Novel modelling strategies for high‑frequency stock trading data  

作　　者：Xuekui Zhang Yuying Huang Ke Xu Li Xing 

机构地区：[1]Mathematics and Statistics Department at University of Victoria,Victoria,Canada [2]Statistics and Actuarial Scienceat University of Waterloo,Waterloo,Canada [3]Economics Department at University of Victoria,Victoria,Canada [4]Mathematics and Statistics Department at University of Saskatchewan,Saskatchewan,Canada

出　　处：《Financial Innovation》2023年第1期1030-1054,共25页金融创新（英文）

基　　金：Canada Research Chair(950231363,XZ),Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grants(RGPIN-20203530,LX);the Social Sciences and Humanities Research Council of Canada(SSHRC)Insight Development Grants(430-2018-00557,KX).

摘　　要：Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.

关 键 词：High-frequency trading Machine learning Mid-price prediction strategy Raw data processing Multi-class prediction Ensemble learning 

分 类 号：F832.51[经济管理—金融学]