Exploring the Endogenous Nature of Meme Stocks Using the Log-Periodic Power Law Model and Confidence Indicator

TL;DR

This paper investigates the endogenous bubble formation in meme stocks using the LPPL Confidence Indicator, highlighting its ability to detect internal bubbles but limitations in predicting externally driven rallies.

Contribution

It applies the LPPL CI to meme stocks, demonstrating its effectiveness in identifying endogenous bubbles and discussing challenges in predicting exogenous events.

Findings

LPPL CI detected multiple bubble formations in meme stocks.

Model struggled to predict social media-driven exogenous rallies.

Evidence of bubbles potentially triggering meme stock rallies.

Abstract

This study examined the endogenous nature of negative bubbles forming in meme stocks with the Log-Periodic Power Law (LPPL) Confidence Indicator (CI). A meme stock is a stock that has gained a significant amount of attention on a large social media platform such as Yahoo! or Reddit. This study examined four meme stocks including Tesla, Inc. (TSLA), GameStop Corp. (GME), Koss Corporation (KOSS), and AMC Entertainment Holdings Inc (AMC). The CI was able to detect numerous bubbles forming in meme stocks, but had difficulty in significantly predicting social media-induced exogenous rallies. This may have been due to price movements affected by external causes such as short squeezes. However, the model did provide proof for the formation of previous bubbles that could have been a catalyst for the meme stocks rallies. This study outlines the real unpredictability of many black-swan events, and further studies could be done examining exogenous bubbles.