[Objective] This paper proposes a weighted network for stock intraday trading, and selects the main parameters for network features, aiming to identify the ultra-short-term market manipulations. [Methods] We constructed the weighted network for stock intraday trading with tick data, and used the order ID as nodes. The lines of network were the dealing orders, and the weights of line values were actual trading volumes. Analytical software Pajek5.03 and Ucinet6 were used to obtain the statistical parameters of complex networks for the proposed model. [Results] The nine network parameters, such as weighted average degree and network density, can be used as the main parameter to determine the stock manipulation. The overall accuracy values of our model with internal and external samples were 93.58% and 87.73%. [Limitations] We only retrieved the bull market data from 2015, while the bear market data were not collected. [Conclusions] This study helps authorities identify and crack down on the stock trading manipulation.
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