Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp011c18dj87v
Title: | FOMO Fever: How the Democratization of Finance Quashed Efficient Markets |
Authors: | Gitelman, Daniel |
Advisors: | Sircar, Ronnie |
Department: | Operations Research and Financial Engineering |
Certificate Program: | Center for Statistics and Machine Learning |
Class Year: | 2021 |
Abstract: | The perfect storm of increasingly gamified trading applications, social-media-driven herding behavior, and pandemic-era stay-at-home orders irreversibly transformed equities markets demographics in 2020. Young gamblers brought a casino-like frenzy to exchanges, buying up highly speculative securities with no regard to fundamentals in anticipation of quick triple-digit returns. Talk of efficient markets became heresy as retail traders waged coordinated wars on institutions, effecting price movements unlike anything we've seen before. This thesis aims to quantify retail behavior in the age of coronavirus. Namely, we will study individual investors on WallStreetBets, a popular online trading forum known for its incredibly speculative nature, to explore how ideas propagate through the retail trading community. Information contagion will be modeled through the framework of self-exciting processes to determine the market impact of "hype", while herding will be analyzed using natural language processing tasks to back out aggregate sentiment. We will then investigate how the "Fear of Missing Out" (FOMO) fuels retail behavior and tell the WallStreetBets "story" through a combination of these mathematical lenses. Finally, we analyze the predictive capabilities of retail traders through regression analysis and portfolio evaluation techniques, and determine that WallStreetBets successfully discriminated equities winners from losers in 2020. |
URI: | http://arks.princeton.edu/ark:/88435/dsp011c18dj87v |
Type of Material: | Princeton University Senior Theses |
Language: | en |
Appears in Collections: | Operations Research and Financial Engineering, 2000-2022 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
GITELMAN-DANIEL-THESIS.pdf | 2.72 MB | Adobe PDF | Request a copy |
Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.