| Author: | Zhang, Ting |
| Title: | "Reinforcing" the power of factors for investment |
| Degree: | DFinTech |
| Year: | 2025 |
| Department: | Faculty of Business |
| Pages: | 146 pages : color illustrations |
| Language: | English |
| Abstract: | This study explores how RL can enhance factor investing for portfolio management. It follows the Design-Science Research (DSR) framework, and by leveraging Design Science principles, my research not only addresses a critical challenge in factor investing but also highlights the transformative potential of machine learning in financial decision-making. My empirical study demonstrates that portfolios optimized with RL algorithms (A2C, PPO, and DDPG) significantly outperform traditional factor-based portfolios in terms of annualized returns, volatility, and risk-adjusted metrics. These findings underscore the adaptability and effectiveness of RL in refining investment strategies under dynamic market conditions, offering a novel methodology for portfolio investment. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8216.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.17 MB | Adobe PDF | View/Open |
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