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Record W3121140277 · doi:10.1093/rof/rft035

Cash Holdings and Mutual Fund Performance

2013· article· en· W3121140277 on OpenAlexaff
Mikhail Simutin

Bibliographic record

VenueEuropean Finance Review · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsBusinessCash and cash equivalentsCash on cash returnCash managementFinanceOperating cash flowCash flow statementCashCash flow forecastingMutual fundClosed-end fundMonetary economicsEconomicsMarket liquidity

Abstract

fetched live from OpenAlex

Abstract Cash holdings of equity mutual funds impose a drag on fund performance but also allow managers to make quick investments in attractive stocks and satisfy outflows without costly fire sales. This article shows that actively managed equity funds with high abnormal cash—that is, with cash holdings in excess of the level predicted by fund attributes—outperform their low abnormal cash peers by over 2% per year. Managers carrying high abnormal cash compensate for the low return on cash by making superior stock selection decisions, whereas less capable managers find abnormal cash costly and remain more fully invested in equities. Managers of high abnormal cash funds also proficiently satisfy fund outflows and control fund transaction costs, whereas low abnormal cash funds lack flexibility to cover outflows and can suffer from costly fire sales. The empirical evidence suggests that managers carrying abnormal cash benefit from the flexibility it provides despite the costs of holding cash.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.210
Teacher spread0.159 · 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

Citations121
Published2013
Admission routes1
Has abstractyes

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