The Last Hurrah Effect: End-of-Period Temporal Landmarks Increase Optimism and Financial Risk-Taking
Bibliographic record
Abstract
Understanding what drives risk-taking is fundamental for the study of choice under uncertainty. One widely discussed question is when and why people engage in risk-taking. Prior work finds evidence for an ending effect, where risk-taking increases on the last gamble in a series when outcomes are immediately realized. The authors test whether socially ubiquitous end-of-period temporal landmarks (e.g., last day of the work week, month, year) alter financial risk-taking even when outcomes are not immediate. Using data from a large peer-to-peer investment platform in the United States, they show that investors make riskier financial investment decisions on Fridays relative to those made earlier in the week. Consistent with a broader end-of-period effect, risk-taking also increases on the last day of the month, on the last day of the year, and on weekdays prior to a long weekend. Follow-up lab experiments identify a novel mechanism driving ending effects: As people near the end of a temporal period, they feel more optimistic that their financial risks will pay off, driving greater financial risk-taking. The shift in risk-taking is not without consequence: In this specific peer-to-peer context, the authors show that end-of-period investments perform worse over time, losing money relative to investments made on other days.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".