International portfolio investment: does the uncertainty matter?
Bibliographic record
Abstract
Purpose This study investigates the effect of global and domestic uncertainty on the dynamics of portfolio investment in 21 economies (mostly advanced and larger emerging economies) from 2001–2016. Design/methodology/approach Specifically, the evolution of the net portfolio equity investment inflows (FPI net inflows) and the evolution of net portfolio investment (FPI net) are investigated in a context in which the degree and the volatility of domestic economic policy uncertainty (EPU) and world uncertainty index (WUI) varied. The authors provide an empirical analysis through the sequential (two-stage) estimation of linear panel data models for unbalanced panel data. Findings An increase in the degree and volatility of domestic EPU has a significant negative influence on FPI net inflows, while an increase in WUI has a significant positive one. Notably, a simultaneous increase in the domestic EPU and WUI enhances the net inflows of FPI, whereas a simultaneous increase in the volatility of these indicators reduces the net inflows of FPI. An increase in the degree and volatility of both domestic EPU and WUI have a significant positive effect on the net portfolio investment, implying that a significant net portfolio investment is going out of the country. Research limitations/implications The results of this study encourage international investors to consider uncertainty indicators (and, more specifically, their variations) in their portfolio strategy to optimize their position on the international markets. The findings of this study invite policy-makers from large countries to reduce the perceived domestic uncertainty since this parameter can influence international investors' sensitivity and willingness to diversify their position out of the country. Originality/value The authors' approach focuses on the variations of uncertainty (existing literature mainly works with the indicators). While the results confirm the role played by large markets in international portfolio investment management, it nuances the changes in the portfolio management behaviors toward other markets when facing a changing uncertainty.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".