Stress Testing a Markowitz Portfolio - AI and Finance Project

This repository contains the notebook Stress Testing a Markowitz Portfolio.ipynb, developed as part of the AI and Finance course.

Overview

This project explores the application of Artificial Intelligence techniques to financial data analysis and modeling.
It involves data preprocessing, feature engineering, model training, and performance evaluation using machine learning and deep learning methods.

Main Components

• Data Loading and Preprocessing: Handling financial datasets, normalization, and feature extraction.
• Modeling: Implementation and training of AI models (e.g., neural networks).
• Evaluation: Performance metrics and visualizations for model comparison.
• Results and Discussion: Interpretation of outcomes and insights derived from experiments.

File Structure

• Stress Testing a Markowitz Portfolio.ipynb: Main Jupyter Notebook containing all analyses and experiments.
• consideration.md: Document with detailed considerations and results analysis.
• README.md: This document.

Authors

• Fidanza Riccardo
• Loda Enrico
• Panariello Luca

Requirements

Typical dependencies include:

numpy
pandas
matplotlib
scikit-learn
tensorflow / pytorch

Install them via:

pip install -r requirements.txt

How to Run

1. Open the notebook:

   jupyter notebook Stress Testing a Markowitz Portfolio.ipynb

2. Execute cells in order to reproduce the analysis and results.

License

This project is for academic and educational purposes only.