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Record W4413220898 · doi:10.3390/jrfm18080448

The Lunar New Year Effect on Stock Market Returns: Evidence from Ho Chi Minh Stock Exchange

2025· article· en· W4413220898 on OpenAlexvenueno aff
Loc Dong Truong, H. Swint Friday, Dung T.K. Nguyen

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsEconometricsOrdinary least squaresHeteroscedasticityStock exchangeHo chi minhStock marketMarket capitalizationNames of the days of the weekEconomicsMarket liquidityStock market indexMathematicsFinancial economicsMonetary economicsGeographyDemographic economicsFinance

Abstract

fetched live from OpenAlex

This study is devoted to investigating the Lunar New Year effect on market returns for the Ho Chi Minh Stock Exchange (HOSE). The data employed in this study include a daily series of the VN30-Index, which is a market capitalization weighted index of 30 large capitalization and high liquidity stocks traded on the HOSE, for the period from 6 February 2012 to 31 December 2024. The empirical findings derived from ordinary least squares (OLS), exponential-generalized autoregressive conditional heteroskedasticity [EGARCH(1,1)] regression models consistently confirm that the average return in the last two days and five days before the Lunar New Year are significantly higher than the average market returns on other days of the year. However, this study finds that the average return during the first two trading days and five trading days following the Lunar New Year are not significantly different from the average market returns on other days throughout the year.

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)0.003
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.234
Teacher spread0.215 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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