Appendix to 'Factor-Based Tactical Bond Allocation and Interest Rate Risk Management'
Bibliographic record
Abstract
This online appendix reports additional backtesting results of our bond market factor strategy. The tested countries span the most important sovereign markets, including Germany, UK, USA, Japan, Australia, and Canada as in the main paper, we use short- and long-term bond durations for every tested country. Abstract of Factor-Based Tactical Bond Allocation and Interest Rate Risk Management: This paper reports two composite bond market factor investment strategies each for the Swiss and global sovereign bond markets. These composite factor strategies can be used as tools for tactical asset allocation decisions between bonds and cash, and to base the duration debate upon. As such, the output of our bond market factors can guide tactical interest rate views and, therefore, interest rate risk management. To construct the composite factors, we use four economically meaningful, individual factors. Following an investment strategy based on a composite bond market factor, constructed as the equally weighted average of individual components, we are able to outperform cash as well as the static buy-and-hold strategy in terms of Sharpe ratio, annualized standard deviation, and maximum drawdown. Testing the composite and individual factors on their performance during periods of historical rising interest rates, we observe improved drawdown results compared to holding the underlying asset passively.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.602 | 0.169 |
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".