Knowledge Patterns in Evolutionary Decision Support Systems for Financial Time Series Analysis

This paper discusses knowledge patterns in evolutionary learning of decision support systems for time series analysis, especially concerning time series of economical or financial data. It focuses on decision support systems, which use evolutionary algorithms to construct efficient expertises built on the basis of a set of specific expert rules analysing time series, such as artificial stock market financial experts composed of popular technical indicators analysing recent price quotations. Discovering common knowledge patterns in such artificial experts not only leads to an additional improvement of system efficiency, in particular - the efficiency of the evolutionary algorithms applied, but also reveals additional knowledge on phenomena under study. This paper shows a numer of experiments carried out on real data, discusses some examples of the knowledge patterns discovered in terms of their financial relevance as well as compares all the results with some popular benchmarks.

[1]  Richard J. Bauer,et al.  Genetic Algorithms and Investment Strategies , 1994 .

[2]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[3]  Brad G. Kyer.  Review of 5 of biologically inspired algorithms for financial modelling by Anthony Brabazon, Michael O'Neill Springer-Verlag Berlin Heidelberg, 2006 , 2010, SIGA.

[4]  Franklin Allen,et al.  Using genetic algorithms to find technical trading rules , 1999 .

[5]  M. Dempster,et al.  A real-time adaptive trading system using genetic programming , 2001 .

[6]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[7]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (2nd, extended ed.) , 1994 .

[8]  Anthony Brabazon,et al.  Adaptive Trading With Grammatical Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[10]  J. Moody,et al.  Performance functions and reinforcement learning for trading systems and portfolios , 1998 .

[11]  Tony Plummer.  Forecasting financial markets , 1993 .

[12]  Piotr Lipinski,et al.  Discovering Stock Market Trading Rules Using Multi-layer Perceptrons , 2007, IWANN.

[13]  Anthony Brabazon,et al.  Biologically inspired algorithms for financial modelling , 2006, Natural computing series.

[14]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[15]  Jerzy J. Korczak,et al.  Evolution Strategy in Portfolio Optimization , 2001, Artificial Evolution.

[16]  Piotr Lipinski.  ECGA vs. BOA in discovering stock market trading experts , 2007, GECCO '07.

[17]  Christopher J. Neely,et al.  Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach , 1996, Journal of Financial and Quantitative Analysis.

[18]  Jerzy J. Korczak,et al.  Performance Measures in an Evolutionary Stock Trading Expert System , 2004, International Conference on Computational Science.

[19]  Jin Li,et al.  EDDIE In Financial Decision Making , 2001 .

[20]  Jerzy J. Korczak,et al.  Evolutionary building of stock trading experts in a real-time system , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[21]  Robert W. Colby,et al.  The Encyclopedia of Technical Market Indicators , 1988 .

[22]  Alberto Prieto,et al.  Computational and ambient intelligence , 2009, Neurocomputing.

[23]  Stephen Gilmore,et al.  Evaluating the Performance of Skeleton-Based High Level Parallel Programs , 2004, International Conference on Computational Science.

[24]  W. Sharpe.  CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* , 1964 .

[25]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .