The Role of Feelings in Investor Decision-Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. This paper surveys the research on the influence of investor feelings on equity pricing and also develops a theoretical basis with which to understand the emerging findings of this area. The theoretical basis is developed with reference to research in the fields of economic psychology and decision-making. Recent advancements in understanding how feelings affect the general decision-making of individuals, especially under conditions of risk and uncertainty [e.g. Loewenstein et al. (2001). Psychological Bulletin 127: 267–286], are covered by the review. The theoretical basis is applied to analyze the existing research on investor feelings [e.g. Kamstra et al. (2000). American Economic Review (forthcoming); Hirshleifer and Shumway (2003). Journal of Finance 58 (3): 1009–1032]. This research can be broadly described as investigating whether variations in feelings that are widely experienced by people influence investor decision-making and, consequently, lead to predictable patterns in equity pricing. The paper concludes by suggesting a number of directions for future empirical and theoretical research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it