Data for Paper: The Impact of the Yield Curve on the Equity Returns of Insurance Companies
Bibliographic record
Abstract
Data for Paper: The Impact of the Yield Curve on the Equity Returns of Insurance Companies This study uses monthly data for all insurance companies listed on the major U.S. and Canadian public equity markets (NYSE, NASDAQ, and TSX) over the period between January 2000 and June 2019. This provides a sample of ninety-five U.S. insures and eight Canadian insurers. The monthly returns for both the U.S. and Canadian insurers are obtained through DataStream. The Fama-French factors, which include the market, size, and value factors, are obtained via AQR for the U.S. and Canada, respectively. The reasoning for obtaining these factors from AQR as opposed to Kenneth French’s website is because AQR has specific factors for Canada, while the Kenneth French website only has North American or Global factors to apply to the Canadian data. The interest rate data for the U.S. is obtained via the U.S. Federal Reserve Economic Database (FRED) and for Canada through Statistics Canada (Table 10-10-0122-01). Various interest rates are obtained to measure the various section of the term structure in both countries. These include the 3-month treasury, the two-, five- ten- and twenty-year notes and bonds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.018 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| 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".