Superstition and Risk Taking: Evidence from “Zodiac Year” Beliefs in China
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".