High-Frequency Runs and Flash Crash Predictability

Posted: 7 Jan 2014 Last revised: 2 May 2014

See all articles by Irene Aldridge

Irene Aldridge

AbleMarkets.com; Cornell University; BigDataFinance.org; ABLE Alpha Trading, LTD

Date Written: January 5, 2014


This research investigates the short-term nature of movements in price data. The key finding of the study is that asset returns do not evolve at the Gaussian increments commonly assumed by continuous pricing models. Instead, prices exhibit strong autocorrelation, often resulting in predictable one-directional sequences or “runs.” These runs are more pronounced ahead of market crashes. Identifying these runs can help predict impending flash crashes as much as a day in advance of a crash. The research further contributes to asset pricing and derivatives literature by deriving discreet and continuous closed-form expressions for the probability of flash crashes.

Keywords: high-frequency trading, market microstructure, flash crash, market crash

JEL Classification: B23, C22, G12, D46

Suggested Citation

Aldridge, Irene, High-Frequency Runs and Flash Crash Predictability (January 5, 2014). Journal of Portfolio Management, Vol. 40, No. 3, 2014. Available at SSRN: https://ssrn.com/abstract=2375834 or http://dx.doi.org/10.2139/ssrn.2375834

Irene Aldridge (Contact Author)

AbleMarkets.com ( email )

New York, NY 10128
United States

HOME PAGE: http://www.AbleMarkets.com

Cornell University ( email )

Ithaca, NY 14853
United States

BigDataFinance.org ( email )

United States

ABLE Alpha Trading, LTD ( email )

New York, NY 10004
United States

HOME PAGE: http://www.ablealpha.com

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