Critical Crashes

TL;DR

This paper investigates stock market crashes, proposing they result from long-range trader correlations leading to critical points, supported by evidence from historical crashes and a novel non-parametric analysis revealing consistent scaling behavior.

Contribution

It introduces a non-parametric method to identify log-periodic oscillations associated with market crashes, confirming the critical phenomena hypothesis across multiple historical events.

Findings

Log-periodic oscillations are statistically significant in all examined crashes.

A consistent preferred scaling ratio around 2 is observed.

Market crashes are linked to critical points caused by trader correlations.

Abstract

We argue that the word ``critical'' in the title is not purely literary. Based on our and other previous work on nonlinear complex dynamical systems, we summarize present evidence, on the Oct. 1929, Oct. 1987, Oct. 1987 Hong-Kong, Aug. 1998 global market events and on the 1985 Forex event, for the hypothesis advanced four years ago that stock market crashes are caused by the slow buildup of long-range correlations between traders leading to a collapse of the stock market in one critical instant. We qualify the log-periodic oscillations using a novel non-parametric method that does not rely on any fit: the corresponding log-periodogram exhibits a strong statistically significant peak for all six crashes examined, pointing at approximately the same prefered scaling ratio around 2.