Did Adoption of Forward-Looking Valuation Methods Improve Valuation Accuracy in Shareholder Litigation?
Bibliographic record
Abstract
Before 1984, Delaware judges relied exclusively on the Delaware Block method—an appraisal formula based on trailing earnings and liquidation value—to price shares in shareholder litigation. In 1984, the Delaware Supreme Court changed the law to permit its judges to use any valuation method they deem appropriate. As a result, judges and litigants began switching from the Block method and adopting forward-looking valuation techniques based on cash flow and earnings forecasts. While the use of forward-looking methods potentially improves valuation accuracy by incorporating forecast information, the use of forecasts allows more room for subjective manipulation. Did the adoption of forward-looking methods improve or reduce valuation accuracy in shareholder litigation? We address this question using a comprehensive hand-collected sample of all Delaware corporate “appraisal-remedy” cases published between 1966 and 2002 in Lexis-Nexis. The sample identifies, on a case-by-case basis, the plaintiff's, the defendant's, and the judge's valuation methods and resulting valuation estimates. We show that the adoption of forward-looking valuation methods improves litigants' valuation accuracy on average.
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How this classification was reachedexpand
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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".