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Record W2936043593

Asymmetric Effects of Macroeconomic shocks on the Stock Returns of the G-7 Countries: The Evidence from the NARDL Approach

2018· article· en· W2936043593 on OpenAlexaboutno aff
Levent Erdoğan, Ahmet Ti̇ryaki̇

Bibliographic record

VenueDergiPark (Istanbul University) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsStock marketStock (firearms)Monetary economicsStock market indexInterest rateExchange rateStock market bubbleFinancial economicsEconometrics
DOInot available

Abstract

fetched live from OpenAlex

The aim of the paperis to investigate the asymmetric effects of changes in industrial production,real exchange rate, consumer price index, interest rate, and the World oilprice index on the stock market returns in G-7 countries by using the NARDLmodel and monthly data from the period of 1999:01 to 2017:12. The study,overall, finds that the effects of the changes in independent domestic andexternal variables on stock returns are significant and asymmetric withexpected signs for the G-7 countries. Also, the effects of the changes in theindustrial production index on stock market returns are asymmetrical only inEuro area countries. The effects of the changes in the real exchange rate onstock market returns are asymmetrical for all countries, except Japan.Interestingly, while the real appreciation of the currency causes stock returnsto increase in the Anglo-Saxon countries, the opposite occurs in Euro areacountries. The effects of the changes in the consumer price index on stockmarket returns are asymmetrical only in Canada and France. The effects of thechanges in the interest rate on stock market returns are asymmetrical for allcountries, except for the UK and the USA. Additionally, there exist inelasticinterest elasticity of stock returns in all countries, except Japan. Empiricalresults suggest that the policymakers as well as the market participants shouldconsider asymmetry between selected macroeconomic variables and stock returnswhen they evaluate any policy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.020
GPT teacher head0.187
Teacher spread0.167 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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