Distinguishing Factors and Characteristics with Characteristic-Mimicking Portfolios
Bibliographic record
Abstract
This dissertation contains three essays on the non-pecuniary preferences pertaining to financial asset characteristics and their implications for asset pricing. The first essay considers the pricing implications of screens adopted by socially responsible investors. A model including such investors reconciles the empirically observed risk-adjusted sin-stock abnormal return with a systematic “boycott risk premium” which has a substantial financial impact that is, however, not limited to the targeted firms. The boycott effect cannot be displaced by litigation risk, a neglect effect, and liquidity considerations, or by industry momentum and concentration. The boycott risk factor is valuable in explaining cross-sectional differences in mean returns across industries and its premium varies directly with the relative wealth of socially responsible investors and with the business cycle. The second essay generalizes Fama (1996)’s concept of Multi-Factor Efficiency without being limited by additional random state variables that must affect future investment opportunities. Incorporating non-pecuniary preferences into a representative investor’s utility function generates multi-factor pricing implications. A representative investor chooses among expected returns, variances, and levels of characteristics according to their taste, which gives rise to an N-fund separation theorem with static characteristics. If a portfolio is built to maximize the exposure to the asset characteristics, the covariance between asset returns and this portfolio returns will be identical to the underlying characteristics. Such identity makes obsolete any attempts to distinguish between characteristics and risk exposures as the driving forces behind the cross-sectional variation in stock returns. The third essay develops a procedure for deriving systematic factors from characteristics, based on maximizing each factor’s exposure to a characteristic subject to a given level of factor variance. The resulting characteristic-mimicking portfolios (CMP) price mean asset returns identically as the original characteristics, irrespective of the underlying model. Accordingly, differences in the performance of mimicking factors and characteristics in explaining mean returns should be interpreted as an artifact of arbitrary procedural choices for generating mimicking factors. Factors and characteristics may be distinguished usefully only by determining if CMPs have significant explanatory power for the time series of returns.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".