Following momentum and avoiding the “Minsky Moment” evidence from investors on the Financial Instability Hypothesis
Bibliographic record
Abstract
Purpose This study aims to find out how institutional investors use momentum in making investment decisions, and whether their actions are consistent with the Financial Instability Hypothesis of Hyman Minsky. Design/methodology/approach The study discusses the findings of interviews with 25 professional investors from the Hong Kong offices of five global financial institutions. All of the participants have several years of practical experience in global and regional markets. Findings Nearly all the managers interviewed said they use momentum in making investment decisions, and they do this in ways that are consistent with the Financial Instability Hypothesis, in which markets alternate between stable and unstable states. The participants are aware they may contribute to this, but they cannot avoid doing it because of short-term constraints in the present financial system. Originality/value This study adds to our knowledge of how professional investors use momentum in their investment strategies. It complements findings of quantitative studies that show momentum strategies have been profitable in many market settings. It also adds evidence that supports the Financial Instability Hypothesis.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".