Proposed Framework for Predicting Stock Return Volatility Using Neural Network: An Applied Study on the Egyptian Stock Exchange

Abstract

Purpose: the primary purpose of the study is to determine the effect of both internal and external factors on stock returns volatility using different statistical methods, applied on Egyptian stock exchange.

Methodology: the researchers have compared the accuracy of (GLS Model, GARCH Model, and Neural Network) in predicting the stock return volatility to choose the most accurate one. Data was collected from the Egyptian Stock Exchange (EGYX 30) for the period (2014 to 2017) on a monthly basis.

Findings: The results of the study revealed that the Neural Network Model has proven to outperform the traditional models in the prediction of stock return volatility.

Originality: the study contributes to literature as it used Artificial Neural Network in two functions (Prediction of stock return volatility) and (Classification of the volatility to –high volatility and Low volatility). Also few studies concerned with stock return volatility in developing countries, especially Egypt.