Implied Expected Returns and the Choice of a Mean–Variance Efficient Portfolio Proxy
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Bibliographic record
Abstract
Implied expected returns are the expected returns for which a supposedly mean–variance efficient portfolio is effectively efficient, given a covariance matrix. The authors analyze the properties of monthly implied expected stock returns and study their sensitivity to the choice of mean–variance efficient portfolio proxy. For the universe of S&P 100 stocks over the period from 1984 to 2014, they find that using as risk-based portfolio proxy with respect to a market capitalization or fundamental value portfolio brings its biggest gains in return forecasts’ stability and precision. For all the proxies considered, they report that the implied expected returns outperform forecasts based on a time-series model in stability and precision. <b>TOPICS:</b>Portfolio management/multi-asset allocation, factor-based models, mutual funds/passive investing/indexing
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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 | 0.003 | 0.000 |
| 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 it