The Influencer Copycats

55 Pages Posted: 30 Nov 2023

See all articles by Haiyang Bian

Haiyang Bian

Tsinghua University - Department of Automation

Emma Li

Department of Finance

Lu Liu

Central University of Finance and Economics (CUFE)

Zhengwei Wang

Tsinghua University - PBC School of Finance

Date Written: September 7, 2023

Abstract

Influencers on social media share insights through their online posts, which substantially impact the perspectives and decisions of follower investors. Our research constructs the similarity of influencer posts gathered from a prominent financial social media platform, leveraging large language models for profound semantic understanding of posts. We find that influencers increase their follower investment amount, short-term following and content monetization potential by imitating the posts of their peers, especially those who have displayed exceptional performance on social media. However, influencers are less likely to imitate their peers during periods of low investor sentiment in the securities market. While influencer post similarity positively affects their own short-term benefits, it does not significantly improve the investment performance of their retail investor followers.

Keywords: Influencers, Social Media, Large Language Models, Retail Investors

JEL Classification: G11, G14, G41

Suggested Citation

Bian, Haiyang and Li, Emma and Liu, Lu and Wang, Zhengwei, The Influencer Copycats (September 7, 2023). PBCSF-NIFR Research Paper, Available at SSRN: https://ssrn.com/abstract=4634986 or http://dx.doi.org/10.2139/ssrn.4634986

Haiyang Bian

Tsinghua University - Department of Automation ( email )

Beijing, 100084
China

Emma Li

Department of Finance ( email )

221 Burwood Highway
Burwood, Victoria 3125
Australia

Lu Liu (Contact Author)

Central University of Finance and Economics (CUFE) ( email )

39 South College Road
Haidian District
Beijing, Beijing 100081
China

Zhengwei Wang

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengfu Road
Haidian District
Beijing 100083
China

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