machine-learning
MASI Index Performance Prediction
2023
Learn MoreA machine learning project to predict the performance of the MASI index using time-series analysis.
This project aims to predict the performance of the Moroccan All Shares Index (MASI), the principal stock market index of the Casablanca Stock Exchange. Using historical MASI index data, the notebook applies machine learning techniques to forecast future values and gain insights into market trends.
LSTM results
Key Features
- Data Collection & Preprocessing: Historical MASI index data is gathered, cleaned, and prepared for analysis.
- Feature Engineering: Various technical indicators are computed to serve as input features for predictive modeling.
- Model Selection & Training: Multiple machine learning models, including Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, are explored.
- Evaluation & Results: The models are evaluated using metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) to determine the most accurate approach.
Achievements
- Demonstrated the effectiveness of LSTM networks in stock market forecasting.
- Achieved an MAE of 39.45, demonstrating strong model performance.
- Outperformed traditional machine learning models with deep learning approaches.
- Provided a framework that can be extended to other stock market indices.