Abstract:
Accurately predicting future stock prices is crucial for investors, particularly during market
stress, enabling informed decisions to mitigate losses and reduce financial exposure. While
machine learning techniques have shown promise in this field, most studies have focused on local
models. Global forecasting models, which are trained on a variety of time series, have
demonstrated encouraging outcomes in outperforming local models. This article presents a global
forecasting approach utilizing LightGBM to predict stock prices in the Moroccan market during
highly volatile period. The study includes a sector-wise analysis, with the most volatile sector
being evaluated, and correlated stocks from other sectors were considered to enhance the data.
Compared to other deep learning models that use a local approach, our findings demonstrate the
effectiveness of utilizing a global forecasting with optimized LightGBM for predicting stock
prices. Additionally, incorporating data from multiple stocks improves accuracy of stock price
prediction.
