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

TL;DR

This paper introduces a machine learning-based method utilizing liquidity measures and technical indicators from high-frequency data to predict intraday stock jumps and their directions, demonstrating the effectiveness of random forests on Shenzhen Stock Exchange data.

Contribution

It presents a novel, portable data-driven approach combining liquidity and technical data for intraday jump prediction using machine learning, especially random forests.

Findings

Random forest outperforms other algorithms in prediction accuracy.

Liquidity measures and technical indicators effectively predict jump occurrences.

The approach is validated on high-frequency data from 1271 stocks in China.

Abstract

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.