data snooping, technical trading rules have statistically significant forecasting power and can generate economically significant profits. This section briefly outlines the different chapters of the thesis. The chapters are written independently from each other with a separate introduction for each chapter. Now and then there is some repetition in the text, but this is mainly done to keep each chapter self contained. Chapters 2 through 5 are mainly empirical, while Chapter 6 describes a theoretical model.
In Chapter 2 a large set of 5350 trend-following technical trading rules is applied to the price series of cocoa futures contracts traded at the London International Financial Futures Exchange (LIFFE) and the New York Coffee, Sugar and Cocoa Exchange (CSCE), in the period January 1983 through June 1997. The trading rule set is also applied to the Pound-Dollar exchange rate in the same period. It is found that 58% of the trading rules generates a strictly positive excess return, even if a correction is made for transaction costs, when applied to the LIFFE cocoa futures prices. Moreover, a large set of trading rules exhibits statistically significant forecasting power if applied to the LIFFE cocoa futures series. On the other hand the same set of strategies performs poor on the CSCE cocoa futures prices, with only 12% generating strictly positive excess returns and hardly showing any statistically significant forecasting power. Bootstrap techniques reveal that the good results found for the LIFFE cocoa futures price series cannot be explained by several popular null models like a random walk, autoregressive and GARCH model, but can be explained by a structural break in trend model. The large difference in the performance of technical trading may be attributed to a combination of the demand/supply mechanism in the cocoa market and an accidental influence of the Pound-Dollar exchange rate, reinforcing trends in the LIFFE cocoa futures but weakening trends in the CSCE cocoa futures. Furthermore, our case study suggests a connection between the success or failure of technical trading and the relative magnitudes of trend, volatility and autocorrelation of the underlying series.
In the next three chapters, Chapters 3-5, a set of trend-following technical trading rules is applied to the price history of several stocks and stock market indices. Two different performance measures are used to select the best technical trading strategy, namely the mean return and the Sharpe ratio criterion. Corrections are made for transaction costs. If technical trading shows to be profitable, then it could be the case that these profits are merely the reward for bearing the risk of implementing technical trading. Therefore Sharpe-Lintner capital asset pricing models (CAPMs) are estimated to test this hypothesis. Furthermore, if technical trading shows economically and statistically significant forecasting power after corrections are made for transaction costs and risk, then it is tested whether the selected technical trading strategy is genuinely superior to the buy-