Reinforcement Learning in Portfolio Management: A Comparative Study of DDPG and PPO

Arodh Lal Karn (Supervisor)

Department of Financial and Actuarial Mathematics

Activity: Supervision › Completed SURF Project

Description

The project provides a useful research-led learning opportunity for students (machine learning, portfolio management). The research questions are concise. The methodology and timeline are realistic. The anticipated research outcomes are realistic. This project addresses concrete research questions leading students to delve into reinforcement learning.

Period	Aug 2024
Degree of Recognition	Local

Keywords

reinforcement learning algorithms
deep Q-learning
proximal policy optimization (PPO)
deep deterministic policy gradient (DDPG)
quantitative finance
artificial intelligence

Documents & Links

SURF_2024_0035
File: application/pdf, 1.3 MB
Type: Text

This research project aims to enhance portfolio management by incorporating modern investment strategies and cutting-edge technology. It focuses on optimizing portfolios through stock selection, building on the foundational work of Markowitz's mean-variance (MV) model. The project extends portfolio optimization to consider different asset time periods, risk measures, and high-quality asset selection. Additionally, it explores the online portfolio selection (OPS) model and its potential to maximize long-term wealth, incorporating concepts like universal portfolio (UP) strategies and transaction cost factors. Leveraging AI advancements, the project investigates the application of reinforcement learning algorithms, including deep Q-learning, proximal policy optimization (PPO), and deep deterministic policy gradient (DDPG), within portfolio management. This interdisciplinary endeavor aims to integrate finance, quantitative finance, and artificial intelligence to improve portfolio management methodologies and strategies.

Reinforcement Learning in Portfolio Management: A Comparative Study of DDPG and PPO

Description

Keywords

Documents & Links

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Reinforcement Learning in Portfolio Management: A Comparative Study of DDPG and PPO