Neural Network Models of the Spot Canadian/U.S. Exchange Rate
Bibliographic record
Abstract
: This paper proposes several predictive nonlinear transfer function models between short term interest rate spread and daily spot Canadian/US foreign exchange rate, using multi-layer feedforward neural networks with backpropagation learning algorithm. A comparative pre-test of the neural network model is constructed to evaluate the network performance and to select the "best" model. All of the testing models yield about 55% - 60% accuracy of the directional forecast on the "out-of-sample test set". Comparing with the linear predictive models, a 2% to 5% gain is obtained by using neural network models. In particular, one of the models proposed in this paper, namely the separate neural networks model, is able to explore the nonlinear relationship between the spot Canadian/US foreign exchange rate and short term interest rate spread during a period of negative interest rate spread. Furthermore it is able to capture a corrective mean reversion when the Canadian dollar is under or over-val...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".