models to departures from normality." Contemporary Accounting Research 5, no. 1 (September 1988): 284–98. http://dx.doi.org/10.1111/j.1911-3846.1988.tb00706.x.

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SHOME, Samik, and Sushma VERMA. "Financial Distress in Indian Aviation Industry: Investigation Using Bankruptcy Prediction Models." Eurasian Journal of Business and Economics 13, no. 25 (May 30, 2020): 91–109. http://dx.doi.org/10.17015/ejbe.2020.025.06.

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Yusra, Irdha, and Novyandri Taufik Bahtera. "Prediction modelling the financial distress using corporate governance indicators in Indonesia." Jurnal Kajian Manajemen Bisnis 10, no. 1 (June 13, 2021): 18. http://dx.doi.org/10.24036/jkmb.11228400.

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We examine whether the indicators of company governance procedures are associated with the risk of bankruptcy or financial distress in Indonesia. An empirical study we conducted using a causal model of corporate governance indicators in forecasting financial distress. The data used in this study is panel data. Using samples from assembling companies registered on the Indonesia Stock Exchange during the 2017-2019 period, we obtained as many as 105 observations selected by the purposive sampling method. Our results indicate that financial distress can be predicted by corporate governance mechanisms, although statistically it is only proven by a few indicators in our study. Specifically, our results demonstrate that institutional ownership, managerial ownership, and independent commissioners do not affect financial distress. Furthermore, our study shows evidence of a significant influence between the size of the board of directors and audit committee on financial distress. Our interpretation is that research on financial distress prediction models using corporate governance indicators has provided empirical evidence.

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Ariyo, Ademola. "Financial Ratios for Bankruptcy Prediction A Consensus Approach." Vikalpa: The Journal for Decision Makers 11, no. 1 (January 1986): 47–54. http://dx.doi.org/10.1177/0256090919860107.

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Bankruptcy prediction is essentially a judgemental task. Yet, most models use statistical methods to select the relevant financial variables for prediction. This study provides behavioural evidence from 31 bank managers and officers of financial institutions in Nigeria on the appropriateness of the choice of variables for bankruptcy prediction. The study finds that there was a consensus on short‐term liquidity ratios being consistent predictors of financial distress, a finding which confirms the usual choice of these variables based on statistical models. Interestingly, the short-term lending orientation of the financial institutions represented on the study appears to have yielded a consensus in the respondents' preference for the short-term liquidity ratios. The study finds that the level of experience of the manager has an important bearing on the choice of variables to be used in predicting bankruptcy.

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Chen, You Shyang. "Building a Hybrid Prediction Model to Evaluation of Financial Distress Corporate." Applied Mechanics and Materials 651-653 (September 2014): 1543–46. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1543.

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Exploring financial distress activity within a listed target of stock markets focused on creating such prediction models can provide insight into the technological requirements of corporate and the demands placed upon a stock investor in this field. This study integrates professional knowledge to financial ratios into the emerging soft computing techniques for building up a hybrid corporate distress prediction of early warning systems in regarding application fields. Conclusively, the empirical results indicate that the proposed procedure is a great potential alternative of helpful hybrid models to demonstrate its technological merit and application value, and it has increasing the application filings. In terms of managerial implications, the analysis results may be relevant to other types of prediction models seeking to identify financial ratios for the planning processes.

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Kim, Kyoung-jae, Kichun Lee, and Hyunchul Ahn. "Predicting Corporate Financial Sustainability Using Novel Business Analytics." Sustainability 11, no. 1 (December 22, 2018): 64. http://dx.doi.org/10.3390/su11010064.

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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.

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Lesáková, Ľubica, Petra Gundová, and Miroslava Vinczeová. "The practice of use of models predicting financial distress in Slovak companies." Journal of Eastern European and Central Asian Research (JEECAR) 7, no. 1 (March 14, 2020): 122–36. http://dx.doi.org/10.15549/jeecar.v7i1.369.

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The aim of the paper is to present the results of the research focused on the identification of the current situation concerning the knowledge and use of the models predicting financial distress in Slovak companies. In the paper three partial goals are formulated. On grounds of the goals of this paper four hypotheses were formulated. Their validity was verified by means of the primary data gained by the questionnaire research with the use of the statistical software. The research results confirmed that Slovak companies did not know the term “models predicting financial distress” neither applied them in practice. The main reasons why they do not apply them involve not knowing them, the company size (too small company) and the use of some own prediction methods. The most often used models are simple methods of point evaluation in business practice. Companies prefer simple methods not demanding of much time.

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Tahmasebi, Rasoul, Ali Asghar Anvary Rostamy, Abbas Khorshidi, and Seyyed Jalal Sadeghi Sharif. "A data mining approach to predict companies’ financial distress." International Journal of Financial Engineering 07, no. 03 (September 2020): 2050031. http://dx.doi.org/10.1142/s2424786320500310.

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Financial distress and companies’ failure have always been a complicated and intriguing problem for businesses. Because of the unfavorable impacts of financial distress on companies and societies, accounting and finance researchers around the world are thinking of ways to anticipate corporate financial distress. Several models are provided in the literature for predicting financial distress. This research develops nonlinear decision tree and linear discriminant analysis models to predict financial distress of companies listed in Iranian Stock Exchange during 2010 to 2015. The drivers are firms’ financial ratios, intellectual capital and performance indicators. According to the results, intellectual capital and financial performance indices have no informational content in decision tree model. Comparing the result show that both models predict financial distress with 90.9% and 81.8% accuracy, respectively. Moreover, the difference between the accuracy of the models however is not meaningful. In other words, two models were very close to each other in terms of predictive power.

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Journal articles on the topic 'Financial distress prediction models'

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