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Record W4311054521 · doi:10.1142/s2010139222500148

Why do Funds Make More When They Trade More?

2022· article· en· W4311054521 on OpenAlex
Jaden Jonghyuk Kim, Jung H. Lee, Shyam Venkatesan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuarterly Journal of Finance · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsWestern University
Fundersnot available
KeywordsMutual fundCash flowIndex fundStock (firearms)BusinessClosed-end fundResidualFinancial economicsEconomicsMonetary economicsFinanceEconometricsOpen-end fundInstitutional investorComputer science

Abstract

fetched live from OpenAlex

In this paper, we introduce a conditional measure of skill, the correlation between funds’ residual trades, net of common trading motives, and future news about the stocks traded. Using this measure, we show that the average mutual fund manager in the cross-section has stock-picking skill. This result is robust to different benchmarks and is mainly driven by the manager’s ability to predict a firm’s cash-flow news. This skill has short-term persistence and is distinctly related to traditional measures of performance. Importantly, consistent with Berk and Green [2004, Mutual Fund Flows and Performance in Rational Markets, Journal of Political Economy 112(6), 1269–1295] fund flows are increasing with respect to managerial skill after controlling for fund performance.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.216
Teacher spread0.194 · 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