IDEAS home Printed from https://ideas.repec.org/a/eee/dyncon/v25y2001i1-2p213-244.html
   My bibliography  Save this article

Financial returns and efficiency as seen by an artificial technical analyst

Author

Listed:
  • 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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Skouras, Spyros, 2001. "Financial returns and efficiency as seen by an artificial technical analyst," Journal of Economic Dynamics and Control, Elsevier, vol. 25(1-2), pages 213-244, January.
  • Handle: RePEc:eee:dyncon:v:25:y:2001:i:1-2:p:213-244
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-1889(99)00074-3
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W, 1994. " Contrarian Investment, Extrapolation, and Risk," Journal of Finance, American Finance Association, vol. 49(5), pages 1541-1578, December.
    2. Scholes, Myron & Williams, Joseph, 1977. "Estimating betas from nonsynchronous data," Journal of Financial Economics, Elsevier, vol. 5(3), pages 309-327, December.
    3. Hansen, Lars Peter & Jagannathan, Ravi, 1991. "Implications of Security Market Data for Models of Dynamic Economies," Journal of Political Economy, University of Chicago Press, vol. 99(2), pages 225-262, April.
    4. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
    5. Taylor, Stephen J, 1992. "Rewards Available to Currency Futures Speculators: Compensation for Risk or Evidence of Inefficient Pricing?," The Economic Record, The Economic Society of Australia, vol. 0(0), pages 105-116, Supplemen.
    6. Richard B. Olsen & Michel M. Dacorogna & Ulrich A. Muller, & Olivier V. Pictet, "undated". "Going Back to the Basics - Rethinking Market Efficiency," Working Papers 1992-09-07., Olsen and Associates.
    7. repec:cdl:ucsbec:13-89 is not listed on IDEAS
    8. Cumby, Robert E. & Modest, David M., 1987. "Testing for market timing ability : A framework for forecast evaluation," Journal of Financial Economics, Elsevier, vol. 19(1), pages 169-189, September.
    9. Christoffersen, Peter F & Diebold, Francis X, 1996. "Further Results on Forecasting and Model Selection under Asymmetric Loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 561-571, Sept.-Oct.
    10. Neely, Christopher & Weller, Paul & Dittmar, Rob, 1997. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, pages 405-426.
    11. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data-Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    12. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
    13. Blake LeBaron, "undated". "Technical Trading Rules and Regime Shifts in Foreign Exchange," Working papers _007, University of Wisconsin - Madison.
    14. Neftci, Salih N, 1991. "Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis."," The Journal of Business, University of Chicago Press, vol. 64(4), pages 549-571, October.
    15. W. Brian Arthur, 1992. "On Learning and Adaptation in the Economy," Working Papers 854, Queen's University, Department of Economics.
    16. Ross, Stephen A, 1987. "The Interrelations of Finance and Economics: Theoretical Perspectives," American Economic Review, American Economic Association, vol. 77(2), pages 29-34, May.
    17. G. Hanoch & H. Levy, 1969. "The Efficiency Analysis of Choices Involving Risk," Review of Economic Studies, Oxford University Press, vol. 36(3), pages 335-346.
    18. Allen, Helen & Taylor, Mark P, 1990. "Charts, Noise and Fundamentals in the London Foreign Exchange Market," Economic Journal, Royal Economic Society, vol. 100(400), pages 49-59, Supplemen.
    19. LeRoy, Stephen F, 1989. "Efficient Capital Markets and Martingales," Journal of Economic Literature, American Economic Association, vol. 27(4), pages 1583-1621, December.
    20. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    21. Spyros Skouras, "undated". "A Theory of Technical Analysis," Computing in Economics and Finance 1997 58, Society for Computational Economics.
    22. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. " Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    23. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
    24. Treynor, Jack L & Ferguson, Robert, 1985. " In Defense of Technical Analysis," Journal of Finance, American Finance Association, vol. 40(3), pages 757-773, July.
    25. Bong-Chan, Kho, 1996. "Time-varying risk premia, volatility, and technical trading rule profits: Evidence from foreign currency futures markets," Journal of Financial Economics, Elsevier, vol. 41(2), pages 249-290, June.
    26. Latham, Mark, 1986. " Informational Efficiency and Information Subsets," Journal of Finance, American Finance Association, vol. 41(1), pages 39-52, March.
    27. H. Dennis Tolley & Rulon D. Pope, 1988. "Testing for Stochastic Dominance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 70(3), pages 693-700.
    28. Hussman, John P., 1992. "Market efficiency and inefficiency in rational expectations equilibria : Dynamic effects of heterogeneous information and noise," Journal of Economic Dynamics and Control, Elsevier, vol. 16(3-4), pages 655-680.
    29. Jensen, Michael C., 1978. "Some anomalous evidence regarding market efficiency," Journal of Financial Economics, Elsevier, vol. 6(2-3), pages 95-101.
    30. Blake LeBaron, "undated". "Do Moving Average Trading Rule Results Imply Nonlinearities in Foreign Exchange?," Working papers _005, University of Wisconsin - Madison.
    31. Cooper, Ian & Kaplanis, Evi, 1994. "Home Bias in Equity Portfolios, Inflation Hedging, and International Capital Market Equilibrium," Review of Financial Studies, Society for Financial Studies, vol. 7(1), pages 45-60.
    32. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    33. Franklin Allen & Risto Karjalainen, "undated". "Using Genetic Algorithms to Find Technical Trading Rules (Revised: 20-95)," Rodney L. White Center for Financial Research Working Papers 20-93, Wharton School Rodney L. White Center for Financial Research.
    34. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    35. Olivier V. Pictet & Michel M. Dacorogna & Rakhal D. Dave & Bastien Chopard & Roberto Schirru & Marco Tomassini, "undated". "Genetic Algorithms with collective sharing for Robust Optimization in Financial Applications," Working Papers 1995-02-06., Olsen and Associates.
    36. Skouras, S., 1997. "Analysing Technical Analysis," Economics Working Papers eco97/36, European University Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Bell, Peter N, 2013. "New Testing Procedures to Assess Market Efficiency with Trading Rules," MPRA Paper 46701, University Library of Munich, Germany.
    3. 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.
    4. Pablo Pincheira, 2006. "Shrinkage Based Tests of the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 376, Central Bank of Chile.
    5. 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.
    6. 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.
    7. 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.
    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. Blaskowitz, Oliver & Herwartz, Helmut, 2011. "On economic evaluation of directional forecasts," International Journal of Forecasting, Elsevier, pages 1058-1065.
    10. Florios, Kostas & Skouras, Spyros, 2008. "Exact computation of max weighted score estimators," Journal of Econometrics, Elsevier, vol. 146(1), pages 86-91, September.

    More about this item

    JEL classification:

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

    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:eee:dyncon:v:25:y:2001:i:1-2:p:213-244. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/jedc .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.