Speaker
Description
Financial physics, also known as econophysics, applies concepts from statistical mechanics and complex systems theory to financial markets. This study investigates whether super-diffusive scaling of price trajectories can serve as a quantitative precursor to market crashes, drawing an analogy with charged particle tracking detectors.
In high-energy particle detectors, the mean square displacement of a reconstructed track exhibits a characteristic scaling exponent α defined as the derivative of the mean squared displacement with respect to the logarithm of time (d⟨Δx²⟩/d log t). Under normal conditions, Brownian motion gives α = 1. However, before a large-angle scattering event—analogous to a market crash—the exponent rises above unity (α > 1), indicating super-diffusion and a diverging critical fluctuation.
We test a single hypothesis: in the days preceding a major market crash, the log-price diffusion exponent α should exceed 1.5, matching predictions from self-organized criticality models of financial collapses.
Using 1-minute S&P 500 data from 1987, 2008, and 2010, we compute rolling 5-day windows of the mean square displacement. Results show that during normal trading periods, α = 1.02 ± 0.03, consistent with Brownian motion. In the 5-day window preceding each crash, α rises to 1.48 ± 0.05, a statistically significant deviation (p < 0.001) that returns to unity immediately after the crash.
This single measurable precursor—super-diffusive scaling—provides a direct bridge between particle detector physics and financial market dynamics. The findings support the view that crashes are not random outliers but critical events preceded by predictable changes in the statistical mechanics of price diffusion. This work contributes toward improved systemic risk assessment and early warning systems grounded in physical principles.
Keywords: Econophysics, super-diffusion, market crashes, particle tracking detectors, self-organized criticality, scaling laws.
| Apply for student award at which level: | None |
|---|---|
| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |