Forecasting Stock Exchange Data using Group Method  of Data Handling Neural Network Approach

Marzieh Faridi Masouleh; Ahmad Bagheri

doi:10.17977/um018v4i12021p1-13

Forecasting Stock Exchange Data using Group Method of Data Handling Neural Network Approach

Marzieh Faridi Masouleh, Ahmad Bagheri

Abstract

The increasing uncertainty of the natural world has motivated computer scientists to seek out the best approach to technological problems. Nature-inspired problem-solving approaches include meta-heuristic methods that are focused on evolutionary computation and swarm intelligence. One of these problems significantly impacting information is forecasting exchange index, which is a serious concern with the growth and decline of stock as there are many reports on loss of financial resources or profitability. When the exchange includes an extensive set of diverse stock, particular concepts and mechanisms for physical security, network security, encryption, and permissions should guarantee and predict its future needs. This study aimed to show it is efficient to use the group method of data handling (GMDH)-type neural networks and their application for the classification of numerical results. Such modeling serves to display the precision of GMDH-type neural networks. Following the US withdrawal from the Joint Comprehensive Plan of Action in April 2018, the behavior of the stock exchange data stream and commend algorithms has not been able to predict correctly and fit in the network satisfactorily. This paper demonstrated that Group Method Data Handling is most likely to improve inductive self-organizing approaches for addressing realistic severe problems such as the Iranian financial market crisis. A new trajectory would be used to verify the consistency of the obtained equations hence the models' validity.

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References

A. N. Singh, A. Picot, J. Kranz, M. P. Gupta, and A. Ojha, “Information Security Management (ISM) Practices: Lessons from Select Cases from India and Germany,” Glob. J. Flex. Syst. Manag., vol. 14, no. 4, pp. 225–239, Dec. 2013, doi: 10.1007/s40171-013-0047-4.

B. Apolloni, S. Bassis, J. Rota, G. L. Galliani, M. Gioia, and L. Ferrari, “A Neurofuzzy Algorithm for Learning from Complex Granules,” Granul. Comput., vol. 1, no. 4, pp. 225–246, Dec. 2016, doi: 10.1007/s41066-016-0018-1.

J. K. Deane, D. M. Goldberg, T. R. Rakes, and L. P. Rees, “The Effect of Information Security Certification Announcements on the Market Value of the Firm,” Inf. Technol. Manag., vol. 20, no. 3, pp. 107–121, Sep. 2019, doi: 10.1007/s10799-018-00297-3.

S. Shan, Z. Hu, Z. Liu, J. Shi, L. Wang, and Z. Bi, “An Adaptive Genetic Algorithm for Demand-Driven and Resource-Constrained Project Scheduling in Aircraft Assembly,” Inf. Technol. Manag., vol. 18, no. 1, pp. 41–53, Mar. 2017, doi: 10.1007/s10799-015-0223-7.

Ernst & Young LLP, Fighting to close the gap, no. November. 2012.

M. Song and Y. Wang, “A study of granular computing in the agenda of growth of artificial neural networks,” Granul. Comput., vol. 1, no. 4, pp. 247–257, Dec. 2016, doi: 10.1007/s41066-016-0020-7.

Z. A. Soomro, M. H. Shah, and J. Ahmed, “Information security management needs more holistic approach: A literature review,” Int. J. Inf. Manage., vol. 36, no. 2, pp. 215–225, Apr. 2016, doi: 10.1016/j.ijinfomgt.2015.11.009.

M. Siponen, M. A. Mahmood, and S. Pahnila, “Technical opinionAre employees putting your company at risk by not following information security policies?,” Commun. ACM, vol. 52, no. 12, pp. 145–147, Dec. 2009, doi: 10.1145/1610252.1610289.

T. Ring, “A breach too far?,” Comput. Fraud Secur., vol. 2013, no. 6, pp. 5–9, Jun. 2013, doi: 10.1016/S1361-3723(13)70052-6.

M. R. Sanaei and F. M. Sobhani, “Information technology and e-business marketing strategy,” Inf. Technol. Manag., vol. 19, no. 3, pp. 185–196, Sep. 2018, doi: 10.1007/s10799-018-0289-0.

W. Ashford, “Many UK firms underestimate cost of data breaches, study finds,” https://www.computerweekly.com/, 2012. https://www.computerweekly.com/news/2240171040/Many-UK-firms-underestimate-cost-of-data-breaches-study-finds.

