Experimental evidence on price manipulability

Last registered on December 15, 2023
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Pre-Trial

Trial Information

General Information

Title
Experimental evidence on price manipulability
RCT ID
AEARCTR-0012543
Initial registration date
December 01, 2023

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
December 15, 2023, 3:57 PM EST

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

Locations

Country
United States of America
Region

Primary Investigator

Name
Itzhak Rasooly
Affiliation
Sciences Po & Paris School of Economics
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Roberto Rozzi
PI Affiliation
University of Padova
Contact Investigator

Additional Trial Information

Status
In development
Start date
2023-12-01
End date
2024-07-01
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this project, we provide experimental evidence on price manipulability. Please see the attached PDF for the experimental design and analysis plan.
External Link(s)

Registration Citation

Citation
Rasooly, Itzhak and Roberto Rozzi. 2023. "Experimental evidence on price manipulability." AEA RCT Registry. December 15. https://doi.org/10.1257/rct.12543-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Please see the attached PDF.
Intervention Start Date
2023-12-01
Intervention End Date
2024-03-01

Primary Outcomes

Primary Outcomes (end points)
Prices (see PDF).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Please see the attached PDF.
Experimental Design Details
To quote from the analysis plan (see PDF):

"The goal of this experiment is to learn whether prediction markets can be manipulated through random trades. To investigate this, we plan to make a series of random bets across hundreds of prediction markets on the Manifold platform. While these bets will automatically shift prices in the short run, our main goal is to learn whether these price effects persist in the long run or are instead ‘undone’ by the reactions of other traders. We will also study which kinds of market can most easily be manipulated.

We will only bet on binary markets, i.e. markets that must resolve as `Yes' or `No' (or N/A). We will exclude:

- Markets that do not resolve based on an external event by the end of 2025 (e.g. self-referential markets). Explanation: self-referential markets (e.g. “will this market resolve at above 50\%?”) may be trivial to manipulate but may not be representative of more normal markets.
- Markets with fewer than 10 traders (at the time of the trade). Explanation: while it may be easy to manipulate very small markets, we want to see if we can manipulate larger markets; particularly since prediction markets of interest to the public (e.g. on elections) will generally have many traders.
- Markets which cost at least 200M to move in either direction by 5 percentage points. Explanation: including such markets would reduce the number of markets that we can manipulate holding fixed our experimental budget.
- Markets that (i) started within the last 7 days or (ii) end within the next 30 days. Explanation: markets can be very volatile within the first 7 days of their life cycle, which reduces statistical power. To examine 30 day effects, we need the second restriction.
- Markets that are closely related to other markets in our sample (e.g. “Will Trump win?” vs “Will Trump lose?”). Explanation: the prices of such markets are linked by no-arbitrage conditions. As a result, betting on one market can in theory alter the price in the other market, which can bias our estimates due to a “spillover” effect."
Randomization Method
Online random number generator.
Randomization Unit
Market level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
849
Sample size: planned number of observations
849
Sample size (or number of clusters) by treatment arms
849
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See attached PDF.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Paris School of Economics
IRB Approval Date
2023-11-23
IRB Approval Number
2023-042
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials