%0 Conference Paper %1 marney:2001:SCE %A Marney, John Paul %A Miller, D. %A Fyfe, Colin %A Tarbert, Heather F. E. %B 7th International Conference of Society of Computational Economics %C Yale %D 2001 %K algorithms, genetic programming %T Risk Adjusted Returns to Technical Trading Rules: a Genetic Programming Approach %X This paper is a continuation of our investigation of the paradox of technical analysis in the stock market (Fyfe, Marney and Tarbert 1999), Marney et. al (2000). The Efficient Markets Hypothesis (hereafter the EMH) holds that there should be no discernible pattern in share price data or the prices of other frequently traded financial instruments, as financial markets are efficient. Prices therefore should follow an information-free random-walk. Nevertheless, technical analysis is a common and presumably profitable practice among investment professionals. Applications of Genetic Programming and Genetic Algorithms to the extraction of Technical Trading Patterns from financial data. The subset of technical trading research which is concerned with the application of GAs, GPs and neural networks is very new and underdeveloped and therefore of considerable potential. The most notable empirical work which has been done in this area is that of Neely, Dittmar and Weller (1996, 1997), Neely and Weller (2001) and Neely (2001). We have also done some work in this area ourselves (Fyfe et al. 1999, Marney et al. 2000). The theoretical underpinning for this kind of approach to finding technical trading patterns is provided by the work of Arthur et al. (1997).

@inproceedings{marney:2001:SCE, abstract = {This paper is a continuation of our investigation of the paradox of technical analysis in the stock market (Fyfe, Marney and Tarbert 1999), Marney et. al (2000). The Efficient Markets Hypothesis (hereafter the EMH) holds that there should be no discernible pattern in share price data or the prices of other frequently traded financial instruments, as financial markets are efficient. Prices therefore should follow an information-free random-walk. Nevertheless, technical analysis is a common and presumably profitable practice among investment professionals. Applications of Genetic Programming and Genetic Algorithms to the extraction of Technical Trading Patterns from financial data. The subset of technical trading research which is concerned with the application of GAs, GPs and neural networks is very new and underdeveloped and therefore of considerable potential. The most notable empirical work which has been done in this area is that of Neely, Dittmar and Weller (1996, 1997), Neely and Weller (2001) and Neely (2001). We have also done some work in this area ourselves (Fyfe et al. 1999, Marney et al. 2000). The theoretical underpinning for this kind of approach to finding technical trading patterns is provided by the work of Arthur et al. (1997).}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Yale}, author = {Marney, John Paul and Miller, D. and Fyfe, Colin and Tarbert, Heather F. E.}, biburl = {https://www.bibsonomy.org/bibtex/29073a3d1de21e7bffa7e7a6a95aece5d/brazovayeye}, booktitle = {7th International Conference of Society of Computational Economics}, interhash = {86bb27810806e6c5e100da9acc736f49}, intrahash = {9073a3d1de21e7bffa7e7a6a95aece5d}, keywords = {algorithms, genetic programming}, month = {28-29 June}, notes = {http://www.econ.yale.edu/sce01/confpage.html 22 aug 2004 http://ideas.repec.org/p/sce/scecf1/147.html CEF 2001}, organisation = {Society for Computational Economics}, timestamp = {2008-06-19T17:46:22.000+0200}, title = {Risk Adjusted Returns to Technical Trading Rules: a Genetic Programming Approach}, year = 2001 }