Can machine improve the wisdom of crowd from social media? Evidence from a field experiment

Last registered on March 23, 2026
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Pre-Trial

Trial Information

General Information

Title
Can machine improve the wisdom of crowd from social media? Evidence from a field experiment
RCT ID
AEARCTR-0018180
Initial registration date
March 21, 2026

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
March 23, 2026, 8:01 AM EDT

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

Locations

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Primary Investigator

Name
Zhiyu Liu
Affiliation
The Chinese University of Hong Kong, Shenzhen
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Other Primary Investigator(s)

PI Name
Tianyu Zhang
PI Affiliation
The Chinese University of Hong Kong, Shenzhen
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PI Name
Susan Shu
PI Affiliation
Boston College
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PI Name
Zihao Cheng
PI Affiliation
Shenzhen Finance Institute
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Additional Trial Information

Status
In development
Start date
2026-01-01
End date
2026-12-31
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
In this study, we examine whether and how introducing greater diversity of viewpoints through machine can improve the wisdom of crowds on an investor-oriented social media. Prior research shows that aggregate opinions extracted from social media posts can predict firm-level earnings surprises and announcement returns. However, user behavior on social media is often shaped by platform-specific biases, such as echo chambers and the spread of misinformation. This project addresses these concerns by using AI-generated, standardized supporting, dissenting, and neutral comments to introduce structured opinion diversity into investor discussions. We expect this intervention to improve how investors process information on social media, strengthen the predictive power of social media signals for earnings outcomes and announcement returns, and shed light on how these effects vary with intervention intensity. This study contributes to the literature by examining how opinion diversity affects the function of the wisdom of crowds on social media and how AI can be used to enhance information quality in capital markets.

External Link(s)

Registration Citation

Citation
Cheng, Zihao et al. 2026. "Can machine improve the wisdom of crowd from social media? Evidence from a field experiment." AEA RCT Registry. March 23. https://doi.org/10.1257/rct.18180-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-03-22
Intervention End Date
2026-04-28

Primary Outcomes

Primary Outcomes (end points)
(1) how users interact with our comments; (2) whether these comments enhance the predictive power of posts with respect to firm-level earnings and stock returns; and (3) where the effective boundaries of these interventions.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Firms are randomly assigned to a control group or to one of three treatment groups: dissenting, supporting, or neutral. For firms in treatment, we post standardized AI-generated replies to qualifying posts shortly after publication. Depending on treatment assignment, replies either challenge, support, or provide non-directional information related to the original post. Control firms receive no intervention. To study treatment intensity, firms within each treatment arm are further randomized so that replies are posted to approximately one-quarter, one-half, three-quarters, or all qualifying posts during the intervention window. The intervention takes place over the nine trading days from day −10 to day −2 relative to the firm’s annual report disclosure date.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
firm
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
719 firms
Sample size: planned number of observations
The study is expected to include approximately 50,000 to 100,000 posts. The exact number of observations cannot be predetermined.
Sample size (or number of clusters) by treatment arms
Dissenting group 179 firms
Supporting group 181 firms
Neutral group 181 firms
Control group 178 firms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
The Chinese University of Hong Kong (Shenzhen) Institutional Review Board
IRB Approval Date
2026-03-20
IRB Approval Number
20260080
Analysis Plan