Title: Investors' opinion divergence and stock return volatility: evidence from user-generated content

Authors: Yang Li; Ruben Xing

Addresses: Department of Information Management and Business Analytics, Montclair State University, Montclair, NJ 07043, USA ' Department of Information Management and Business Analytics at Montclair State University, Montclair, NJ 07043, USA

Abstract: This paper examines the relationship between investors' opinion divergence and stock return volatility using daily UGC data of 66 most discussed stocks from one of the most popular social media platforms for investors in the USA. Specifically, we use an unsupervised learning method to measure opinion divergence and apply both dynamic panel regression and panel vector autoregressive regression (pVAR) to explore its role in return volatility. We find that investors' opinion divergence is negatively associated with future return volatility across a variety of holding periods. Moreover, the impact of opinion divergence will become attenuated over time. Our research adds to the emerging body of literature on the impact of UGC on the stock market regarding: 1) novel techniques for systematically measuring sentiment divergence in large-scale UGC data; 2) uncovering the dynamic interdependence of the relationship between investors' opinion divergence and stock return volatility.

Keywords: opinion divergence; user-generated content; volatility; pVAR; social media; KL distance; return; stock market; dynamic panel data.

DOI: 10.1504/IJDATS.2023.136682

International Journal of Data Analysis Techniques and Strategies, 2023 Vol.15 No.4, pp.302 - 322

Received: 11 Nov 2022
Accepted: 18 Sep 2023
Published online: 15 Feb 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article