A Simple Framework for Time Diversification
Bibliographic record
Abstract
In this article the authors provide a simple but rigorous mathematical framework for time diversification. Based on this framework, we provide a measure of time diversification that can be computed for any return distribution model and any risk measure; this measure of time diversification can be empirically ascertained with non-parametric estimates of risk and with bootstrap techniques to simulate the return distribution. The authors argue that the critical issue of time diversification is not how to interpret time diversification in sequences of IID returns, but how to make long-term forecasts. The latter involves complex issues related to the distributional properties of returns, as well as memory effects and regime shifts. The authors then discuss how the distributional properties of stock returns, long memory, and regime shifts affect time diversification. <bold>TOPICS:</bold> <ext-link>Volatility measures</ext-link>, <ext-link>portfolio management/multi-asset allocation</ext-link>
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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 | 0.000 |
| 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".