Superstition and Risk Taking: Evidence from “Zodiac Year” Beliefs in China
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
We show that superstitions—beliefs without scientific grounding—impact the investment and risk-taking of Chinese firms. We focus on widely held beliefs in bad luck during one’s “zodiac year,” which occurs on a 12-year cycle around a person’s birth year, to study superstitions and risk taking. We first show a direct correspondence between zodiac year and risk taking via survey data: respondents are two percentage points more likely to favor no-risk investments if queried during their zodiac year. Turning to corporate decision making, we find that return volatility declines in the chairman’s zodiac year, suggesting a reduction in risk taking overall. Focusing on specific types of risk taking, investment in R&D and corporate acquisitions both decline during the chairman’s zodiac year; returns around acquisition announcements are also lower, suggesting real allocative consequences of zodiac year beliefs. This paper was accepted by Gustavo Manso, finance. Funding: W. Huang thanks the Major Project of National Social Science Foundation of China [Grant 17ZDA090] and the “National Program for Special Support of Eminent Professions” for financial support. Y. Pan thanks the National Natural Science Foundation of China [Grant 71790601] for financial support. Y. Wang thanks the National Natural Science Foundation of China [Grant 72172090] for financial support. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4594 .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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