Application of artificial neural networks in predicting financial distress in the JSE financial services and manufacturing companies

References

• Alifiah, M., and M. Tahir. 2018. “Predicting Financial Distress Companies in the Manufacturing and non-Manufacturing Sectors in Malaysia Using Macroeconomic Variables.” Management Science Letters 8 (6): 593–604.
   Google Scholar
• Altman, E. I. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” The Journal of Finance 23 (4): 589–609.
   Web of Science ®Google Scholar
• Altman, E. I., G. Marco, and F. Varetto. 1994. “Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience).” Journal of Banking & Finance 18 (3): 505–529.
   Web of Science ®Google Scholar
• Bagheri, M., M. Valipour, and V. Amin. 2012. “The Bankruptcy Prediction In Tehran Share Holding Using Neural Network And Its Comparison With Logistic Regression.” Journal of Mathematics and Computer Science 05 (3): 219–228.
   Google Scholar
• Banda, H., H. Ngirande, and F. Hogwe. 2016. “The Impact of Economic Growth on Unemployment in South Africa: 1994-2012.” Investment Management and Financial Innovations 13 (2): 246–255.
   Google Scholar
• Barboza, F., H. Kimura, and E. Altman. 2017. “Machine Learning Models and Bankruptcy Prediction.” Expert Systems with Applications 83: 405–417.
   Web of Science ®Google Scholar
• Beaver, W. H. 1966. “Financial Ratios as Predictors of Failure.” Journal of Accounting Research 4 (1): 71–111.
   Google Scholar
• Bešlić Obradović, D., D. Jakšić, I. Bešlić Rupić, and M. Andrić. 2018. “Insolvency Prediction Model of the Company: The Case of the Republic of Serbia.” Economic Research-Ekonomska Istraživanja 31 (1): 139–157.
   Web of Science ®Google Scholar
• Bonnell, J. A. 2011. “Implementation of a New Sigmoid Function in Backpropagation Neural Networks.” (Masters dissertation). East Tennessee State University, Tennessee, United States of America.
   Google Scholar
• Cassim, R. J. 2014. “The Value of Financial Ratio Analysis in Predicting the Failure of JSE Listed Companies.” (Masters dissertation). North-West University, Noth-West, South Africa.
   Google Scholar
• Cassim, Z. 2016. “Predicting Financial Distress of JSE-Listed Companies Using Bayesian Networks.” (Master's dissertation). University of Cape Town, South Africa: Cape Town.
   Google Scholar
• Cassim, R. J., and M. J. Swanepoel. 2021. “The Bankruptcy Prediction Approach: An Empirical Study of Comparison Between the Emerging Market Score Model and Bankruptcy Prediction Indicators Approach in the Johannesburg Stock Exchange.” Journal of Economic and Financial Sciences 14 (1): a539..
   Google Scholar
• Chan, P. T., V. Edwards, G. Manger, and H. Yu. 2009. Corporate Failure Prediction (Bankruptcy) in Australia: From Zeta to Neural Networks. Available at SSRN 1347351.
   Google Scholar
• Chancharat, N. 2008. “An Empirical Analysis of Financial Distressed Australian Companies: The Application of Survival Analysis.” (Doctoral dissertation). University of Wollongong, Sydney, Australia.
   Google Scholar
• Chava, S., and R. A. Jarrow. 2004. “Bankruptcy Prediction with Industry Effects.” Review of Finance 8 (4): 537–569.
   Google Scholar
• Chen, W. S., and Y. K. Du. 2009. “Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model.” Expert Systems with Applications 36 (2): 4075–4086.
   Web of Science ®Google Scholar
• Cohen, T. 2019. Group Five: Another SA Construction Giant Buckles Under the Pressure. [online], https://www.dailymaverick.co.za/article/2019-03-13-group-five-another-sa-construction-giant-buckles-under-the-pressure/. [Accessed 2019/08/28].
   Google Scholar
• Diaz, J. F., and T. T. Nguyen. 2021. “Application of Grey Relational Analysis and Artificial Neural Networks on Corporate Social Responsibility (CSR) Indices.” Journal of Sustainable Finance & Investment, 1–19. doi:10.1080/20430795.2021.1929805.
