Using PPP deviations as a trading rule : an indirect joint test of PPP and foreign exchange market efficiency
Bibliographic record
Abstract
In this thesis an international investment filter rule is used to test both the tendency for Purchasing Power Parity (PPP) to hold in the long run and the hypothesis of foreign exchange market efficiency for the four most actively traded currencies in the world vis-a-vis the United States dollar: the British pound, the Japanese yen, the Germany mark, and the Canadian dollar. One way to examine whether there is a tendency for PPP to hold in the long run and whether the foreign exchange market is efficient, is to place more money in the 'undervalued' currency according to PPP deviations or to invest according to PPP deviations, putting more money into interest bearing securities in the 'undervalued' currency, the more this currency is undervalued. The return can then be compared with a reference rule which does not use this filter, but instead puts an equal value of money into the currencies or the securities of each country. This thesis has produced three results. First, using the PPP filter in the exchange money market yields no significantly abnormal rate of return compared with the reference rule. The result suggests that we can not reject the hypothesis that the tendency for PPP to hold in the long run does not exist. Second, using the PPP filter to invest in securities also yields no significantly higher rate of return compared with the reference rule. And third, when comparing the domestic (or foreign) interest rates with the rates of return for the domestic (or foreign) investor who uses the PPP filter, there is no significant difference between these rates in the long run. The last two results suggest that we can not reject the hypothesis that the foreign exchange-market is efficient.
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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".