Financial Education and Advice: The Case of Robo-advisors
Last registered on May 15, 2019


Trial Information
General Information
Financial Education and Advice: The Case of Robo-advisors
Initial registration date
April 11, 2019
Last updated
May 15, 2019 8:49 AM EDT
Primary Investigator
Ludwig Maximilian University of Munich
Other Primary Investigator(s)
PI Affiliation
Ludwig Maximilian University of Munich
Additional Trial Information
In development
Start date
End date
Secondary IDs
Some scholars argue that financial literacy is a complement rather than a substitute to financial advice as it allows investors to better assess (and demand) quality and thus have more confidence in the advisor. While financial advisors and managers rely on information asymmetry as part of their business model, several arguments point toward a positive impact of educating investors on financial matters on the use of their services. Thus, we intend to study in a laboratory setting the impact of financial education on the adoption of technology-based advisory algorithms, so-called robo-advisors, as well as the interaction with investor characteristics. On the one hand, they might be in a good position to effectively educate investors due to their broad customer base and digital service provision. On the other hand, they might benefit from communicating the underlying investment logic to oppose algorithm aversion due to the perception of a “black-box” decision-making process.
External Link(s)
Registration Citation
Litterscheidt, Rouven and David Streich. 2019. "Financial Education and Advice: The Case of Robo-advisors." AEA RCT Registry. May 15.
Former Citation
Litterscheidt, Rouven, David Streich and David Streich. 2019. "Financial Education and Advice: The Case of Robo-advisors." AEA RCT Registry. May 15.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Use of robo-advisor in incentivized financial decision-making task
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Participants first earn a budget in a real effort task, which is then used in an incentivized four-period financial decision-making game. In each period, subjects allocate their budget to three distinct assets that evolve probabilistically in value. The value of their portfolio after the last period determines their payout. Subjects can delegate decision-making to a robo-advisory service for all or parts of their budget at a proportional fee. The service elicits the subjects’ risk preferences through a risk questionnaire, assigns a risk profile, implements a corresponding portfolio and re-establishes the portfolio weights after each trading period. To investigate the impact of financial education on the willingness to use the robo-advisor, some subjects are provided with additional information on the principles of operation of the robo-advisory algorithm.
Experimental Design Details
Randomization Method
Randomization done in laboratory by a computer
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
200 students
Sample size (or number of clusters) by treatment arms
100 students in treatment group, 100 students in control group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers