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Evolving decision rules to predict investment opportunities

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Abstract

This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules (EDR) to detect financial opportunities. The objective of EDR is to classify the minority class (positive cases) in imbalanced environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are: 1) to show that EDR produces a range of solutions to suit the investor’s preferences and 2) to analyze the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive cases in imbalanced data sets. Experimental results show that: 1) EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2) a bigger collection of rules is able to classify more positive cases in imbalanced environments.

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Authors and Affiliations

  1. Department of Computer Science, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Alma Lilia Garcia-Almanza & Edward P. K. Tsang

Authors
  1. Alma Lilia Garcia-Almanza
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  2. Edward P. K. Tsang
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Correspondence to Alma Lilia Garcia-Almanza.

Additional information

Alma Lilia Garcia-Almanza held a first degree in actuary from the Universidad Nacional Autonoma de Mexico, in 1995, and the M. Sc. in computer science from the University of Essex in 2003. She is currently a Ph. D. candidate at the University of Essex.

Her research interests include time series forecasting using genetic programming by means of the analysis of decision trees.

Edward P. K. Tsang is a professor in computer science at University of Essex. He is also the deputy director of Centre for Computational Finance and Economic Agents (CCFEA) and a founding member of the Centre for Computational Intelligence at University of Essex.

He has broad interest in business application of artificial intelligence, including computational finance, computational economics, and constraint satisfaction. Main techniques used included heuristic search, optimization, and evolutionary computation.

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Garcia-Almanza, A.L., Tsang, E.P.K. Evolving decision rules to predict investment opportunities. Int. J. Autom. Comput. 5, 22–31 (2008). https://doi.org/10.1007/s11633-008-0022-2

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  • DOI: https://doi.org/10.1007/s11633-008-0022-2

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