Abstract
The intrinsic capability of functional link artificial neural network (FLANN) to recognize the complex nonlinear relationship present in the historical stock data made it popular and got wide applications for stock market prediction. Contrasting to multilayer neural networks, FLANN uses functional expansion units to expand the input space into higher dimensions, thus generating hyperplanes which offer better discrimination capability in the input space. The feedback properties of recurrent neural networks make them more proficient and dynamic to model nonlinear systems accurately. Artificial chemical reaction optimization (ACRO) requires less number of tuning parameters with faster convergence speed. This article develops an ACRO-based recurrent functional link neural network (RFLN) termed as ACRRFLN, in which optimal structure and parameters of a RFLN are efficiently searched by ACRO. Also two evolutionary optimization techniques, i.e., particle swarm optimization (PSO) and genetic algorithm (GA) are employed to train RFLN separately. All the models are experimented and validated on forecasting stock closing prices of five stock markets. Results from extensive simulation studies clearly reveal the outperformance of ACRRFLN over other models similarly trained. Further, the Deibold-Mariano test justifies the statistical significance of the proposed model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barak, S., Modarres, M.: Developing an approach to evaluate stocks by forecasting effective features with data mining methods. Expert Syst. Appl. 42(3), 1325–1339 (2015)
Ballings, M., Van den Poel, D., Hespeels, N., Gryp, R.: Evaluating multiple classifiers for stock price direction prediction. Expert Syst. Appl. 42(20), 7046–7056 (2015)
Yu, L.Q., Rong, F.S.: Stock market forecasting research based on neural network and pattern matching. In: 2010 International Conference on E-Business and E-Government (ICEE), pp. 1940–1943. IEEE (2010, May)
Tahersima, H., Tahersima, M., Fesharaki, M., Hamedi, N.: Forecasting stock exchange movements using neural networks: a case study. In: 2011 International Conference on Future Computer Sciences and Application (ICFCSA), pp. 123–126. IEEE (2011, June)
Dutta, G., Jha, P., Laha, A.K., Mohan, N.: Artificial neural network models for forecasting stock price index in the Bombay stock exchange. J. Emerg. Market Financ. 5(3), 283–295 (2006)
Huang, F.Y.: Integration of an improved particle swarm algorithm and fuzzy neural network for Shanghai stock market prediction. In: Workshop on Power Electronics and Intelligent Transportation System, 2008. PEITS’08, pp. 242–247. IEEE (2008, August)
Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5), 76–79 (1992). Mishra, B.B., Dehuri, S.: Functional link artificial neural network for classification task in data mining. J. Comput. Sci. 3, 948–955 (2007)
Mishra, B.B., Dehuri, S., Panda, G., Dash, P.K.: Fuzzy swarm net (FSN) for classification in data mining. CSI J. Comput. Sci. Eng. 5(2&4 (b)), 1–8 (2008)
Dehuri, S., Cho, S.B.: A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. Neural Comput. Appl. 19(2), 317–328 (2010)
Majhi, R., Majhi, B., Panda, G.: Development and performance evaluation of neural network classifiers for Indian internet shoppers. Expert Syst. Appl. 39(2), 2112–2118 (2012)
Purwar, S., Kar, I.N., Jha, A.N.: On-line system identification of complex systems using Chebyshev neural networks. Appl. Soft Comput. 7(1), 364–372 (2007)
Majhi, R., Panda, G., Sahoo, G.: Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Syst. Appl. 36(3), 6800–6808 (2009)
Nayak, S.C., Misra, B.B., Behera, H.S.: ACFLN: artificial chemical functional link network for prediction of stock market index. Evolving Systems, pp. 1–26 (2018)
Nayak, S.C., Misra, B.B., Behera, H.S.: Comparison of performance of different functions in functional link artificial neural network: a case study on stock index forecasting. In: Computational Intelligence in Data Mining-Volume 1, pp. 479–487. Springer, New Delhi (2015)
Patra, J.C., Thanh, N.C., Meher, P.K.: Computationally efficient FLANN-based intelligent stock price prediction system. In: International Joint Conference on Neural Networks, 2009. IJCNN 2009, pp. 2431–2438. IEEE (2009, June)
Dehuri, S., Roy, R., Cho, S.B., Ghosh, A.: An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J. Syst. Softw. 85(6), 1333–1345 (2012)
Mili, F., Hamdi, M.: A hybrid evolutionary functional link artificial neural network for data mining and classification. In: 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 917–924. IEEE (2012, March)
Lam, A.Y., Li, V.O.: Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans. Evol. Comput. 14(3), 381–399 (2010)
Alatas, B.: A novel chemistry based metaheuristic optimization method for mining of classification rules. Expert Syst. Appl. 39(12), 11080–11088 (2012)
Nayak, S.C., Misra, B.B., Behera, H.S.: Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices. Ain Shams Eng. J. (2015)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer, US (2011)
Nayak, S.C., Misra, B.B., Behera, H.S.: Index prediction with neuro-genetic hybrid network: a comparative analysis of performance. In: 2012 International Conference on Computing, Communication and Applications (ICCCA), pp. 1–6. IEEE (2012, February)
Nayak, S.C., Misra, B.B., Behera, H.S.: An adaptive second order neural network with genetic-algorithm-based training (ASONN-GA) to forecast the closing prices of the stock market. Int. J. Appl. Metaheuristic Comput. (IJAMC) 7(2), 39–57 (2016)
Nayak, S.C., Misra, B.B., Behera, H.S.: Efficient financial time series prediction with evolutionary virtual data position exploration. Neural Comput. Appl., 1–22 (2017)
Nayak, S.C., Misra, B.B., Behera, H.S.: On developing and performance evaluation of adaptive second order neural network with GA-based training (ASONN-GA) for financial time series prediction. In: Advancements in Applied Metaheuristic Computing, pp. 231–263. IGI Global (2018)
Nayak, S.C., Misra, B.B., Behera, H.S.: Impact of data normalization on stock index forecasting. Int. J. Comp. Inf. Syst. Ind. Manag. Appl. 6, 357–369 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nayak, S.C., Kumar, K.V., Jilla, K. (2020). ACRRFLN: Artificial Chemical Reaction of Recurrent Functional Link Networks for Improved Stock Market Prediction. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_28
Download citation
DOI: https://doi.org/10.1007/978-981-13-8676-3_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8675-6
Online ISBN: 978-981-13-8676-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)