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Financial Returns and Efficiency as seen by an Artificial Technical Analyst

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  • Spyros Skouras

    (European University Institute)

Abstract

Previous research has shown that simple trading rules can be useful tools for evaluating financial models. Here we introduce trading rules which are selected by an artificially intelligent agent who learns from experience - an Artificial Technical Analyst. We show that the rules used by this agent can lead to the recognition of subtle regularities in return processes whilst suffering from lesser data-mining problems than other rules commonly used as model evaluation devices. The relationship between the efficiency of financial markets and the efficacy of technical analysis is investigated and it is shown that the Artificial Technical Analyst can be used to provide a quantifiable measure of market efficiency. The measure is applied to the DJIA daily index from 1962 to 1986 and it is shown that a quantification of efficiency based on the profits of an Artificial Technical Analyst can lead to interesting results concerning the behaviour of other investors.

Suggested Citation

  • Spyros Skouras, 1998. "Financial Returns and Efficiency as seen by an Artificial Technical Analyst," Finance 9808001, University Library of Munich, Germany, revised 24 Aug 1998.
    • Handle: RePEc:wpa:wuwpfi:9808001
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    Cited by:

    1. Bell, Peter N, 2013. "New Testing Procedures to Assess Market Efficiency with Trading Rules," MPRA Paper 46701, University Library of Munich, Germany.
    2. Pablo Pincheira, 2006. "Shrinkage Based Tests of the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 376, Central Bank of Chile.
    3. Edward R Dawson & James M. Steeley, 2003. "On the Existence of Visual Technical Patterns in the UK Stock Market," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(1‐2), pages 263-293, January.
    4. Stephan Schulmeister, 2000. "Technical Analysis and Exchange Rate Dynamics," WIFO Studies, WIFO, number 25857, April.
    5. Saacke, Peter, 2002. "Technical analysis and the effectiveness of central bank intervention," Journal of International Money and Finance, Elsevier, vol. 21(4), pages 459-479, August.
    6. Fong, Wai Mun & Yong, Lawrence H. M., 2005. "Chasing trends: recursive moving average trading rules and internet stocks," Journal of Empirical Finance, Elsevier, vol. 12(1), pages 43-76, January.
    7. Blaskowitz, Oliver & Herwartz, Helmut, 2011. "On economic evaluation of directional forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1058-1065, October.
    8. Dewachter, Hans & Lyrio, Marco, 2006. "The cost of technical trading rules in the Forex market: A utility-based evaluation," Journal of International Money and Finance, Elsevier, vol. 25(7), pages 1072-1089, November.
    9. Wang, Zi-Mei & Chiao, Chaoshin & Chang, Ya-Ting, 2012. "Technical analyses and order submission behaviors: Evidence from an emerging market," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 109-128.
    10. Isakov, Dusan & Marti, Didier, 2011. "Technical Analysis with a Long-Term Perspective: Trading Strategies and Market Timing Ability," FSES Working Papers 421, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    11. Chris Doucouliagos, 2005. "Price exhaustion and number preference: time and price confluence in Australian stock prices," The European Journal of Finance, Taylor & Francis Journals, vol. 11(3), pages 207-221.
    12. Florios, Kostas & Skouras, Spyros, 2008. "Exact computation of max weighted score estimators," Journal of Econometrics, Elsevier, vol. 146(1), pages 86-91, September.

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    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • G - Financial Economics

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