Fair Value Accounting and Implications for the Auditing Profession: Historical Overview
Bibliographic record
Abstract
This paper explores the link between the introduction of fair value measurements (FVM) and the development of the Global Financial Crisis (GFC) in 2008-9. The paper aims to provide an historical analysis of the development of the Enron scandal with a focus on fair value accounting (FVA) and provides a narrative literature review of the subsequent economic downturn, its effect on the auditing profession and audit fees arrangements, implications for FVA, and response of global institutions and standard setters. It provides a theoretical explanation of the underlying antecedents using existing accounting theories. For each reviewed stream of research, the paper establishes the theoretical underpinning and discusses its application supported by the context. The content analysis using NVivo software was employed to analyse existing research and available published information. Based on the comprehensive literature review, the study arrives at two main findings. First, the paper concludes with the controversial use of FVM and establishes a direct connection between Enron’s collapse, the GFC, and improper FVM practices. Secondly, the study identifies the current pricing strategy for audit as an industry response to the abuse of external audit arrangements regarding FVA. Supported by the literature review findings, the paper contributes to the audit research by providing deep insights into the connection between FVA practices, the GFC and development of the audit profession. The paper addresses the lack of foundational research in FVA and establishes a solid background for further studies on FVA and audit profession development in general.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".