IDEAS home Printed from https://ideas.repec.org/p/sce/scecf1/147.html
   My bibliography  Save this paper

Risk Adjusted Returns to Technical Trading Rules: a Genetic Programming Approach

Author

Listed:
  • JP Marney, Colin Fyfe, Heather Tarbert, David Miller

Abstract

Background 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). Using the main six trading currencies, Neely et al. (1996, 1997) find strong evidence of economically significant out-of-sample excess returns to technical trading rules identified by their genetic program. In Allen and Karjaleinen (1999) a genetic algorithm is used to find technical trading rules for the S&P index. Compared to a simple buy-and-hold strategy, these trading rules lead to positive excess returns which are statistically and economically significant. In Fyfe et. al. (1999), a GP is used to discover a successful ÎbuyÌ rule. This discovery, as such, however, was not really a refutation of the EMH, as it was really a form of timing specific buy and hold, which was triggered only once. Nevertheless, the return is superior to buy and hold. Using the S&P 500 index, Neely (2001) finds no evidence that technical trading rules identified by a GP significantly outperform buy-and-hold on a risk-adjusted basis. For the case of intraday trading on the forex market, Neely and Weller (2001) find no evidence of excess returns to trading rules derived from a GP and an optimised linear forecasting model. Indeed Neely (2001) observes that a number of studies÷'have generally evaluated raw excess returns rather than explicitly risk-adjusted returns, leaving unclear the implications of their work for the efficient markets hypothesis' (2001, p.1). On the other hand, Neely et al. (1996, 1997) did calculate betas associated with foreign currency portfolio holdings, and did not find evidence of excessive risk bearing. Brown, Geotzman and Kumar (1998) and Bessember and Chan (1998) can also be cited in favour of the hypothesis of superior risk-adjusted returns from technical trading signals. Marney et al. (2000) looked again at their 1999 findings by, amongst other things, adjusting for risk. It was found that although there were other rules which apparently performed well by being very active in the market, the impressive returns to these rules turn out on closer inspection to be illusory, as risk adjusted returns did not compare well with simple buy and hold. Nevertheless, paradoxically, we did find a useful role for technical trading. It is possible to substantially improve on buy and hold by timing it right. Hence our argument is that it is worth analysing the market to find a good intervention point. Purpose and method of the investigation Given that very little work has been done on generating technical trading rules which produce excess risk-adjusted profits, and given that the empirical evidence is somewhat ambiguous, there is clearly considerable scope for additional work in this area. What we propose to do then is to re-examine our previous findings, this time within a more rigorous framework which makes use of a wider data set, more extensive use of techniques of risk adjustment, and more demanding assessment of the robustness of the result with respect to GP representation. 1. Hypotheses Can the GP generate technical trading rules which will generate risk-adjusted excess returns out of sample? Secondly, the is there any further evidence for 'timing-specific' buy and hold. Thirdly, are there any technical trading rules which generalise across data sets or time-periods? 2. Data Set Our data set is drawn from long time series for 5 US shares from a disparate set of industrial sectors and also the S&P 500. 3. Risk adjustment In this study we look at a variety of risk measures including Betas, Sharpe ratios and the X* statistic. 4. The GP - As in Marney et al. (2000) we consider how robust our conclusion is with respect to the GP method used.

Suggested Citation

  • JP Marney, Colin Fyfe, Heather Tarbert, David Miller, 2001. "Risk Adjusted Returns to Technical Trading Rules: a Genetic Programming Approach," Computing in Economics and Finance 2001 147, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:147
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Keywords

    Technical Trading; Efficient Markets Hypothesis; Genetic Programming; Parallel Computing;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sce:scecf1:147. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/sceeeea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.