An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression
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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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When predicting financial distress, the logistic regression model has exhibited better results in comparison to neural networks.
“However, when it comes to predicting financial distress, the logistic regression model has exhibited better results in comparison to neural networks (Zizi et al. 2021).”
From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Zizi et al. 2021
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SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works
FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY
Altman, E. I. (1968) · The Journal of Finance · doi:10.1111/j.1540-6261.1968.tb00843.x · Cited by 14,888 · see citing works
Financial Ratios and the Probabilistic Prediction of Bankruptcy
Ohlson, J. A. (1980) · Journal of Accounting Research · doi:10.2307/2490395 · Cited by 6,166 · see citing works
Financial Ratios As Predictors of Failure
Beaver, W. H. (1966) · Journal of Accounting Research · doi:10.2307/2490171 · Cited by 4,882 · see citing works
A Discriminant Analysis of Predictors of Business Failure
Deakin, E. B. (1972) · Journal of Accounting Research · doi:10.2307/2490225 · Cited by 1,176 · see citing works
Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., & Suvas, A. (2016) · Journal of International Financial Management and Accounting · doi:10.1111/jifm.12053 · Cited by 1,111 · see citing works
A neural network model for bankruptcy prediction
Odom, M. D., & Sharda, R. (1990) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1990.137710 · Cited by 849 · see citing works
Balcaen, S., & Ooghe, H. (2005) · The British Accounting Review · doi:10.1016/j.bar.2005.09.001 · Cited by 796 · see citing works
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
Zhang, G., Y. Hu, M., Eddy Patuwo, B., & C. Indro, D. (1999) · European Journal of Operational Research · doi:10.1016/s0377-2217(98)00051-4 · Cited by 677 · see citing works
Prediction of financial distress: An empirical study of listed Chinese companies using data mining
Geng, R., Bose, I., & Chen, X. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.08.016 · Cited by 480 · see citing works
A Review of Bankruptcy Prediction Studies: 1930 to Present
Gissel, J. L., Giacomino, D. E., & Akers, M. D. (2006) · e-Publications@Marquette (Marquette University) · Cited by 405 · see citing works
Sun, J., Li, H., Huang, Q., & He, K.-Y. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.12.006 · Cited by 393 · see citing works
Using neural networks and data mining techniques for the financial distress prediction model
Chen, W.-S., & Du, Y.-K. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.03.020 · Cited by 283 · see citing works
Financial distress prediction: The case of French small and medium-sized firms
Mselmi, N., Lahiani, A., & Hamza, T. (2017) · International Review of Financial Analysis · doi:10.1016/j.irfa.2017.02.004 · Cited by 220 · see citing works
Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks
López Iturriaga, F. J., & Sanz, I. P. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.11.025 · Cited by 199 · see citing works
Remaining Financially Healthy and Competitive: The Role of Financial Predictors
Klieštik, T., Valášková, K., Lăzăroiu, G., Kováčová, M., & Vrbka, J. (2020) · Journal of Competitiveness · doi:10.7441/joc.2020.01.05 · Cited by 193 · see citing works
Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks
Jones, S., Johnstone, D. J., & Wilson, R. (2016) · Journal of Business Finance & Accounting · doi:10.1111/jbfa.12218 · Cited by 191 · see citing works
Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries
Kováčová, M., Klieštik, T., Valášková, K., Ďurana, P., & Juhászová, Z. (2019) · Oeconomia Copernicana · doi:10.24136/oc.2019.034 · Cited by 153 · see citing works
Bankruptcy prediction using terminal failure processes
du Jardin, P. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.09.059 · Cited by 152 · see citing works
Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms
