Bankruptcy in Indian Private Sector Banks: A Neural Network Analysis
DOI:
https://doi.org/10.12725/ujbm.52.6Keywords:
bankruptcy, neural network, predictionAbstract
This paper aims to predict the bankruptcy in Indian private banks using financial ratios such as ROA, GNPA, EPS, PAT, and GNP of the country. This paper also explains the importance of Ohlson’s number, Graham’s number and Zmijewski number as the major predictors of bankruptcy while developing a model using neural networks. For the prediction, the financial data for private sector banks of India such as HDFC, HDFC, ICICI, AXIS, YES bank, KOTAK MAHINDRA Bank, FEDERAL BANK, INDUSIND Bank, RBL and KARUR VYSYA for the last 10 years from 2010-2019 have been analysed. The model developed during the research will help the financial institutions and banks in India to understand the economic condition of the banking industry.
References
Addo, P. M., Guegan, D., & Hassani, B. (2018). Credit risk analysis using machine and deep learning models. Risks, 6(2), 38.
Al-hroot, Y. A. K. (2016). Bankruptcy prediction using multilayer perceptron neural networks in Jordan. European Scientific Journal, 12(4).
Al-shayea, Q. K., El-refae, G. A., & El-itter, S. F. (2010). Neural networks in bank insolvency prediction. IJCSNS International Journal of Computer Science and Network Security, 10(5), 240–245.
Bansal, R., & Mohanty, A. (2013). A Study on financial performance of commercial banks in India: Application of Camel model. Al-Barkaat Journal of Finance & Management, 5(2), 60-79.
Begley, J., Ming, J., & Watts, S. (1996). Bankruptcy classification errors in the 1980s: An empirical analysis of Altman's and Ohlson's models. Review of accounting Studies, 1(4), 267-284.
Bellovary, J. L., & Giacomino, D. E. (2007). A review of bankruptcy prediction studies: 1930-Present. Accounting Faculty Research and Publications, 33, 1–42.
Bhatia, M., & Mulenga, M. J. (2019). Value relevance of accounting information: Comparative study of Indian public and private sector banks. International Journal of Indian Culture and Business Management, 18(1).
Bredart, X. (2014). Bankruptcy prediction model using neural networks. Accounting and Finance Research, 3(2).
Burke, A. (2007). Neutralizing cognitive bias: An invitation to prosecutors. NYU Journal of Law & Liberty, 2, 512–530.
Chintala, B. (2017). A comparative study on financial performance of selected public and private sector banks in India. Journal of Commerce and Trade, 11, 89–96.
Coelho, L., & John, K. (2011). Gambling on the market: who buys the stock of bankrupt firms? In BFWG April 2011 conference Cass Business School, 1–53.
Cole, S. A., Sampson, T. A., & Zia, B. H. (2009). Financial literacy, financial decisions, and the demand for financial services: evidence from India and Indonesia (pp. 09-117). Cambridge, MA: Harvard Business School.
Constand, R., Yazidpour, R. (2010). Predicting firm failure: A behavioral finance perspective. The Journal of Entrepreneurial Finance, 14(3), 90–104.
Du Jardin, P. (2010). Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy. Neurocomputing, 73(10–12), 2047–2060.
Edward, A. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4).
Feng, M., Shaonan, T., & Ling, M. (2019). Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research, 274(2), 743–758.
Gautam, J., Joshi, N., Singh, S., & Kumar, D. (2014). Analyzing performance of banks & predicting bank failure. SSRN, 1–15.
Ghosh, B., & Srinivasan, P. (2014). BSE 100 market capitalization follows sentiment of investors or technical methods- An analytical study. Scholarly Research Journal for Humanity Science and English Language, 1(3), 400–404.
Ghosh, B., & Srinivasan, P. (2015). Detection of sentiment in CNX Nifty–An investigative attempt using probabilistic neural network. International Journal of Business Quantitative Economics and Applied Management Research, 1(12), 1–11.
Ghosh, B., Krishna, M. C., & Ramachandran, T. (2016). PSU bank modeling- A comparative modeling approach involving Artificial Neural Network and Panel Data Regression. Asian Journal of Research in Business Economics and Management, 6(6), 27–36.
Ghosh, B. (2017). Bankruptcy modelling of indian public sector banks: Evidence from neural trace. International Journal of Applied Behavioral Economics, 6(2), 15.
