Dynamic Defaults

Last registered on June 29, 2026
View Trial History

Pre-Trial

Trial Information

General Information

Title
Dynamic Defaults
RCT ID
AEARCTR-0019009
Initial registration date
June 24, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 29, 2026, 8:47 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
United States of America
Region
Prolific

Primary Investigator

Name
Avner Strulov-Shlain
Affiliation
University of Chicago
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Rawley Heimer
PI Affiliation
W.P. Carey School of Business, Arizona State University
Contact Investigator

Additional Trial Information

Status
In development
Start date
2026-06-25
End date
2026-07-31
Keywords
Behavior, Finance & Microfinance, Lab
Additional Keywords
overconfidence, misattribution, default effects
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Observational data from a trading platform (henceforth "the Platform"), covering 555,195 traders and 98.7M trades over 2013–2020, show three patterns that motivate this experiment: (i) traders typically accept the Platform's default parameters for leverage, stop-loss, and take-profit (mean "defaultism" score of 3.13 on a 0–4 scale); (ii) defaultism declines with tenure while monthly profits become more negative; (iii) traders in the *lowest* first-month defaultism quartile are the most lossy over 22 active trading months.
This correlation between departing from defaults and worse performance is consistent with several mechanisms. The channel we test here is skill misattribution: if platform defaults are informative, subjects who accept them may do well early, misattribute the performance to their own trading skill, and therefore (a) report higher confidence about their ability and (b) subsequently invest more aggressively when defaults are removed. If skill is in fact the platform's rather than theirs, this greater aggression translates into worse realized outcomes.
External Link(s)

Registration Citation

Citation
Heimer, Rawley and Avner Strulov-Shlain. 2026. "Dynamic Defaults." AEA RCT Registry. June 29. https://doi.org/10.1257/rct.19009-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Participants complete a two-round investment task on Prolific, hosted in Qualtrics. In each round they trade six risky assets over five periods starting from a 1,000-point endowment; points not allocated to an asset stay in cash (the risk-free option). Round 1 is a non-incentivized practice round; only Round 2 earnings determine the bonus.

Three factors are randomized independently at the participant level: defaults, skill elicitation, and leverage choices.

The design tests whether experiencing a good default in the practice round causes participants to misattribute the resulting good outcome to their own skill — rating themselves as more skilled and then, in the incentivized round, investing more and choosing higher leverage.
Intervention Start Date
2026-06-25
Intervention End Date
2026-07-31

Primary Outcomes

Primary Outcomes (end points)
- Round-1 final account balance (first-stage / manipulation check).
- Self-rated skill on a 7-point scale, elicited after Round 1 and after Round 2.
- Round-2 amount invested at the start of the round (1,000 minus cash).
- Chosen leverage factor L (participants offered the leverage tool).
Primary Outcomes (explanation)
Five pre-specified hypotheses, all two-sided (alpha = 0.05), estimated by OLS with HC3 robust standard errors and corrected as one family by the Holm step-down method. Predicted signs follow the skill-misattribution channel (good-default > no-default > bad-default).

H1 (first stage): round1_final_account ~ arm, full sample.

H2a (skill, primed): skill_post_R1 and skill_post_R2 each regressed on arm controlling for the pre-Round-1 skill baseline (skill_pre_R1); show_skill = 1 subsample.

H2b (skill, un-primed): post-Round-2 skill regressed on arm; show_skill = 0 subsample (this group's only skill elicitation, no baseline).

H3 (investment): Round-2 initial amount invested regressed on arm controlling for Round-1 initial amount invested; good- and bad-default arms only (both face the identical Round-1 default), good-default the reference.

H4 (risk appetite): chosen leverage L regressed on arm; leverage_offered = 1 subsample.

Secondary Outcomes

Secondary Outcomes (end points)
- Round-2 realized return / final account balance.
- Average Round-2 amount invested (mean over Round-2 trading periods).
- Round-2 liquidation (binary: account reached zero before the round ended).
- Stated interest in trading after the task (7-point).
- Moderators: prior trading experience (yes/no), general trading self-assessment (7-point), and willingness to take risks in general and in financial matters (two 0-10 items).
Secondary Outcomes (explanation)
Each secondary outcome is regressed on the treatment arm (OLS, HC3 robust SEs, two-sided, unadjusted p-values; reported as exploratory). Additional pre-specified secondary analyses: (a) within-arm OLS gradients of the leverage choice and Round-2 amount invested on realized Round-1 profit (round1_final_account - 1000), within the good- and bad-default arms; (b) descriptive splits of Round-2 amount invested by Round-1 gain vs. loss and by Round-1-outcome decile; (c) two-stage least squares estimates of the effect of realized Round-1 profit on later skill ratings, Round-2 amount invested, and the leverage choice, using the arm indicators as instruments for Round-1 profit (exclusion restriction: the default affects later outcomes only through realized Round-1 profit; first-stage F reported, with Anderson-Rubin weak-instrument-robust inference when F < 10). The post-task measures are also examined as moderators of the treatment effects.

Experimental Design

Experimental Design
Online experiment on Prolific (UK and US participants, English-fluent, age 18+), hosted in Qualtrics. Varying defaults in a trading task, and measuring market participation, self-rated skill, and leverage choices.
Experimental Design Details
Not available
Randomization Method
Randomization is performed in the survey software (Qualtrics). The Round-1 default condition and the skill-elicitation factor are each assigned by a Qualtrics block randomizer with even presentation; the leverage-offered factor is assigned by a 50/50 draw in JavaScript at the start of the experiment. The three factors are mutually independent.
Randomization Unit
Individual participant.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters; randomization is at the individual level (1,750 individual participants).
Sample size: planned number of observations
1,750 participants, retained after the pre-specified exclusions.
Sample size (or number of clusters) by treatment arms
Target ~583 participants per Round-1 default arm (good-default / bad-default / no-default). Orthogonally, ~875 receive the skill elicitation and ~875 do not; ~875 are offered the leverage choice and ~875 are not. The skill (show_skill = 1) and leverage (leverage_offered = 1) subsamples therefore contain ~292 participants per default arm each; the H3 contrast (good + bad arms) uses ~1,167.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
At N = 1,750 (two-sided alpha = 0.05, power = 0.80): - H1 (Round-1 final account): about 10 points. - H2a self-rated skill (post-R1 / post-R2, baseline-adjusted): about 0.29 / 0.34 scale points. - H2b un-primed post-R2 skill: about 0.43 scale points. - H3 (Round-2 amount invested): about 50 points. - H4 (chosen leverage L): about 0.08 leverage units.
Supporting Documents and Materials

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Chicago SBS IRB
IRB Approval Date
2026-04-16
IRB Approval Number
IRB26-0653
Analysis Plan

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information