Technical analysis and artificial neural networks as prediction tools in the equity markets
This study investigates the possible forecast power of a wide spectrum of technical rules on the equity markets. Two equity indices (Nasdaq Composite and Athens General Index) have been chosen as case studies. The results of the tests show that there is evidence of forecast power for many of the technical strategies. The optimization methodology improves substantially the achieved returns. The performance is higher in the case of Nasdaq Composite which could be a paradox since it is a much more developed and efficient market. The performance of technical strategies deteriorates dramatically during the most recent period. When transaction costs are taken into account in a realistic way, the technical trading strategies fail in the majority of the cases to generate higher returns than a naIve buyand- hold strategy. It is proved that transaction costs generate an inflated effect on the total profits which can be more than double the nominal amount paid in these costs. The importance of trading cost has been underestimated by many previous studies mainly due to unrealistic ways for their calculation. The unsatisfactory performance of technical analysis during different time horizons and data is mainly due to its static nature that deprives the method from adjusting to the constantly changing market and economic conditions. The proposed solution is a trading model that combines artificial neural networks, genetic algorithms and technical analysis. The results are very optimistic since there is evidence for significant forecast power and consistent abnormal returns in a very difficult short term prediction as it is to predict next day's market direction.