Ghosh, B., Roux, C. Le, & Ianole, R. (2017). Fear estimation-evidence from BRICS and UK. International Journal of Applied Business and Economic Research, 15(4), 15.
Hamid, S. A. (2004). Primer on using neural networks for forecasting market variables. The Center for Financial Studies.
Janger, E. J., Block-lieb, S., Block-lieb, S., & Janger, E. J. (2006). The Myth of the Rational Borrower: Behaviorism, Rationality and the Misguided Reform of Bankruptcy Law Bankruptcy Law. Texas Law Review, 84, 1481–1565.
Jardin, P. Du. (2008). Bankruptcy prediction and neural networks: the contribution of variable selection methods. Research Gate.
John, Y., Hilscher, J. D., & Szilagyi, J. (2010). Predicting Financial Distress and the Performance of Distressed Stocks Predicting Financial Distress and the Performance of Distressed Stocks. Digital Access to Scholarship at Harvard.
Jouzbarkand, M., Aghajani, V., & Khodadabi, M. (2013). Creation Bankruptcy Prediction Model with Using Ohlson and Shirata Models. International Proceedings of Economics Development and Research, 54(1), 1–5.
Karim, R. Al. (2013). An Evaluation of Financial Performance of Private Commercial Banks in An Evaluation of Financial Performance of Private Commercial Banks in Bangladesh: Ratio Analysis. Journal of Business Studies Quarterly, 5(2), 65–77.
Kasgari, A. A., & Ebadi, F. (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.
Kumar, M., Goel, V., Jain, T., & Singhal, S. (2018). Neural network approach to loan default prediction. International Research Journal of Engineering and Technology (IRJET), 5(4), 4–7.
Li, Y., & Ma, W. (2011). Applications of Artificial Neural Networks in Financial Economics: A Survey. In 2010 International Symposium on Computational Intelligence and Design.
López-de-Foronda, O., & Pastor-Sanz, I. (2000). Predicting bankruptcy using neural networks in the current financial crisis: A study of U.S. commercial banks. SSRN Electronic Journal.
M, B. V., & Chakraborty, S. (2017). Efficiency of private sector banks performance comparison between old and new generation private sector banks. RUAS-JMC, 03(02), 6–10.
Malyadri, P., & Sirisha, S. (2015). An analytical study on trends and progress of Indian banking industry business & financial affairs. Journal of Business & Financial Affairs, 4(1), 1–7.
Mansouri, A., Nazari, A., & Ramazani, M. (2016). A comparison of artificial neural network model and logistics regression inprediction of companies’ bankruptcy- A case study of Tehran stock exchange. International Journal of Advanced Computer Research, 6(24), 81–92.
Motamedi, P. (2013). Investigating different factors influencing on return of private banks. Management Science Letters, 3, 2467–2472.
Murari, K. (2012). Insolvency risk and Z-Index for Indian banks: A probabilistic interpretation of bankruptcy. IUP Journal of Bank Management, 11(3), 7–21.
Nadine, D. M. M. (2001). Bankruptcy prediction: Literature survey of the last ten years.
Naidu, PG. and Govinda, K. (2018). Bankruptcy prediction using neural networks. In Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018 (pp. 248–251).
Ohlson, A, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1).
Pandey, A. (2016). The Indian insolvency and bankruptcy bill: Sixty years in the making. In Munich Personal RePEc Archive, 8, 26–34.
Piramuthu, S., Ragavan, H., & Shaw J., M. (1998). Using feature construction to improve the performance of neural networks. Management Science, 44(3), 285–432.
Raj, U. (2019). Prediction of bankruptcy risk in Indian banks: An application of Altman’ s model. International Journal of Research (IJR), 1–23.
Samek, W., & Montavon, G. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1–18.
Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), 16.
Singh, S., & Makkar, A. (2015). Assessing the probability of financial distress. Journal of Business Thought, 5, 126–137.
Sun, L. (2007). A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction. Review of Quantative Finance and Accounting, 28(1), 55–78.
Tam, K. Y. (1991). Neural network models and the prediction of bank bankruptcy. Omega, 19(05), 429–445.
Vardar, G. (2013). Efficiency and stock performance of banks in transition countries: Is there a relationship? International Journal of Economics and Financial Issues, 3(2), 355–369.
Veganzones, D., Severin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111–124.
Wilson, R. l., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(05), 545–557.
Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, 6(1), 34-45.
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