(Mis-)Perceived Loss Probability and Investments

Last registered on July 21, 2022

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Pre-Trial

Trial Information

General Information

Title

(Mis-)Perceived Loss Probability and Investments

RCT ID

AEARCTR-0009588

Initial registration date

July 14, 2022

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

July 21, 2022, 11:23 AM EDT

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

Locations

Country

Germany

Region

Primary Investigator

Name

Markus Strucks

Affiliation

Radboud University

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Other Primary Investigator(s)

PI Name

Christine Laudenbach

PI Affiliation

Goethe University Frankfurt

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PI Name

Stephan Siegel

PI Affiliation

University of Washington

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PI Name

Stefan Zeisberger

PI Affiliation

Radboud University & University of Zurich

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Additional Trial Information

Status

In development

Start date

2022-07-15

End date

2022-07-25

Keywords

Behavior, Finance & Microfinance

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

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

Abstract

Traditional models of asset market participation and asset allocation focus on general return and volatility expectations and neglect the role of individual beliefs in form of loss expectations. Yet, recent research suggests that biased expectations in the domain of loss potential may deter stock investment. In a computerized survey we elicit individual loss expectations to study their role in investment decision making. We test whether a simple intervention changes respondents’ beliefs and finally influences hypothetical allocation to future stock investments.

External Link(s)

Registration Citation

Citation

Laudenbach, Christine et al. 2022. "(Mis-)Perceived Loss Probability and Investments." AEA RCT Registry. July 21. https://doi.org/10.1257/rct.9588-1.0

Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2022-07-15

Intervention End Date

2022-07-25

Primary Outcomes

Primary Outcomes (end points)

Accuracy of historical loss frequency estimates over different investment horizons, beliefs and investment allocations before and after the intervention

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Perceived effectiveness of intervention, motives for stock market non-participation

Secondary Outcomes (explanation)

Experimental Design

We elicit estimates of stock market index characteristics in the past 50 years and for the future along pre-defined investment horizons. The display order of investment horizons is random for the past, but fixed for the future. Respondents state their (possibly revised) beliefs for the future again and make another hypothetical investment decision.

Experimental Design Details

Investment horizons are 1 Day, 1 Year and 10 Years. For each horizon, we elicit expected returns, loss likelihoods, and points of respondents’ subjective return distributions. We also ask about hypothetical allocations to a stock index investment before and after the intervention. Respondents are randomly placed in a control or treatment group, the latter of which features an intervention displaying actual historical stock market characteristics next to own estimates. In both groups we ask about actual planned future allocations of participants. The survey will be sent out to three different groups of bank clients: Those without an investment account, those who opened an investment account last year, and those who have an investment account for at least five years.

Randomization Method

Computerized coin flip. Participants in each cluster (see below) have an equal chance to get assigned to either control or treatment group.

Randomization Unit

Individual

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

3 different groups of bank clients

Sample size: planned number of observations

Invited 10,000, expected 1,700

Sample size (or number of clusters) by treatment arms

Expected around at least 255 participants per treatment arm

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Power is 0.922 to detect a small- to medium-sized difference (i.e., a standardized effect size of Cohen’s d of 0.3) in independent means (t-test); calculated with G* Power 3.1

Supporting Documents and Materials

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