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Record W2470376747 · doi:10.5539/ijef.v8n7p132

Impacts of Monetary Policy and Information Shock on Stock Market: Case Study in Vietnam

2016· article· en· W2470376747 on OpenAlexvenueno aff
Trung Thành Nguyễn, Thi Linh, Van Duy Nguyen

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsMonetary policyStock marketMonetary economicsStock market indexAutoregressive conditional heteroskedasticityStock (firearms)Stock exchangeStock market bubbleShock (circulatory)Financial economicsVolatility (finance)Finance

Abstract

fetched live from OpenAlex

<p>Evaluation of the impact of monetary policy on Vietnam stock market plays an important role for economists as well as stock investors. Stock price index not only gets impacts from the macroeconomic factors such as oil price, gold prices…but also be very sensitive to the changes in monetary policy. For each different markets, stock index are also different from each other. Hence, this artical is conducted to evaluate the impacts of monetary policy on Vietnam Stock Index (VNIDEX) in the period of the time from 2006 to 2015. The author uses GJR - GARCH model and ARDL research with time-serie data by statistical methods and quantitative analysis to evaluate the above impact related to lag and shocks in the market. The result shows that the monetary policy including interests, exchange rate and required reserve ratio has a negative impact on stock price in long term. Besides, both bad or good market shock cause changes of stock price at stable level.</p>

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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