Other Comprehensive Income: Do Nonprofessional Investors Value It as Much as Net Income?
Bibliographic record
Abstract
This study examines how investors incorporate unrealized gains or losses reported in Other Comprehensive Income (OCI) into their investment judgments. Since unrealized gains or losses can be presented in either OCI or net income—gains from trading securities are included in net income, while those from available-for-sale securities are reported in OCI (ASC 320 and ASC 851)—it raises the question of whether OCI items are perceived as equally significant as net income items. To explore this, we conducted a 2 × 2 experiment with 240 individual investors, manipulating the presentation of unrealized gains or losses in either net income or OCI. Our findings reveal that unrealized gains are valued significantly lower when presented in OCI compared to net income, indicating that investors see OCI-reported gains as less relevant. However, for unrealized losses, the incorporation degree remained consistent across both presentations, reflecting a general aversion to unrealized losses regardless of how they are reported.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".