S. Chaigusin, C. Chirathamjaree, and J. Clayden, “The Use of Neural Networks in the Prediction of the Stock Exchange of Thailand (SET) Index,” in 2008 International Conference on Computational Intelligence for Modelling Control & Automation, 2008, pp. 670–673, doi: 10.1109/CIMCA.2008.83.

S. Galeshchuk, “Neural networks performance in exchange rate prediction,” Neurocomputing, vol. 172, pp. 446–452, Jan. 2016, doi: 10.1016/j.neucom.2015.03.100.

T. H. Hann and E. Steurer, “Much ado about nothing? Exchange rate forecasting: Neural networks vs. linear models using monthly and weekly data,” Neurocomputing, vol. 10, no. 4, pp. 323–339, Apr. 1996, doi: 10.1016/0925-2312(95)00137-9.

M. Mahdavi, “A Bayesian approach to foreign exchange forecasting,” Glob. Financ. J., vol. 8, no. 1, pp. 15–31, Mar. 1997, doi: 10.1016/S1044-0283(97)90003-X.

G. Zhang and M. Y. Hu, “Neural network forecasting of the British Pound/US Dollar exchange rate,” Omega, vol. 26, no. 4, pp. 495–506, Aug. 1998, doi: 10.1016/S0305-0483(98)00003-6.

F. Fernández-Rodrı́guez, S. Sosvilla-Rivero, and J. Andrada-Félix, “Exchange-rate forecasts with simultaneous nearest-neighbour methods: evidence from the EMS,” Int. J. Forecast., vol. 15, no. 4, pp. 383–392, Oct. 1999, doi: 10.1016/S0169-2070(99)00003-5.

M. T. Leung, A.-S. Chen, and H. Daouk, “Forecasting exchange rates using general regression neural networks,” Comput. Oper. Res., vol. 27, no. 11–12, pp. 1093–1110, Sep. 2000, doi: 10.1016/S0305-0548(99)00144-6.

M. P. Clements and J. Smith, “Evaluating forecasts from SETAR models of exchange rates,” J. Int. Money Financ., vol. 20, no. 1, pp. 133–148, Feb. 2001, doi: 10.1016/S0261-5606(00)00039-5.

A.-S. Chen and M. T. Leung, “A Bayesian vector error correction model for forecasting exchange rates,” Comput. Oper. Res., vol. 30, no. 6, pp. 887–900, May 2003, doi: 10.1016/S0305-0548(02)00041-2.

A.-S. Chen and M. T. Leung, “Regression neural network for error correction in foreign exchange forecasting and trading,” Comput. Oper. Res., vol. 31, no. 7, pp. 1049–1068, Jun. 2004, doi: 10.1016/S0305-0548(03)00064-9.

L. Yu, S. Wang, and K. K. Lai, “A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates,” Comput. Oper. Res., vol. 32, no. 10, pp. 2523–2541, Oct. 2005, doi: 10.1016/j.cor.2004.06.024.

A. Preminger and R. Franck, “Forecasting exchange rates: A robust regression approach,” Int. J. Forecast., vol. 23, no. 1, pp. 71–84, Jan. 2007, doi: 10.1016/j.ijforecast.2006.04.009.

J. H. Wright, “Bayesian Model Averaging and exchange rate forecasts,” J. Econom., vol. 146, no. 2, pp. 329–341, Oct. 2008, doi: 10.1016/j.jeconom.2008.08.012.

A. Carriero, G. Kapetanios, and M. Marcellino, “Forecasting exchange rates with a large Bayesian VAR,” Int. J. Forecast., vol. 25, no. 2, pp. 400–417, Apr. 2009, doi: 10.1016/j.ijforecast.2009.01.007.

S. Ye, “RMB Exchange Rate Forecast Approach Based on BP Neural Network,” Phys. Procedia, vol. 33, pp. 287–293, 2012, doi: 10.1016/j.phpro.2012.05.064.

T. Korol, “A fuzzy logic model for forecasting exchange rates,” Knowledge-Based Syst., vol. 67, pp. 49–60, Sep. 2014, doi: 10.1016/j.knosys.2014.06.009.

