The Impact of Foreign Direct Investments on the Performance of Egyptian Stock Market Sectors Using Machine Learning Techniques

This study attempts to investigate the impact of foreign direct investments, represented by net foreign direct investment, considering economic changes on the Performance of Egyptian Stock Market Sectors. This has been applied on 13 sectors in the Egyptian exchange on an annual basis during 2007-2021. The stock return is measured by calculating the change in sectoral indices. Additionally, this study employs Panel Generalized Method of Moments (GMM), Support Vector Regression (SVR), and K-Nearest Neighbors (k-NN) to compare the results and identify the best approach in prediction. The findings indicate that there is a positive impact of foreign direct investments on sectors’ performance. Macroeconomic factors, including GDP growth, inflation, real interest rate, and market capitalization, are also identified as determinants of performance of exchange sectors. The results reveal that the model based on the K-Nearest Neighbors approach performs better in prediction compared to other techniques. The empirical results provide several practical implications and new insights for policymakers and investors, highlighting the need for further studies can examine the impact of decision of investments on the financial performance under unstable political and health conditions.