Digital engagement practices in trading apps: addressing biases through financial education

Last registered on November 25, 2025

Pre-Trial

Trial Information

General Information

Title
Digital engagement practices in trading apps: addressing biases through financial education
RCT ID
AEARCTR-0017266
Initial registration date
November 19, 2025

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
November 25, 2025, 7:37 AM EST

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

Locations

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Primary Investigator

Affiliation
Bank of Italy

Other Primary Investigator(s)

PI Affiliation
University of Calabria
PI Affiliation
Bank of Italy
PI Affiliation
University of Calabria
PI Affiliation
Bank of Italy
PI Affiliation
University of Calabria

Additional Trial Information

Status
On going
Start date
2025-11-19
End date
2026-02-28
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Digitalisation and social media have transformed investment platforms, making trading more accessible and interactive. Many apps employ Digital Engagement Practices (DEPs) - behavioural techniques, gamification, and design features - to boost user engagement. While these features lower barriers to entry and promote participation, academia and regulators warn that DEPs may exploit behavioural biases, particularly among young, less financially literate investors, encouraging riskier decisions misaligned with their profile.

Financial literacy is considered a key defence against these risks. Yet, empirical evidence on its mitigating role remains scarce. This study addresses this gap through a randomized experiment conducted jointly by the University of Calabria (UniCal) and Bank of Italy, involving master’s students from UniCal University. The study investigates: (i) whether financial education messages (information treatment) actively protect individuals by reducing or offsetting the influence of engagement practices; (ii) whether individuals’ pre-existing financial knowledge acts as a defence, making them less susceptible to app-induced decision-making errors (heterogeneous effect of financial education). Additionally, we explore heterogeneity by gender, academic background, financial experience, and risk profile.
External Link(s)

Registration Citation

Citation
De Paola , Maria et al. 2025. "Digital engagement practices in trading apps: addressing biases through financial education." AEA RCT Registry. November 25. https://doi.org/10.1257/rct.17266-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-11-19
Intervention End Date
2025-12-21

Primary Outcomes

Primary Outcomes (end points)
Amount invested in the crypto-asset
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Respondents enter a fictitious investment app where they are presented with information on a new crypto-asset that they might chose to invest in. The performance can be with equal probability either positive and double the initial investment, or negative and fully lose its value.
Respondents are randomly assigned to one of three groups. Participants within the first treatment group (gamification) receive a series of gamified messages designed to boost confidence and encourage risk-taking. Those in the second treatment group (gamification + financial education), in addition to the gamification messages, receive a financial education message reminding them of the risks involved and advising careful consideration before making investment decisions. The control group receive no additional message beyond the basic information about the crypto-asset. To encourage participation, the experiment incorporates a reward mechanism whereby participants’ payoffs are linked to their investment choices.
Experimental Design Details
Not available
Randomization Method
The randomization process is automatically handled by the survey software (LimeSurvey) used to conduct the experiment.
Randomization Unit
Individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
About 900 master students are expected to join the RCT
Sample size: planned number of observations
About 900 master students are expected to join the RCT
Sample size (or number of clusters) by treatment arms
About 300 students per treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number