Determinants of Undetected Unintentional Errors in Audited Financial Statements
Bibliographic record
Abstract
This dissertation investigates the associations between financial restatements and characteristics of the parties responsible for preventing and detecting unintentional errors, i.e., boards (through their audit committees), management (through chief financial officers (CFOs)), and auditors. To conduct this investigation, I developed a theoretical model of restatement determinants that is more complete than models used in previous archival research as it includes characteristics of all three parties and the moderating effects of chief financial officers’ financial expertise and influence on the disruptive effects of organizational change. To identify restatements that correct unintentional error, I conducted automated text searches of over 10,000 restatement disclosures for language asserting or implying lack of intent. This language-based proxy is automated, direct, transparent, easily replicable, scalable, and classifies as error-correcting a smaller proportion of restatements as error-correcting than other proxies. I validated this proxy by contrasting the characteristics of the unintentional error restatements against other restatements based on theory-derived expectations. I find that annual financial statements restated to correct unintentional error(s) for years with Sarbanes Oxley Act of 2002 (SOX) Section 404 auditor’s opinions exhibit less net income smoothing, less earnings persistence, and less positive accruals than such firm-years of other restatements. Finally, I tested the theoretical model using logistic regressions and data from financial statements, proxy statements, and auditor’s SOX 404 opinions of 346 companies (i.e., 121 companies that restate to correct unintentional error; 121 companies without restatement matched by year, industry, and company size; and 104 companies with other restatements that proxy for restatements of intentional misstatement). \n \nResults show that of the three parties responsible for financial reporting quality, the CFO plays the major role with respect to unintentional error: The likelihood of restatement to correct unintentional error is decreasing in CFO financial expertise and influence, but only when companies are undergoing organizational change. Results also show that CFOs’ (audit committees’) financial expertise is more strongly associated with restatements that correct unintentional error (intentional misstatement) than intentional misstatement (unintentional error). However, I find no evidence of significant associations between auditor quality and either restatements that correct unintentional error or intentional misstatement. \n \nThis research contributes to the emerging literature that examines variation in associations between type or severity of restatements and the influence of parties responsible for financial reporting quality. The new language-based proxy for restatements that correct unintentional error developed in this thesis will facilitate future research that uses type of restatement to proxy for constructs of interest.
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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 | 0.012 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".