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Record W2042599419 · doi:10.1016/j.rfe.2003.09.002

The survivorship bias, share price effect, and small firm effect in Canadian markets

2003· article· en· W2042599419 on OpenAlexafffundabout
Said Elfakhani, Jason Zhanshun Wei

Bibliographic record

VenueReview of Financial Economics · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersUniversity of Saskatchewan
KeywordsSurvivorship curveEconomicsDemographic economicsMonetary economicsBiology

Abstract

fetched live from OpenAlex

Abstract After controlling for survivorship bias, we examine the relation between average returns, firm size, and price levels for Canadian stocks during the 1975–1994 period. Our findings indicate that there is a significant inverse share price level effect in Canadian markets. When we compare the results of the overall sample with the groups of surviving firms and delisted stocks, the latter group shows strong performance for large‐size, high‐priced stocks. Evidence that supports an independent size effect is less clear for Canadian stocks. A small size effect exists only among the higher share price denominations, which suggests a confounded size‐price effect. Although the delisted group returns are statistically different from those of the survivor and the overall groups, which implies some evidence of survivorship bias, the difference between the survivor group and the overall group is weak at best.

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)0.005
metaresearch head score (Gemma)0.021
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 score0.084
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.210
Teacher spread0.187 · 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

Citations10
Published2003
Admission routes3
Has abstractyes

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