Application of advanced Machine Learning algorithms in Finance - Rózsahegyi, Dávid (2025)
This thesis explores the application of advanced machine learning techniques in finance, specifically focusing on portfolio management using deep reinforcement learning. We propose and implement a hybrid PPO-LSTM model, combining the stability of Proximal Policy Optimization with the memory capacity of a recurrent LSTM network. The trained agent learns to optimize daily portfolio weights across a diversified basket of stocks, explicitly accounting for transaction costs. We evaluate three architectural variants along with a simple ensemble created by averaging their outputs. Using walk-forward validation, we assess model performance on data from 2010 to 2023. The results show that the agents tend to mitigate extreme drawdowns and achieve Sharpe ratios comparable to a passive benchmark, though often at the cost of lower exposure and higher turnover. The compact model performs best, offering a favorable balance between network complexity, gradient stability, and generalization. Additionally, we analyze turnover dynamics and sector exposure, which helps us to interpret the agent's behavior over time.