Bettors' Curse

Last registered on April 29, 2026

Pre-Trial

Trial Information

General Information

Title
Bettors' Curse
RCT ID
AEARCTR-0018433
Initial registration date
April 21, 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
April 29, 2026, 3:25 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
Adelaide University

Other Primary Investigator(s)

PI Affiliation
Adelaide University

Additional Trial Information

Status
In development
Start date
2026-04-26
End date
2026-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We define the bettor’s curse as the tendency for individual bettors to overvalue their private signals about the likelihood of events relative to the public information contained in market odds. In this study, we examine the extent to which betting behaviour is driven by the bettors’ curse.

Under Bayesian updating, private signals mechanically update posterior beliefs. Given correct priors and signal precision, posterior beliefs are unbiased. If market odds fully aggregate the private information of all bettors, then all relevant information is summarised by the posterior probability. Consequently, conditional on the posterior, betting behaviour should be identical regardless of whether a bettor observes the private signal or not. While private signals may be correlated with the true probability, they are not perfectly correlated and therefore not fully informative. In contrast, market odds—if efficient—perfectly reflect the true probability (up to the bookmaker’s margin).


In our experimental setting, individuals are not required to acquire private information themselves. Instead, we exogenously provide them with a private signal with known precision. Posterior beliefs are then computed based on all these signals and signal precision according to Bayesian rule and presented directly to participants in the form of market odds. This design removes the need for participants to perform belief updating themselves. Under the Bayesian benchmark, if individuals solely depend on the posterior probability, irrespective of the signal observed, their behaviour should be identical.


H1 (Bettors’ Curse): We hypothesise that when individuals observe private signals, they overweight these signals and perceive posterior probabilities as higher or lower than they objectively are, which lead their betting behaviour deviating from the Bayesian benchmark. On average, they place higher bets when they observe a private signal than when no signal is observed.


H2 (Directional Betting): If bettors overweight their private signals, betting behaviour should move in the direction indicated by the signal. In our experiment, odds are fair, so the expected value of betting on either outcome is zero, and there is no ex ante reason to favour one outcome over the other. However, if private signals are overweighted, participants will systematically bet in favour of the outcome suggested by their signal. Such directional behaviour indicates that bettors treat their private information as more informative than the information already incorporated into market odds.


H3 (Sole Dependence on Signals): If bettors fully disregard the information contained in market odds and believe that probabilities depend solely on their private signals, then, conditional on observing the same signal, they will place higher bets when the odds are more favourable (i.e., when potential payouts are higher).
External Link(s)

Registration Citation

Citation
Bayer, Ralph-Christopher and Rubayat Sarwar. 2026. "Bettors' Curse." AEA RCT Registry. April 29. https://doi.org/10.1257/rct.18433-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-04-26
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
The key outcome variable is bet size at the participant level. Each participant will be asked to decide how much of the provided endowment they would like to bet on the outcome of fifteen (15) card-drawing games. Thus, one independent observation within a treatment corresponds to a vector of 15 betting decisions made by a single participant, with each stake ranging from $0 to $10. We will then compare these decisions across treatment with and without private signals.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will conduct an incentivised laboratory experiment in which participants make betting decisions in 15 rounds of card-drawing games. For each round, one card was pre-drawn at random from the deck of cards. The drawing process was recorded, showing a dealer shuffling the cards and a person drawing one card randomly. In each round, participants receive a $10 endowment and choose how much to bet on an outcome, with stakes ranging from $0 to $10.

Treatments vary whether a participant observes only public market odds (baseline treatment) or also receives a private signal with known precision.

The baseline treatment is a no-signal treatment, where participants observe only the publicly available betting information: the market odds associated with the possible outcomes. No private signal is provided. Since participants do not observe additional private information, their betting decisions should depend only on the public information embodied in market odds and their individual risk preferences.

In the treatment with private signals, participants observe the same public market information (market odds) as in the no-signal treatment, but they also receive a private signal regarding the likely outcome of the card-drawing game. The signal is exogenously provided by the experimenter and has a known precision. Recall that posterior beliefs are then computed based on the signals of all bettors involved and signal precision according to Bayesian rule and presented directly to participants in the form of market odds. The purpose of these treatments is to test whether participants respond solely to the information contained in market odds—which aggregate private signals of all bettors—or whether they place excessive weight on their own private signal.

In two treatments (baseline and with private signals), the prior probability of the two mutually exclusive outcomes is set at 50–50. In two additional treatments, the prior probabilities are set at 75–25. This variation introduces additional heterogeneity in the betting environment and allows us to examine behaviour under different priors.

The experimental design allows us to compare betting behaviour across treatments that vary the availability of private signals while holding constant the underlying structure of the betting environment. By controlling priors, signal precision, and market odds, the design provides a clear benchmark for Bayesian updating and enables us to identify deviations consistent with the bettors’ curse.

All four treatments will present the payout of each dollar bet (odds) explicitly. In our experiment, as the card-drawing games are objective, the value of probability information given in the experiment is free from human judgment. Each participant will undergo only one of these four treatments, making this experiment a cross-subject design. For each round, participants will be given a virtual endowment of 10 dollars in the programmed experiment in Z-tree, from which they can allocate their stakes between 0 and 10 dollars. The difference between their endowment and the betting stake will be part of their earnings from the experiment. The other part will be the payout from the bet, received only if they win.
Experimental Design Details
Not available
Randomization Method
We randomly assign each experimental session to one of the 4 treatments. Participants voluntarily sign up to attend in our sessions. But at the time of registration, they do not know the treatment they will receive.
Randomization Unit
Session
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
As one independent observation will be a vector of 15 betting decisions by an individual participant, we will do clustering at individual level. The planned number of clusters is 200 and 15 observations per cluster. This approach captures the similarities in betting behavior across participants.
Sample size: planned number of observations
200 participants x 15 betting decisions = 3,000 betting decisions (observations)
Sample size (or number of clusters) by treatment arms
Each treatment has 50 participants, and with 4 treatments, the total number of participants is 4 × 50 = 200.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Faculty of Arts, Business, Law and Economics Lower Risk Human Research Ethics Committee, The University of Adelaide
IRB Approval Date
2024-07-22
IRB Approval Number
H-2024-101
Analysis Plan

Analysis Plan Documents

Pre-analysis plan.pdf

MD5: 0aaf56fbc6f2327244e56b30a757d188

SHA1: 6a2919ec5a1fd13ba7d4469c0f1dbebf3b486463

Uploaded At: April 21, 2026