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

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

Ramazan Gençay
Department of Economics
University of Windsor

Thanasis Stengos
Department of Economics
University of Guelph


Pages 23-34

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.

Bibliography

  • Brock, W. A., J. Lakonishok, and B. LeBaron (1992). "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns." Journal of Finance 47:1731-1764.
  • Chong, Y., and D. Hendry (1986). "Econometric Evaluation of Linear Macroeconomic Models." Review of Economic Studies LIII:671-690.
  • Chopra, N., J. Lakonishok, and J. R. Ritter (1992). "Performance Measurement Methodology and the Question of Whether Stocks Overreact." Journal of Financial Economics 31:235-268.
  • Colby, R. W., and T. A. Meyers (1988). The Encyclopedia of Technical Market Indicators . Irwin, Illinois: Business One.
  • Conrad, J., and G. Kaul (1988). "Time-Variation in Expected Returns." Journal of Business 61:409-425.
  • Cutler, D. M., J. M. Poterba, and L. H. Summers (1991). "Speculative Dynamics." Review of Economic Studies 58:529-546.
  • Cybenko, G. (1989). "Approximation by Superposition of a Sigmoidal Function." Mathematics of Control, Signals and Systems 2:303-314.
  • De Bondt, W. F. M., and R. H. Thaler (1985). "Does the Stock Market Overreact?" Journal of Finance 40:793-805.
  • Diebold, F. X., and R. S. Mariano (1995). "Comparing Forecasting Accuracy." Journal of Business and Economic Statistics 13:253-263.
  • Engle, R. F., D. Lilien, and R. P. Robins (1987). "Estimating Time-Varying Risk Premia in the Term Structure: The ARCH-M Model." Econometrica 55:391-407.
  • 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 25:383-417.
  • Fama, E. F. (1991). "Efficient Capital Markets: II." Journal of Finance 46:1575-1617.
  • Fama, E. F., and K. R. French (1986). "Permanent and Temporary Components of Stock Prices." Journal of Political Economy 98:246-274.
  • Fisher, L. (1966). "Some New Stock-Market Indexes." Journal of Business 39:191-225.
  • French, K. R., and R. Roll (1986). "Stock Return Variances: The Arrival of Information and the Reaction of Traders." Journal of Financial Economics 17:5-26.
  • Funahashi, K.-I. (1989). "On the Approximate Realization of Continuous Mappings by Neural Networks." Neural Networks 2:183-192.
  • Gallant, A. R., and H. White (1988). "There Exists a Neural Network that Does Not Make Avoidable Mistakes." In Proceedings of the Second Annual IEEE Conference on Neural Networks . New York: IEEE Press, pp. 657-664.
  • Gallant, A. R., and H. White (1992). "On Learning the Derivatives of an Unknown Mapping with Multilayer Feedforward Networks." Neural Networks 5:129-138.
  • Hardle, W. (1990). Applied Nonparametric Regression . Cambridge, UK: Cambridge University Press.
  • Hecht-Nielsen, R. (1989). "Theory of the Backpropagation Neural Networks." In Proceedings of the International Joint Conference on Neural Networks . New York: IEEE Press, pp. 593-605.
  • Hornik, K., M. Stinchcombe, and H. White (1989). "Multilayer Feedforward Networks are Universal Approximators." Neural Networks 2:359-366.
  • Hornik, K., M. Stinchcombe, and H. White (1990). "Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks." Neural Networks 3:551-560.
  • Jegadeesh, N. (1990). "Evidence of Predictable Behavior of Security Returns." Journal of Finance 45:881-898.
  • Jensen, M. C. (1978). "Some Anomalous Evidence Regarding Market Efficiency." Journal of Financial Economics 6:95-101.
  • Kuan, C.-M., and H. White (1994). "Artificial Neural Networks: An Econometric Perspective." Econometric Reviews 13:1-91.
  • Lehmann, B. N. (1990). "Fads, Martingales and Market Efficiency." Quarterly Journal of Economics 105:1-28.
  • Lo, A. W., and A. C. MacKinlay (1988). "Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test." Review of Financial Studies 1:41-66.
  • Lo, A. W., and A. C. MacKinlay (1990). "When are Contrarian Profits Due to Stock Market Overreaction?" Review of Financial Studies 3:175-205.
  • Mizrach, B. (1995). "Forecast Comparison in L2." Working Paper 95-24, Rutgers University.
  • Nelson, D. B. (1991). "Conditional Heteroskedasticity in Asset Returns: A New Approach." Econometrica 59:347-370.
  • Poterba, J. M., and L. H. Summers (1988). "Mean Reversion in Stock Prices: Evidence and Implications." Journal of Financial Economics 22:27-59.
  • Robinson, P. M. (1987). "Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form." Econometrica 55:875-891.
  • Stone, C. J. (1977). "Consistent Nonparametric Regression." Annals of Statistics 5:595-645.
  • Yakowitz, S. (1987). "Nearest-Neighbor Methods for Time Series Analysis." Journal of Time Series Analysis 8:235-247.