A Genetic Programming Approach for Optimal Trading Strategies
Keywords:
Computer applications, evolutionary computing and genetic algorithms, learning, natural language processing, web text analysis.Abstract
This paper is about an outline for computerized development of information in stock trade strategy. Actions are extracted from information post offered in open content with no explanation. We study the introduced plan by deriving trade strategy base on scientific indicator and impact of the extract actions. The strategy take the structure of policy that merge scientific trade indicator with a consecutively adaptable, and are exposed throughout the utilize of genetic programming. We discovery that the information changeable is frequently incorporated in the best possible trading policy, representing the further charge of information for projecting purpose and validate our future structure for consequentially incorporate reports in stock trade strategy.
References
 [1] M.-A. Mittermayer and G.F. Knolmayer, “Text Mining Systems for Market Response to News: A Survey,†technical report Institute of Information Systems University of Bern, http://www.ie.iwi.unibe.ch/publikationen/berichte/resource/WP-184, 2006.
[2] S.B. Achelis, Technical Analysis from A to Z. McGraw-Hill, 2000.
[3] J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press,1992.
[4] H. Zhao, “A Multi-Objective Genetic Programming Approach to Developing  Pareto Optimal Decision Trees,†Decision Support Systems, vol. 43, no. 3, pp. 809-826,  2007.
[5] W. Fan, P. Pathak, and L. Wallace, “Nonlinear Ranking Function Representations in Genetic Pro-gramming-Based Ranking Discovery for Personal-ized Search,†Decision Support Systems, vol. 42, no. 3, pp. 1338-1349, 2006.
[6] W.S. Chan, “Stock Price Reaction to News and No-News: Drift and Reversal After Headlines,†J. Fi-nancial Economics, vol. 70, no. 2, pp. 223-260, 2003.
[7] S.G. Ewalds, M.B.J. Schauten, and O.W. Steenbeek, “De Informatiewaarde Van Kwartaalcijfers,† Maandblad voor Accountancy en Bedrijfseconomie, no. 7/8, pp. 333-341, 2000.
[8] R.J. Rosen, “Merger Momentum and Investor Sen-timent: The Stock Market Reaction to Merger An-nouncements,†J. Business, vol. 79, no. 2, pp. 987-1017, 2006.
[9] 9.  J.B. Warner, R.L. Watts, and K.H. Wruck, “Stock Prices and Top Management Changes,†J. Financial Economics, vol. 20, no. 1, pp. 461-492, 1988.
[10] 10.  A.J. Keown and J.M. Pinkerton, “Merger An-nouncements and Insider Trading Activity: An Em-pirical Investigation,†J. Finance, vol. 36, no. 4, pp. 855-869, 1981.
[11] P.C. Tetlock, “More than Words: Quantifying Language to Measure Firms’ Fundamentals,†J. Fi-nance, vol. 63, no. 3, pp. 1437-1467, 2008.
[12] W. Leigh, R. Purvis, and J.M. Ragusa, “Forecasting the NYSE Composite Index with Technical Analy-sis, Pattern Recognizer, Neural Network, and Genet-ic Algorithm: A Case Study in Romantic Decision Support,†Decision Support Systems, vol. 32, no. 4,pp. 361-377, 2002.
[13] 13.  On the information role of stock recommendation revisions$ Oya Altınkılıc- a , Robert S. Hansen b, a Joseph M. Katz Graduate School of Business, Uni-versity of Pittsburgh, Pittsburgh, PA 15260, USA b A.B. Freeman School of Business, Tulane Universi-ty, New Orleans, LA 70118, USA.
[14] V. Milea, F. Frasincar, and U. Kaymak, “tOWL: A Temporal We Ontology Language,†IEEE Trans. Systems, Man and Cybernetics, Part B, Cybernetics, vol. 42, no. 1, pp. 268-281, Feb. 2012.
[15] F. Allen and R. Karjalainen, “Using Genetic Algorithms to Find Technical Trading Rules,†J. Economics, vol. 51, no. 2, pp. 245-271,1999.
[16] F. Hogenboom,  M. de Winter, M. Jansen, A. Ho-genboom,  F.Frasincar, and U. Kaymak, “Event-Based Historical Value-at-Risk,†Proc. IEEE Conf. Computational Intelligence for Financial Eng. Eco-nomics (CIFEr ’12), 2012.
[17] F. Allen and R. Karjalainen, “Using Genetic Algo-rithms to Find Technical Trading Rules,†J. Eco-nomics, vol. 51, no. 2, pp. 245-271,1999.
[18] F. Hogenboom, M. de Winter, M. Jansen, A. Ho-genboom,  F.Frasincar, and U. Kaymak, “Event-Based Historical Value-at-Risk,†Proc. IEEE Conf. Computational Intelligence for Financial Eng. Eco-nomics(CIFEr’12),2012
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