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Record W2054517012 · doi:10.1002/bdm.595

To buy or to sell: cultural differences in stock market decisions based on price trends

2008· article· en· W2054517012 on OpenAlexaff
Li‐Jun Ji, Zhiyong Zhang, Tieyuan Guo

Bibliographic record

VenueJournal of Behavioral Decision Making · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsQueen's University
Fundersnot available
KeywordsStock marketStock (firearms)Stock priceFinancial economicsEconomicsBusinessGeography

Abstract

fetched live from OpenAlex

Abstract Four studies compared the stock market decisions of Canadians and Chinese. In two studies using simple stock market trends, compared with Chinese, Canadians were more willing to sell and less willing to buy falling stock. But when the stock price was rising, the opposite occurred: Canadians were more willing to buy and less willing to sell. A third study showed that for complex stock price trends, Canadians were strongly influenced by the most recent price trends: they tended to predict that recent trends would continue and made selling decisions without considering the rest of the trend patterns; whereas the Chinese made reversal predictions for the dominant trends and made decisions that took both recent and early trends into consideration. Study 4 replicated the finding with experienced individual investors. These findings are consistent with the previous literature on different lay theories of change held by Chinese and North Americans. Copyright © 2008 John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.108
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.277
GPT teacher head0.459
Teacher spread0.182 · 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

Citations102
Published2008
Admission routes1
Has abstractyes

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