Rezaei, N., and Elmi, Z., Behavioral finance models and behavioral biases in stock price forecasting,
Advances in mathematical finance and applications, 2018, 3 (4), P.67-82. DOI: 10.22034/AMFA.2019.576127.1118
 Adnan, A. M., and Humayon, D., Predicting Corporate Bankruptcy, Department of Economics, Loughborough University, UK, 2002. DOI: 10.1108/14720700610649436
 Saghafi, A., and Amiri, G. A., Investigating the Indicators of Bankruptcy Predictors in Iran's Environment, Ph.D. thesis, Faculty of Management Tehran University, Tehran, Iran, 2003, (in Persian).
 Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic multiplicative DEA model for measuring the efficiency and ranking of DMUs under VRS technology, Indian Journal of Science and Technology,2014, 7 (11), P. 1765–1773.
 Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic Modified MAJ Model for Measuring the Efficiency and Ranking of DMUs, Indian Journal of Science and Technology,2015, 8 (8), P. 549–555.
 Mohseni, R., and Rahimian, S. Y., Investigating the Factors Affecting Bankruptcy Using Efficiency as a Predictive Variable Based on Data Logic Panel Approach
, Journal of Quantities Economics, 2018, 15 (2), P.111-130, (in Persian). DOI: 10.22055/JQE.2018.23087.1711
 Pereira, J., Survival analysis employed in predicting corporate failure: A forecasting model proposal. IBR, 2014, 7 (5), P.9-22. DOI:10.5539/ibr.v7n5p9
 Miryekemami, S. A., Sadeh, E., and Amini, Z, S., Using Genetic algorithm in solving stochastic programming for multi-objective portfolio selection in Tehran stock exchange
, Advances in mathematical finance and applications, 2017, 2 (4), P.107-120. DOI: 10.22034/AMFA.2017.536271
 Azar, A., Hamidian, M., Saberi, M., and Norozi, M., Evaluating the performance of forecasting models for portfolio allocation purposes with generalized GRACH method
, Advances in mathematical finance and applications,2017, 2 (1), P.1-7. DOI: 10.22034/AMFA.2017.529057
 Richards, C. A., The effect of hospital financial distress on immediate breast reconstruction. Ph.D. thesis, Columbia University, New York, USA. DOI:10.7916/D8C53HZ2
 Rosenberg H. J., and Ferlie, E., Applying strategic management theories in public sector organizations: Developing a typology, Public Management Review, 2014, 18 (1), P.1-19. DOI:10.1080/14719037.2014.957339
 Geng, R., Bose, I., and Chen, X., Prediction of financial distress: An empirical study of listed Chinese companies using data mining, European Journal of Operational Research, 2014, 241 (1), P.236-247. DOI:10.1016/j.ejor.2014.08.016
 Nazemi, A. M., and Zare, M. V., Bankruptcy prediction of companies according to industry characteristics, Quarterly journal of accounting research, 2017, 7 (2), P.122-139, (in Persian).
 Ghazanfari, M., Eghbal, R., and Asgari, A., Bankruptcy prediction of companies based on hybrid smart systems, Financial Accounting and Auditing Research, 2018, 10 (27), P.159-193, (in Persian).
 Saeidi, A., and Aghaei, A., The prediction of financial distress among companies accepted in the Tehran Stock Exchange using bayes networks, Accounting and auditing reviews, 2009, 16 (56), P.59-78, (in Persian).
 Saruei, S., The study of performance of Springerit, Zimsky and Ahlson models in predicting bankruptcy of companies accepted in Tehran Stock Exchange, M. A. thesis, Arak Islamic Azad University, Arak, Iran, (2010), (in Persian).
 Asgari, H., Study of the Efficiency of Springerit, Zowin and Fuler Patterns in Bankruptcy Forecast of Companies Accepted in Tehran Stock Exchange, M.A. thesis, Islamic Azad University, Tehran Central Branch, Tehran, Iran, 2010, (in Persian).
 Ghadiri, A., Golampourfard, M., and Nasirzadeh, F., Investigating the ability of Altman and Ohlson bankruptcy prediction models following the bankruptcy of the companies listed in the stock exchange, Journal of Knowledge and Development, 2009, 16 (28), P.193-220, (in Persian).
 Raei, R., and Fallahpour, S., The prediction of financial distress for companies using artificial neural networks, Financial research, 2004, 17, (in Persian).
 Shariatpanahi, M., and Araghi, S. M., Providing a model for predicting financial crisis in Iranian companies. Empirical studies on financial accounting, 2006, 4(16), P.19-41, (in Persian).
 Farajzadeh, H. D., Application of Genetic Algorithms in Bankruptcy Forecast Modeling, M.A. thesis, Tarbiat Modarres University, Tehran, Iran, 2007, (in Persian).
 Ahadidolatsara, R., The Effectiveness of Financial Crisis Forecast Models Using Financial Ratios in Iran's Economic Environment. M.A. thesis, Alzahra University, Tehran, Iran, 2008, (in Persian).
 Poormehr, S., Investigating the feasibility of using Springerit and Zemjiecki patterns in predicting bankruptcy of companies admitted to Tehran Stock Exchange, M. A. thesis, Islamic Azad University, Tehran Central Branch, Tehran, Iran, 2007, (in Persian).
 Hosaka, T., Bankruptcy prediction using imaged financial ratios and convolution neural networks
. Expert systems with applications, 2019, 117, P.287-299. DOI:10.1016/j.eswa.2018.09.039
 Feng, M., Shaonan, T.. Chihoon, L., and Ling, M., Deep learning models for bankruptcy prediction using textual disclosures,
European journal of operational research, 2019, 274 (2), P.743-758. DOI:10.1016/j.ejor.2018.10.024
 Zeiba, M., Tomczak, S. K., and Tomczak, M., Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction
. Expert systems with applications, 2016, 58, P.93-101. DOI:10.1016/j.eswa.2016.04.001
 Cho, S., Kim, W., and Ba, J., An integrative model with subject weight based on neural network learning for bankruptcy prediction, Expert Systems with applications, 2009, 36, P.403-410.
 Hyun-Jung, K., Nam-Ok, J., and Kyung-shik, S., Optimization of cluster-based evolutionary under sampling for the artificial neural networks in corporate bankruptcy prediction,
Expert systems with applications, 2016, 59, P.226-234. DOI:10.1016/j.eswa.2016.04.027
 Chandra, K., Ravi, V., and Bose, I., Failure prediction of dotcom companies using hybrid intelligent techniques,
Expert Systems with applications, 2006, 36 (3), P.4830-4837. DOI:10.1016/j.eswa.2008.05.047
 Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis
, Socio-economic planning science, 42 (3) P.206-220. DOI:10.1016/j.seps.2006.11.002
 Chuang, L. C., Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction,
Information Science, 2013, 236, P.174-185. DOI:10.1016/j.ins.2013.02.015
 Cochran, J., and Darrat, Kh. E., Bankruptcy of internet Companies: An Empirical Inquiry, Journal of Business Research, 2006, 59, P.1193-1200.
 Tsakonas A., Dounias G., Doumpos M., and Zopounidis, C., Bankruptcy Prediction with Neural Logic Networks by Means of Grammar-Guided Genetic Programming, Expert Systems with Applications, 2006, 30, P.449-461.
 Wallace Wanda A. Risk Assessment by Internal Auditors Using Past Research on Bankruptcy Applying Bankruptcy Models, 2004.
 Mazumder, B., and Miller, S., The effects of the Massachusetts health reform on financial distress, Working Paper, Federal Reserve Bank of Chicago. DOI:10.2139/ssrn.2390186