Evolution of high-frequency systematic trading: a performance-driven gradient boosting model

DOI10.1080/14697688.2015.1032541zbMath1406.91515OpenAlexW1600654029MaRDI QIDQ4619504

Wen Cheng, Yichen Qin, Zongcheng Yin, Nan Zhou

Publication date: 6 February 2019

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2015.1032541

zbMATH Keywords

gradient boostinghigh-frequencysystematic tradingtrading performance

Mathematics Subject Classification ID

Actuarial science and mathematical finance (91G99)

Related Items (1)

Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

Uses Software

• rpart
• AdaBoost.MH
• ElemStatLearn

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• Sure independence screening in generalized linear models with NP-dimensionality
• Estimation of continuous-time stochastic volatility models with jumps using high-frequency data
• Correlated variables in regression: clustering and sparse estimation
• High frequency market microstructure noise estimates and liquidity measures
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• High-frequency trading in a limit order book
• Realized kernels in practice: trades and quotes
• Boosting With theL₂Loss
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Statistical properties of stock order books: empirical results and models
• Modeling and Forecasting Realized Volatility
• A Tale of Two Time Scales
• Stochastic gradient boosting.

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