   Web of Science ®Google Scholar
• Dithebe, K., C. Aigbavboa, A. Oke, and M. A. Muyambu. 2018. Factors Influencing the Performance of the South African Construction Industry: A Case of Limpopo Province. In International Conference on Industrial Engineering and Operations Management, Pretoria, South Africa.
   Google Scholar
• Duarte, D. L., and F. L. de Moraes Barboza. 2020. “Forecasting Financial Distress With Machine Learning – A Review.” Future Studies Research Journal: Trends and Strategies 12 (3): 528–574.
   Google Scholar
• Durica, M., K. Valaskova, and K. Janoskova. 2019. “Logit Business Failure Prediction in V4 Countries.” Engineering Management in Production and Services 11 (4): 54–64.
   Google Scholar
• Eliasy, A., and J. Przychodzen. 2020. “The Role of AI in Capital Structure to Enhance Corporate Funding Strategies.” Array 6 (1): 1–13.
   Google Scholar
• Ewing, B. T., S. M. Forbes, and J. E. Payne. 2003. “The Effects of Macroeconomic Shocks on Sector-Specific Returns.” Applied Economics 35 (2): 201–207.
   Web of Science ®Google Scholar
• Fitz Patrick, P. J. 1932. “A Comparison of Ratios of Successful Industrial Enter- Prises with Those of Failed Firms.” Journal of Accounting Research 2 (9): 598–605.
   Google Scholar
• Gameel, M. S., and K. El-Geziry. 2016. “Predicting Financial Distress: Multi Scenarios Modeling Using Neural Network.” International Journal of Economics and Finance 8 (11): 159–166.
   Google Scholar
• Giacomini, E. 2003. “Neural networks in Quantitative Finance.” (Masters dissertation). Humboldt University of Berlin, Mitte, Berlin.
   Google Scholar
• Giamporcaro, S. 2017. Lest we Forget - Lessons from African Bank. [online], https://www.fin24.com/Opinion/lest-we-forget-lessons-from-african-bank-20170927. [Accessed 2019/08/28].
   Google Scholar
• Gregova, E., K. Valaskova, P. Adamko, M. Tumpach, and J. Jaros. 2020. “Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods.” Sustainability 12 (10): 3954.
   Web of Science ®Google Scholar
• Groves, C., and H. Buthelezi. 2000. “Predicting Financial Distress Using Quantitative and Qualitative Performance Measures, with Specific Reference to the South African Life Assurance Industry.” (Masters dissertation). University of Cape Town, Cape Town, South Africa.
   Google Scholar
• Gupta, R., and M. P. Modise. 2011. “Macroeconomic Variables and South African Stock Return Predictability.” Economic Modelling 30 (1): 612–622.
   Google Scholar
• Hájek, P., V. Olej, and R. Myšková. 2015. “Advances in Intelligent Systems and Computing.” In Artificial Intelligence Perspectives and Applications 347: 131–140.
   Google Scholar
• Hastings, C., F. Mosteller, J. W. Tukey, and C. P. Winsor. 1947. “Low Moments for Small Samples: A Comparative Study of Order Statistics.” The Annals of Mathematical Statistics 18 (3): 413–426.
   Google Scholar
• Hauser, R. P., and D. Booth. 2011. “Predicting Bankruptcy with Robust Logistic Regression.” Journal of Data Science 9 (4): 565–584.
   Google Scholar
• Hu, H., and M. Sathye. 2015. “Predicting Financial Distress in the Hong Kong Growth Enterprises Market from the Perspective of Financial Sustainability.” Sustainability 7 (2): 1186–1200.
   Web of Science ®Google Scholar
• Ihsan, I., and S. Anjum. 2013. “Impact of Money Supply (M2) on GDP of Pakistan.” Global Journal of Management and Business Research 13 (6): 1–8.
   Google Scholar
• Irina, I., and I. Ilya. 2020. “What Impact Does Artificial Intelligence Have on Corporate Governance?” Journal of Corporate Finance Research 14 (4): 90–101.
   Google Scholar
• Kasgari, A. A., S. H. Salehnezhad, and F. Ebadi. 2013. “The Bankruptcy Prediction by Neural Networks and Logistic Regression. International Journal of Academic Research in Accounting.” Finance and Management Sciences 3 (4): 146–152.
