  The Chinese stock market experienced an abrupt crash in 2015, and over
one-third of its market value evaporated. Given its associations with fear and
the fine resolution with respect to frequency, the illiquidity of stocks may
offer a promising perspective for understanding and even signaling a market
crash. In this study, by connecting stocks with illiquidity comovements, an
illiquidity network is established to model the market. Compared to noncrash
days, on crash days, the market is more densely connected due to heavier but
more homogeneous illiquidity dependencies that facilitate abrupt collapses.
Critical stocks in the illiquidity network, particularly those in the finance
sector, are targeted for inspection because of their crucial roles in
accumulating and passing on illiquidity losses. The cascading failures of
stocks in market crashes are profiled as disseminating from small degrees to
high degrees that are usually located in the core of the illiquidity network
and then back to the periphery. By counting the days with random failures in
the previous five days, an early signal is implemented to successfully predict
more than half of the crash days, especially consecutive days in the early
phase. Additional evidence from both the Granger causality network and the
random network further testifies to the robustness of the signal. Our results
could help market practitioners such as regulators detect and prevent the risk
of crashes in advance.
