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Technical Trading Rules and the Size of the Risk Premium in Security Returns

Author

Listed:
  • Gencay Ramazan

    (Department of Economics University of Windsor)

  • Stengos Thanasis

    (Department of Economics University of Guelph)

Abstract

Among analysts, technical trading rules are widely used for forecasting security returns. Recent literature provides evidence that these rules may provide positive profits after accounting for transaction costs. This would be contrary to the theory of the efficient market hypothesis which states that security prices cannot be forecasted from their past values or other past variables. This paper uses the daily Dow Jones Industrial Average Index from 1963 to 1988 to examine the linear and nonlinear predictability of stock market returns with simple technical trading rules, by using the nearest neighbors and the feedforward network regressions. Evidence of nonlinear predictability is found in the stock market returns by using the past returns and the buy and sell signals of the moving average rules.

Suggested Citation

  • Gencay Ramazan & Stengos Thanasis, 1997. "Technical Trading Rules and the Size of the Risk Premium in Security Returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(2), pages 1-14, July.
    • Handle: RePEc:bpj:sndecm:v:2:y:1997:i:2:n:1
    DOI: 10.2202/1558-3708.1026
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    Cited by:

    1. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    2. Neely, Christopher J., 2003. "Risk-adjusted, ex ante, optimal technical trading rules in equity markets," International Review of Economics & Finance, Elsevier, vol. 12(1), pages 69-87.
    3. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    4. Mariano Matilla-Garcia & Carlos Arguello, 2005. "A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market," Applied Economics Letters, Taylor & Francis Journals, vol. 12(5), pages 303-308.
    5. Bokhari, Jawaad & Cai, Charlie & Hudson, Robert & Keasey, Kevin, 2005. "The predictive ability and profitability of technical trading rules: does company size matter?," Economics Letters, Elsevier, vol. 86(1), pages 21-27, January.
    6. Nam, Kiseok & Washer, Kenneth M. & Chu, Quentin C., 2005. "Asymmetric return dynamics and technical trading strategies," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 391-418, February.
    7. Ni, Yensen & Liao, Yi-Ching & Huang, Paoyu, 2015. "MA trading rules, herding behaviors, and stock market overreaction," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 253-265.
    8. Gaunersdorfer, Andrea, 2000. "Endogenous fluctuations in a simple asset pricing model with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 799-831, June.
    9. Bill Cai & Charlie Cai & Kevin Keasey, 2005. "Market Efficiency and Returns to Simple Technical Trading Rules: Further Evidence from U.S., U.K., Asian and Chinese Stock Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 12(1), pages 45-60, March.
    10. Nowman, K. Ben & Saltoglu, Burak, 2003. "Continuous time and nonparametric modelling of U.S. interest rate models," International Review of Financial Analysis, Elsevier, vol. 12(1), pages 25-34.
    11. Dibeh, Ghassan, 2005. "Speculative dynamics in a time-delay model of asset prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 199-208.
    12. Kwang-il Choe & Joshua Krausz & Kiseok Nam, 2011. "Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 36(3), pages 323-353, April.
    13. Jiali Fang & Ben Jacobsen & Yafeng Qin, 2014. "Predictability of the simple technical trading rules: An out‐of‐sample test," Review of Financial Economics, John Wiley & Sons, vol. 23(1), pages 30-45, January.
    14. Walid Omrane & Hervé Oppens, 2006. "The performance analysis of chart patterns: Monte Carlo simulation and evidence from the euro/dollar foreign exchange market," Empirical Economics, Springer, vol. 30(4), pages 947-971, January.
    15. Guanqing Liu, 2019. "Technical Trading Behaviour: Evidence from Chinese Rebar Futures Market," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 669-704, August.
    16. Yochanan Shachmurove & Uri BenZion & Paul Klein & Joseph Yagil, 2001. "A Moving Average Comparison of the Tel-Aviv 25 and S&P 500 Stock Indices," Penn CARESS Working Papers 4731f3394c43bebf4d3191c81, Penn Economics Department.

    More about this item

    Keywords

    market efficiency; technical trading rules; feedforward networks;
    All these keywords.

    JEL classification:

    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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