Discretionary Goodwill Accounting: Do the Institutional Investors Matter?
Bibliographic record
Abstract
Discretionary goodwill accounting is a long-debated issue in accounting and finance literature. Issuing new standards (IFRS 3‐ Business Combination and SFAS 141 (R)) jointly by International Accounting Standard Board (IASB) and Financial Accounting Standard Board (FASB) was an attempt to end the debate in this domain. However, rather than resolving the issue, IFRS‐3 and SFAS 141 (R) provided new dimensions in arguments among academics and practitioners. Existing goodwill literature was mainly focused on its’ subsequent treatment (Impairment‐ IAS 36) in relation to other business environmental and macro‐economic factors. However, this study was conducted emphasizing the focus on the evaluation of initial goodwill recognition and measurement. More specifically, this study examines; whether institutional investors impact goodwill recognition during the purchase price allocation (PPA) in ‘Merger and Acquisition’ (M&A) transactions. This study found that number of institutional investors does not have any influence on the M&A purchase price allocation toward goodwill; rather, the percentage of holding matters and the relationship is negative. Moreover, the study identified that both active and passive institutions significantly impact the recognition of goodwill identification. In addition to that, there was also a statistically significant relationship between the acquired weight on the investor portfolio return and the M&A purchase price allocation, and the coefficient of the relation is negative. Keyword: Goodwill, M&A, IFRS3, Institutional Investor, Corporate Governance.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".