The 2020 Global Stock Market Crash: Endogenous or Exogenous?
Ruiqiang Song, Min Shu, Wei Zhu

TL;DR
This study uses the LPPLS methodology to analyze the 2020 global stock market crash, distinguishing between endogenous and exogenous causes across major indexes, revealing systemic bubble formations and external shocks.
Contribution
It introduces a novel classification method using LPPLS confidence indicators to differentiate endogenous and exogenous market crashes.
Findings
Seven indexes showed endogenous bubble patterns indicating systemic instability.
Three indexes' crashes were driven by external shocks, not endogenous bubbles.
The classification method can be applied to analyze regime changes in financial markets.
Abstract
Starting on February 20, 2020, the global stock markets began to suffer the worst decline since the Great Recession in 2008, and the COVID-19 has been widely blamed on the stock market crashes. In this study, we applied the log-periodic power law singularity (LPPLS) methodology based on multilevel time series to unravel the underlying mechanisms of the 2020 global stock market crash by analyzing the trajectories of 10 major stock market indexes from both developed and emergent stock markets, including the S&P 500, DJIA, NASDAQ, FTSE, DAX, NIKKEI, CSI 300, HSI, BSESN, and BOVESPA. In order to effectively distinguish between endogenous crash and exogenous crash, we proposed using the LPPLS confidence indicator as a classification proxy. The results show that the apparent LPPLS bubble patterns of the super-exponential increase, corrected by the accelerating logarithm-periodic oscillations,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
