الفهرس 
Applying data preprocessing techniques for egyptian stock market price prediction
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Abstract
The primary purpose of trading in stock markets is to profit from buying and
selling listed stocks. Therefore, forecasting stock prices is crucial for successful
investment in financial markets. However, numerous factors can influence the stock
prices, such as the company’s present financial situation, news, rumor,
macroeconomics, psychological, economic, political, and geopolitical factors.
Consequently, tremendous challenges already exist in predicting noisy stock prices.
Recently, artificial neural networks (ANNs) attained remarkable outcomes in
predicting stock markets. Nevertheless, the nonstationarity and the interaction
between hidden features of the price time series lessen forecasting accuracy.
Consequently, data preprocessing techniques such as discrete wavelet transform
(DWT) and singular spectrum analysis (SSA) are required to improve the prediction
accuracy of ANNs by reducing the noise and extracting hidden characteristics of the
time series.
This thesis proposes hybrid models that integrate data preprocessing techniques,
i.e., DWT and SSA, with the nonlinear autoregressive neural network (NARNN) to
predict stock prices in the Egyptian Exchange. The NARNN involves a time delay
line (TDL) in the input layer representing the memory that recognizes subsequent or
changing patterns over time and fades the short-term volatility. The DWT-NARNN
and SSA-NARNN models first divide the stock prices into training and testing sets.
Then the training set is decomposed using data preprocessing techniques to reduce
the noise and lessen the data’s nonlinearity. Afterward, each extracted component is
utilized for training a separate NARNN. To predict the future components, the model
decomposes the preceding available prices at each time step in the testing set and
utilizes the latest points as input to the NARNNs. Eventually, the outputs from the
NARNNs are aggregated to provide the final predicted prices. The weekly closing prices for twenty-four stocks from the Egyptian Exchange
(EGX-30) are used to verify the proposed models’ performance. The DWT-NARNN
and SSA-NARNN models are compared with other methods, including the
backpropagation neural network (BPNN), NARNN, DWT-BPNN, and SSA-BPNN
models. The empirical results reveal that the proposed models perform better than the
other
s, indicating the data preprocessing techniques’ ability
to extract hidden
information and reduce the noise effect of the original time series. Moreover, this
thesis proves that the old approach of decomposing the entire dataset
and
partitio
then
ning it into training and testing sets is unrealistic. The unrealistic approach
causes the testing set to inherit
the
stock’s future performance, leading to optimistic
deceptive results. In contrast to the old method, our point
simul
by
point decomposition
ates the actual trading process, and the validation process is reliabl