Back, B., Laitinen, T., Sere, K., & van Wezel, M. (1996) · Cited by 137 · see citing works
Valášková, K., Klieštik, T., Švábová, L., & Adamko, P. (2018) · Sustainability · doi:10.3390/su10072144 · Cited by 127 · see citing works
An overview of bankruptcy prediction models for corporate firms: A Systematic literature review
Shi, Y., & Li, X. (2019) · Intangible Capital · doi:10.3926/ic.1354 · Cited by 125 · see citing works
Zhang, L., Altman, E. I., & Yen, J. (2010) · Frontiers of Computer Science in China · doi:10.1007/s11704-010-0505-5 · Cited by 119 · see citing works
Company Accounts-Based Modelling of Business Failures and the Implications for Financial StabilityPreprint
Bunn, P., & Redwood, V. (2004) · SSRN Electronic Journal · doi:10.2139/ssrn.598276 · Cited by 119 · see citing works
Bankruptcy prediction using Partial Least Squares Logistic Regression
Jabeur, S. B. (2017) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2017.02.005 · Cited by 100 · see citing works
Gregová, E., Valášková, K., Adamko, P., Tumpach, M., & Jaroš, J. (2020) · Sustainability · doi:10.3390/su12103954 · Cited by 89 · see citing works
du Jardin, P., & Séverin, É. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2012.04.006 · Cited by 86 · see citing works
Firm failure processes and components of failure risk: An analysis of European bankrupt firms
Lukason, O., & Laitinen, E. K. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.06.025 · Cited by 81 · see citing works
Financial Distress Prediction for Small and Medium Enterprises Using Machine Learning Techniques
Malakauskas, A., & Lakštutienė, A. (2021) · Engineering Economics · doi:10.5755/j01.ee.32.1.27382 · Cited by 79 · see citing works
A Race for Long Horizon Bankruptcy Prediction
Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., & Suvas, A. (2020) · Applied Economics · doi:10.1080/00036846.2020.1730762 · Cited by 75 · see citing works
Charalambakis, E. C., & Garrett, I. (2018) · Review of Quantitative Finance and Accounting · doi:10.1007/s11156-018-0716-7 · Cited by 75 · see citing works
Financial Distress Prediction Through Cash Flow Ratios Analysis
Kamaluddin, A., Ishak, N., & Mohammed, N. F. (2019) · International Journal of Financial Research · doi:10.5430/ijfr.v10n3p63 · Cited by 71 · see citing works
Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya
Ogachi, D. O., Ndege, R. M., Gaturu, P., & Zéman, Z. (2020) · Journal of risk and financial management · doi:10.3390/jrfm13030047 · Cited by 59 · see citing works
Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach
Zizi, Y., Oudgou, M., & Moudden, A. E. (2020) · Risks · doi:10.3390/risks8040107 · Cited by 58 · see citing works
Self-citationPrediction of financial distress in the Spanish banking system
Paule-Vianez, J., Fernández, M. G., & Coca-Pérez, J. L. (2019) · Applied Economic Analysis · doi:10.1108/aea-10-2019-0039 · Cited by 58 · see citing works
How to Predict Financial Distress in the Wholesale Sector: Lesson from Indonesian Stock Exchange
Universitas Trilogi, I., Kisman, Z., & Krisandi, D. (2019) · Journal of Economics and Business · doi:10.31014/aior.1992.02.03.109 · Cited by 56 · see citing works
Jeong, C., Min, J. H., & Kim, M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.056 · Cited by 52 · see citing works
Auditors and early signals of financial distress in local governments
Cohen, S., Costanzo, A., & Rossi, F. M. (2017) · Managerial Auditing Journal · doi:10.1108/maj-05-2016-1371 · Cited by 52 · see citing works
Business Failure Prediction for Slovak Small and Medium-Sized Companies
Švábová, L., Michalkova, L., Ďurica, M., & Nica, E. (2020) · Sustainability · doi:10.3390/su12114572 · Cited by 52 · see citing works
Rationnement du crédit et PME: une tentative de mise en relation
Psillaki, M. (2012) · Revue internationale P M E Économie et gestion de la petite et moyenne entreprise · doi:10.7202/1008359ar · Cited by 45 · see citing works
Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model
Rahman, M., Li, C., & Masud, M. A. K. (2021) · Journal of risk and financial management · doi:10.3390/jrfm14050199 · Cited by 43 · see citing works
Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis
