Hybrid Multilayer Perceptron Neural Network with Grey Wolf Optimization for Predicting Stock Market Index

Advances in Mathematical Finance and Applications

مقاله 14، دوره 6، شماره 4، دی 2021، صفحه 883-894   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2021.1903474.1452

نویسندگان

Meysam Doaei^{* 1}؛ Seyed Ahmad Mirzaei²؛ Mohammad Rafigh³

¹Department of Management, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran

²Faculty of Management and Accounting, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran

³Department of Finance, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran

چکیده

Stock market forecasting is a challenging task for investors and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. With the development of computationally intelligent method, it is possible to predict stock price time series more accurately. Artificial neural networks (ANNs) are one of the most promising biologically inspired techniques. ANNs have been widely used to make predictions in various research. The performance of ANNs is very dependent on the learning technique utilized to train the weight and bias vectors. The proposed study aims to predict daily Tehran Exchange Dividend Price Index (TEDPIX) via the hybrid multilayer perceptron (MLP) neural networks and metaheuristic algorithms which consist of genetic algorithm (GA), particle swarm optimization (PSO), black hole (BH), grasshopper optimization algorithm (GOA) and grey wolf optimization (GWO). We have extracted 18 technical indicators based on the daily TEDPIX as input parameters. Therefore, the experimental result shows that grey wolf optimization has superior performance to train MLPs for predicting the stock market in metaheuristic-based.

کلیدواژه‌ها

Neural Networks؛ Metaheuristic Algorithms؛ Stock Market Forecasting

مراجع

[1] Alba, E., Chicano, J.F., Training neural networks with GA hybrid algorithms, In Genetic and Evolutionary Computation Conference, Springer, Berlin, Heidelberg, 2004, P.852-863. Doi: 10.1007/978-3-540-24854-5_87

 

[2] Aljarah, I., Faris, H., Mirjalili, S., Optimizing connection weights in neural networks using the whale optimization algorithm, Soft Computing, 2018, 22(1), P.1-5. Doi: 10.1007/s00500-016-2442-1

 

[3] Chen, J.F., Do, Q.H., Hsieh, H.N., Training artificial neural networks by a hybrid PSO-CS algorithm, Algorithms, 2015, 8(2), P.292-308. Doi: 10.3390/a8020292

 

[4] 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. 1-7, Doi: 10.17485/ijst/2015/v8iS8/71505

 

[5] Eberhart, R., Kennedy, J., A new optimizer using particle swarm theory, InMHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, P.39-43.

Doi: 10.1109/MHS.1995.494215

 

[6] Ecer, F., Ardabili, S., Band, S.S., Mosavi, A., Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction, Entropy, 2020, 22(11), P.1239.

Doi: 10.3390/e22111239

 

[7] Faris, H., Aljarah, I., Al-Madi, N., Mirjalili, S., Optimizing the learning process of feedforward neural networks using lightning search algorithm, International Journal on Artificial Intelligence Tools, 2016, 25(06), P.1650033. Doi: 10.1142/S0218213016500330

 

[8] Faris, H., Aljarah, I., Mirjalili, S., Training feedforward neural networks using multi-verse optimizer for binary classification problems, Applied Intelligence, 2016, 45(2), P.322-32. Doi: 10.1007/s10489-016-0767-1

 

[9] Ghasemiyeh, R., Moghdani, R., Sana, S.S., A hybrid artificial neural network with metaheuristic algorithms for predicting stock price, Cybernetics and Systems, 2017, 48(4), P.365-92.

Doi: 10.1080/01969722.2017.1285162

 

 

[10] Ghasemzadeha, M., Mohammad-Karimi, N., Ansari-Samani, H., Machine learning algorithms for time series in financial markets, Advances in Mathematical Finance and Applications, 2020, 5(4), P.479-490.

Doi: 10.22034/amfa.2020.674946

 

[11] Hatamlou, A., Black hole: A new heuristic optimization approach for data clustering, Information sciences, 2013, 222, P.175-84. Doi: 10.1016/j.ins.2012.08.023

 

[12] Izadikhah, M., Azadi, M., Shokri Kahi, V., Farzipoor Saen, R., Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains, International Journal of Production Research, 2019, 57(3), P. 662-682, Doi: 10.1080/00207543.2018.1480840

[13] Heidari, A.A., Faris, H., Aljarah, I., Mirjalili, S., An efficient hybrid multilayer perceptron neural network with grasshopper optimization, Soft Computing, 2019, 23(17), P.7941-58.

Doi: 10.1007/s00500-018-3424-2

 

[14] Heidari, A.A., Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M., Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks, Nature-Inspired Optimizers, 2020, P.23-46.

Doi: 10.1007/978-3-030-12127-3_3

 

[15] Holland, J. H, Adaptation in natural and artificial systems, University of Michigan press. Ann arbor, MI, 1975, 1(97), 5.

 

[16] Izadikhah, M., Using goal programming method to solve DEA problems with value judgments, Yugoslav Journal of Operations Research, 2016, 24 (2), P. 267–282. Doi: 10.2298/YJOR121221015I

[17] Khodayari, M.A., Yaghobnezhad, A., Khalili Eraghi, K. E., A Neural-Network Approach to the Modeling of the Impact of Market Volatility on Investment, Advances in Mathematical Finance and Applications, 2020, 5(4), P.569-581. Doi: 10.22034/amfa.2020.674953

 

[18] Kumar, G., Jain, S., Singh, U.P., Stock Market Forecasting Using Computational Intelligence: A Survey, Archives of Computational Methods in Engineering, 2020, P.1-33. Doi: 10.1007/s11831-020-09413-5

 

[19] Mirjalili, S., Mirjalili, S.M., Lewis, A., Grey wolf optimizer, Advances in engineering software, 2014, 69, P.46-61. Doi: 10.1016/j.advengsoft.2013.12.007

 

[19] Murphy, J.J., Technical analysis of the financial markets: A comprehensive guide to trading methods and applications, Penguin, 1999.

 

[20] Ojha, V.K., Abraham, A., Snášel, V., Metaheuristic design of feedforward neural networks: A review of two decades of research, Engineering Applications of Artificial Intelligence, 2017, 60, P.97-116.

Doi: 10.1016/j.engappai.2017.01.013

 

[20] Pillay, B.J., Ezugwu, A.E., Metaheuristics optimized feedforward neural networks for efficient stock price prediction, arXiv preprint arXiv, 2019, 1906.10121.

 

[21] Rezaei, N., Javaheri, M., The Predictability Power of Neural Network and Genetic Algorithm from Firms’ Financial crisis, Advances in Mathematical Finance and Applications, 2020, 5(2), P.1-17.

Doi: 10.22034/amfa.2019.1863963.1195

 

[22] Saremi, S., Mirjalili, S., Lewis, A., Grasshopper optimisation algorithm: theory and application, Advances in Engineering Software, 2017,105, P.30-47. Doi: 10.1016/j.advengsoft.2017.01.004

 

[23] Tone, K., Toloo, M., Izadikhah, M., A modified slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research, 2020, 287 (2), P. 560-571, Doi: 10.1016/j.ejor.2020.04.019.

 

[24] Thakkar, A., Chaudhari, K., A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm Optimization, Archives of Computational Methods in Engineering, 2020, P.1-32. Doi: 10.1007/s11831-020-09448-8