Summary: LSTM and GRU are types of Recurrent Neural Networks that have grown in popularity in recent years. In addition to their ability to ‘learn’ from their previous mistakes, these types of Neural Networks have expanded memory capacities, which allows them to be used on applications involving time series data. One of the most significant applications that requires time series data analysis is trading in financial markets, such as the stock market. The above research article describes a revolutionary application of Machine Learning where LSTM and GRU neural networks can be trained on financial time series data and used to predict the price of an asset in the future, essentially making it easier for individuals and financial companies to generate positive alpha (large profits) without the need for manual trading. The article trains two different types of neural networks (LSTM and GRU), which are deep learning tools, on time series data from the Moroccan stock market and tests and compares the models’ performance against a test set. I found this article to be interesting and relevant since finance and economics are the backbones of our society. Machine Learning and Artificial Intelligence are undoubtedly being heavily adopted in the financial industry, and training Machine Learning models to generate profits in financial markets is probably the best example of ML and AI being used in finance.