الفهرس 
صفحة العنوان
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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 modem 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’t affected by the 25th of Jan revolution and is not
diminishing overtime which may indicate that the 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.