Fundamental Analysis of Banks: The Use of Financial Statement Information to Screen Winners from Losers.
Bibliographic record
Abstract
This study investigates the efficacy of a fundamental analysis-based approach to screen U.S. bank stocks. We construct an index (BSCORE) based on fourteen bank–specific valuation signal. We document a positive association between BSCORE and future profitability changes, as well as current and one-year-ahead stock returns, implying that BSCORE captures forward looking information that the markets are yet to impound. A hedge strategy based on BSCORE yields positive hedge returns for all but two years during our 1994–2014 sample period. Results are robust to partitions of size, analyst following, and exchange listing, and persist after adjusting for risk factors. We further document a positive relation between BSCORE and future analyst forecast surprises as well as earnings announcement period returns, and a negative relation between BSCORE and future performance-based delistings. Overall, our results show that a fundamental analysis-based approach can provide useful insights for analyzing banks.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 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".