Dynamic Density Estimation of Market Microstructure Variablesvia Auxiliary Particle Filtering

Nehren, Daniel; Fellah, David; Ruiz-Mata, Jesus; Qin, Yichen

doi:10.3905/jot.2012.7.4.055

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These huge amounts of trades and quotes information from exchanges and markets have attracted significant attentions from both academia and industry. Many interesting research topics have been studied, such as market microstructure noise (O'Hara 1995, Harris 2002, Aït-Sahalia and Yu 2009, Nehren et al 2012, dynamics of limit order book (Bouchaud et al 2002, Avellaneda andStoikov 2008) and intra-day volatility estimations (Andersen et al 2003, Zhang et al 2005, Todorov 2009, Zhou 2011.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou¹,

Cheng²,

Qin

et al. 2015

Quantitative Finance

Self Cite

View full text Add to dashboard Cite

This paper proposes a performance-driven gradient boosting model (pdGBM) which predicts shorthorizon price movements by combining nonlinear response functions of selected predictors. This model performs gradient descent in a constrained functional space by directly minimizing loss functions customized with different trading performance measurements. To demonstrate its practical applications, a simple trading system was designed with trading signals constructed from pdGBM predictions and fixed holding period in each trade. We tested this trading system on the highfrequency data of SPDR S&P 500 index ETF (SPY). In the out-of-sample period, it generated an average of 0.045% return per trade and an annualized Sharpe ratio close to 20 after transaction costs. Various empirical results also showed the model robustness to different parameters. These superior performances confirm the predictability of short-horizon price movements in the US equity market. We also compared the performance of this trading system with similar trading systems based on other predictive models like the gradient boosting model with L2 loss function and the penalized linear model. Results showed that pdGBM substantially outperformed all other models by higher returns in each month of the testing period. Additionally, pdGBM has many advantages including its capability of automatic predictor selection and nonlinear pattern recognition, as well as its simply structured and interpretable output function.

show abstract