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...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.展开更多
Nonsynchronous trading is one of the hot issues in financial high-frequency data processing. This paper extends the nonsynchronous trading model studied in [1] and [2] for the financial security, and considers the mom...Nonsynchronous trading is one of the hot issues in financial high-frequency data processing. This paper extends the nonsynchronous trading model studied in [1] and [2] for the financial security, and considers the moment functions of the observable return series for the extended model. At last, the estimators of parameters are obtained.展开更多
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).
文摘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.
文摘Nonsynchronous trading is one of the hot issues in financial high-frequency data processing. This paper extends the nonsynchronous trading model studied in [1] and [2] for the financial security, and considers the moment functions of the observable return series for the extended model. At last, the estimators of parameters are obtained.
