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Record W3123197064 · doi:10.1108/mf-01-2017-0003

Portfolio turnover activity and mutual fund performance

2018· article· en· W3123197064 on OpenAlex

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

VenueManagerial Finance · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsWestern UniversityYork UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsPortfolioEconometricsPredictabilityVolatility (finance)ChurningMutual fundEconomicsActive managementActuarial scienceFinancial economicsProject portfolio managementStatisticsMathematicsFinance

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to propose a new measure of portfolio activity, the modified turnover (MT), which represents the portion of the portfolio that the manager changes from one quarter to the next. Compared with the traditional turnover, the MT measure has a distinct interpretation, relies on portfolio holdings, includes the effects of flows and ignores the effects of offsetting trades. Design/methodology/approach Using quarterly holdings data, the authors examine the relationship between fund turnover, performance, and flows for a sample of 2,856 actively managed mutual funds over the period 1991-2012. The authors provide numerical examples to illustrate how the suggested measure, MT, is different from the traditional turnover measure. The authors use panel regressions, simple and double sorts to examine the predictability of performance. Findings The authors find evidence that high MT predicts lower performance. The comparison between the highest and lowest quintiles sorted based on MT reveals a difference of −2.41 percent in the annual risk-adjusted return. Furthermore, high MT predicts lower net flows. The authors also find that MT relates positively to other activeness measures while volatility, flows, size, number of stocks, and the expense ratio are significant determinants of MT. Overall, the results suggest that frequent churning of a portfolio is value destroying for investors and signals a manager’s lack of skill. Originality/value The authors offer a simple measure, namely, MT, for estimating the fraction of a portfolio that changes from one quarter to the next. Armed with this tool, the authors investigate whether funds deviate from their previous quarter’s holdings because of valuable or noisy information, and whether such signals are exploited by fund 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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.030
GPT teacher head0.214
Teacher spread0.184 · 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