Bateni, L., & Asghari, F. (2016) · Computational Economics · doi:10.1007/s10614-016-9590-3 · Cited by 39 · see citing works
Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis
Affes, Z., & Hentati-Kaffel, R. (2016) · Computational Economics · doi:10.1007/s10614-017-9698-0 · Cited by 34 · see citing works
Bankruptcy Prediction of Construction Businesses: Towards a Big Data Analytics Approach
Alaka, H., Oyedele, L. O., Bilal, M., Akinadé, O. O., Owolabi, H. A., & Ajayi, S. O. (2015) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice.2015.30 · Cited by 33 · see citing works
Insolvenzprognosen aus aktienrechtlichen Jahresabschlüssen
Gebhardt, G. (1980) · Bochumer Beiträge zur Unternehmungsführung und Unternehmensforschung · doi:10.1007/978-3-322-87942-4 · Cited by 33 · see citing works
Business Distress Prediction Using Bayesian Logistic Model for Indian Firms
Shrivastava, A., Kumar, K., & Kumar, N. (2018) · Risks · doi:10.3390/risks6040113 · Cited by 32 · see citing works
Shrivastav, S. K., & Ramudu, P. J. (2020) · Risks · doi:10.3390/risks8020052 · Cited by 31 · see citing works
Predicting Corporate Financial Sustainability Using Novel Business Analytics
Kim, K.-J., Lee, K., & Ahn, H. (2018) · Sustainability · doi:10.3390/su11010064 · Cited by 31 · see citing works
Bankruptcy Prediction: The Case of the Greek Market
Papana, A., & Spyridou, A. E. (2020) · Forecasting · doi:10.3390/forecast2040027 · Cited by 27 · see citing works
The business failure process : towards an integrative model of the literature
Crutzen, N., & Caillie, D. V. (2007) · ORBi (University of Liège) · Cited by 27 · see citing works
La prévision de la faillite fondée sur l’analyse financière de l’entreprise : un état des lieux
Refait-Alexandre, C. (2004) · Économie & prévision · doi:10.3406/ecop.2004.6937 · Cited by 24 · see citing works
Kherrazi, S., & Ahsina, K. (2016) · La Revue Gestion et Organisation · doi:10.1016/j.rgo.2016.03.006 · Cited by 20 · see citing works
Tax Arrears Versus Financial Ratios in Bankruptcy Prediction
Lukason, O., & Andresson, A. (2019) · Journal of risk and financial management · doi:10.3390/jrfm12040187 · Cited by 19 · see citing works
Tong, Y., & Serrasqueiro, Z. (2021) · JOURNAL OF INTERNATIONAL STUDIES · doi:10.14254/2071-8330.2021/14-2/1 · Cited by 19 · see citing works
Vu, L. T., Vu, L. T., Nguyen, N. T. T., Thuy, P. T., & Dao, D. P. (2019) · Investment Management and Financial Innovations · doi:10.21511/imfi.16(1).2019.22 · Cited by 18 · see citing works
LES DEFAILLANCES: UN ESSAI D'EXPLICATION
Malécot, J.-F. (1981) · Persée · Cited by 16 · see citing works
Empirics of Korean Shipping Companies’ Default Predictions
Park, S., Kim, H., Kwon, J., & Kim, T. (2021) · Risks · doi:10.3390/risks9090159 · Cited by 10 · see citing works
Financial distress prediction in Slovakia: An application of the CART algorithm
Ďurica, M., Frnda, J., & Švábová, L. (2021) · JOURNAL OF INTERNATIONAL STUDIES · doi:10.14254/2071-8330.2021/14-1/14 · Cited by 9 · see citing works
Azayite, F. Z., & Achchab, S. (2017) · International Conference on Cloud Computing Technologies and Applications (CloudTech) · doi:10.1109/cloudtech.2017.8284724 · Cited by 7 · see citing works
Guan, R., Wang, H., & Zheng, H. (2019) · Computational Statistics · doi:10.1007/s00180-019-00916-9 · Cited by 6 · see citing works
Modèle prévisionnel de la défaillance financière des PME québécoises emprunteuses
Amor, S. B., Khoury, N. T., & Savor, M. (2009) · Journal of Small Business & Entrepreneurship · doi:10.1080/08276331.2009.10593469 · Cited by 5 · see citing works
A Framework for Business Failure Prediction
İşlek, İ., Atakli, I. M., & Öğüdücü, Ş. G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59060-8_8 · Cited by 1 · see citing works
Flagged first, then by citations
2 of the 64 references couldn’t be matched to a known paper.
Research works printed but not linked: 27
- Altman, Edward I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23: 589-609.
- Amor, S. Ben, Nabil Khoury, and Marko Savor. 2009. Modèle prévisionnel de la défaillance financière des PME québécoises emprunteuses. Journal of Small Business and Entrepreneurship 22: 517-34.
