Abstract
Biological systems are inspiration for the design of optimisation and classification models. Applying various forms of bio-inspired algorithms may be a very high-complex system. Modelling of financial markets is challenging for several reasons, because many plausible factors impact on it. An automated trading on financial market is not a new phenomenon. The model of bio-inspired hybrid adaptive trading system based on technical indicators usage by grammatical evolution and moving window is presented in this paper. The proposed system is just one of possible bio-inspired system which can be used in financial forecast, corporate failure prediction or bond rating company.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Walczak, S.: An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks. Journal of Management Information Systems 17(4), 203–222 (2001)
Kim, M.J., Han, I.: The Discovery of Experts’ Decision Rules from Quantitative Bankruptcy Data Using Genetic Algorithms. Expert System Application (25), 637–646 (2003)
Alfaro-Cid, E., Sharman, K., Esparcia-Alcázar, E.: A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 169–178. Springer, Heidelberg (2007)
Marinakis, Y., Marinaki, M., Doumpos, M., Zopounidis, C.: Ant Colony and Particle Swarm Optimisation for Financial Classification Problems. Expert Systems with Applications 36(7), 10604–10611 (2009)
Marinaki, M., Marinakis, Y.M., Zopounidis, C.: Honey Bees Mating Optimization Algorithm for Financial Classification Problems. Applied Soft Computing 10(3), 806–812 (2010)
Singh, R., Sengupta, R.N.: Bankruptcy Prediction Using Artificial Immune Systems. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 131–141. Springer, Heidelberg (2007)
Rada, R.: Expert Systems and Evolutionary Computing for Financial Investing. Expert Systems with Applications 34(4), 2232–2240 (2008)
Simić, D., Simić, S.: An Approach to Efficient Business Intelligent System for Financial Prediction. Soft Computing 11(12), 1185–1192 (2007)
O’Neill, M., Ryan, C.: Grammatical Evolution. IEEE Transactions Evolutionary Computation 5(4), 349–358 (2001)
Brock, W., Lakonishok, J., LeBaron, B.: Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance 47(5), 1731–1764 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Simić, D., Gajić, V., Simić, S. (2010). An Approach of Bio-inspired Hybrid Model for Financial Markets. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-13769-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13768-6
Online ISBN: 978-3-642-13769-3
eBook Packages: Computer ScienceComputer Science (R0)