Predicting Equity Crises, Critical Exponents, and Earthquakes - II

46 Pages Posted: 21 Jul 2016 Last revised: 17 Sep 2016

See all articles by Jan Dash

Jan Dash

Fordham University; Bloomberg L.P.

Xipei Yang

Bloomberg L.P.

Date Written: September 14, 2016


We present further encouraging evidence for the Critical Exponent Earthquake Crisis (CEEC) Model that gives the probabilities of equity crises one year in advance. The CEEC model uses suitable precursor signals and is agnostic regarding dynamical origins of crises. The precursors accumulate in time between crises, like precursors to some earthquakes. The model uses a sophisticated noise filter to separate out crisis signals. The main metric is the anomalous exponent of an equity series describing the difference of the data variance scaling exponent from the Brownian variance scaling exponent of 1. No extra non-equity variables are used. The CEEC Model results for predicting crises are not perfect, but are much better than chance, including out-of-sample tests. The details here supplement our previous CEEC crisis paper (2013).

In another paper (2016) we give details showing that various markets - not just equities - that are already in crisis are on the average described by a critical exponent of the nonlinear-diffusion Reggeon Field Theory (RFT), calculated in 1974, with no free parameters. Nonlinear diffusion naturally extends Brownian motion. Rich/cheap crisis behavior is suggested as a paradigm. This supplements our previous CEEC crisis paper (2013).

The CEEC Model crisis predictions use the same scaling form described by the anomalous exponent as does the RFT. This consistency for crisis predictions and crisis behavior is significant.

Keywords: Probability of Equity Crises One Year in Advance, CEEC Model, Critical Exponent Earthquake Crisis Model, Nonlinear Diffusion Reggeon Field Theory

JEL Classification: E44, G01

Suggested Citation

Dash, Jan and Yang, Xipei, Predicting Equity Crises, Critical Exponents, and Earthquakes - II (September 14, 2016). Available at SSRN: or

Jan Dash (Contact Author)

Fordham University ( email )

113 West 60th Street
New York, NY 10023
United States

Bloomberg L.P. ( email )

731 Lexington Ave
New York, NY 10022
United States

Xipei Yang

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States

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