Persistent Leverage in Residual-Based Portfolio Sorts: An Artifact of Measurement Error?
Bibliographic record
Abstract
University, the Bank of Canada, and the NFA Conference, as well as an anonymous referee for their Firm leverage has been documented to be a slow-moving, persistent variable, even after controlling for leverage determinants. I show that if a firm’s leverage dynamics are driven by a persistent explanatory variable that is measured with error, the mismeasured explanatory variable creates leverage persistence in a Lemmon et al. portfolio sort framework. In regression residual-sorted portfolios, a large positive residual will forecast above average future leverage. If a single factor drives leverage (we can think of this factor as a composite of many tradeoff theorybased explanatory variables), then the measurement error variance of this single “composite ” variable needs to be 42 % larger than its cross-sectional variance to reproduce the stylized facts of portfolio leverage persistence. Even small levels of measurement error produce a remarkable level of persistence in residual-based portfolio sorts. Furthermore, low quantities of measurement error in profitability, tangibility, and industry leverage, coupled with a measurement error variance equal to about 80 % of the cross-sectional variation in the market to book ratio, produce a good fit of simulated sample data moments to empirical moments. This suggests that unobserved investment opportunities may play an important role in explaining leverage ratios. EFM Classification: 140 1 1
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".