Exogenous and Endogenous Price Jumps Belong to Different Dynamical Classes

TL;DR

This study distinguishes between exogenous and endogenous stock price jumps by analyzing their unique dynamical features, revealing different relaxation patterns and enabling classification without news data.

Contribution

The paper introduces a method to classify large price jumps into endogenous or exogenous categories based on their dynamical relaxation patterns, independent of news information.

Findings

Exogenous jumps show abrupt increases followed by power-law decay.

Endogenous jumps exhibit accelerating volatility growth with slower decay.

Power-law fitting enables classification of events without news data.

Abstract

Synchronising a database of stock specific news with 5 years worth of order book data on 300 stocks, we show that abnormal price movements following news releases (exogenous) exhibit markedly different dynamical features from those arising spontaneously (endogenous). On average, large volatility fluctuations induced by exogenous events occur abruptly and are followed by a decaying power-law relaxation, while endogenous price jumps are characterized by progressively accelerating growth of volatility, also followed by a power-law relaxation, but slower than for exogenous jumps. Remarkably, our results are reminiscent of what is observed in different contexts, namely Amazon book sales and YouTube views. Finally, we show that fitting power-laws to {\it individual} volatility profiles allows one to classify large events into endogenous and exogenous dynamical classes, without relying on the news feed.