Support Vector Auto-Regression and Neural Network for Prediction of Construction Stock Prices in Egypt

Accurately predicting stock prices in the construction industry is crucial as it can help reduce losses for construction companies and mitigate the impact of economic downturns on the industry. However, uncertainties often lead to inaccurate predictions in the Egyptian construction industry. To address this issue, researchers used mathematical models and machine learning models to predict stock prices and assist decision-makers in responding to sudden changes. The main challenge is identifying the factors that drive stock prices. This paper proposes two models: artificial neural networks (ANN) and support vector auto regression (SVAR) to provide predictions for construction companies' stock prices, which can be useful tools for contractors and construction material companies to predict and understand fluctuations in major construction stock prices and take appropriate measures to avoid negative impacts on their stability. The aim of this study is to provide a comprehensive evaluation of different methodologies in predicting stock prices in the Egyptian construction industry.