Wisdom of the Crowds or Ignorance of the Masses? A data-driven guide to WSB

TL;DR

This paper analyzes the WallStreetBets forum using topic modeling and network analysis to understand its influence on asset price fluctuations, revealing causal links and generating trade signals.

Contribution

It introduces a comprehensive data-driven approach combining topic and network analysis to study WSB's influence on markets, including new datasets and interactive tools.

Findings

WSB discussion topics show persistent and sporadic patterns over time.

Forum activity Granger-causes asset returns, especially meme stocks.

Trade signals derived from forum dynamics have predictive power.

Abstract

A trite yet fundamental question in economics is: What causes large asset price fluctuations? A tenfold rise in the price of GameStop equity, between the 22nd and 28th of January 2021, demonstrated that herding behaviour among retail investors is an important contributing factor. This paper presents a data-driven guide to the forum that started the hype -- WallStreetBets (WSB). Our initial experiments decompose the forum using a large language topic model and network tools. The topic model describes the evolution of the forum over time and shows the persistence of certain topics (such as the market / S\&P500 discussion), and the sporadic interest in others, such as COVID or crude oil. Network analysis allows us to decompose the landscape of retail investors into clusters based on their posting and discussion habits; several large, correlated asset discussion clusters emerge, surrounded by smaller, niche ones. A second set of experiments assesses the impact that WSB discussions have had on the market. We show that forum activity has a Granger-causal relationship with the returns of several assets, some of which are now commonly classified as `meme stocks', while others have gone under the radar. The paper extracts a set of short-term trade signals from posts and long-term (monthly and weekly) trade signals from forum dynamics, and considers their predictive power at different time horizons. In addition to the analysis, the paper presents the dataset, as well as an interactive dashboard, in order to promote further research.