The Predictive Ability of Fair Values for Future Financial Performance of Commercial Banks and the Relation of Predictive Ability to Banks' Share Prices
Bibliographic record
Abstract
For a sample of commercial banks during 1994 – 2008, we find that accumulated fair value adjustments for interest-bearing investment securities are positively associated with future interest income and total realized income from these investments. Additional tests reveal that accumulated fair value adjustments on investment securities also have predictive ability for future investment-security-related cash flows. Our analyses reveal that our predictive ability proxy for interest-bearing investment securities is positively related to the measurement precision of reported fair value measurements. We also provide evidence that the relative ability of fair values to predict reported accounting income is a factor that strengthens the association between fair values and commercial banks’ stock prices. Taken together, our study suggests that fair values have predictive ability for future income realization despite the low persistence of changes in fair value that has been extensively documented in prior studies. Further, we find that our proxy for predictive ability captures banks’ expectations of future above- or below-market relative interest income, which is a real economic condition that is incrementally reflected in current equity prices.
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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 | 0.008 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".