Determinants of Foreign Portfolio Investment and Its Effects on China
Bibliographic record
Abstract
<p>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.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".