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 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 | 0.048 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".