This survey provides the first comprehensive review of the academic literature on mutual fund style drift---the deviation of a fund's actual investment characteristics from its stated mandate. Synthesizing 91 papers spanning 1997-2025, we trace the evolution of measurement methods from returns-based style analysis to holdings-based approaches such as Active Share, and organize the empirical evidence around a central tension: whether style drift reflects managerial skill or agency problems. Our review identifies three critical gaps: (1) methodological vacuum---zero ML/NLP papers; (2) governance blind spot---only 2% address regulation; (3) synthesis deficit---no prior comprehensive survey.
| Journal | Papers |
|---|---|
| SSRN Electronic Journal | 9 |
| The Journal of Investing | 6 |
| Financial Analysts Journal | 4 |
| Review of Financial Studies | 4 |
| Finance Research Letters | 3 |
| Journal of Finance | 2 |
| Journal of Banking & Finance | 2 |
Database: OpenAlex (comprehensive academic finance coverage)
Search Strategy: (1) "style drift" AND "mutual fund"; (2) "fund misclassification", "Active Share", "style consistency"; (3) backward/forward citation searches from seminal papers (Cremers & Petajisto 2009, diBartolomeo & Witkowski 1997)
Screening: 5,190 candidates -> 342 after title/abstract -> 91 after full-text review and quality filtering
Classification: CORE (75 papers directly on style drift) + RELATED (16 papers on adjacent topics)
1. Style drift is pervasive. Fund misclassification ranges 9-40% (diBartolomeo & Witkowski 1997). Chen, Cohen & Gurun (2021) find 31.4% of bond funds misclassified.
2. Performance consequences contested. Cremers & Petajisto (2009): high Active Share outperforms. Frazzini et al. (2016): no effect after controlling for risk factors.
3. Investors cannot detect drift in real time. Chua & Tam (2020): style drift is "shrouded"---investors fail to detect deviations as they occur.
4. Governance underexplored. Only 2% of papers address regulatory themes despite SEC 2023 Names Rule amendments.
5. Methodological stagnation. Zero papers apply ML, NLP, or modern causal inference to style drift detection.
6. Extensions emerging. Recent work on bond funds, Chinese markets, ESG "greenwashing" (Lin, Pan & Sha 2025).
Sorted by citations. Click title for DOI link where available.
| Section | Content |
|---|---|
| 1. Introduction | Motivation, scope, methodology, key findings preview |
| 2. Defining Style Drift | Conceptual definition, types, related concepts |
| 3. Measurement Methods | RBSA, Holdings-based (Active Share), comparison |
| 4. Empirical Evidence | Causes, performance, investor behavior, risk, international |
| 5. Governance & Policy | Fund governance, SEC Names Rule, ESG oversight |
| 6. Future Research | ML/NLP opportunities, governance research, emerging topics |
| 7. Conclusions | Implications for practice, research agenda |