Review of Prospective Financial Statements: Stationary vs. Forward-Looking Assessments
Bibliographic record
Abstract
Prospective financial statements (PFSs) and their examination have become more and more important in recent years as a result of various regulating pressures and market needs. Despite this growing importance, the literature about PFS review appears to be dated and generic and only proposes backward-looking models. This paper examines and integrates the literature, standard setters’ guidelines, and best practices regarding PFS analysis in order to identify the objectives of PFS review and categorize the criteria for its examination. We develop a conceptual and operational framework to achieve the following: (a) we define a structured estimation process in the light of PFS review criteria; (b) we operationalize the estimation process to guide PFS validation. We find that PFS review mainly relies on a consistency analysis between the results of the company analyzed and its drivers, with the aim of identifying reasonable weights of each driver on the forecasts. Our work represents a first attempt to build a method to assess the reasonableness and uncertainty of PFSs under both backward-looking and forward-looking perspectives. It supports auditors and managers in evaluating the likelihood that a company’s plan will ensure business continuity. It also supports external users (banks, analysts, valuers) involved in formulating estimates and corporate valuations based on prospective information.
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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 | 0.150 | 0.533 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".