This paper extends the work of Boudt and Pertitjean(2014) and investigates the trading patterns before price jumps in the stock market based on a new multivariate time classification technique . Different from Boudt and Pertitjean(2014), our analyzing scheme can explore the "time-series information " embedded in the trading-related attributes and provides a set of jump indicators for abnormal pattern recognition . In addition to the commonly used liquidity measures , our analysis also involves a set of technical indicators to describe the micro-trading behaviors. An empirical study is conducted on the level-2 data of the constituent stocks of  China Security Index 300. It is found that among all the candidate attributes, several volume and volatility-related attributes exhibit the most significant abnormality before price jumps . Though some of the abnormalities start just shortly before the occurrence of the jumps, some start much earlier. We also find that most of our attributes have low mutual dependencies with each other from the perspective of time-series analysis, which allows various perspectives to study the market trading behaviors. To this end, our experiment provides a set of jump indicators that can effectively detect the stocks with extremely abnormal trading behaviors before price jumps. More importantly, our study offers a new framework and potential useful directions for trading-related pattern recognition problem using the time series classification techniques .
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 .
R
This paper extends the work of Boudt and Pertitjean(2014) and investigates the trading patterns before price jumps in the stock market based on a new multivariate time classification technique
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
R
Boudt and Pertitjean(2014), our analyzing scheme can explore the "time-series information "
the existing literature, our methodology takes into account the temporal information
R
provides a set of jump indicators for abnormal pattern recognition
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
D
to the commonly used liquidity measures
D
also
D
the
R
An empirical study is conducted
To illustrate the viability of the proposed methodology, an application case is conducted based
R
the
189
A
the
R
It is found that among all the candidate attributes, several volume and volatility-related attributes exhibit the most significant abnormality before price jumps . Though some of the abnormalities start just shortly before the occurrence of the jumps, some start much earlier. We also find that most of our attributes have low mutual dependencies with each other from the perspective of time-series analysis, which allows various perspectives to study the market trading behaviors.
The individual and joint informativeness levels of the attributes in predicting price jumps are evaluated and compared.
R
effectively detect the
represent the pre-jump trading behaviors in the Chinese stock market and have detected some
R
trading behaviors before price jumps. More importantly, our study offers a new framework and potential useful directions for trading-related pattern recognition problem using the time series classification techniques
pre-jump trades