المستخلص: |
This paper examines the accuracy of technical analysis in forecasting stock prices by applying artificial neural networks analysis to technical analysis dataset. It has been done by investigating the predictive ability of eight technical analysis indicators: volume, EMA 50 days, %RSI, %K, %D, ADX, +DI, and –DI. The research data involve the daily closing prices for 29 companies listed on EGX30 in the Egyptian stock market over the period 2009-2018 to forecast stock prices in 2019. For statistical inference, the artificial neural networks analysis has been applied. The results of the study illustrate that the technical indicators can be used in predicting stock prices accurately particularly with the help of ANN analysis. The moving average is the best indicator to forecast the stock prices. Furthermore, %RSI is the second most important indicator, because its normalized importance is above (40%) for some companies. The ANN analysis can be applied to technical analysis dataset to enhance the accuracy the prediction.
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