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Studying the micro-trading behaviors before stock price jumps is an importantproblem for financial regulations and investment decisions. In this study, weprovide a new framework to study pre-jump trading behaviors based onmultivariate time series analysis. Different from the existing literature, ourmethodology takes into account the temporal information embedded in thetrading-related attributes and can better evaluate and compare the abnormalitylevels of different attributes. Moreover, it can explore the jointinformativeness of the attributes as well as select a subset of highlyinformative but minimally redundant attributes to analyze the homogeneous andidiosyncratic patterns in the pre-jump trades of individual stocks. Inaddition, our analysis involves a set of technical indicators to describemicro-trading behaviors. To illustrate the viability of the proposedmethodology, an application case is conducted based on the level-2 data of 189constituent stocks of the China Security Index 300. The individual and jointinformativeness levels of the attributes in predicting price jumps areevaluated and compared. To this end, our experiment provides a set of jumpindicators that can represent the pre-jump trading behaviors in the Chinesestock market and have detected some stocks with extremely abnormal pre-jumptrades.

Pattern recognition in micro-trading behaviors before stock price jumps: A framework based on multivariate time series analysis