Predicting Financial Distress Within Indian Enterprises: A Comparative Study on the Neuro-Fuzzy Models and the Traditional Models of Bankruptcy Prediction
Keywords:
artificial intelligence, convolution neural network, Artificial neural networks, multi-layer perceptron, bankruptcy, Fuzzy C-mean algorithmAbstract
The financial distresses is of major importance in the financial management system particularly in the case of this competitive environs. There are several traditional methods existing for predicting the financial distress within the country. Major factors influencing the financial distress is the stock market, credit risk and so on. Hence there is a need of models which could make dynamic predictions with the use of dynamic variables. There are several machine learning and artificial intelligence-based bankruptcy prediction models available. The neural network concepts and the computational intelligence-based methods are highly acceptable in the prediction arena. This research presents a comprehensive review of the existing prediction approaches and suggests future research directions and ideas. Some of the existing methods are support vector machines, artificial neural network, multi-layer perceptron, and the linear models such as principal component analysis. Neuro-fuzzy approaches, Deep belief neural networks, Convolution neural networks are also discussed.
References
. H. A. Alaka, L. O. Oyedele, H. A. Owolabi, V. Kumar, S. O. Ajayi, O. O. Akinade, and M. Bilal, “Systematic review of bankruptcy prediction models: Towards a framework for Tool Selection,”Expert Systems with Applications, vol. 94, pp. 164–184, 2018.
. F. Barboza, H. Kimura, and E. Altman, “Machine learning models and bankruptcy prediction,” ExpertSystems with Applications, vol. 83, pp. 405–417, 2017.
. N. Chen, B. Ribeiro, and A. Chen, “Financial Credit Risk Assessment: A Recent Review,” ArtificialIntelligence Review, vol. 45, no. 1, pp. 1–23, 2016.
. O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, “Financial time series forecasting with Deep Learning : A Systematic Literature Review: 2005–2019,” Applied Soft Computing, vol. 90, 2020.
. W. Bao, J. Yue, and Y. Rao, “A deep learning framework for financial time series using stacked autoencoders and long-short term memory,” PLOS ONE, vol. 12, no. 7, 2017.
. V. K. Ojha, A. Abraham, and V. Snášel, “Metaheuristic design of Feedforward Neural Networks: A review of two decades of research,” Engineering Applications of Artificial Intelligence, vol. 60, pp. 97–116, 2017.
. Y. Deng, F. Bao, Y. Kong, Z. Ren, and Q. Dai, “Deep Direct Reinforcement Learning for financial signal representation and trading,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 3, pp. 653–664, 2017.
. J. Zhang, “Investment risk model based on intelligent fuzzy neural network and Var,” Journal of Computational and Applied Mathematics, vol. 371, 2020.
. M. Dixon, D. Klabjan, and J. H. Bang, “Implementing Deep Neural Networks for financial market prediction on the Intel Xeon Phi,” Proceedings of the 8th Workshop on High Performance Computational Finance, 2015.
. D. C. M. Dickson and S. Li, “The distributions of the time to reach a given level and the duration of negative surplus in the Erlang(2) risk model,” Insurance: Mathematics and Economics, vol. 52, no. 3, pp. 490–497, 2013.
. W. Bao, J. Yue, and Y. Rao, “A deep learning framework for financial time series using stacked autoencoders and long-short term memory,” PLOS ONE, vol. 12, no. 7, 2017.
. D.M.Q. Helson, A.C.M. Pereira, R.A. Oliveira, Stock market’s price movement prediction with LSTM neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2017, pp. 1419–1426.
. Y. Xia, C. Liu, and N. Liu, “Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending,” Electronic Commerce Research and Applications, vol. 24, pp. 30–49, 2017.
. Y. Jin and Y. Zhu, “A data-driven approach to predict default risk of loan for online peer-to-peer (P2P) lending,” 2015 Fifth International Conference on Communication Systems and Network Technologies, 2015.
. M. Gong, “Research and application of credit rating model in small and micro enterprises based on Fuzzy Neural Network,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 20, no. 4, pp. 817–834, 2017.
. H. V. Long, H. B. Jebreen, I. Dassios, and D. Baleanu, “On the statistical garch model for managing the risk by employing a fat-tailed distribution in finance,” Symmetry, vol. 12, no. 10, p. 1698, 2020.
. Sartori, F., Mazzucchelli, A. and Gregorio, A.D. (2016) “Bankruptcy forecasting using case-based reasoning: The CREPERIE approach,” Expert Systems with Applications, 64, pp. 400–411. Available at: https://doi.org/10.1016/j.eswa.2016.07.033.
. Uthayakumar, J., Vengattaraman, T. and Dhavachelvan, P. (2020) “Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: An application of bankruptcy prediction and credit risk analysis,” Journal of King Saud University - Computer and Information Sciences, 32(6), pp. 647–657. Available at: https://doi.org/10.1016/j.jksuci.2017.10.007.
. Wang, G., Ma, J. and Yang, S. (2014) “An improved boosting based on feature selection for corporate bankruptcy prediction,” Expert Systems with Applications, 41(5), pp. 2353–2361. Available at: https://doi.org/10.1016/j.eswa.2013.09.033.
. Qiu, W., Rudkin, S. and D?otko, P. (2020) “Refining understanding of corporate failure through a topological data analysis mapping of Altman’s Z-Score Model,” Expert Systems with Applications, 156, p. 113475. Available at: https://doi.org/10.1016/j.eswa.2020.113475.
. Altman, E.I. et al. (2016) “Financial distress prediction in an international context: A review and empirical analysis of Altman's z-score model,” Journal of International Financial Management & Accounting, 28(2), pp. 131–171. Available at: https://doi.org/10.1111/jifm.12053.
. Son, H. et al. (2019) “Data Analytic Approach for bankruptcy prediction,” Expert Systems with Applications, 138, p. 112816. Available at: https://doi.org/10.1016/j.eswa.2019.07.033.
. Zi?ba, M., Tomczak, S.K. and Tomczak, J.M. (2016) “Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction,” Expert Systems with Applications, 58, pp. 93–101. Available at: https://doi.org/10.1016/j.eswa.2016.04.001.
. Karamzadeh, M.S. (2013) “Application and comparison of Altman and Ohlson models to predict bankruptcy of companies,” Research Journal of Applied Sciences, Engineering and Technology `, 11(9), pp. 2007–2011. Available at: https://doi.org/10.19026/rjaset.5.4743.
. Imani Khoshkhoo, O., Seyed Nezhad Fahim, S.R. and Mokhtari, M. (2013) “The impact of net value added on predicting the earnings and operating cash flow: An empirical study based on Tehran Stock Exchange,” Management Science Letters, pp. 2923–2932. Available at: https://doi.org/10.5267/j.msl.2013.11.005.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 International Journal of Computer (IJC)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
