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Record W2759879269 · doi:10.5430/afr.v6n4p197

Analyzing Foreign Investors Behavior in the Emerging Stock Market: Evidence from Qatar Stock Market

2017· article· en· W2759879269 on OpenAlexvenueno aff
Elsayed Elsiefy, Moustafa Ahmed AbdElaal

Bibliographic record

VenueAccounting and Finance Research · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHerdingStock marketInstitutional investorBusinessMonetary economicsStock exchangeFinancial economicsStock (firearms)EconomicsFinanceCorporate governance

Abstract

fetched live from OpenAlex

This paper examines the effect of the foreign investors fund flow into the domestic stock market. We investigated whether foreign investors are only herders or if they have also the ability to push the market up and down. To answer this question, we include investors’ types as an independent factor in Markov-Switching Model used by Hamilton (1989) to examine the asymmetric effect of the foreign investors during the bull and bear states. Empirical results from Qatar Stock Market suggested that foreign institutional traders are only herding in the market and they cannot play the role of the market maker. We have also found that neither foreign investors nor domestic investors have the ability to switch the regime of the market. The time-varying relationship between the various investors’ types has been investigated. We reported that, although the correlation matrixes of the investors’ categories with the market are time-varying, the foreign institutional trader is still the leader of the market during the bull (bear) states of the market. Finally, we proposed some procedures to minimize the harmful of the foreign investors bad trading.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.132
GPT teacher head0.337
Teacher spread0.205 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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