Active fund managers and the rise of passive investing: Epistemic opportunism in financial markets
Bibliographic record
Abstract
Financial markets have witnessed a dramatic shift in financial flows in recent years from Active fund management where professional investors attempt to beat the market (generate ‘alpha’) to Passive investment where portfolios are assembled that follow existing market indicators (track ‘beta’). This transition has important implications for both corporate governance and wider society, with potentially significant distributive effects. Passive investing is predicated upon different bodies of knowledge and is suggestive of an epistemic shift of sorts in financial markets. In such circumstances, field theory suggests that incumbent groups like Active players will try to adapt to the new rules of the investment game. However, drawing from an empirical study which explores the views of the Active investment community in both the United Kingdom and the United States, we document significant defensiveness vis-à-vis the rise of Passive investing. Whereas behavioural approaches might explain this defensiveness in terms of irrationality, the conceptual approach advanced here instead emphasizes the epistemic opportunism (convoluted and self-serving attempts to demonstrate superior knowledge) that communities strategically engage in to justify their position. As such, we conclude that financial markets should be understood as constituted by slowly evolving communities of practice whose habits, routines and ways of knowing can be difficult to shift, even when faced with overwhelming evidence that what they are doing doesn’t work most of the time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".