Earnings Disclosures and Investor Judgments: The Joint Effect of Incidental Affect and Emotion‐Understanding Ability<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This study uses an experiment to investigate the influence of investors' irrelevant feelings (e.g., positive vs. negative) on their financial judgments, with a specific focus on the role that emotion‐understanding ability plays in mitigating their biases. The participants in the experiment were exposed to emotionally charged social media posts before a positive earnings announcement was made by a company in which they had invested. The results indicate that investors with lower emotion‐understanding ability displayed biased judgments influenced by their feelings that were evoked by irrelevant content. Notably, the findings show that the negative and positive feelings elicited by irrelevant information led to lower investor judgments. Conversely, those with higher emotion‐understanding ability were able to resist these biases, focusing on the relevant information. This research underscores the critical role of emotional intelligence in financial decision‐making and highlights how investors' feelings can inadvertently distort their perceptions, particularly in environments saturated with irrelevant, emotionally charged information, such as social media.
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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 | 0.002 | 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 | 0.001 |
| 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".