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Record W4413035294 · doi:10.1108/cafr-09-2023-0110

Long-term institutional shareholdings and stock price informativeness of analyst target prices

2025· article· en· W4413035294 on OpenAlexaff
Amanjot Singh, Harminder Singh, Venura Welagedara

Bibliographic record

VenueChina Accounting and Finance Review · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsTerm (time)Stock priceStock (firearms)Financial economicsEconomicsMonetary economicsEconometricsBusinessSeries (stratigraphy)GeographyGeology

Abstract

fetched live from OpenAlex

Purpose This study examines whether long-term institutional shareholdings affect the informativeness of analyst target price revisions. Design/methodology/approach Using institutional investors’ portfolio holdings and a sample of 53,988 target prices from 2000 to 2019, we relate cumulative abnormal returns (CARs) to long-term institutional shareholdings to investigate whether long-term investment horizon influences the stock market response to analyst target price revisions. We identify long-term institutional shareholdings based on their quarterly portfolio churn ratios. Findings We find that firms with more long-term institutional shareholdings experience positive returns on target price revisions. This positive response is pronounced for (1) firms with more long-term motivated institutional shareholdings with higher incentives to monitor, (2) firms with higher idiosyncratic volatility and (3) firms with a higher probability of informed trading. Investors find more value in target price revisions when issued by sophisticated analysts, i.e. with higher experience, higher earnings accuracy and a bigger brokerage size. Specifically, we report that target price revisions provide more incremental information to investors than earnings forecasts and stock recommendation revisions. Our findings remain robust to several alternative specifications, addressing the endogeneity issues. Originality/value Our study contributes to the extant literature on the informativeness of analyst target prices while exploring how the investment horizon influences investors’ reliance on firm-level information.

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 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.242
Teacher spread0.224 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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