Emerging Stock Market Prediction using GRU Algorithm: Incorporating endogenous and exogenous variables

Abstract

Stock market prediction poses significant challenges due to the inherent noise and volatility of the data. These challenges are further amplified in emerging stock markets, where data volatility increases due to numerous endogenous and exogenous variables. Despite the progress made in models for stock market prediction, such as Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVMs), and deep learning models, there is still a need for further research in emerging stock markets. This study addresses the complexity and non-linearity of emerging stock market data by proposing a deep learning model that utilizes Gated Recurrent Unit (GRU) algorithm to predict the next-day closing price. The proposed model leverages the inclusion of exogenous variables to enhance the model�s performance. Three datasets are constructed for three main emerging market indices, specifically in Qatar, Saudi Arabia, and China. Using mean absolute percentage error (MAPE), the inclusion of exogenous variables led to a noticeable improvement over the related work results from 0.74, 1.68, and 0.72 for indices of Qatar, Saudi Arabia, and China respectively to 0.16, 0.6, and 0.2. Furthermore, the results demonstrate the appropriateness of GRU algorithm for predicting emerging stock markets.