Bibliographic record
Abstract
Purpose The random walk forecast of exchange rate serves as a standard benchmark for forecast comparison. The purpose of this paper is to assess whether this benchmark is unbiased and directionally accurate under symmetric loss. The focus is on the random walk forecasts of the dollar/euro for 1999‐2007 and the dollar/pound for 1971‐2007. Design/methodology/approach A forecasting framework to generate the one‐ to four‐quarter‐ahead random walk forecasts at varying lead times is designed. This allows to compare forecast accuracy at different lead times and forecast horizons. Using standard evaluation methods, this paper further evaluates these forecasts in terms of unbiasedness and directional accuracy. Findings The paper shows that forecast accuracy improves with a reduction in the lead time but deteriorates with an increase in the forecast horizon. More importantly, the random walk forecasts are unbiased and accurately predict directional change under symmetric loss and thus are of value to a user who assigns similar cost to incorrect upward and downward move predictions in the exchange rates. Research limitations/implications The one‐ to four‐quarter‐ahead random walk forecasts evaluated here are for averages of daily figures and not for the (end‐of‐quarter) rates in 3‐, 6‐, 9‐ and 12‐months. Thus, the framework is of value to a market participant who is interested in forecasting quarterly average rates rather than the end‐of‐quarter rates. Originality/value The exchange rate forecasting framework presented in this paper allows the evaluation of the random walk forecasts in terms of directional accuracy which (to the best of knowledge) has not been done before.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".