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

Ao Kong, Hongliang Zhu and Robert Azencott

Journal of Forecasting, 2021, vol. 40, issue 3, 416-438

Abstract: Predicting 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‐min 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‐min interval. An empirical study is conducted using level‐2 high‐frequency data of 1271 stocks on the Shenzhen Stock Exchange of China to validate our approach. The results provide 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 for such prediction. We also reveal the superiority of using random forest compared with 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.

Date: 2021
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Citations: View citations in EconPapers (8)

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https://doi.org/10.1002/for.2721

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:40:y:2021:i:3:p:416-438

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