   Google Scholar
• Khan, U. E. 2016. “Bankruptcy Prediction for Financial Sector of Pakistan: Evaluation of Logit and Discriminant Analysis Approaches.” Pakistan Journal of Engineering Technology and Science 6 (2): 210–220.
   Google Scholar
• Kidane, H. W. 2004. “Predicting Financial Distress in IT and Services Companies in South Africa.” (Doctoral dissertation). University of the Free State, Bloemfontein, South Africa.
   Google Scholar
• Kriesel, D. 2007. A Brief Introduction to Neural Networks. [Online] http://www.dkriesel.com.
   Google Scholar
• Kücher, A., S. Mayr, C. Mitter, C. Duller, and B. Feldbauer-Durstmüller. 2020. “Firm Age Dynamics and Causes of Corporate Bankruptcy: Age Dependent Explanations for Business Failure.” Review of Managerial Science 14 (3): 633–661.
   Web of Science ®Google Scholar
• Mabe, Q. M., and W. Lin. 2018. Determinants of Corporate Failure: The Case of the Johannesburg Stock Exchange. Germany: University Library of Munich.
   Google Scholar
• Malik, V. 2019. Interest Rate Risk for Financial Institutions: A Simple Guide. [Online]. https://www.drvijaymalik.com/2019/01/interest-rate-risk-bank-nbfc-hfc.html. [Accessed 15 November 2020].
   Google Scholar
• Marso, S., and M. El Merouani. 2020. “Predicting Financial Distress Using Hybrid Feedforward Neural Network with Cuckoo Search Algorithm.” Procedia Computer Science 170: 1134–1140.
   Google Scholar
• Masekesa, W. M. 2010. “Macroeconomic Determinants of Corporate Failures in South Africa (1994-2009).” (Doctoral dissertation). University of Witwatersrand, Johannesburg, South Africa.
   Google Scholar
• Mattsson, B., and O. Steinert. 2017. “Corporate Bankruptcy Prediction Using Machine Learning Techniques.” (Bachelor’s dissertation). University of Gothenburg, Gothenburg, Sweden.
   Google Scholar
• Merwin, C. L. 1942. Financing Small Corporations in Five Manufacturing Indus- Tries, 1926-36. New York: National Bureau of Economic Research, 1942.
   Google Scholar
• Mihalovič, M. 2016. “Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction.” Economics and Sociology 9 (4): 101–118.
   Web of Science ®Google Scholar
• Mirzaei, M., S. Ramakrishnan, and M. Bekri. 2016. “Corporate Default Prediction with Industry Effects: Evidence from Emerging Markets.” International Journal of Economics and Financial Issues 6 (3S): 161–169.
   Google Scholar
• Mraihi, F. 2015. “Distressed Company Prediction Using Logistic Regression: Tunisians Case.” Global Journal of Management and Business Research 15 (3): 17–33.
   Google Scholar
• Muller, G. H., B. W. Steyn-Bruwer, and W. D. Hamman. 2009. “Predicting Financial Distress of Companies Listed on the JSE: A Comparison of Techniques.” South African Journal of Business Management 40 (1): 21–32.
   Web of Science ®Google Scholar
• Narkhede, S. 2018. “Understanding auc-roc Curve.” Towards Data Science 26 (1): 220–227.
   Google Scholar
• Ngwenya, S. 2018. “Assessing the State of Financial Distress of Listed Gold and Platinum Mining Companies in South Africa.” Acta Universitatis Danubius. Œconomica 14 (4): 655–677.
   Google Scholar
• Odom, M. D., and R. Sharda. 1990. “A Neural Network Model for Bankruptcy Prediction.” International Joint Conference on Neural Networks, 163–168. IEEE.
   Google Scholar
• Ohlson, J. S. 1980. “Financial Ratios and the Probabilistic Prediction of Bankruptcy.” Journal of Accounting Research 18 (1): 109–131.
   Web of Science ®Google Scholar
• Okoth, M. N. 2013. “The Effect of Interest Rate and Inflation Rate on Exchange Rates in Kenya.” (Doctoral dissertation). University of Nairobi, Nairobi, Kenya.
