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Record W6988191855

Who are the value and growth investors? : [Version April 2014]

2014· report· en· W6988191855 on OpenAlexfundno aff

Bibliographic record

VenuePublication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · 2014
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaFondation HECAgence Nationale de la RechercheSveriges RiksbankenStrong
KeywordsReal estateValue (mathematics)PortfolioOffset (computer science)Market valueFinancial marketPanel data
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates the determinants of value and growth investing in a large administrative panel of Swedish residents over the 1999-2007 period. We document strong relationships between a household’s portfolio tilt and the household’s financial and demographic characteristics. Value investors have higher financial and real estate wealth, lower leverage, lower income risk, lower human capital, and are more likely to be female than the average growth investor. Households actively migrate to value stocks over the life-cycle and, at higher frequencies, dynamically offset the passive variations in the value tilt induced by market movements. We verify that these results are not driven by cohort effects, financial sophistication, biases toward popular or professionally close stocks, or unobserved heterogeneity in preferences. We relate these household-level results to some of the leading explanations of the value premium.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.135
Threshold uncertainty score0.452

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.008
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1350.056

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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 designNot applicable
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

Citations0
Published2014
Admission routes1
Has abstractyes

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