Abstract
Predicting future prices is challenging for both scholars and traders due to the high frequency and complexity of stock markets (SMs). The efficient market hypothesis (EMH) states that stock prices (SPs) follow a random walk and are unpredictably fluctuating. Furthermore, the price contains all accessible data, and we can’t extrapolate profitability from previous or current data, thus technical analysis (TA) is ineffective for projecting future prices. Technical indicators (TI) are calculated using past prices, and they are divided into two categories: trend TI and oscillators. The purpose of this study is to evaluate the accuracy of predictions for three stocks traded on the Casablanca Stock Exchange (CSE): IAM, Attijari Wafa Bank (ATW), and Banque Centrale Populaire (BCP). We combined trend TI with Long Short Term Memory model (LTSM) to make predictions and compared the results to the Random Forest model (RF). We also use Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to assess prediction accuracy. As a result, LSTM outperforms the RF model in terms of prediction.