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

Author's Department

Construction Engineering Department

Second Author's Department

Construction Engineering Department

Third Author's Department

Construction Engineering Department

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https://doi.org/10.1061/9780784485262.131

All Authors

Mohamed T. Ramadan, Ossama Hossny, Khaled Nassar

Document Type

Research Article

Publication Title

Construction Research Congress 2024, CRC 2024

Publication Date

1-1-2024

doi

10.1061/9780784485262.131

Abstract

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.

First Page

1288

Last Page

1298

Comments

Conference Paper. Record derived from SCOPUS.

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