Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms
Subject Areas : ICT
Vahid Safari dehnavi
1
*
,
masoud shafiee
2
1 -
2 -
Keywords: Prediction, optimized grasshopper optimization algorithm, GMDH neural network, Modeling,
Abstract :
Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the volume of networks and learning complexity, all of which ultimately reduce the accuracy of forecasting. This article proposes a method for forecasting the stock market that can effectively predict the stock market. In this paper, the past market price is used to reduce the volume of input data and this data is placed in a regressor model.
[1]. Buettner R. Predicting user behavior in electronic markets based on personality-mining in large online social networks: A personality-based product recommender framework. The International Journal on Networked Business. Springer, 2016. pp. 1–19.
[2]. Mosavi A, Vaezipour A. Developing effective tools for predictive analytics and informed decision. University of Tallinn, Technical Report. 2013.
[3] Jones ER. Neural Networks’ Role in Predictive Analytics. DM Review Special Report. 2008.
[4] Krauss C, Do XA, Huck N. Deep neural networks, gradient boosted trees, random forests: Statistical arbitrage on the S&P 500. Eur J Oper Res. 2017;259:689–702.
[5] Hiransha M, Gopalakrishnan EA, Menon VK, Soman KP. NSE stock market prediction using deep-learning models. Procedia Comput Sci. 2018;132: 1351–1362.
[6] Abraham A, Nath B, Mahanti PK. Hybrid intelligent systems for stock market analysis. In: Proceedings of International Conference on Computational Science. San Francisco, CA. Springer-Verlag, Berlin Heidelberg, 2001. pp. 337–345.
[7] Vijaya P, Raju G, Ray SK. Artificial neural network-based merging score for Meta search engine. J Central South Univ. 2016;23:2604–2615.
[8] Evans C, Pappas K, Xhafa P. Utilizing artificial neural networks and genetic algorithms to build an algo-trading model intra-day foreign exchange speculation. Math Comput Model. 2013;58:1249–1266.
[9] Sun BQ, Guo H, Karimi HR, et al. Prediction of stock market futures based on fuzzy sets and multivariate fuzzy time series. Neurocomputing. 2015; 151:1528–1536.
[10] Efendi R, Arbaiy N, Deris MM. A new procedure in stock market forecasting based on fuzzy random auto-regression time series model. Inform Sci. 2018;441:113–132.
[11] Khedr AE, Yaseen N. Predicting stock market behavior using data mining technique and news sentiment analysis. Int J Intell Syst Appl. 2017;9:22.
[12] Ahmadi E, Jasemi M, Monplaisir L, et al. New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic. Expert Syst Appl. 2018;94:21–31.
[13]. Safari Dehnavi, V., & Shafiee, M. (2020). »LQR for Generalized Systems Using Metaheuristic Algorithms Based on Disturbance Observer«. 28th Iranian Conference on Electrical Engineering (ICEE), 2020, 08, 04, Tabriz, Iran.
[14]. Sabri, M. (2018). »Stabilization and control of power system using meta-heuristic algorithms«. Karafan (TVU), 14 (42), 33-55.
[15]. Pourdadashi Komachali, F., & Shafiee, M. (2020). »Sensor fault diagnosis in fractional-order singular systems using unknown input observer«. International Journal of Systems Science, 51 (1), 116-132.
[16] Huang, W. (2007). »Neural networks in finance and economics forecasting«. International Journal of Information Technology & Decision Making, 6 (1), 113-140.
[17] Dennis, O. & Mossman, CH. (2003). »Neural network forecasts of Canadian stock returns using accounting ratios«. International Journal of Forecasting, 19 (3), 453-465.
[18] Kelly, J. (2011). »The current stock of money: an aggregation theoretic measure of narrowly defined money«. Applied Economics Letters, 18 (7), 659-664.
[19] Moayedi, H. (2020). »Optimization of ANFIS with GA and PSO estimating α ratio in driven piles«. Engineering with Computers, 36 (1), 227-238.
[20] Sajadi, A. (2020). »Estimation of cetane numbers of biodiesel and diesel oils using regression and PSO-ANFIS models«. Renewable Energy, 158 (1), 465-473.
[21] Çakıt, E. (2020). »Assessing safety at work using an adaptive neuro-fuzzy inference system (ANFIS) approach aided by partial least squares structural equation modeling (PLS-SEM)«. International Journal of Industrial Ergonomics, 76(1), 102925- (in press).
[22] Choo, K. (2018). »Symmetries and many-body excitations with neural-network quantum states«. Physical review letters, 121 (16), 167-204.
[23] Dai, A., & Quoc V. (2020). »Training a document classification neural network«, U.S. Patent No. 10,528,866. 7.
[24] Shqair, M. (2020). »Adaptation of conformable residual power series scheme in solving nonlinear fractional quantum mechanics problems«. Applied Sciences, 10 (3), 890-912.
[25] Erik, C., Gálvez, J., & Avalos, O. (2020). »Gravitational Search Algorithm for Non-linear System Identification Using ANFIS-Hammerstein Approach«. Recent Metaheuristics Algorithms for Parameter Identification (Springer), 854 (1), 97-134.
[26] Bobyr, M., & Sergey G. (2020). »A nonlinear method of learning neuro-fuzzy models for dynamic control systems«. Applied Soft Computing, 88 (1), 106030-106044.
[27] SafariDehnavi, Vahid, and Masoud Shafiee. "The prediction of stock value by using the proposed fuzzy neural network and hybrid algorithm." Karafan Quarterly Scientific Journal 18.1 (2021): 203-220.
[28] Pourzamani, Z. & Miralavi, H. (2018). »Provide a model for stock price forecasting using meta-innovative methods and neural networks«. Quarterly Journal of Financial Management and Securities, 10 (40), 57-83 (in Persian).
[29] Samadipour, S., Matinfard, R. & Torkashvand, A. (2020). »Investigating and predicting the decline or growth of stock companies in a certain period of time using data mining classification technique«. The Third International Conference on New Strategies in Engineering, Information Science and Technology in the Next Century, 2020, 05, 20, Tehran, Iran (in Persian).