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Record W1527228427 · doi:10.3386/w15038

Is Investor Rationality Time Varying? Evidence from the Mutual Fund Industry

2009· report· en· W1527228427 on OpenAlexfundno aff
Vincent Glode, Burton Hollifield, Marcin Kacperczyk, Shimon Kogan

Bibliographic record

VenueNational Bureau of Economic Research · 2009
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsRationalityMutual fundBusinessFinancial economicsTarget date fundEconomicsMonetary economicsEconometricsFinanceOpen-end fundInstitutional investorCorporate governancePolitical scienceLaw

Abstract

fetched live from OpenAlex

We provide new empirical evidence suggesting that the marginal investor in mutual funds behaves differently across market conditions.If the marginal investor allocates capital across mutual funds rationally, then the relative performance of funds should be unpredictable.We find however that relative fund performance is predictable after periods of high market returns but not after periods of low market returns.The asymmetric predictability in performance we document cannot be explained by time-varying differences in transaction costs or style exposures between funds, or by sample selection.Consistent with the hypothesis that the asymmetric predictability in performance may be driven by unsophisticated investors' mistakes when allocating capital, we document that performance predictability is more pronounced for funds that cater to retail investors than for funds that cater to institutional investors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.579
GPT teacher head0.477
Teacher spread0.101 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2009
Admission routes1
Has abstractyes

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