Explaining Investors' Fixation on Increasing Revenue: An Experimental Investigation of the Differential Reaction to Revenues versus Expenses*
Bibliographic record
Abstract
ABSTRACT Prior research has demonstrated that investors have differential reactions to revenue increases versus expense decreases in firms with positive earnings surprises. We use the experimental method to explain how this differential reaction is, at least in part, due to a heuristic‐like process in investors' decision‐making processes. Critical to our study, we further demonstrate that when revenue increases explain even a small portion of that surprise, investors make more positive judgments about the firm than one might expect. This somewhat surprising result occurs even though those same investors provide similar earnings forecasts for the next period, indicating that our results are not entirely caused by differential judgments regarding the persistence of the earnings surprise. These findings are consistent with the halo effect, a phenomenon described in psychology literature in which, when making evaluations, one's focus on a certain salient factor impacts assessments regarding other factors. This article helps to explain some of the complexities with individual investor behavior when evaluating the impact of changes in revenues and expenses. Specifically, investors' preference for revenue increases as compared to expense decreases is partially caused by biases that are not obvious to investors, making it difficult for them to adjust for these biases.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".