Managerial responses to changes in fair value accounting for equity securities
Bibliographic record
Abstract
Abstract Accounting Standards Update (ASU) 2016‐01 requires that unrealized gains and losses on equity investments (equity‐URGL) previously recognized in other comprehensive income now be included in net income. Using a sample of public insurers, we examine how this accounting standard change influences managerial investment decisions, with a particular focus on the moderating effects of compensation contracting and financial reporting practices. We find that prior to ASU 2016‐01, equity‐URGL was positively associated with CEO compensation, but this association dissipates in the post‐adoption period, when equity‐URGL is more frequently excluded from CEO performance metrics. Despite purported concerns about increased earnings volatility due to the new reporting requirements, highly affected insurers do not significantly reduce the size or risk of their equity investment portfolios following ASU 2016‐01, particularly when compensation metrics exclude equity‐URGL. We also find that equity‐URGL is more frequently excluded from non‐GAAP earnings post‐adoption, suggesting that managers adjust financial reporting practices as a response to the change. Moreover, highly affected insurers maintain the size and risk of their equity portfolios when equity‐URGL is excluded from non‐GAAP earnings. These findings suggest that managerial responses to ASU 2016‐01 are influenced by a balance between incentive structures and the costs associated with adjusting investment strategies.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".