الفهرس 
Testing the ability of technical analysis indicators in managing the effect of information asymmetry and predicting the future returns in the Egyptian stock exchange
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Abstract
This research tested the predicting ability of technical analysis rules including the most popular indicators and price patterns. It tested also the ability of technical analysis in managing the effect of information asymmetry in the Egyptian stock market. As an emerging market, Egyptian stock market is inefficient and includes high degree of information asymmetry which is confirmed empirically in this research. Classical technical patterns can predict future price movements based on insider trading (sell/ buy) actions and consequently giving sell or buy signals that lead to saving capital loss, or achieving abnormal return. The research tested the accuracy of neural network application as one of most popular modern techniques used for enhancing the prediction of future price movements. Different statistical techniques have been applied including GARCH model, t-test boot strapping technique and Al-Trilogy neural network application. The empirical results revealed that sell signals of technical classical patterns could reduce the negative effect of both financial crisis and 25th of Jan revolution (saving large portion of capital loss till the official announcement and occurrence of the events respectively). Classical technical patterns in general had a significant positive effect on the future returns. The results revealed also that price indicators including RSI and ROC had a significant positive effect on the future return regardless the trend direction, while MACD and MAs crossovers had more significant positive effect during uptrend periods. ROC had the most powerful effect based on R-square value. The indicators performance wasn{u2019}t affected by the 25th of Jan revolution and is not diminishing overtime which may indicate that Egyptian stock market is still a good ground for using technical rules in investment decisions. It is indicated finally that the neural network model is effective in predicting the future returns of the Egyptian stock market and can be used to enhance the prediction accuracy of the future returns