Modeling Goodwill for Banks: A Residual Income Approach with Empirical Tests*
Bibliographic record
Abstract
Abstract This paper uses the residual income valuation technique outlined in Feltham and Ohlson 1996 to examine the relation between stock valuations and accounting numbers for a prototypical banking firm. Prior work of this nature typically assumes a manufacturing setting. This paper contributes to the prior research by clarifying how the approach can be extended to settings where value is created from financial assets and liabilities. Key elements of our model include allowing banks to generate positive net present value from either lending or borrowing activities, and allowing for accounting policy to affect valuation through the loan loss allowance. We validate our model using archival data analysis, and interpret coefficients in light of our modeling assumptions. These results suggest that banks create value more from deposit‐taking activities than from lending activities. Vuong tests confirm that our model outperforms adaptations of the unbiased accounting model of Ohlson 1995 and adaptations of the base model proposed by Beaver, Eger, Ryan, and Wolfson 1989. However, our model is outperformed by the popular net income‐book value model used in many empirical studies, and we can formally reject one of our key modeling assumptions. These tests of our model suggest future avenues for improving upon the theoretical analysis.
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.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".