Mental Models of the Stock Market

Last registered on June 09, 2023

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Pre-Trial

Trial Information

General Information

Title

Mental Models of the Stock Market

RCT ID

AEARCTR-0011505

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.

Locations

Country

Germany

Region

Country

United States of America

Region

Primary Investigator

Name

Peter Andre

Affiliation

SAFE and Goethe University Frankfurt

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Philipp Schirmer

PI Affiliation

University of Bonn

PI Name

Johannes Wohlfart

PI Affiliation

University of Copenhagen

Additional Trial Information

Status

On going

Start date

2023-05-30

End date

2023-08-31

Keywords

Behavior, Finance & Microfinance

Additional Keywords

JEL code(s)

D83, D84, G11, G12, G41, G51

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We explore the mental models of the stock market among different groups of economic agents.

External Link(s)

Registration Citation

Citation

Andre, Peter, Philipp Schirmer and Johannes Wohlfart. 2023. "Mental Models of the Stock Market." AEA RCT Registry. June 09. https://doi.org/10.1257/rct.11505-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2023-05-30

Intervention End Date

2023-08-31

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

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

Individual

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

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