Volume 5 Number 1
January 2008
Article Contents
Alma Lilia Garcia-Almanza and Edward P. K. Tsang. Evolving Decision Rules to Predict Investment Opportunities. International Journal of Automation and Computing, vol. 5, no. 1, pp. 22-31, 2008. doi: 10.1007/s11633-008-0022-2
Cite as: Alma Lilia Garcia-Almanza and Edward P. K. Tsang. Evolving Decision Rules to Predict Investment Opportunities. International Journal of Automation and Computing, vol. 5, no. 1, pp. 22-31, 2008. doi: 10.1007/s11633-008-0022-2

Evolving Decision Rules to Predict Investment Opportunities

  • Received: 2007-08-20
  • 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 investors 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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  • [1] A.L.Garcia-Almanza,E.P.K.Tsang,E.Galvan-Lopez.Evolving Decision Rules to Discover Patterns in Financial Data Sets.Computational Methods in Financial Engineering,to be published.
    [2] N.Japkowicz,The Class Imbalance Problem:Significance and Strategies.In Proceedings of International Conference on Artificial Intelligence,Las Vegas,Nevada,USA,vol.1,pp.111-117,2000.
    [3] G.M.Weiss.Mining with Rarity:A Unifying Framework.ACM SIGKDD Explorations Newsletter,vol.6,no.1,pp.7-19,2004.
    [4] G.E.A.P.A.Batista,R.C.Prati,M.C.Monard.A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data.ACM SIGKDD Explorations Newsletter,vol.6,no.1,pp.20-29,2004.
    [5] K.McCarthy,B.Zabar,G.Weiss.Does Cost-sensitive Learning Beat Sampling for Classifying Rare Classes? In Proceedings of the 1st International Workshop on Utilitybased Data Mining,ACM Press,New York,NY,USA,pp.69-77,2005.
    [6] M.Kubat,R.C.Holte,S.Matwin.Machine Learning for the Detection of Oil Spills in Satellite Radar Images.Machine Learning,vol.30,no.2-3,pp.195-215,1998.
    [7] T.Fawcett,F.J.Provost.Adaptive Fraud Detection.Data Mining and Knowledge Discovery,vol.1,no.3,pp.291-316,1997.
    [8] M.Greiner,D.Pfeiffer,R.D.Smith.Principles and Practical Application of Receiver-operating Characteristic Analysis for Diagnostic Tests,Preventive Veterinary Medicine,vol.45,no.1-2,pp.23-41,2000.
    [9] G.Vanagas.Receiver Operating Characteristic Curves and Comparison of Cardiac Surgery Risk Stratification Systems.Interactive Cardiovascular and Thoracic Surgery,vol.3,no.2,pp.319-322,2004.
    [10] A.L.Garcia-Almanza,E.P.K.Tsang.Forecasting Stock Prices Using Genetic Programming and Chance Discovery.In Proceedings of the 12th International Conference on Computing in Economics and Finance,2006,[Online] ,Available:http://ideas.repec.org/p/sce/scecfa/489.html,July 4,2006.
    [11] A.L.Garcia-Almanza,E.P.K.Tsang.Detection of Stock Price Movements Using Chance Discovery and Genetic Programming.Innovation in Knowledge-based and Intelligent Engineering Systems,to be published.
    [12] E.P.K.Tsang,J.Li,J.M.Butler.Eddie Beats the Bookies.Software:Practice and Experience,vol.28,no.10,pp.1033-1043,1998.
    [13] J.Li,A Genetic Programming Based Tool for Financial Forecasting,Ph.D.dessertation,Department of Computer Science,University of Essex,UK,2001.
    [14] E.P.K.Tsang,P.Yung,J.Li.Eddie-automation:A Decision Support Tool for Financial Forecasting.Journal of Decision Support Systems,vol.37,no.4,pp.559-565,2004.
    [15] E.P.K.Tsang,S.Markose,H.Er.Chance Discovery in Stock Index Option and Future Arbitrage.New Mathematics and Natural Computation,vol.1,no.3,pp.435-447,2005.
    [16] A.L.Garcia-Almanza,E.P.K.Tsang.The Repository Method for Chance Discovery in Financial Forecasting.In Proceedings of the 10th International Conference on Knowledge-based and Intelligent Information and Engineering Systems,Lecture Notes in Computer Science,SpringerVerlag,Boumemouth,UK,pp.30-37,2006.