   Google Scholar
• Paule-Vianez, J., M. Gutiérrez-Fernández, and J. L. Coca-Pérez. 2019. “Prediction of Financial Distress in the Spanish Banking System.” Applied Economic Analysis 28 (82): 69–87.
   Google Scholar
• Phillips, D. W., and M. D. Saft. 2007. Enron and its Ripple Effects. In Global Executive Forum, Colorado, United States of America.
   Google Scholar
• Premachandra, I. M., G. S. Bhabra, and T. Sueyoshi. 2009. “DEA as a Tool for Bankruptcy Assessment: A Comparative Study with Logistic Regression Technique.” European Journal of Operational Research 193 (2): 412–424.
   Web of Science ®Google Scholar
• Rado, M., and N. van der Sar. 2013. “Testing and Calibrating the Altman Z-Score for the UK.” (Bachelor thesis). Erasmus University Rotterdam, Rotterdam, Netherlands.
   Google Scholar
• Raei, R., and S. Fallahpour. 2004. “Corporate Financial Distress Prediction Using Artificial Neural Networks.” Journal of Financial Research 17 (1): 159–178.
   Google Scholar
• Rama, K. D. 2013. “An Empirical Evaluation of the Altman (1968) Failure Prediction Model on South African JSE Listed Companies.” (Doctoral dissertation). University of the Witwatersrand, Johannesburg, South Africa.
   Google Scholar
• Sabela, S. W., L. M. Brummer, J. H. Hall, and H. P. Wolmarans. 2018. “Using Fundamental, Market and Macroeconomic Variables to Predict Financial Distress: A Study of Companies Listed on the Johannesburg Stock Exchange.” Journal of Economic and Financial Sciences 11 (1): 1–11.
   Google Scholar
• Schumacher, K., H. Chenet, and U. Volz. 2020. “Sustainable Finance in Japan.” Journal of Sustainable Finance & Investment 10 (2): 213–246.
   Web of Science ®Google Scholar
• Sfakianakis, E. 2021. “ Bankruptcy Prediction Model for Listed Companies in Greece.” Investment Management and Financial Innovations 18 (2): 166–180.
   Google Scholar
• Shin, H. 2009. Integration of Unascertained Method and Neural Networks in Financial Early Warning. Paper Presented at the Second 2009 International Conference on Business Intelligence and Financial Engineering: Beijing, China.
   Google Scholar
• Stats, S. A. 2020. Quarterly Employment Statistics (QES). Statistical release, P0277. [Online]. Available from: http://www.statssa.gov.za [Accessed 18 September 2020].
   Google Scholar
• Sundararaj, S. 2017. “Predicting the Academic Performance of Potential Electrical Engineering Majors.” (Masters dissertation). Texas A and M University, Texas, United States of America.
   Google Scholar
• Tchantcheu, B. G. 2015. “Prediction of Financial Distress and Bankruptcy in Alternative Exchange (AltX) Listed Companies.” (Masters dissertation). Tshwane University of Technology, Pretoria, South Africa.
   Google Scholar
• Vaez, G. M., and C. S. Ramezanpour. 2018. “Predicting Bankruptcy of Companies Listed on the Stock Exchange Using the Artificial Neural Network.” Investment Knowledge 7 (26): 277–296.
   Google Scholar
• Van de Colff, F. 2017. “An Artificial Intelligence Model to Predict Financial Distress in Companies Listed on the JSE.” (Doctoral dissertation). University of Pretoria, Pretoria, South Africa.
   Google Scholar
• Wang, Q., Q. Yang, and M. Zhang. 2012. Analysing Financial Distress of Listed Companies using Neural Network. Paper Presented at the 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization: Chengdu, China.
   Google Scholar
• Williams, H. T. 2018. “An Empirical Investigation Of The Impact Of Exchange Rate Fluctuations On The Performance Of Selected Listed Firms In Nigeria.” Journal of Business Management and Economic Research 2 (3): 1–10.
   Google Scholar
• Winakor, A., and R. Smith. 1935. Changes in the Financial Structure of Unsuccessful Industrial Corporations. Bulletin no. 51. Bureau of Business Research, University of Illinois, Urbana, Ill.
   Google Scholar