ESTIMATING THE NEGATIVE IMPACT OF "NOISE" ON THE RETURNS OF CAP-WEIGHTED PORTFOLIOS IN VARIOUS SEGMENTS OF THE EQUITY MARKETS
Bibliographic record
Abstract
Capital Market Theory assumes that the ex ante market portfolio (which is cap-weighted) lies on the (ex ante) efficient frontier. However, we show that ex ante cap-weighted portfolios will always be interior portfolios relative to the end-of-investment-period ex post efficient frontier. This is due to the arrival of unanticipated information, which we refer to as “noise” that causes unexpected price changes and creates either “winner” or “loser” stocks. By construction, ex ante cap-weighted portfolios will be overweighted in “loser” stocks and underweighted in “winners” during the return measurement period. To estimate the negative impact of noise on the returns of ex antecap-weighted portfolios, we use the concept of a “perfect foresight” (PF) portfolio. The PF portfolio for any given equity segment is a buy-and-hold portfolio of all stocks in that segment with weights at the beginning of the return period set to be proportional to the market capitalization of the stocks at the end of the return period.We show that the PF portfolio will always be on the ex post efficient frontier and outperform its ex ante cap-weighted counterpart. Because the PF portfolio has risk characteristics that are similar to the ex ante capweighted portfolio for a particular equity segment, the excess return of the PF portfolio provides an estimate of the maximum annual amount of available alpha to all investors involved in that segment in a given year. For example, the total excess return of the PF portfolio for the “large-cap US equity segment” (which we define as the 1,000 largest US stocks based on market values at the beginning of each year) is about 7%, on average, per year. This can be thought of as the maximum amount of alpha, or ex ante mispricing (in percentage terms), available to all investors in the large-cap US equity market segment.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".