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The Chinese stock market experienced an abrupt crash in 2015, and overone-third of its market value evaporated. Given its associations with fear andthe fine resolution with respect to frequency, the illiquidity of stocks mayoffer a promising perspective for understanding and even signaling a marketcrash. In this study, by connecting stocks with illiquidity comovements, anilliquidity network is established to model the market. Compared to noncrashdays, on crash days, the market is more densely connected due to heavier butmore homogeneous illiquidity dependencies that facilitate abrupt collapses.Critical stocks in the illiquidity network, particularly those in the financesector, are targeted for inspection because of their crucial roles inaccumulating and passing on illiquidity losses. The cascading failures ofstocks in market crashes are profiled as disseminating from small degrees tohigh degrees that are usually located in the core of the illiquidity networkand then back to the periphery. By counting the days with random failures inthe previous five days, an early signal is implemented to successfully predictmore than half of the crash days, especially consecutive days in the earlyphase. Additional evidence from both the Granger causality network and therandom network further testifies to the robustness of the signal. Our resultscould help market practitioners such as regulators detect and prevent the riskof crashes in advance.

The illiquidity network of stocks in China's market crash