Abstract
This paper proposes a hybrid decision support system for financial time series analysis. It uses evolutionary algorithms to construct an artificial expert, consisting of some expert rules, which processes financial time series and provides an expertise. Such expert rules are either static coming from the domain knowledge or are in the form of multi-layer perceptrons representing expert rules extracted from data.
Results of a large number of experiments on real-life data from the Paris Stock Exchange confirm that the model of the hybrid decision support system is reasonable and it is able to generate reasonable solutions, not only over a training period, used in the training process, but also over a test period, unknown during constructing artificial experts.
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References
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Heidelberg (2006)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)
Korczak, J.J., Lipinski, P., Roger, P.: Evolution Strategy in Portfolio Optimization. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 156–167. Springer, Heidelberg (2002)
Lipinski, P.: Discovering Stock Market Trading Rules using Multi-Layer Perceptrons. In: Sandoval, F., Gonzalez Prieto, A., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 1114–1121. Springer, Heidelberg (2007)
Lipinski, P., Korczak, J.: Performance Measures in an Evolutionary Stock Trading Expert System. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3039, pp. 835–842. Springer, Heidelberg (2004)
Loraschi, A., Tettamanzi, A.: An Evolutionary Algorithm for Portfolio Selection within a Downside Risk Framework. In: Dunis, C.L. (ed.) Forecasting Financial Markets, pp. 275–286. Wiley, Chichester (1996)
Moller, M.: A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks 6(4), 525–533 (1993)
Murphy, J.: Technical Analysis of the Financial Markets. NUIF (1998)
Tsang, E., Li, J., Markose, S., Er, H., Salhi, A., Iori, G.: EDDIE In Financial Decision Making. Journal of Management and Economics 4(4) (2000)
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Lipinski, P. (2008). Neuro-evolutionary Decision Support System for Financial Time Series Analysis. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_23
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DOI: https://doi.org/10.1007/978-3-540-87656-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87655-7
Online ISBN: 978-3-540-87656-4
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