Bibliographic record
Abstract
The US Senate Committee that investigated the Enron disaster assessed the role of analysts. At issue was whether analysts misled the public by ignoring warning signals that included a high proportion of non-operating income. Non-operating income derives from secondary activities like investments, but operating income is from the primary business activities like manufacturing. While the analysts admitted their limited ability to forecast Enron’s earnings, they denied any intentional deceit and claimed that they were misled by Enron. This study asks whether analysts’ ability to predict earnings is generally negatively associated with the proportion of non-operating income. The rationale is to determine whether the limited ability of analysts to predict earnings for Enron was an isolated incident or a pervasive one that applies to other firms. If pervasive, then another such disaster could occur without a warning from analysts. First, I examine the incentives for firms to resort more to non-operating income rather than focus on Operating income. Then I examine the association between analysts’ forecast attributes and the ratio of nonoperating to operating income. The results show that non-operating income and operating income are negatively associated, suggesting that firms use non-operating income to manage their operating results. Also, analysts’ forecast inaccuracy and dispersion are positively associated with the ratio of non-operating income to operating income. These results imply that analysts are generally inefficient in predicting earnings of firms with a high proportion of non-operating income.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".