Abbasi and Samavi, (2020). Performance Evaluation of the Technical Analysis Indicators in Comparison with the Buy and Hold Strategy in Tehran Stock Exchange Indices. Advances in Mathematical Finance and Applications, 5(3), 285-301.
Adebiyi, et al. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014, 1-7.
Al Galib, et al. (2014) Prediction of stock price based on hidden Markov model and nearest neighbour algorithm’, Int. J. Information and Decision Sciences, 6 (3), 262–292.
Alborzi et al. (2009). Application of artificial neural networks in predicting cash efficiency index and stock price. Empirical Studies in Financial Accounting, 6 (22), 119-137.
Amiri and Bigleri Kami (2015). Predicting stock method using the Markov chain model. Financial Engineering and Securities Management, 5 (20), 79-97.
Asgari, et al. (2018). Predicting the stock market index by the hidden Markov model and the K-mean clustering method. Financial Knowledge of Securities Analysis, 10 (36), 71-82.
Awartani and Corradi, V. (2005). Predicting the volatility of the S&P-500 stock index via GARCH models: The role of asymmetries. International Journal of Forecasting, 21, 167–183.
Balaban, E. (2004). Comparative forecasting performance of symmetric and asymmetric conditional volatility models of an exchange rate. Economics Letters, 83, 99–105
Bathaee, M., Nozari, H., & Szmelter-Jarosz, A. (2023). Designing a new location-allocation and routing model with simultaneous pick-up and delivery in a closed-loop supply chain network under uncertainty. Logistics, 7(1), 3.
Berkes and Horváth, L. (2004). The efficiency of the estimators of the parameters in GARCH processes. The Annals of Statistics, 32, 633–655.
Campbell and Thompson. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average? The Review of Financial Studies, 21, 1509– 1531
Dabo-Niang, et al. (2010). Combining nonparametric and optimal linear time series predictions. Journal of the American Statistical Association, 105, 1554–1565.
Dulu and Heydari (2018). Predicting stock index using a combination of artificial neural network and meta-innovative harmonic search models and genetic algorithms. Financial Economics Quarterly, 11(40), 1-23.
Ehsani, (2020). The structure of stock markets as signed networks, 13(1), 136-146.
Fadaei Nejad and Farahani (2018). The effects of macroeconomic variables on the total index of Tehran Stock Exchange. Financial Economics Quarterly, 11(39), 1-26.
Faghihinejad and Minaei. (2019). Predicting stock market behavior based on artificial neural networks with an intelligent collective learning approach. Industrial Management, 10 (2), 315-334.
Fallah, M., & Nozari, H. (2021). Neutrosophic mathematical programming for optimization of multi-objective sustainable biomass supply chain network design. Computer Modeling in Engineering & Sciences, 129(2), 927-951.
Fallahpour and Alipour Rikandeh. (2015). Predicting stock index using wavelet neural networks in Tehran Stock Exchange. Financial Management Strategy, 2 (4), 15-31.
Farmanara. (2011). Predicting the effect of macro variables on stock price index using GMDH neural network Financial Economics Quarterly, 3(7), 45-62.
Hejazi and Fatemi. (2006). Predict stock efficiency using financial ratios. Experimental Studies in Financial Accounting, 3 (12), 83-111.
Hung-ChunLiuaJui-ChengHung (2010), Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models, Expert Systems with Applications, 37(7), 4928-4934.
JAROSLAV,(2011)” Computer M0deling using Hidden Markov Model approach applied to the financial markets , Doctoral dissertation, Oklahoma State University, United states of America.
Kelly, Logan (2007). Measuring the Economic Stock of Money, MPRA Paper ,5528(7). Bryant University .
Khajehzadeh, et al .(2020), Predicting the Optimal Stock Portfolio Approach of Meta-Heuristic Algorithm and Markov Decision Process, Journal of Decisions & Operations Research, 5 (4), 426-445
Li, X., Yang, L., Xue, F., & Zhou, H. (2017). Time series prediction of stock price using deep belief networks with intrinsic plasticity. Paper presented at the Control and Decision Conference (CCDC), 29.
Lotfi, F. H. Z., Najafi, S. E., & Nozari, H. (Eds.). (2016). Data envelopment analysis and effective performance assessment. IGI Global.
Md. Rafiul Hassan and Baikunth Nath,(2005) stock market forecasting using hidden markov model: a new approach, Proceedings of the 2005 5th International Conference on Intelligent Systems Design and Applications (ISDA’05)
Ming-Chi Lee,(2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction, Department of Computer Science and Information Engineering, National Pingtung Institute of Commerce, 51 Minsheng E. Rd., Pingtung 900, Taiwan, ROC.
Nozari, H., & Ghahremani-Nahr, J. (2023). A Comprehensive Strategic-Tactical Multi-Objective Sustainable Supply Chain Model with Human Resources Considerations. Supply Chain Analytics, 4, 100044.
Nozari, H., Najafi, S. E., Jafari-Eskandari, M., & Aliahmadi, A. (2016). Providing a model for virtual project management with an emphasis on IT projects. In Project Management: Concepts, Methodologies, Tools, and Applications (pp. 476-496). IGI Global.
Oliver C. Ibe,(2009) "Markov Processes for Stochastic Modeling", Elsevier Academic Press Inc.
PrateekSharmaa1Vipul (2016), Forecasting stock market volatility using Realized GARCH model: International evidence, The Quarterly Review of Economics and Finance ,59, 222-230.
Rabiner R L, (1989), A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, Vol. 77(2), 257-286.
Rijeka ,Przemyslaw Dymarski,(2011),"Hidden Markov Models, Theory and Applications" InTech, ,under CC BY-NC-SA 3.0 license .
Safari, Mozhdeh Peykari (2022) Improving the multilayer Perceptron neural network using teaching-learning optimization algorithm in detecting credit card fraud, Journal of Industrial and Systems Engineering
Saghafi, Ali, Shari, Saber. (2005). The role of basic accounting information in predicting stock efficiency. Empirical Studies in Financial Accounting, 2 (8), 87-120.
Tavakoli, Mahsa, Doosti, hassan, (2020). Forecasting the Tehran Stock market by Machine Learning Methods using a New Loss Function. Advances in Mathematical Finance and Applications, 6 (2), 1-24.
Tkáč, M., & Verner, R. (2016). Artificial neural networks in business: Two decades of research. Applied Soft Computing, 38(1), 788-804.
Tofallis (2015). "A Better Measure of Relative Prediction Accuracy for Model Selection and Model Estimation", Journal of the Operational Research Society, 66(8),1352-1362.
Tsang, et al. (2007). Design and implementation of NN5 for Hong Kong stock price forecasting, Engineering Applications of Artificial Intelligence 453–461.
Vahidpour, et al. (2023). Optimizing Dynamic Portfolio Management in the Cryptocurrency Market Using Multi-Agent Deep Reinforcement Learning and the Fear and Greed Index, Journal of Industrial and Systems Engineering,15(2). 94-111.
Yakup Kara, et al., (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications 38.5311–5319.
Yan, et al. (2017). Bayesian regularisation neural network based on artificial intelligence optimization, International Journal of Production Research, 55(8), 2266-2287.
Yim, (2002). A comparison of neural networks with time series models for forecasting returns on a stock market index. paper. School of Economics and Finance.
Zhang, Y. (2004)," Prediction of Financial Time Series with Hidden Markov Models", Doctoral dissertation, Simon Fraser University, British Columbia, Canada.
Zhang, Z.Y., et al. (2005). Stock time series forecasting using support vector machines employing analyst recommendations, Springer-Verlag Berlin Heidelberg.
