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Record W4405185130 · doi:10.1016/j.pacfin.2024.102620

How retail investors affect the stock market?

2024· article· en· W4405185130 on OpenAlexafffund
Xiaozhou Zhou, Feng Zhan, Chang Chan

Bibliographic record

VenuePacific-Basin Finance Journal · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsWestern UniversityUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council
KeywordsBusinessAffect (linguistics)Stock marketStock (firearms)Monetary economicsFinancial systemFinancial economicsEconomicsPsychologyGeography

Abstract

fetched live from OpenAlex

Using the expansion of price limit regulation as a quasi-natural experiment, this study explores the trade-off between stock market liquidity and price efficiency where retail investors act as de facto market makers. Our empirical results show a significant variation of retail investors' trading activities after the expansion of the daily price limit. Economically, this variation represents around $26.73 million fewer shares per day traded after the regulatory change. Furthermore, we show that this reduction of retail investors' participation leads to a decrease in liquidity, but an improvement in stock price efficiency. Our empirical findings complement the theoretical work of Subrahmanyam (1991) and Han et al. (2016) and highlight the importance and complexity of retail investors' trading activities. • This study explores the trade-off between stock market liquidity and price efficiency where retail investors act as de facto market makers. • A significant variation of retail investors' trading activities after the expansion of the daily price limit. • We show that this reduction of retail investors' participation leads to a decrease in liquidity, but an improvement in stock price efficiency.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.039
GPT teacher head0.216
Teacher spread0.177 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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