Bibliographic record
Abstract
Several recent papers have presented evidence from foreign exchange and other markets suggesting that the log of excess returns can be characterized as first-order integrated processes (I(1)). This contrasts sharply with the "conventional" wisdom that log prices are integrated of order one I(1) and that log returns should therefore be integrated of order zero I(0), and even more sharply with the view that past returns have no ability to predict future returns (weak market efficiency). It has been suggested that this should be interpreted as evidence of the importance of regime-switching in asset prices, since such non-linear processes can produce these results even when returns are truly I(0). This paper suggests an alternative interpretation. We consider whether the above results can be explained away as an artifact of the estimation procedure used. At first glance, this is not a likely explanation because - the significance level of some of the results is very high - the methodologies vary considerably across papers, so that a problem with any one statistical test cannot account for all the results - simulation experiments are used to check the validity of the tests Despite this, we suggest that the above evidence of unit roots may be spurious. Our explanation relies on the presence of several factors, including - severe size distortion in more than one statistical test - sensitivity to the design of the simulation experiments used to validate those tests Once these factors are taken into account, we think that the "anomaly" vanishes. We find that there is no remaining evidence of unit roots in excess returns once we account for the size distortion. We also show that the test results seem to be consistent with simple linear data generating processes -- regime-switching is not needed to account for them.
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 | 0.034 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".