Financial markets are very rich with information and variables. In contradiction to the Efficient Market Hypothesis, much research has been conducted to predict asset prices with promising accuracy. However, ensuring good models requires extracting important information from given data sets. This paper investigates the main Egyptian Stock Exchange index (EGX 30) and constructs some alternative portfolios by identifying important linear combinations of EGX 30 constituents. This could be approached by a dimensionality reduction technique, which is performed following the principal components analysis (PCA). The results show that the first three Principal Components (PCs) could summarize 83% of data variability. Each one of the first three PCs highlights the most contributed individual stocks. These three PCs provide investors with alternative portfolios. Moreover, a Principal Component Regression (PCR) model is built to predict the future behavior of the EGX 30. The performance of the obtained PCR model is very well. This result is reached by comparing observed values of EGX 30 with the predicted ones (R-squared estimated as 0.98).