- Beaver, William H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research 4: 71-111.
- Charalambakis, Evangelos C., and Ian Garrett. 2019. On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece. Review of Quantitative Finance and Accounting 52: 467-91.
- Chawla, Nitesh V., Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002 . SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16: 321-57.
- Cohen, Sandra, Antonella Costanzo, and Francesca Manes-Rossi. 2017. Auditors and early signals of financial distress in local governments. Managerial Auditing Journal 32: 234-50.
- Deakin, Edward B. 1972. A discriminant analysis of predictors of business failure. Journal of Accounting Research 10: 167-79.
- Du Jardin, Philippe, and Éric Séverin. 2012. Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time. European Journal of Operational Research 221: 378-96.
- Durica, Marek, L. Svabova, and Jaroslav Frnda. 2021. Financial distress prediction in Slovakia: An application of the cart algorithm. Journal of International Studies 14: 201-15.
- Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2017. The Elements of Statistical Learning. New York: Springer Series in Statistics, vol.
- Geng, Ruibin, Indranil Bose, and Xi Chen. 2014. Prediction of financial distress: An empirical study of listed chinese companies using data mining. European Journal of Operational Research 241: 236-47.
- Islek, Irem, Idris Murat Atakli, and Sule Gunduz Oguducu. 2017. A Framework for Business Failure Prediction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI. Zakopane: Springer, pp. 74-83.
- Jabeur, Sami Ben. 2017. Bankruptcy prediction using Partial Least Squares Logistic Regression. Journal of Retailing and Consumer Services 36: 197-202.
- Kamaluddin, Amrizah, Norhafizah Ishak, and Nor Farizal Mohammed. 2019. Financial distress prediction through cash flow ratios analysis. International Journal of Financial Research 10: 63-76.
- Khlifa, Selma Haj. 2017. Predicting default risk of SMEs in developing economies: Evidence from Morocco. Journal of WEI Business and Economics 6: 3.
- Kisman, Zainul, and Dian Krisand. 2019. How to Predict Financial Distress in the Wholesale Sector: Lesson from Indonesian Stock Exchange. Journal of Economics and Business 2: 569-85.
- Kliestik, Tomas, Katarina Valaskova, George Lazaroiu, Maria Kovacova, and Jaromir Vrbka. 2020. Remaining financially healthy and competitive: The role of financial predictors. Journal of Competitiveness 12: 74-92.
- Kovacova, Maria, Tomas Kliestik, Katarina Valaskova, Pavol Durana, and Zuzana Juhaszova. 2019. Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries. Oeconomia Copernicana 10: 743-72.
- Long, J. Scott, and Jeremy Freese. 2006. Regression models for categorical dependent variables using Stata. College Station: Stata press.
- Lukason, Oliver, and Art Andresson. 2019. Tax arrears versus financial ratios in bankruptcy prediction. Journal of Risk and Financial Management 12: 187.
- Lukason, Oliver, and Erkki K. Laitinen. 2019. Firm failure processes and components of failure risk: An analysis of European bankrupt firms. Journal of Business Research 98: 380-90.
- Mselmi, Nada, Amine Lahiani, and Taher Hamza. 2017. Financial distress prediction: The case of French small and medium-sized firms. International Review of Financial Analysis 50: 67-80.
- CrossRef · DOI
- Shi, Yin, and Xiaoni Li. 2019. An overview of bankruptcy prediction models for corporate firms: A systematic literature review. Intangible Capital 15: 114-27.
- Sun, Jie, Hui Li, Qing-Hua Huang, and Kai-Yu He. 2014. Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems 57: 41-56.
- Van Caillie, Didier. 1993. Apports de l'analyse factorielle des correspondances multiples à l'étude de la détection des signaux annonciateurs de faillite parmi les PME. Cahier de Recherche Du Service d'Informatique de Gestion (Reprint: Cahier de Recherche Du CEPE). Ph.D. thesis, Université de Liège, Liège, Belgiquepp, pp. 1-36.
- Zhang, Guoqiang, Michael Y. Hu, B. Eddy Patuwo, and Daniel C. Indro. 1999. Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European Journal of Operational Research 116: 16-32.
