Portfolio rebalancing and the dynamics between equity flow, exchange rates and equity returns
Bibliographic record
Abstract
In this paper we use simple panel regression augmented by a VAR framework and impulse response function to test the presence of portfolio rebalancing between US and 7 developed countries. We find that overall portfolio rebalancing does hold, however some countries in the sample, specifically Australia and Canada display information asymmetry whereby US investors are less informed than local investors, and chase the returns in foreign markets during bullish times. We hypothesize that this phenomena and the increasing supply elasticity of the FOREX markets interfere with the explanatory power of equity flow over the portfolio rebalancing channel in the long term. We find that the portfolio rebalancing channel itself generates a fleeting exchange rate change, however the effect persists through two distinct channels of magnification, with the causality running from exchange rates to equity flow which further appreciates the USD. We hold that US faces a tradeoff between current account and capital account inflows. We also hold that equity flow can be an effective measure to assess foreign exchange intervention in the US only with currencies belonging to countries it has no information frictions with, and countries that have minimal intervention in their FOREX markets.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".