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Record W2556275019 · doi:10.5539/ijef.v8n12p143

Determinants of Foreign Portfolio Investment and Its Effects on China

2016· article· en· W2556275019 on OpenAlexvenueno aff
Muhammad Afaq Haider Jafri, Muhammad Asif Khan, Elyas Abdulahi

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentForeign portfolio investmentPortfolio investmentMonetary economicsPortfolioChinaExchange rateDebtEconomicsBusinessInvestment (military)PopulationInternational economicsFinancial systemFinancial economicsFinanceMacroeconomicsOpen-ended investment companyReturn on investment

Abstract

fetched live from OpenAlex

Foreign portfolio investment consists of securities and other capital inflows of assets possibly held by other foreign countries. Foreign Portfolio Investment (FPI) provides the investor with indirect ownership of financial assets. This study intends to investigate the economic factors which attract the investors to invest in the host country. We observed what the impact of FPI determinants on the Chinese economy. To elaborate our results we used multiple regression models by using E-views. This study investigates the effects of FPI and its determinants of the economic structure of China. The data of FPI, GDP, FDI, EXD and P has been taken from the World Bank. GDP and External Debt are the strong determinants of the FPI, the Exchange Rate, Population shows that these variables have a significant impact on the FPI. The investors speculate the high returns, more secure investors’ rights and feel safer to invest in the country.

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)0.002
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 score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.215
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 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

Citations31
Published2016
Admission routes1
Has abstractyes

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