Mental Models of the Stock Market

Last registered on March 13, 2024


Trial Information

General Information

Mental Models of the Stock Market
Initial registration date
May 30, 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
June 09, 2023, 3:55 PM EDT

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

Last updated
March 13, 2024, 11:39 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

SAFE and Goethe University Frankfurt

Other Primary Investigator(s)

PI Affiliation
University of Bonn
PI Affiliation
University of Copenhagen

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
We explore the mental models of the stock market among different groups of economic agents.
External Link(s)

Registration Citation

Andre, Peter, Philipp Schirmer and Johannes Wohlfart. 2024. "Mental Models of the Stock Market." AEA RCT Registry. March 13.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
- investors’ return expectations
- the reasoning behind their expectation
Primary Outcomes (explanation)
Return expectations: In which scenario is the future expected return of an investment in the stock over the next year higher?
Reasoning 1: Open-ended explanation of prediction
Reasoning 2:Structured question about the reasoning behind respondents' prediction

See separate preregistration plan and full survey instrument.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In our surveys, we present participants with different hypothetical scenarios describing news about the future earnings stream of a company. We explore whether investors still believe that the old news are relevant for future returns and why they think or do not think so. Moreover, we explore how investors' beliefs differ across the different investor classes.
Experimental Design Details
In our surveys, we present participants with different hypothetical scenarios describing news about the future earnings stream of a company. For example, in the Nike good news case, we ask respondents to consider the following two scenarios.

"Nike maintains supplier partnership" - Four weeks ago, on [date], Nike Inc. announced the continuation of its partnership with major polyester supplier Toray Industries Inc., in a move aimed at retaining its current supply chain. The continuation of the partnership is expected to maintain the company's current cost structure. Industry experts were not surprised by the announcement, as continuity in supplier relationships is a common practice in the industry.

"Nike secures cost-saving partnership" - Four weeks ago, on [date], Nike Inc. announced a new strategic partnership with leading recycled polyester supplier Unifi Inc., aimed at slashing raw material costs by 20%. The deal is expected to have a significant impact on Nike's bottom line, making its products more price-competitive. Industry experts were pleasantly surprised by the news and dubbed it an "unexpected success" for the company. They projected the move to significantly enhance Nike's market position in the sports apparel industry.

In both scenarios, the announcement was made four weeks ago and received a lot of attention by stock market traders.

Afterwards, we ask the following questions.

Prediction: In which scenario is the future expected return of an investment in the stock over the next year higher?
Open-ended explanation of prediction (not for Bundesbank Online Panel)
Prediction of future expected return difference for the years 2--5 (not among academic experts / in Bundesbank Online Panel)
Prediction of future return uncertainty, factor exposure, and earnings for the years 1--5 (for academic experts / Bundesbank Online Panel: only year 1)
Quantitative first- and second-order predictions for both scenarios for year 1 (not among academic experts / in Bundesbank Online Panel)
Structured question about the reasoning behind respondents' prediction
Various background characteristics (precise questions can differ across samples)

The full instructions of the general population survey are available in the separate instructions document.
Randomization Method
Participants will be randomly assigned to different hypothetical news cases. The randomization is computerized in Qualtrics.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Equal to number of observations.
Sample size: planned number of observations
General population (US), Dynata, n = 2,400 Financial advisors (US), CloudResearch, n = 200 Financial professionals (US), CloudResearch, n = 200 Academic experts (global), invited via email, n = 150* General population (Germany), Bundesbank Online Panel, 4,350** *Sample size is approximate/aspirational because we cannot perfectly predict the response rate. **Sample size estimated by Bundesbank Online Panel team.
Sample size (or number of clusters) by treatment arms
General population (US): All prediction conditions (6 individual stocks, 4 aggregate cases). 200 respondents per condition, except for the two Nike cases, for which we plan to collect 400 respondents each.

Financial advisor / financial professionals / academic experts: Randomly assigned to the Nike good news or the Nike bad news case with equal chance.

General population (Germany): All six individual stock prediction conditions. Condition selected randomly with equal chance.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials


Document Name
Instructions: Main
Document Type
Document Description
Instructions: Main

MD5: e879acb160e113e2286758bf7e45ba2e

SHA1: 80eddec7e41504c660e1e9b2d8df477ceb4efc48

Uploaded At: May 30, 2023

Document Name
Instructions: Bundesbank Online Panel
Document Type
Document Description
Instructions: Bundesbank Online Panel

MD5: c5024d83c504a5da7fc695cbf24b71cd

SHA1: 160c6d063258594c2d60f0fe808861b858196924

Uploaded At: May 30, 2023


Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Preregistration: Downstream Consequences Study

MD5: deffc698260821ab645200926de331eb

SHA1: 8d52aa5efe1f7fec270125f24a7bd2fbd728cebb

Uploaded At: March 05, 2024

Preregistration: Real News Study

MD5: 1c837057bce6f95f3398e92c9e2370f6

SHA1: 0f71a23ae3001cb6e16e32063304ab2069957084

Uploaded At: September 12, 2023

Preregistration: Asset manager sample

MD5: 9e1376846cb9ffbaee04281deae819e6

SHA1: 0b614b6ceba413d2b42dac40200d992dc3871fdb

Uploaded At: November 14, 2023

Preregistration Plan

MD5: a2a5465a0d7ff855c0af4e9242ef52fd

SHA1: 277a16753c529080e0f459ec70f8440b8fef6461

Uploaded At: May 30, 2023

Preregistration: Explaining Equilibrium Study

MD5: 432c35e4725301e8a171f72415a49687

SHA1: 2b7389f4c3c57a38d785cfbdf3a2de2c021f45f5

Uploaded At: June 13, 2023

Preregistration: Attention Study

MD5: 4b8035cdcdff8d5795897659d9db096b

SHA1: 67d31528e3cd71c749ef96ebc5802f4ff717399d

Uploaded At: June 12, 2023

Pregistration: Detection Study

MD5: 167a0fbf29904096b2eee6a0644ad859

SHA1: b76134246b9cfb68889f72fe49c259087243c9c7

Uploaded At: June 15, 2023

Preregistration: Downstream Consequences Study Addendum

MD5: d36e5b69de6c1dabac1b881a57621462

SHA1: 6b4d2a63f085405bf498dca09df28becdc29c9dc

Uploaded At: March 13, 2024

Preregistration: US retail investor sample

MD5: 018a155126fe25bb1b9eb1f1e2b8c4b1

SHA1: 9567538b1af5058140d1e8b38979f3e69cb4577f

Uploaded At: August 18, 2023


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