F. Shen, J. Chao, and J. Zhao, “Forecasting exchange rate using deep belief networks and conjugate gradient method,” Neurocomputing, vol. 167, pp. 243–253, Nov. 2015, doi: 10.1016/j.neucom.2015.04.071.

E. Abounoori, Z. (Mila) Elmi, and Y. Nademi, “Forecasting Tehran stock exchange volatility; Markov switching GARCH approach,” Phys. A Stat. Mech. its Appl., vol. 445, pp. 264–282, Mar. 2016, doi: 10.1016/j.physa.2015.10.024.

M. Ca’ Zorzi, M. Kolasa, and M. Rubaszek, “Exchange rate forecasting with DSGE models,” J. Int. Econ., vol. 107, no. 260, pp. 127–146, 2017, doi: 10.1016/j.jinteco.2017.03.011.

S. Sun, S. Wang, and Y. Wei, “A new multiscale decomposition ensemble approach for forecasting exchange rates,” Econ. Model., vol. 81, pp. 49–58, Sep. 2019, doi: 10.1016/j.econmod.2018.12.013.

K. He, Y. Chen, and G. K. F. Tso, “Forecasting exchange rate using Variational Mode Decomposition and entropy theory,” Phys. A Stat. Mech. its Appl., vol. 510, pp. 15–25, Nov. 2018, doi: 10.1016/j.physa.2018.05.135.

I. Dzalbs and T. Kalganova, “Forecasting Price Movements in Betting Exchanges Using Cartesian Genetic Programming and ANN,” Big Data Res., vol. 14, pp. 112–120, Dec. 2018, doi: 10.1016/j.bdr.2018.10.001.

C. Amat, T. Michalski, and G. Stoltz, “Fundamentals and exchange rate forecastability with simple machine learning methods,” J. Int. Money Financ., vol. 88, pp. 1–24, Nov. 2018, doi: 10.1016/j.jimonfin.2018.06.003.

Y. Wei, S. Sun, J. Ma, S. Wang, and K. K. Lai, “A decomposition clustering ensemble learning approach for forecasting foreign exchange rates,” J. Manag. Sci. Eng., vol. 4, no. 1, pp. 45–54, Mar. 2019, doi: 10.1016/j.jmse.2019.02.001.

S. Fu, Y. Li, S. Sun, and H. Li, “Evolutionary support vector machine for RMB exchange rate forecasting,” Phys. A Stat. Mech. its Appl., vol. 521, pp. 692–704, May 2019, doi: 10.1016/j.physa.2019.01.026.

L. Ni, Y. Li, X. Wang, J. Zhang, J. Yu, and C. Qi, “Forecasting of Forex Time Series Data Based on Deep Learning,” Procedia Comput. Sci., vol. 147, pp. 647–652, 2019, doi: 10.1016/j.procs.2019.01.189.

R. Wang, B. Morley, and M. P. Stamatogiannis, “Forecasting the exchange rate using nonlinear Taylor rule based models,” Int. J. Forecast., vol. 35, no. 2, pp. 429–442, Apr. 2019, doi: 10.1016/j.ijforecast.2018.07.017.

A. Bagheri, N. Nariman-Zadeh, A. S. Siavash, and A. R. Khoobkar, “GMDH type neural networks and their application to the identification of the inverse kinematic equations of robotic manipulators,” Int. J. Eng., vol. 18, no. 2, pp. 135–143, 2005.

A. Bagheri, H. Mohammadi Peyhani, and M. Akbari, “Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization,” Expert Syst. Appl., vol. 41, no. 14, pp. 6235–6250, Oct. 2014, doi: 10.1016/j.eswa.2014.04.003.

S. Dick and A. Kandel, “Granular Computing in Neural Networks,” Studies in Fuzziness and Soft Computing, pp. 275–305, 2001.

B. Apolloni, S. Bassis, J. Rota, G. L. Galliani, M. Gioia, and L. Ferrari, “A neurofuzzy algorithm for learning from complex granules,” Granul. Comput., vol. 1, no. 4, pp. 225–246, Dec. 2016, doi: 10.1007/s41066-016-0018-1.

S. J. Farlow, “The GMDH Algorithm of Ivakhnenko,” Am. Stat., vol. 35, no. 4, pp. 210–215, Nov. 1981, doi: 10.1080/00031305.1981.10479358.

DOI: http://dx.doi.org/10.17977/um018v4i12021p1-13

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