  Predicting the intraday stock jumps is a significant but challenging problem
in finance. Due to the instantaneity and imperceptibility characteristics of
intraday stock jumps, relevant studies on their predictability remain limited.
This paper proposes a data-driven approach to predict intraday stock jumps
using the information embedded in liquidity measures and technical indicators.
Specifically, a trading day is divided into a series of 5-minute intervals, and
at the end of each interval, the candidate attributes defined by liquidity
measures and technical indicators are input into machine learning algorithms to
predict the arrival of a stock jump as well as its direction in the following
5-minute interval. Empirical study is conducted on the level-2 high-frequency
data of 1271 stocks in the Shenzhen Stock Exchange of China to validate our
approach. The result provides initial evidence of the predictability of jump
arrivals and jump directions using level-2 stock data as well as the
effectiveness of using a combination of liquidity measures and technical
indicators in this prediction. We also reveal the superiority of using random
forest compared to other machine learning algorithms in building prediction
models. Importantly, our study provides a portable data-driven approach that
exploits liquidity and technical information from level-2 stock data to predict
intraday price jumps of individual stocks.
