Alirezaie, A., Hajmohammad, M. H., Ahangar, M. R. H., & Esfe, M. H. (2018). Price-performance evaluation of thermal conductivity enhancement of nanofluids with different particle sizes. Applied Thermal Engineering, 128, 373-380.
Al-Waeli, A. H., Sopian, K., Kazem, H. A., Yousif, J. H., Chaichan, M. T., Ibrahim, A., ... & Ruslan, M. H. (2018). Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network. Solar Energy, 162, 378-396.
Chen, M. Y., & Chen, B. T. (2015). A hybrid fuzzy time series model based on granular computing for stock price forecasting. Information Sciences, 294, 227-241.
Dash, R. (2018). Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate prediction. Applied Soft Computing, 67, 215-231.
Di Persio, L., & Honchar, O. (2016). Artificial neural networks architectures for stock price prediction: Comparisons and applications. International journal of circuits, systems and signal processing, 10(2016), 403-413.
Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517.
Esfe, M. H., Rostamian, H., Esfandeh, S., & Afrand, M. (2018). Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data. Physica A: Statistical Mechanics and its Applications, 510, 625-634.
Ghorbani, N., Babaei, E., & Sadikoglu, F. (2017). Exchange market algorithm for multi-objective economic emission dispatch and reliability. Procedia computer science, 120, 633-640.
Granger, C. W. (1992). Forecasting stock market prices: Lessons for forecasters. International Journal of Forecasting, 8(1), 3-13.
Hamid, S. A., & Habib, A. (2014). Financial forecasting with neural networks. Academy of Accounting and Financial Studies Journal, 18(4), 37.
Li, X., Wang, S. S., & Wang, X. (2017). Trust and stock price crash risk: Evidence from China. Journal of Banking & Finance, 76, 74-91.
Li, X., Xie, H., Wang, R., Cai, Y., Cao, J., Wang, F., ... & Deng, X. (2016). Empirical analysis: stock market prediction via extreme learning machine. Neural Computing and Applications, 27(1), 67-78.
Li, X., Yang, L., Xue, F., & Zhou, H. (2017, May). Time series prediction of stock price using deep belief networks with intrinsic plasticity. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 1237-1242). IEEE.
Mankiw, N. G., Romer, D., & Shapiro, M. D. (1991). Stock market forecastability and volatility: a statistical appraisal. The Review of Economic Studies, 58(3), 455-477.
Mishra, S. K., Panda, G., Majhi, B., & Majhi, R. (2012, July). Improved portfolio optimization combining multiobjective evolutionary computing algorithm and prediction strategy. In World Congress on Engineering (Vol. 1).
Montgomery, D. C., Johnson, L. A., & Gardiner, J. S. (1990). Forecasting and time series analysis. McGraw-Hill Companies.
Park, S. K., Moon, H. J., Min, K. C., Hwang, C., & Kim, S. (2018). Application of a multiple linear regression and an artificial neural network model for the heating performance analysis and hourly prediction of a large-scale ground source heat pump system. Energy and Buildings, 165, 206-215.
Rezaee, M. J., Jozmaleki, M., & Valipour, M. (2018). Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange. Physica A: Statistical Mechanics and its Applications, 489, 78-93.
Sureshkumar, K. K., & Elango, N. M. (2012). Performance analysis of stock price prediction using artificial neural network. Global journal of computer science and Technology.
Ticknor, J. L. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14), 5501-5506.
Vega Ezpeleta, E. (2016). Modeling volatility for the Swedish stock market.
Wei, L. Y. (2016). A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing, 42, 368-376.
Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
Messaoudi, L., & Rebai, A. (2013, April). A fuzzy stochastic Goal Programming approach for solving portfolio selection problem. In Modeling, Simulation and Applied Optimization (ICMSAO), 2013 5th International Conference on (pp. 1-5). IEEE.
Bhattacharyya, R., Hossain, S. A., & Kar, S. (2018). Fuzzy cross-entropy, mean, variance, skewness models for portfolio selection. Journal of King Saud University-Computer and Information Sciences, 26(1), 79-87.
Hiller, R. S., & Eckstein, J. (1993). Stochastic dedication: Designing fixed income portfolios using massively parallel Benders decomposition. Management Science, 39(11), 1422-1438.
Kouwenberg, R. (2001). Scenario generation and stochastic programming models for asset liability management. European Journal of Operational Research, 134(2), 279-292.
Chatsanga, N., & Parkes, A. J. (2017). Two-Stage Stochastic International Portfolio Optimisation under Regular-Vine-Copula-Based Scenarios. arXiv preprint arXiv:1704.01174.
Zhang, W. G., Liu, Y. J., & Xu, W. J. (2017). A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs. European Journal of Operational Research, 222(2), 341-349.
Bermúdez, J. D., Segura, J. V., & Vercher, E. (2012). A multi-objective genetic algorithm for cardinality constrained fuzzy portfolio selection. Fuzzy Sets and Systems, 188(1), 16-26.
Pagnoncelli, B. K., Reich, D., & Campi, M. C. (2012). Risk-return trade-off with the scenario approach in practice: a case study in portfolio selection. Journal of Optimization Theory and Applications, 155(2), 707-722.
Köksalan, M., & Şakar, C. T. (2016). An interactive approach to stochastic programming-based portfolio optimization. Annals of Operations Research, 245(1-2), 47-66.
Mansini, R., Ogryczak, W., & Speranza, M. G. (2007). Conditional value at risk and related linear programming models for portfolio optimization. Annals of operations research, 152(1), 227-256.