Capturing Profitability in Asset Pricing Models for Japanese Equities 1994-2016
Bibliographic record
Abstract
We follow Ball et al. (2015) to investigate and compare firms’ gross profit, operating profit, and net income as predictors of returns for a cross-section of traded Japanese equities spanning 1994-2016. We test the predictive power of profit measures on cross-sectional stock returns using portfolio tests and Fama-MacBeth regressions, find that gross-profit-to-book-equity significantly predict returns on sampled stocks. Consistent with Novy-Marx (2013), we also find that sorting portfolios by gross profitability and book-to-market ratios outperform in the Japanese market. Hence, we create a Market-Profitability-Value model that captures value and profitability premium among returns of sampled stocks. Based on Gibbons-Ross-Shanken test and economic value, we demonstrate that our enhanced model outperforms Fama–French multiple-factor model in isolating influences on equity 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".