Analytical Modelling of Share Price Value Using Computational Intelligence Methods

  • Marius Petrescu "Valahia" University of Târgoviste
  • Mădălina Cuc "Valahia" University of Târgoviste
  • Ionica Oncioiu "Titu Maiorescu" University
  • Anca-Gabriela Petrescu "Valahia" University of Târgoviste
  • Florentina-Raluca Bîlcan "Valahia" University of Târgoviste
  • Lucian Ivan "Valahia" University of Târgoviste


The liberalization, volatility and high competition of financial markets are all factors that expose companies to new risks and challenges, requiring a continuous innovation of models, techniques and tools for managing share price value and other related risks. This paper provides a model to implement a subsystem that should support the management decision on the trading segment of its own shares, based on intelligent agents with regards to identifying, collecting, structuring and updating data instruments, with analysis mechanisms of the main price and volatility indicators. Our conclusion is that the use of computational intelligence methods in modeling of share price value is both a requirement and an option in managing financial-foreign exchange risks, given that companies are subject to a wide range of risks and volatility is the defining feature in which they operate.


[1] Alberola, J. M.; Such, J. M.; Botti, V.; Espinosa, A.; Garcia-Fornes, A. (2013). A Scalable Multiagent Platform for Large Systems, Computer Science and Information Systems, 10, 51-77, 2013.

[2] Allen, F., Karjalainen R., 1999 Using Genetic Algorithms to Find Technical Trading Rules Journal of Financial Economics, 51, 245-271, 1999.

[3] Bahna, M.; Cepoi, C.-O.; Dumitrescu, B. A.; Damian, V. (2018). Estimating the price impact of market orders on the Bucharest Stock Exchange, Romanian Journal of Economic Forecasting, XXI(4), 120-133, 2018.

[4] Benoudjit, N.; Verleysen, M. (2003). On the kernel widths in radial-basis function networks, Neural Processing Letters, 18(2), 139-154, 2003.

[5] Boden, M. (2002). A guide to recurrent neural networks and backpropagation, The Dallas Project, 2002.

[6] Bordini, R.H.; Braubach, L.; Dastani, M.; Seghrouchni, A.E.F.; Gomez-Sanz, J.J.; Leite, J.; O'Hare, G.; Pokahr, A.; Ricci, A. (2006). A Survey of Programming Languages and Platforms for Multi-Agent Systems, Informatica, 30(1), 33-44, 2006.

[7] Dodd, O.; Gilbert, A. (2016). The Impact of Cross-Listing on the Home Market's Information Environment and Stock Price Efficiency, Financial Review, 51(3), 299-328, 2016.

[8] Dzitac, S.; Felea, I.; Dzitac, I. et al. (2008). An application of neuro-fuzzy modelling to prediction of some incidence in an electrical energy distribution center, International Journal of Computers Communications & Control, 3(S), 287-292, 2008.

[9] Georgescu, V. (2010). Robustly Forecasting the Bucharest Stock Exchange Bet Index Through a Novel Computational Intelligence Approach, Economic Computation and Economic Cybernetics Studies and Research, 44(3), 23-42, 2010.

[10] Georgescu, V. (2011). An Econometric Insight into Predicting Bucharest Stock Exchange Mean- Return and Volatility-Return Processes, Economic Computation and Economic Cybernetics Studies and Research, 3, 25-42, 2011..

[11] Grigore, L.S, t.; Soloi, A.; Tiron, O.; Racuciu, C. I. (2013). Fundamentals of Autonomous Robot Classes with a System of Stabilization of the Gripping Mechanism, Advanced Materials Research, 646(1), 164-170, 2013.

[12] Haykin, S. (1994). Neural networks: a comprehensive foundation, Prentice Hall PTR, 1994.

[13] Haykin, S. (2009). Neural networks and Learning machines, Prentice Hall Publishing, 2009.

[14] Kaplan, D.; Glass, L. (2012). Understanding nonlinear dynamics, Springer Science & Business Media, 2012.

[15] McCulloch, W.; Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, 5, 115-133, 1943.

[16] Rehar, T.; Ogrizek, B.; Leber, M.; Pisnic, A.; Buchmeister, B. (2017). Product Lifecycle Forecasting Using System's Indicators, International Journal of Simulation Modelling, 16(1), 45-57, 2017.

[17] Sun, Y.F.; Zhang M.L., Chen, S.; Shi, X.H. (2018). A Financial Embedded Vector Model and Its Applications to Time Series Forecasting, International Journal of Computers Communications & Control, 13(5), 881-894, 2018.

[18] Tsai, C.W.; Lai, C.F.; Chiang, M.C.; Yang, L.T. (2013). Data mining for internet of things: A survey, IEEE Communications Surveys & Tutorials, 16(1), 77-97, 2013.

[19] Tse, Y.; Tsui, A. (2002). A Multivariate GARCH Model with Time-Varying Correlations, Journal of Business and Economic Statistics, 20, 351-362, 2002.

[20] Vapnik, V. (2006). Estimation of Dependences Based on Empirical Data, Springer, 2006.

[21] Wang, SC. (2003). Artificial Neural Network. In: Interdisciplinary Computing in Java Programming. The Springer International Series in Engineering and Computer Science, Springer, Boston, MA., 2003.

[22] Weiss, G. (2000). Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, Cambridge, Massachusetts London, England, 2000.

[23] [Online]. Bucharest Stock Exchange. Available:, Accessed on 22 January 2020.
How to Cite
PETRESCU, Marius et al. Analytical Modelling of Share Price Value Using Computational Intelligence Methods. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 4, june 2020. ISSN 1841-9844. Available at: <>. Date accessed: 12 july 2020. doi:


neural networks, intelligent systems, share price, financial daily vector, exchange risks