Predicting Stock Closing Prices in Emerging Markets with Transformer Neural Networks: The Saudi Stock Exchange Case

Abstract

Deep learning has transformed many fields including computer vision, self-driving cars, product recommendations, behaviour analysis, natural language processing (NLP), and medicine, to name a few. The financial sector is no surprise where the use of deep learning has produced one of the most lucrative applications. This research proposes a novel fintech machine learning method that uses Transformer neural networks for stock price predictions. Transformers are relatively new and while have been applied for NLP and computer vision, they have not been explored much with time-series data. In our method, self-attention mechanisms are utilized to learn nonlinear patterns and dynamics from time-series data with high volatility and nonlinearity. The model makes predictions about closing prices for the next trading day by taking into account various stock price inputs. We used pricing data from the Saudi Stock Exchange (Tadawul) to develop this model. We validated our model using four error evaluation metrics. The applicability and usefulness of our model to fintech are demonstrated by its ability to predict closing prices with a probability above 90%. To the best of our knowledge, this is the first work where transformer networks are used for stock price prediction. Our work is expected to make significant advancements in fintech and other fields depending on time-series forecasting.