Informative Inputs Over Complex Features: Long Short-Term Memory Forecasting in the Saudi Stock Market

Abstract

This study investigates the effectiveness of deep learning, specifically Long Short-Term Memory (LSTM) networks, for forecasting stock closing prices in the Saudi Arabian market. Unlike prior research that focuses on narrow stock subsets or individual technical indicators, we present the first comprehensive evaluation across the top thirty companies by market capitalization on the Tadawul Exchange. We compare eight LSTM variants trained on different combinations of feature families, including technical indicators, calendar effects, and macroeconomic variables such as Brent oil prices and the Tadawul All Share Index (TASI). Despite the popularity of complex feature engineering, our results show that models using simpler macroeconomic inputs consistently outperform those based on technical indicators. In particular, combining the TASI with closing prices yielded the best results for 44.8% of stocks, improving median accuracy by 6.19% over the closing price-only baseline. Conversely, models incorporating extensive technical indicators or applying advanced feature selection techniques underperformed the baseline by 8 to 12%. These findings challenge the assumption that greater complexity leads to better performance in financial forecasting and highlight the value of focused, economically interpretable features in the Saudi market context.