🧠 BBAN5000 Master Thesis — Portfolio Performance on RL Optimised Portfolios

Contributors:

Jakob Lindstrøm & Marcus Hjertaas

Course:

BBAN5000 - Master's thesis in Business Analytics

Keywords:

Reinforcement Learning, Portfolio Optimization, Portfolio Performance, Attribution Analysis, Markowitz Portfolio Theory, Investment Strategies, Reward Functions

Table of Content

• Overview
• Project Structure
• Instructions
• Features
• License

📚 Overview

This project analyzes the performance of optimized investment portfolios using attribution methods. It evaluates allocation and selection effects, generates visualizations, calculates financial metrics (P/L, Sharpe, Sortino, Sterling ratios), and integrates ESG scoring analysis.

🛠 Instructions

Install python version 3.11.8

Check that it is present:

py -3.11 --version

Create enviroment:

py -3.11 -m venv MyVenv

Activate the enviroment:

MyVenv\Scripts\activate

Install requirements:

pip install -r requirements.txt

Run main file:

python main.py

✨ Features

• RL/MPT/equal-weight portfolio optimisation
• ESG-aware and ESG-naive optimisation strategies
• Return, Sharpe, Sortino and Sterling optimisation strategies
• Performance, ESG and attribution analysis

📜 License

This project is part of an academic thesis and is not intended for commercial use. If you use parts of this code, cite the contributors appropriately.