Foreign Exchange Responses to Macroeconomic Surprises: Playing “Peek-a-Boo” with Financial Markets
Bibliographic record
Abstract
This paper explores the relationship between precisely timed macroeconomic “news” (or “surprises”) and the immediate currency price fluctuations that surround them. Using data from 2005-2011, I find significant movements in foreign exchange markets around a variety of announcements (unemployment, GDP, retail sales, inflation) for three different countries (United States, Australia, Canada). My results demonstrate that in the very short-run, as in the long run, the value of a country’s currency is driven by its macroeconomic fundamentals. Upon further investigation, this paper also uncovers the following financial phenomena in these foreign exchange responses to macroeconomic surprises: asymmetric response, nonlinearity, financial stress, liquidity, and exchange rate specificity. These phenomena refer to the difference in responses between: positive and negative surprises, big versus small surprises, pre-crisis versus crisis surprises, ten- versus sixty-minute returns, and two distinct reference currencies, respectively.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".