  Studying the micro-trading behaviors before stock price jumps is an important
problem for financial regulations and investment decisions. In this study, we
provide a new framework to study pre-jump trading behaviors based on
multivariate time series analysis. Different from the existing literature, our
methodology takes into account the temporal information embedded in the
trading-related attributes and can better evaluate and compare the abnormality
levels of different attributes. Moreover, it can explore the joint
informativeness of the attributes as well as select a subset of highly
informative but minimally redundant attributes to analyze the homogeneous and
idiosyncratic patterns in the pre-jump trades of individual stocks. In
addition, our analysis involves a set of technical indicators to describe
micro-trading behaviors. To illustrate the viability of the proposed
methodology, an application case is conducted based on the level-2 data of 189
constituent stocks of the China Security Index 300. The individual and joint
informativeness levels of the attributes in predicting price jumps are
evaluated and compared. To this end, our experiment provides a set of jump
indicators that can represent the pre-jump trading behaviors in the Chinese
stock market and have detected some stocks with extremely abnormal pre-jump
trades.
