Abstract
In this paper we assesss whether some simple forms of technical analysis can predict stock price movements in the Madrid Stock Exchange. To that end, we use daily data for General Index of the Madrid Stock Exchange, covering the thirtyone- year period from January 1966-October 1997.
Our results provide strong support for profitability of these technical trading rules. By making use of bootstrap techniques, we show that returns obtained from these trading rules are not consistent with several null models frequently used in finance, such as AR(1). GARCH and GARCH-M.
Introduction
"Time present and time past are both perhaps present in future time and time future contained in time past" T. S. Eliot ("Burnt Norton", Four Quarters)
Technical analysis test historical data attempting to establish specific rules for buying and selling securities with the objective of maximising profits and minimising risk of loss. The basic idea is that "prices moves in trends which are determined by changing attitudes of investors toward a variety of economic, monetary, political and psychological forces" (Pring, 1991, p. 2).
Although technical trading rules have been used in financial markets for over a century (see, e. g., Plummer, 1989), it is only during the last decade, with growing evidence that financial markets may be less efficient than was originally believed, that the academic literature is showing a growing interest in such rules. Furthermore, surveys among market participant show that many use technical analysis to make decisions on buying and selling.
For example, Taylor and Allen (1992) report that 90% of the respondents (among 353 chief foreign exchange dealers in London) say that they place some weight on technical analysis when forming views for one or more time horizons. A considerable amount of work has provided support for the view that technical trading rules are capable of producing valuable economic signals. On the one hand, technical analysis has been placed on more firmer theoretical foundations.
Brown and Jennings (1989), for instance, demonstrate that, under a dynamic equilibrium model with heterogeneous market participants, rational investors use past prices in forming their demands. Neftci (1991) shows that trading rules derived by technical analysis could be formalized as nonlinear predictors. Finally, Clyde and Osler (1997) provide a theoretical foundation for technical analysis as a method for doing nonlinear forecasting on high dimension systems.
On the other hand, in empirical work, Brock et al. (1992) (BLL from now on) use bootstrap simulations1 of various null asset pricing models and find that simple technical trading rule profits cannot be explained away by the popular statistical models of stock index returns. Levich and Thomas (1993) use the same bootstrap simulation technique to provide evidence on the profitability and statistical significance of technical trading rules in the foreign exchange market with currency future data. Finally, Gençay (1998) investigates the nonlinear predictability of stock market combinibg simple technical trading rules and feedforward networks.
His results indicate strong evidence of nonlinear predictability in the stock merket returns by using the buy-sell signals of the moving average rules. These are findings of potential importance, and we consider that it is of interest to investigate whether similar results hold for other stock markets. Therefore, the purpose of this paper is to examine the predictive ability of technical trading rules in the Madrid Stock Exchange, by analysing daily data on the General Index for the thirty-one-year period from 1966 to 1997.
Our study can be viewed as contributing to a growing body of research testing nonlinear dependencies in financial prices. Early tests for the presence of nonlinearities, testing the null hypothesis of independent and identical distribution (iid), indicate that nonlinearities are indeed present in stock markets [see Ramsey (1990), Hsieh (1991) and Pununzi and Ricci (1993) among others, and, for the Spanish case, Olmeda and Pérez (1995) and Fernández-Rodríguez et al. (1997)].
The rest of the paper is organised as follows. In Section 2 we describe the data set and introduce the technical rules used. Section 3 offers some preliminary results. In Section 4 the empirical results from the bootstrap simulations are presented. Finally, Section 5 provides some concluding remarks.
Note:
1. Bootstraping is a method, introduced by Efron (1979), for estimating the distributions of statistics that are otherwise dificult or imposible to determine. The general idea behind the bootstrap is to use resampling to estimate an empirical distribution for the targert statistic. Artificial samples are drawn from the original data, being the statistic of interest recalculated on the basis of each artificial sample. The resulting "bootstrapped" measures are then used to construct a sampling distribution for the statistic of interest.
By F. Fernández, S. Sosvilla and J. Andrada
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