Implementability of Trading Strategies Based on Accounting Information: Piotroski (2000) Revisited
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The return accumulation approach used in studies on accounting-related anomalies cannot be replicated in a practical context because the number and identity of individual observations within a portfolio are assigned within a research context before the accounting information of all firms in the portfolio would actually be available in real time. We explore this issue by re-examining the results in Piotroski (2000) [Value investing: the use of historical financial statement information to separate winners from losers, Journal of Accounting Research, 38(supplement), 1−44]. We find that the relationship between Piotroski's fundamental signals and subsequent returns is partly driven by the choice of return accumulation periods. Because the method used in Piotroski is typical of those often employed in the accounting literature, this study suggests that evidence of profitable trading strategies and market inefficiency in the literature is likely to be overstated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it