Liquidity dynamics around intraday price jumps in Chinese stock market

Wan, Die; Wei, Xianhua; Yang, Xibei

doi:10.1007/s11424-016-5033-4

Cited by 9 publications

(27 citation statements)

References 45 publications

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“…Patton and Sheppard (2015) show that signed jumps have an asymmetric effect on volatility forecasts. Wan et al (2017) find that positive jumps are more frequent than the negative jumps in the Chinese market. The occurrence of positive jumps is associated with high trading activity and reveals information that impacts subsequent trading activity.…”

Section: Relevant Literaturementioning

confidence: 78%

Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds

Jurdi

2020

JRFM

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This paper uses two highly liquid S&P 500 and gold exchange-traded funds (ETFs) to evaluate the impact of liquidity and macroeconomic news surprises on the frequency of observing intraday jumps. It explicitly addresses market microstructure noise-induced biases in realized estimators used in jump detection tests and applies non-parametric intraday jump detection tests. The results show a significant increase in trading costs and elevated levels of information asymmetry before observing jumps. Depth, resiliency, and trading activity are associated with the frequency of observing intraday jumps and cojumps. The ability of liquidity variables to predict intraday jumps persists after controlling for news surprises. Results show that intraday jump realizations affect the price discovery of ETFs.

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Section: Relevant Literaturementioning

confidence: 78%

Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds

Jurdi

2020

JRFM

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“…Our intraday jump detection phase is based on the LM test developed by Lee (2008). To improve the performance, we combine this technique with the robust volatility estimation methods proposed by Andersen et al (2012) and Boudt et al (2011), as in the work of (Wan et al, 2017). The choice of working with 5-minute return series is consistent with many influential studies (Lee, 2008;Jiang and Lo, 2011;Boudt and Pertitjean, 2014, e.g.).…”

Section: Jump Detection Techniquementioning

confidence: 99%

“…While the majority of works is devoted to the analysis of developed markets, Wan et al (2017) focused on the Chinese stock market and observed, based on a large number of jumps, that liquidity measures show abnormal patterns around jumps. Moreover, in contrast to other developed countries, the Chinese government does not release new data and policies at a fixed time interval, and no data suppliers provide estimations of news expectations and surprises to the public.…”

Section: Liquidity Dynamics Before Price Jumpsmentioning

confidence: 99%

“…where {r i , i = 1, 2, • • • , nK} are the return series of all the intervals in recent K days. Here, we choose K = 5 as in Wan et al (2017). In addition, the weighted standard deviation (WSD) estimator f W SD t,i of f t,i , suggested by Boudt et al (2011), is included as a periodic component to account for the intraweek periodicity in high-frequency returns.…”

Section: Jump Detection Techniquementioning

confidence: 99%

“…Based on these intraday jump detection algorithms, some studies have investigated the intraday liquidity dynamics and found significant abnormal movements before stock jumps (Boudt and Pertitjean, 2014;Wan et al, 2017;Bȩdowska-Sójka, 2016). Although some of the liquidity measures have proved to be associated with the occurrence of stock jumps, they perform poorly in predicting stock jumps individually.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Kong

Zhu

Azencott

2019

Preprint

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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.

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

Kong

Zhu

Azencott

2020

Journal of Forecasting

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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.

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Liquidity dynamics around intraday price jumps in Chinese stock market

Cited by 9 publications

References 45 publications

Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds

Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds

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

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

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