DOI: https://doi.org/10.18371/fcaptp.v1i24.128242

PREDICTION OF FINANCIAL DISTRESS, USING METAHEURISTIC MODELS

Ahmadreza Ahmadreza Ghasemi, Mohsen Seyghalib, Maryam Moradi

Abstract


Investors need to assess and analyze the financial statement, to make the logical decision. Using financial ratios is one of the most common methods. The main purpose of this research is to predict the financial crisis, using ratios of liquidity. Four models, Support vector machine, neural network back propagation, Decision trees and Adaptive Neuro–Fuzzy Inference System has been compared.Furthermore, the ratios of liquidity considered in a period of 89_93. The research method is qualitative and quantitative and type of casual comparative. The result indicates that the accuracy of the neural network, Decision tree, and Adaptive Neuro–Fuzzy Inference System showed that there is a significant differently 0/000 and 0/005 years this is more than support vector machine result. Therefore the result of support vector machine showed that there is a significant differently 0/001 in years. This has been shown that neural network in 2 years before the bankruptcy has the ability to predict a right thing. Therefore, the results have been shown that all four models were statistically significant. Consequently, there are no significant differences. All models have the precision to predict the financial crisis.

Keywords


financial crisis; neural network; Decision tree; Adaptive Neuro–Fuzzy Inference System; support vector machine.

Full Text:

PDF

References


Salehi, M., & Pour, M. D. (2016). Bankruptcy prediction of listed companies in Tehran Stock Exchange. International Journal of Law and Management, 58(5), 545–561.

Han, T. T., & Zhao, Q. C. (2015, August). Financial crisis predictions based on biorthogonal wavelet hybrid kernel support vector machine. 11th International Conference on Natural Computation (ICNC), 719–724.

Mathur, N., Glesk, I., & Buis, A. (2016). Comparison of adaptive neuro–fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. Medical engineering & physics, 38(10), 1083–1089.‏

Gepp, A., & Kumar, K. (2015). Predicting financial distress: A comparison of survival analysis and decision tree techniques. Procedia Computer Science, 54, 396–404.‏

Altman, E. I. (1984). The success of business failure prediction models: An international survey. Journal of Banking and Finance, 8(2), 171–198.

Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 18(1), 109–131.‏

Edmister, R. O. (1972). An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction. The Journal of Financial and Quantitative Analysis, 7(2), 1477–1493.

Williams, C. K. I., & Rasmussen, C. E. (1996). Gaussian processes for regression. Advances in Neural information processing systems 8 : Proceedings of the 1995 Conference, 514–520.

Giovanis, E. (2012). Study of Discrete Choice Models and Adaptive Neuro–Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA. Economic Analysis and Policy, 42(1), 79–95.

Jardin, P., & Séverin, E. (2011). Predicting Corporate Bankruptcy Using a Self–Organizing Map: An Empirical Study to Improve the Forecasting Horizon of a Financial Failure Model. Decision Support Systems, 51(3), 701–711.

Appiah, K. O. (2011). Corporate Failure Prediction: Some Empirical Evidence from Listed Firms in Ghana. China–USA Business Review, 10(1), 32–41.

Mateos–Ronco, A., & Mas, A. L. (2011). Developing a business failure prediction model for cooperatives: Results of an empirical study in Spain. African Journal of Business Management, 5(26), 10565–10576.

Orabi, M. M. A. (2014). Empirical Tests on Financial Failure Prediction Models. Interdisciplinary journal of contemporary research in business, 5(9), 29–43.

Wang, L., & Wu, C. (2017). Business failure prediction based on two–stage selective ensemble with manifold learning algorithm and kernel–based fuzzy self–organizing map. Knowledge–Based Systems, 121, 99–110.‏

Sun, J. (2017). Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble. Knowledge–Based Systems, 120, 4–14.‏

Li, Z., Crook, J., & Andreeva, G. (2017). Dynamic prediction of financial distress using Malmquist DEA. Expert Systems with Applications, 80, 94–106.‏

Soleymani Amiri, Gh. (2010). Investigating of the efficiency of financial distress prediction models in Iranian Companies, Journal of Accounting Knowledge, 1(2), 139–158.

Kusagur, A., Kokad, S. F., & Ram, B. V. S. (2010). Modeling, design &simulation of an adaptive neuro fuzzy inference system (ANFIS) for speed control of induction motor. International Journal of Computer Applications, 12.

Campbell, C., & Ying, Y. (2011). Learning with support vector machines. Morgan & Claypool Publishers.


GOST Style Citations


Salehi, M. Bankruptcy prediction of listed companies in Tehran Stock Exchange [Text] / Mahdi Salehi, Mojdeh Davoudi Pour // International Journal of Law and Management. – 2016. – Vol. 58, Issue 5. – P. 545–561.

