| Author: | Leung, Helen |
| Title: | Assessing qualitative ratings’ predictive power and risk metrics for mutual fund performance : a study on AI-integrated investment strategies |
| Advisors: | Cho, Vincent (MM) |
| Degree: | D.B.A. |
| Year: | 2026 |
| Department: | Faculty of Business |
| Pages: | iv, 177 pages : color illustrations |
| Language: | English |
| Abstract: | This research evaluates the predictive power of forward- looking, rigorous investment due diligence-based qualitative ratings—specifically Mercer FundWatch™—on mutual fund future performance across various time horizons. The qualitative rating assessment focuses at the investment management process of mutual funds that affects future performance. To complement such qualitative evaluation, this research examined quantitative fund risk metrics as an important aspect for investors to consider in fund selection. Information Richness Theory explains the qualitative rating predictive power that brings different theoretical perspectives to past research. It emphasizes that rigorous investment due diligence through in person interviews and the depth and breadth of information being gathered during the qualitative assessment, enriches the rating predictability. The research results demonstrated that mutual funds with higher Mercer FundWatch™ ratings consistently outperform lower rated funds across different time horizons. The relationships between quantitative fund risk metrics and fund performance showed that diversification across asset classes yields a sustained performance benefit. The historical volatility was a significant factor for the medium term fund performance but their relationship was negative. The other fund risk metric investment diversification by markets of investments was not a significant factor in fund performance. These are consistent with past research suggesting that the risk-return relationship is complex and varies over time. Control variables such fund expenses and management fees are significant predictors. Its negative relationship with fund performance is consistent with past research. The other control variables are investment style, fund size and age, of which the bond fund and the middle-aged fund showed a significant and positive relationship with fund performance. This research also studied the integration of artificial intelligence in investment management at the firm level and their impact on mutual fund performance. While the technologies applications in investment management of mutual funds are reviewed during the qualitative assessment, considering the growing trend of artificial intelligence adoption, it is worth exploring separately their impact on fund performance. The correlation analysis findings showed positive relationships in the medium to longer term and not in the short term. This implies that the advantages of firm artificial intelligence integration such as improved data processing, adaptive risk management, and enhanced forecasting and investing in artificial intelligence strategies, may require time to materialize. However, the regression analysis results do not support the relationships, the expected advantages of artificial intelligence have yet to manifest in quantifiable superior performance. This research finding strongly supports the qualitative rating as an effective tool for investors navigating increasingly complex investment choices of mutual funds. The emphasis of rigorous investment due diligence as explained by the Information Richness Theory provides new perspectives to past research on the value and predictive power of qualitative ratings. Fund risk and fund features are key considerations for fund selection; however, their impact may vary and be complex across different time horizons. The artificial intelligence integration impact on mutual fund performance tends to be materialized in a medium to longer term, which is worth further exploration such as artificial intelligence integration at the specific fund level with a broader sample set and longer time horizon. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8949.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 10.68 MB | Adobe PDF | View/Open |
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