Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Business | en_US |
| dc.creator | Zhang, Ting | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13692 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | "Reinforcing" the power of factors for investment | en_US |
| dcterms.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. | en_US |
| dcterms.abstract | 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. | en_US |
| dcterms.extent | 146 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | DFinTech | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Portfolio management | en_US |
| dcterms.LCSH | Machine learning | en_US |
| dcterms.LCSH | Reinforcement learning | en_US |
| dcterms.LCSH | Investments | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
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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