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Financial returns and efficiency as seen by an artificial technical analyst

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  • Skouras, Spyros

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 25 (2001)
Issue (Month): 1-2 (January)
Pages: 213-244

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Handle: RePEc:eee:dyncon:v:25:y:2001:i:1-2:p:213-244

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Citations

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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. Dewachter, H.D.R. & Lyrio, M., 2003. "The Cost of Technical Trading Rules in the Forex Market: A Utility-based Evaluation," ERIM Report Series Research in Management ERS-2003-052-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  3. 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.
  4. Pablo Pincheira, 2006. "Shrinkage Based Tests of the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 376, Central Bank of Chile.
  5. Hristos Doucouliagos, 2003. "Price Exhaustion and Number Preference: Time and Price Confluence in Australian Stock Prices," Economics Series 2003_06, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  6. Blaskowitz, Oliver & Herwartz, Helmut, 2011. "On economic evaluation of directional forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1058-1065, October.
  7. Florios, Kostas & Skouras, Spyros, 2008. "Exact computation of max weighted score estimators," Journal of Econometrics, Elsevier, vol. 146(1), pages 86-91, September.
  8. 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.
  9. 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.

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