The informativeness of consolidated and parent‐only earnings to investors: Evidence from India
Bibliographic record
Abstract
Abstract We examine whether earnings from parent‐only financial statements are incrementally informative to those from consolidated financial statements. We use a unique mandate in India that requires firms to provide both consolidated and parent‐level financial statements, since currently neither US GAAP nor IFRS mandates this level of disaggregation. While disaggregation provides additional information, it also imposes costs, raising the empirical question of whether its benefits outweigh the costs. Our analyses reveal that disaggregated quarterly earnings components inform investors, with investors placing more weight on parent‐level unexpected earnings than on subsidiaries' unexpected earnings. We do not find evidence of mispricing associated with disaggregation; rather, the higher weight on the parent's earnings reflects higher persistence, consistent with semi‐strong market efficiency. Moreover, parent earnings provide incremental informativeness, especially in the context of poor earnings quality and high mergers and acquisitions intensity. Our results endure when we examine annual parent‐ and subsidiary‐level earnings, where available, in 98 countries around the world. Our results contribute to the literature on disaggregation in accounting and earnings informativeness in equity markets, offering insights that may influence regulatory considerations on the usefulness of financial statement disaggregation.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".