Abstract
The principle objective of this paper is to obtain trading rules with a low risk level which are also capable of obtaining high returns. To that purpose a methodology has been defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. In GAP a chromosome is composed of a tree with language operators and a vector with numeric values. The GAP algorithm implements the automatic search for trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. In order to diminish high over-fitting, a technique of incremental training has been used. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. Insert your abstract here. Include keywords, PACS and mathematical subject classification numbers as needed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alcal-Fdez J, Snchez L, Garca S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernndez JC, Herrera F (2008) Keel: A software tool to assess evolutionary algorithms to data mining problems. Softw Comput doi:10.1007/s00500-008-0323-y
Allen F, Karjalainen R (1999) Using genetic algorithms to find technical trading rules. J Financ Econ 51:245–271
Bao D (2008) A generalized model for financial time series representation and prediction. Appl Intell 29(1):1–11. doi:10.1007/s10489-007-0055-1
Becker LA, Seshadri M (2003) Comprehensibility and overfitting avoidance in genetic programming for technical trading rules. Technical report, Worcester Polytechnic Institute
Becker LA, Seshadri M (2003) GP-evolved technical trading rules can outperform buy and hold. In: Procceedings of the sixth international conference on computational intelligence and natural computing. Embassy Suites Hotel and Conference Center, Cary, North Carolina USA, September 26–30
Brenda M, Blanning R, Ho S (2006) Genetic algorithms in logic tree decision modeling. Eur J Oper Res 170:597–612
Chen H (2003) Equity premium prediction and investment strategy searching with genetic programming. Technical report, Faculty of Wiskunde en Natuurwetenschappen of the Universiteit Leiden, July
Coello-Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems. Kluwer Academic, Amsterdam
Elaoud S, Loukil T, Teghem J (2007) The pareto fitness genetic algorithm: Test function study. Eur J Oper Res 177:1703–1719
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading. ISBN 0-201-15767-5
Holland JH (1975) Adaptation in natural and artificial system. University of Michigan Press, Michigan
Howard L, D’Angelo D (1995) The GA-P: A genetic algorithm and genetic programming hybrid. IEEE Expert, 11–15
Kapishnikov A, Borisov A (2001) Technical rules optimization using intelligent hybrid systems. Technical report, Institute of Information Technology Riga Technical University
Koza J (1992) Genetic programming: On the programming of computers by means of natural selection and genetic. MIT press, Cambridge
Lam SS, Lam KP, Ng HS (2002) Genetic fuzzy expert trading system for nasdaq stock market timing. In: Genetic algorithms and genetic programming in computational finance. The Open University of Hong Kong and The Chinesse University of Hong Kong
Li J, Tsang EPK (2000) Reducing failures in investment recommendations using genetic programming. In: Computing in economics and finance, Universitat Pompeu Fabra, Barcelona, Spain, 6–8 July
Matilla-Garcia M (2006) Are trading rules based on genetic algorithms profitable? Appl Econ Lett 13:123–126
Michalewicz Z (1992) Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin
Mochn A, Quintana D, Sez Y, Isasi P (2008) Soft computing techniques applied to finance. Appl Intell 29(2):111–115. doi:10.1007/s10489-007-0051-5
Neely CJ (2001) Risk-adjusted, ex ante, optimal, technical trading rules in equity markets. Technical report, Federal Reserve Bank of St. Louis
O’Neill M, Brabazon A, Ryan C (2002) Forecasting market indices using evolutionary automatic programming: A case study. In: Genetic algorithms and genetic programming in computational finance. University of Limerick (Ireland) and University College Dublin (Ireland)
Potvin J-Y, Soriano P, Valle M (2004) Generating trading rules on the stock markets with genetic programming. Comput Oper Res 31:1033–1047
Prez Serrada A (1996) Una introduccin a la computacin evolutiva. Instituto de Tecnologas Avanzadas de la Produccin, Universidad de Valladolid, Marzo
Sharpe WF (1966) Mutual fund performance. J Bus Suppl Secur Prices 39:119–38
Xufre Casqueiro P, Rodrigues AJL (2006) Neuro-dynamic trading methods. Eur J Oper Res 175:1400–1412
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fernandez Garcia, M.E., de la Cal Marin, E.A. & Quiroga Garcia, R. Improving return using risk-return adjustment and incremental training in technical trading rules with GAPs. Appl Intell 33, 93–106 (2010). https://doi.org/10.1007/s10489-008-0151-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-008-0151-x