Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model
Journal of Economics, Management and Trade,
The prediction of the time series has always attracted much interest from investors and researchers to evaluate financial risk. Stock market movements are extremely complex and are influenced by different factors. Hence it is very important to find the most important factors for the stock market. But the high level of noise and complexity of the financial data makes this job very difficult. Many authors have already used artificial neural network for this kind of forecasting tasks, but hybridization model of artificial neural network is considered to be widely used and better performing forecasting model among others. The dormant high noises data mess up the performance, so to enhance the prediction accuracy. We considered a set of seven technical attribute of stock market to perform the hybrid model of Artificial Neural Network (ANN) and Particle Swarm Optimization algorithms. The efficiency of the proposed method is measured by the stock price of Bharat Immunological & Biological Corporation Ltd with 3945 number of daily transactional data. Empirical prediction analysis shows that the proposed model enhances the performance in comparison to simple ANN model.
- Artificial Neural Network
- Bharat Immunological & Biological Corporation Ltd
- Financial Forecasting
- Particle Swarm Optimization
- Stock Market
- data mining
How to Cite
Budiharto W. Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM). J Big Data. 2021;8:47. Available: https://doi.org/10.1186/s40537-021-00430-0
Reid MJ. Combining three estimates of gross domestic product. Economica. 1968;35:31–444.
Bates JM, Granger WJ. The combination of forecasts. Operation Research. 1969;20: 451–468.
Clemen, R. Combining forecasts: A review and annotated bibliography with discussion. International Journal of Forecasting. 1989;5:559–608.
Rajesh P, Srinivas N, Vamshikrishna Reddy K, VamsiPriya G, Dwija V, Himaja D Stock trend prediction using Ensemble learning techniques in python. Int. J. Innov. Technol. Explanatory Eng. 2019;8(5):150–155.
Farahani SM, Razavi Hajiagha SH. Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models. Soft Computer. 2021;25:8483–8513. Available:https://doi.org/10.1007/s00500-021-05775-5
Wedding DK, Cios KJ. Time series forecasting by combining networks, certainty factors, RBF and the Box–Jenkins model. Neuro-computing. 1996; 10:149–168.
Kumar Chandar S. Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms. Elsevier, Pattern Recognition Letters. 2021;147:124-133. Available:https://doi.org/10.1016/j.patrec.2021.03.030
Luxhoj JT, Riis JO, Stensballe B. A hybrid econometric-neural network modelling approach for sales forecasting. International Journal of Production Economics. 1996;43:175–192.
Pelikan E, De Groot C, Wurtz D. Power consumption in West-Bohemia: Improved forecasts with decorrelating connectionist networks. Neural Network World. 1992;2: 701–712.
Ginzburg I, Horn D. Combined neural networks for time series analysis. Advance Neural Information Processing Systems. 1994;6:224–231.
Kumar PN, Seshadri GR, Hariharan A, Mohandas VP, Balasubramanian P. Financial Market Prediction Using Feed Forward Neural Network. In: Shah K., Lakshmi Gorty VR, Phirke A. (eds) Tech. Systems and Management. Communi- cations in Computer and Inf. Sc., Springer. 2011;145. Available:https://doi.org/10.1007/978-3-642-20209-4_11
Tsaih R, Hsu Y, Lai CC. Forecasting S&P 500 stock index futures with a hybrid AI system. Decision Support Systems. 1998;23:161–174.
Medeiros MC, Veiga A. A hybrid linear-neural model for time series forecasting. IEEE Transaction on Neural Networks. 2000;11(6):1402–1412.
Pai PF, Lin CS. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega. 2005;33:505–597.
Chen KY, Wang CH. A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Systems with Applications. 2007;32:54–264.
Khashei M, Hejazi SR, Bijari M. A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets and Systems. 2008;159: 769–786.
Lean Y, Shouyang W, Lai KK A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates, Computer and Operation Research. 2005;32(10):2523-2543.
Khashei M, Bijari M. An artificial neural network (p, d, q) model for time series forecasting, Expert Systems with Applications, Elsevier. 2010;37:479–489.
Siddique Md, Mohanta D. Ku, Mishra SP. A Hybrid Model of Artificial Neural Network and Particle Swarm Optimization for Forecasting of Stock Price of Tata Motors, Indian Journal of Natural Sciences (IJNOS). 2020;10(59);12999-13005.
Armaghani JD, Shoib RSNSBR, Faizi K. et al. Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput & Applic. 2017;28:391–405. Available: https://doi.org/10.1007/s00521-015-2072-z
Pranav D. Raval, Ashit S. Pandya. A Hybrid PSO-ANN-Based Fault Classification System for EHV Transmission Lines, IETE Journal of Research; 2020. DOI: 10.1080/03772063.2020.1754299
Gourav Kumar, Singh UP, Jain S, Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting. Willey online library, Int. J. of intelligent systems; 2021. Available:https://doi.org/10.1002/int.22495
Chen CH. Neural networks for financial market prediction. in Proceedings of the 1994 IEEE International Conference on Neural Networks. Orlando, Fla, USA. 1994;1199-1202.
Burges CA tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery. 1998;2(2):121–167, 1998.
Majhi R, Panda G, Sahoo G, Panda A, Choubey A. Prediction of S&P 500 and DJIA stock indices using particle swarm optimization technique,” in Proceeding of the IEEE Congress on Evolutionary Computation (CEC '08), Hong Kong, China. 2008;1276-1282.
Gupta S, Wang LP. Stock forecasting with feedforward neural networks and gradual data sub-sampling. Aust J Intell Inf Process Syst. 2010;11(4):14–17.
Ahmed MK, Wajiga GM, Blamah NV, Modi B. Stock market forecasting using ant colony optimization based algorithm. Am J Math Comput Model. 2019;4(3):52–57.
Siddique M, Panda D. A hybrid forecasting model for prediction of stock index of Tata Motors using principal component analysis, support vector regression and particle swarm optimization, International Journal of Engineering and Advanced Technology. 2019;1(9):3032-3037.
Zhang GP, Qi GM. Neural network forecasting for seasonal and trend time series. European Journal of Operational Research. 2005;160: 501–514.
Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. 1995;1942–1948.
AlRashidi MR, AlHajri MF, Al-Othman AK, El-Naggar KM. Particle Swarm Optimization and Its Applications in Power Systems. In: Panigrahi BK, Abraham A, Das S. (eds) Computational Intelligence in Power Engineering. Studies in Computational Intelligence, Springer, Berlin, Heidelberg. 2010;302. Available:https://doi.org/10.1007/978-3-642-14013-6_10
Wang D, Dapei T, Liu L, Particle swarm optimization algorithm: an overview. Soft Computing, Springer. 2017;1-22. DOI 10.1007/s00500-016-2474-6
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