  Can unstructured text data from social media help explain the drivers of
large asset price fluctuations? This paper investigates how social forces
affect asset prices, by using machine learning tools to extract beliefs and
positions of `hype' traders active on Reddit's WallStreetBets (WSB) forum. Our
stylized model shows that peer effects help explain return predictability and
reversals, as well as bubble dynamics. We empirically document that sentiments
expressed by WSB users about assets' future performances (bullish or bearish)
are in part due to sentiments of their peers and past asset returns. The paper
directly estimates the effect of WSB activity on asset prices. We document:
that retail trader demand follows WSB discussions through using Trade and Quote
(TAQ) data, the predictability of prices from retail trader discourse, the
amplified market impact of idiosyncratic investor sentiment from viral content
online, and the greater exposure of hype investors to bubbles in the markets.