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Seseang, R. H., Habbe, A. H., Rasyid, S., & Nirwana, N. (2023) · Verslas teorija ir praktika · doi:10.3846/btp.2023.18123 · Cited by 1 · see citing works
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Zhao, D. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831262 · Cited by 1 · see citing works
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Adel, M., Hamada, B., Solayman, M., & Hassan, M. (2025) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa65733.2025.11166759 · Cited by 1 · see citing works
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Ni, J., Le, H., Nguyen, H. T. T., & Huynh, V. (2026) · Studies in big data · doi:10.1007/978-3-032-06179-9_4 · Cited by 1 · see citing works
Nurcahyono, N., Hanum, A. N., & Sinarasri, A. (2023) · Advances in economics, business and management research/Advances in Economics, Business and Management Research · doi:10.2991/978-94-6463-154-8_29 · Cited by 1 · see citing works
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Nayan, A., Ilias, M. R., Ishak, S. S., Rahim, A. H. A., & Morat, B. N. (2024) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2024.02.001 · Cited by 1 · see citing works
Ananda, Y. Y., Permana, A. H., & Pohan, E. R. (2022) · Syntax Idea · doi:10.46799/syntax-idea.v4i2.1767
Lu, Y., Wang, K., Sun, H., Qu, H., Chen, J., Liu, W., & Chang, C. (2024) · Applied Sciences · doi:10.3390/app14135711
Employing AI to Predict the Cambodia Securities Exchange Index
Lim, S., Flores, E. P., & Dash, T. R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3302-6_5
Nokairi, W., Amine, N. B., & AMINE, S. E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70411-6_23
Oudgou, M., & Boudhar, A. (2025) · Scientific Annals of Economics and Business · doi:10.47743/saeb-2025-0010
Self-citationBoutfssi, A., Zizi, Y., & Quamar, T. (2025) · Theoretical and Practical Research in Economic Fields · doi:10.14505/tpref.v16.2(34).04
Self-citationPerformance of Different Machine Learning Algorithms for Credit Risk Classification
Kasina, M. M., Kihoro, J. M., & Kibet, A. (2025) · Journal of Data Analysis and Information Processing · doi:10.4236/jdaip.2025.134029
Enhancing Predictive Efficiency: Feature Engineering Approaches to Financial Distress Prediction
Antony, T. M., & Thomas, T. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-7292-2_6
AI-Powered Predictive Models for Early Detection of Business Failures in the U.S.
Rahul, D. D., Mahmud, I., Tariquzzaman, M., Akter, S., Aqif, H., Osman, F. Y., Shafi, S. B., & Sozib, H. M. (2025) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet63943.2025.11472153
Chen, J., & Alataş, B. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3864
Financial Risk Early Warning Framework Integrating Stream Processing and Complex Event Processing
Liu, C., & Zhan, D. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.05.227
Bazán-Ramírez, W., Prudencio-Vidal, J. A., Osorio, A. G. M., Gutiérrez-Gómez, E., Palomino-Malpartida, Y. G., Bazán-Ramírez, A., & Bello-Bernal, M. Á. (2026) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2026.1955286
- Authors
- Zizi, Y., Jamali-Alaoui, A., El Goumi, B., Oudgou, M., & El Moudden, A.
- Affiliations
- Université Ibn-Tofail (MA)
- Faculty of Science and Technology, Sidi Mohammed Ben Abdellah University, Fez 3000, Morocco
- INSA EUROMED, University EUROMED of Fez, Fez 3000, Morocco
- Université Sultan Moulay Slimane (MA)
- Laboratory of Research in Organizational Management Sciences, ENCG Kenitra, Ibn Tofail University, Kenitra 14000, Morocco
- Corresponding author
- Zizi, Youssef
- Venue
- Risks
- Publisher
- MDPI AG
- Published
- 2021-11-08
- Volume
- 9
- Issue
- 11
- Pages
- 200
- ISSN
- 2227-9091 (ISSN-L)
- OpenAlex
- W3214597067
- Cited by
- 47
- References
- 64
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 12 · 2023: 9 · 2022: 11
- FWCI
- 13.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Risks (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Econstor (Econstor) (submittedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Social Sciences › Business, Management and Accounting › Accounting › Corporate Insolvency and Governance
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- financial distress prediction, early warning model, small and medium-sized enterprises, financial ratios, return on assets, logistic regression, artificial neural networks, LASSO feature selection, stepwise selection, Moroccan SMEs
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