Han, T. T. Financial crisis predictions based on biorthogonal wavelet hybrid kernel support vector machine [Text] / Ting–ting Han, Qiu–chen Zhao // 11th International Conference on Natural Computation (ICNC), 15–17 Aug. 2015. – P. 719–724.

Mathur, N. Comparison of adaptive neuro–fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses [Text] / Neha Mathur, Ivan Glesk, Arjan Buis // Medical engineering & physics. – 2016. – Vol. 38, Issue 10. – P. 1083–1089.‏

Gepp, A. Predicting financial distress: A comparison of survival analysis and decision tree techniques [Text] / Adrian Gepp, Kuldeep Kumar // Procedia Computer Science. – 2015. – Vol. 54. – P. 396–404.‏

Altman, E. I. The success of business failure prediction models: An international survey [Text] / Edward I. Altman // Journal of Banking and Finance. – 1984. – Vol. 8, Issue 2. – P. 171–198.

Ohlson, J. A. Financial ratios and the probabilistic prediction of bankruptcy [Text] / James A. Ohlson // Journal of Accounting Research. – 1980. – Vol. 18, № 1. – P. 109–131.‏

Edmister, R. O. An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction [Text] / R. O. Edmister // The Journal of Financial and Quantitative Analysis. – 1972. – Vol. 7, № 2. – P. 1477–1493. – Supplement: Outlook for the Securities Industry.

 Williams, C. K. I. Gaussian processes for regression [Text] / Christopher K. I. Williams, Carl Edward Rasmussen // Advances in Neural information processing systems 8 : Proceedings of the 1995 Conference / Eds
David S. Touretzky, Michael C. Mozer, Michael E. Hasselmo. – MIT press, 1996. – P. 514–520.

Giovanis, E.  Study of Discrete Choice Models and Adaptive Neuro–Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA [Text] / Eleftherios Giovanis // Economic Analysis and Policy. – 2012. – Vol. 42, Issue 1. – P. 79–95.

Jardin, P. Predicting Corporate Bankruptcy Using a Self–Organizing Map: An Empirical Study to Improve the Forecasting Horizon of a Financial Failure Model [Text] / Philippe du Jardin, Eric Séverin // Decision Support Systems. – 2011. – Vol. 51, Issue 3. – P. 701–711.

Appiah, K. O. Corporate Failure Prediction: Some Empirical Evidence from Listed Firms in Ghana [Text] / Kingsley Opoku Appiah // China–USA Business Review. – 2011. – Vol. 10, № 1. – P. 32–41.

Mateos–Ronco, A. Developing a business failure prediction model for cooperatives: Results of an empirical study in Spain [Text] / Alicia Mateos–Ronco, Ángela López Mas // African Journal of Business Management. – 2011. – Vol. 5, № 26. – P. 10565–10576.

Orabi, M. M. A. Empirical Tests on Financial Failure Prediction Models [Text] / Marwan Mohammad Abu Orabi // Interdisciplinary journal of contemporary research in business. – 2014. – Vol. 5, № 9. – P. 29–43.

Wang, L. Business failure prediction based on two–stage selective ensemble with manifold learning algorithm and kernel–based fuzzy self–organizing map [Text] / Wang Lu, Wu Chong // Knowledge–Based Systems. – 2017. – Vol. 121. – P. 99–110.‏

Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble [Text] / J. Sun et al. // Knowledge–Based Systems. – 2017. – Vol. 120. – P. 4–14.‏

Li, Z. Dynamic prediction of financial distress using Malmquist DEA [Text] / Li Zhiyong, Jonathan Crook, Galina Andreeva // Expert Systems with Applications. – 2017. – Vol. 80. – P. 94–106.‏

Soleymani Amiri, Gh. Investigating of the efficiency of financial distress prediction models in Iranian Companies [Text] / Gholamreza Soleimany Amiri // Journal of Accounting Knowledge. – 2010. – Vol. 1, № 2. – P. 139–158.

Kusagur, A. Modeling, design &simulation of an adaptive neuro fuzzy inference system (ANFIS) for speed control of induction motor [Text] / Ashok Kusagur, S. F. Kodad, B V. Sankar Ram // International Journal of Computer Applications. – 2010. – № 12.

Campbell, C. Learning with support vector machines [Text] / Colin Campbell, Yiming Ying. – Morgan & Claypool Publishers, 2011. – 100 p. – (Synthesis lectures on artificial intelligence and machine learning).





Copyright (c) 2018 Ahmadreza Ahmadreza Ghasemi, Mohsen Seyghalib, Maryam Moradi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN (print) 2306-4994, ISSN (on-line) 2310-8770