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Predicting the intraday stock jumps is a significant but challenging problemin finance. Due to the instantaneity and imperceptibility characteristics ofintraday stock jumps, relevant studies on their predictability remain limited.This paper proposes a data-driven approach to predict intraday stock jumpsusing the information embedded in liquidity measures and technical indicators.Specifically, a trading day is divided into a series of 5-minute intervals, andat the end of each interval, the candidate attributes defined by liquiditymeasures and technical indicators are input into machine learning algorithms topredict the arrival of a stock jump as well as its direction in the following5-minute interval. Empirical study is conducted on the level-2 high-frequencydata of 1271 stocks in the Shenzhen Stock Exchange of China to validate ourapproach. The result provides initial evidence of the predictability of jumparrivals and jump directions using level-2 stock data as well as theeffectiveness of using a combination of liquidity measures and technicalindicators in this prediction. We also reveal the superiority of using randomforest compared to other machine learning algorithms in building predictionmodels. Importantly, our study provides a portable data-driven approach thatexploits liquidity and technical information from level-2 stock data to predictintraday price jumps of individual stocks.

Predicting intraday jumps in stock prices using liquidity measures and technical indicators