The Stability of Factor Sensitivities of German Stock Market Sector Indices: Empirical Evidence and Some Thoughts about Practical Implications
Bibliographic record
Abstract
This empirical study estimates 18 single and 18 three-factor models and then tests for structural change. Break dates are identified where possible. In general, there is some empirical evidence for parameter instabilities of the estimated beta coefficients. In most cases there is no or one break point, and in some cases, there are two structural breaks examining the three factor models. The estimated factor sensitivities of single beta models seem to be even less strongly affected by structural change. Consequently, beta factors are probably more stable than some observers might believe. The break dates that have been identified generally seem to coincide with crises or recoveries after stock market slumps. This empirical finding is compatible with the point of view that bull-markets or bear-markets could matter when estimating beta coefficients. In general, the timing of structural change often seems to coincide with either the bursting of the dot-com bubble or the recovery of stock prices thereafter. The banking industry is the most notable exception. In this sector of the German economy, the global financial meltdown and the sovereign debt crisis in Europe have been of high relevance. Consequently, the internet hype of the late 1990s and the early 2000s seems to be more important for the German stock market than the US subprime debacle and the accompanying European sovereign debt crisis.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".