    [17] A.L.Garcia-Almanza,E.P.K.Tsang.Repository Method to Suit Different Investment Strategies.In Proceedings of IEEE Congress on Evolutionary Computation,Singapore,pp.790-797,2007.
    [18] J.Koza.Genetic Programming:On the Programming of Computers by Means of Natural Selection,MIT Press,Cambridge,Massachusetts,UK,1992.
    [19] L.J.Fogel,A.J.Owens,M.J.Walsh.Adaptation in Natural and Artificial Systems,University of Michigan Press,Ann Arbor,MI,USA,pp.131-156,1975.
    [20] S.Smith.Flexible Learning of Problem Solving Heuristics through Adaptive Search.In Proceedings of the 8th International Joint Conference on Artificial Intelligence,Karlsruhe,FRG,pp.422-425,1983.
    [21] K.A.De Jong,W.M.Spears.Learning Concept Classification Rules using Genetic Algorithms.In Proceedings of the 12th International Conference on Artificial Intelligence,Sidney,Australia,pp.651-656,1991.
    [22] A.Niimi,E.Tazaki.Rule Discovery Technique Using Genetic Programming Combined with Apriori Algorithm.In Proceedings of the 3rd International Conference on Discovery Science,Lecture Notes in Computer Science,Springer,Kyoto,Japan,vol.1967,pp.273-277,2000.
    [23] C.C.Bojarczuk,H.S.Lopes,A.A.Freitas.An Innovative Application of a Constrained-syntax Genetic Programming System to the Problem of Predicting Survival of Patients.In Proceedings of the 6th European Conference on Genetic Programming,Lecture Notes in Computer Science,Springer-Verlag,vol.2610,pp.11-59,2003.
    [24] C.C.Bojarczuk,H.S.Lopes,A.A.Freitas,E.L.Michalkiewicz.A Constrained-syntax Genetic Programming System for Discovering Classification Rules:Application to Medical Data Sets.Artificial Intelligence in Medicine,vol.30,no.1,pp.27-48,2004.
    [25] P.Angeline.Genetic Programming and Emergent Intelligence.Advances in Genetic Programming,vol.1,pp.75-98,1994.
    [26] T.Soule.Code Growth in Genetic Programming,Ph.D.dissertation,College of Graduate Studies,University of Idaho,Moscow,Idaho,USA,1998.
    [27] A.L.Garcia-Almanza,E,P.K.Tsang.Simplifying Decision Trees Learned by Genetic Algorithms.In Proceedings of Congress on Evolutionary Computation,San Francisco,California,USA,pp.7906-7912,2006.
    [28] T.Fawcett.Roc Graphs:Notes and Practical Considerations for Researchers,Technical Report HPL-2003-4,HP Laboratories,CA,2003.
    [29] F.J.Provost,T.Fawcett.Robust Classification for Imprecise Environments.Machine Learning,vol.42,no.3,pp.203-231,2001.
    [30] C.C.Bojarczuk,H.S.Lopes,A.A.Freitas.Discovering Comprehensible Classification Rules by Using Genetic Programming:A Case Study in a Medical Domain.In Proceedings of the Genetic and Evolutionary Computation Conference,Morgan Kaufmann,Orlando,Florida,USA,vol.2,pp.953-958,1999.
    [31] J.J.Huang,G.H.Tzeng,C.S.Ong.Two-stage Genetic Programming (2SGP) for the Credit Scoring Model.Applied Mathematics and Computation,vol.174,no.2,pp.1039-1053,2006.
    [32] C.Yin,S.Tian,H.Huang,J.He.Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection,In proceedings of the 1st International Conference on Advances in Natural Computation,Lecture Notes in Computer Science,Springer,Changsha,China,vol.3612,pp.323-331,2005.
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Evolving Decision Rules to Predict Investment Opportunities

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 investors 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.

Alma Lilia Garcia-Almanza and Edward P. K. Tsang. Evolving Decision Rules to Predict Investment Opportunities. International Journal of Automation and Computing, vol. 5, no. 1, pp. 22-31, 2008. doi: 10.1007/s11633-008-0022-2
Citation: Alma Lilia Garcia-Almanza and Edward P. K. Tsang. Evolving Decision Rules to Predict Investment Opportunities. International Journal of Automation and Computing, vol. 5, no. 1, pp. 22-31, 2008. doi: 10.1007/s11633-008-0022-2
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