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How Informed Are Actively Trading Institutional Investors? Evidence from Their Trading Behavior before a Break in a String of Consecutive Earnings Increases

2004· article· en· 298 citations· W3124455480 on OpenAlex· 10.1111/j.1475-679x.2004.00160.x

Why is this work in the frame?

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

About CanadaIts subject is Canada, wherever its authors sit.

No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Full frame distilled prediction

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.

Candidate categories
none
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: ObservationalConsensus signal: Observational
Genre
Candidate signal: EmpiricalConsensus signal: Empirical
Teacher disagreement score
0.004
Threshold uncertainty score
0.666
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.124
GPT teacher head0.308
Teacher spread
0.185 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

ABSTRACT We examine whether transient institutional investors (i.e., institutions that trade actively to maximize short‐term profits) have information that allows them to predict a break in a string of consecutive quarterly earnings increases and thereby avoid the economically significant negative stock price response associated with the break announcement. We show that transient institutions predict the break at least one quarter in advance of the break quarter. We also provide evidence that is consistent with transient institutions obtaining information regarding the impending break from private communications with management.

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.

The record

Venue
Journal of Accounting Research
Topic
Financial Markets and Investment Strategies
Field
Economics, Econometrics and Finance
Canadian institutions
not available
Funders
not available
Keywords
Quarter (Canadian coin)EarningsStock (firearms)Institutional investorBusinessPrivate information retrievalStock priceMonetary economicsStructural breakTransient (computer programming)Earnings managementFinanceEconomicsEconometricsCorporate governanceComputer science
Has abstract in OpenAlex
yes