This project implements a dynamic portfolio optimization framework that combines machine learning and classical financial theory to improve risk-adjusted returns.
Specifically, it integrates:
- LSTM neural networks for asset return prediction
- GARCH models for time-varying volatility estimation
- Markowitz mean–variance optimization with Sharpe ratio maximization
The strategy is evaluated on Dow Jones Industrial Average stocks using out-of-sample data.