Optimizing the Prediction Model of Stock Price in Pharmaceutical Companies Using Multiple Objective Particle Swarm Optimization Algorithm (MOPSO)
محورهای موضوعی : Design of Experiment
Ali Khazaei
1
(Department of Management Science, Abhar branch, Islamic Azad University,Abhar, Iran)
Babak Haji Karimi
2
(Department of Management Science, Abhar branch, Islamic Azad University,Abhar, Iran)
Mohammad Mahdi Mozaffari
3
(Faculty of Social Science, Imam Khomeini International University, Qazvin, Iran)
کلید واژه: Pharmaceutical Companies, Price forecasting, particle swarm algorithm (MOPSO), meta-innovation,
چکیده مقاله :
The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used was a multilayer perceptron network using the error propagation learning algorithm.After predicting the stock price with the neural network, the forecast price data in the second phase has been used to optimize the stock portfolio.In this phase, a multi-objective genetic algorithm is used to optimize the portfolio, and the optimal weights are assigned to the stock and the optimal stock portfolio is created.Having a regression model, the design of the relevant genetic algorithm has been done using MATLAB software.The results show that the stock portfolio created by MOPSO algorithm has a better performance compared to the algorithms used in the article under comparison under all four risk criteria except the criterion of conditional risk exposure. In all models, except the conditional risk-averaged value model, the stock portfolios created by the MOPSO algorithm used in the research have more and more appropriate performance.
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.
