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