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Record W3123985161 · doi:10.1177/0148558x11409159

Conflict of interest reforms and analysts’ research biases

2011· article· en· W3123985161 on OpenAlexaff
Hai Lu, Yuyan Guan

Bibliographic record

VenueTSpace · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of Toronto
FundersUniversity of Hong KongChulalongkorn UniversityCity University of Hong KongResearch Foundation of CFA Institute
KeywordsEarningsOptimismProfitability indexStock (firearms)Investment decisionsInvestment bankingEconomicsInvestment (military)BusinessMonetary economicsFinanceBehavioral economicsPolitical science

Abstract

fetched live from OpenAlex

This study examines the consequences of the series of reforms targeting investment banking–related conflicts of interest. The authors compare and contrast optimism biases in analysts’ stock recommendations and earnings forecasts across different types of analyst firms in the postreform period of 2004 to 2007 versus the prereform period of 1998 to 2001. The authors document a significant reduction in the relative optimism of sanctioned investment bank analysts’ stock recommendations but not in their earnings forecasts. Moreover, the authors find little change in the profitability of their stock recommendations but detect a drop in the accuracy of earnings forecasts made by investment bank analysts. In sum, the reforms achieve the objective of mitigating the apparent optimism in investment bank stock recommendations, but they do not provide benefit to investors in terms of more profitable recommendations or more accurate earnings forecasts.

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)0.042
metaresearch head score (Gemma)0.248
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.248
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.506
GPT teacher head0.375
Teacher spread0.131 · 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.

Study designObservational
DomainIncentives
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

Citations50
Published2011
Admission routes1
Has abstractyes

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