Spot piyasalarda geliştirilen teknik kurallarin vadeli i̇şlem piyasalarinda uygulanabilirliğinin test edilmesi

Futures markets have seen a phenomenal success since their inception in developed countries as well as in developing countries during the last four decades. Though futures markets are novel in developing countries, the transaction volume is growing at a rapid rate. This fast pace is mainly due to the tremendous leverage the futures provides to market participants. Margining system in futures allows the investors to commit only a fraction of the amount needed to take a similar position in spot market. This may give rise to a much higher return as compared to spot market. Technical analysis, despite all of its criticisms from academics, is still being used by practitioners in making investment decision in some form. There is also a huge body of literature studying the performance of the technical trading rules in spot market with lots of limitations like data snooping, difficulty of short selling, high market frictions etc. This study, aiming to overcome these limitations, applies the technical trading rules, developed from spot market prices, on futures market prices using a CAPM (Capital Asset Pricing Model) based hedge ratio. The most recent daily data of twenty stocks from each of the five developed markets (US, UK, Australia, Germany, South Korea) and five emerging markets (China, Hong Kong, India, Mexico, Turkey) are used. Popular technical indicators, along with artificial intelligence techniques like Neural Networks and Genetic Algorithms, are used to generate buy and sell signals for each stock individually and for portfolios as well for each of the markets. The performance of the trading strategies is then calculated and compared. The results show, although equal amounts invested in both spot and futures markets, the profit from the strategies applied on futures is significantly higher than that on spot market in both developed and emerging markets. Moreover, the overall performance of the artificial intelligence strategies is far better than the traditional ones. The performance of the rules is more stable in portfolios than individual stocks. Furthermore, the trading rules perform better in emerging markets as compared to the developed ones. The findings also support the viability of the advanced trading rules in financial markets. Keywords: Technical Indicators, Futures, CAPM, Artificial Intelligence, Neural Networks,Genetic Programming