Abstract
The variance ratio test suggests that we cannot reject the random walk null hypothesis for three major US stock market indexes between 1990 and 2007. Moreover, we find that the naïve forecasting model based on the random walk assumption frequently generates more accurate forecasts than those generated by the autoregressive integrated moving average forecasting model. Consistent with this finding, we find that the regular application of three commonly used technical trading rules (the moving average crossover rule, the channel breakout rule and the Bollinger band breakout rule) underperform the buy-and-hold strategy between 1990 and 2007. However, we observe significant positive returns on trades generated by the contrarian version of these three technical trading rules, even after considering a 0.5 per cent transaction costs on all trades.
Similar content being viewed by others
Notes
These test statistics of forecasting efficiency are defined by Greene (2000) as follows:
where P*=forecast price, P=actual price, T=number of forecast horizons.
References
Alexander, S. S. (1961) Price movements in speculative markets: Trends or random walks. Industrial Management Review 2: 7–26.
Bollinger, J. (2002) Bollinger on Bollinger Bands. New York: McGraw Hill.
Box, G. P. E. and Jenkins, G. M. (1994) Time Series Analysis: Forecasting and Control. New Jersey: Prentice Hall.
Brock, W., Lakonishok, J. and Lebaron, B. (1992) Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47: 1731–1764.
Darrat, A. F. and Zhong, M. (2000) On testing the random-walk hypothesis: A model-comparison approach. The Financial Review 35: 105–124.
Fama, E. F. (1965) The behavior of stock market prices. Journal of Business 38: 34–105.
Fama, E. F. (1970) Efficient capital markets: A review of theory and empirical work. Journal of Finance 25: 383–423.
Fama, E. F. (1995) Random walks in stock market prices. Financial Analysts Journal 51 (1): 75–80.
Fama, E. F. and Blume, M. (1966) Filter rules and stock market trading profits. Journal of Business 39: 226–241.
Greene, W. H. (2000) Econometric Analysis, 4th edn. New Jersey: Prentice Hall.
Jensen, M. C. (1967) Random walks: Reality or myth? – Comment. Financial Analysts Journal 23: 77–85.
Jensen, M. C. and Bennington, G. (1970) Random walks and technical theories: Some additional evidences. Journal of Finance 25: 469–482.
Kwon, K. Y. and Kish, R. J. (2002) Technical trading strategies and return predictability: NYSE. Applied Financial Economics 12: 639–653.
Levy, R. A. (1967) Random walks: Reality or myth? Financial Analysts Journal 23: 69–77.
Lo, A. and MacKinlay, A. C. (1988) Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies 1: 41–66.
Malkiel, B. (1981) A Random Walk Down Wall Street, 2nd edn. New York: Norton.
Samuelson, P. (1965) Proof that properly anticipated prices fluctuate randomly. Industrial Management Review 6: 41–49.
Sweeney, R. (1988) Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis 23: 285–300.?
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Balsara, N., Chen, J. & Zheng, L. Profiting from a contrarian application of technical trading rules in the US stock market. J Asset Manag 10, 97–123 (2009). https://doi.org/10.1057/jam.2008.44
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1057/jam.2008.44