基于豆图时间序列对贵州茅台股票的高频金融交易数据进行了重构,提取了每个豆图的均值,组成一个新的时间序列进行实证分析.在进行实证分析中,对比多个时间序列模型,最终选定 ARIMA 模型和 TAR 模型对成交价进行拟合,经过比较得到基于 TAR 模型进行预测更为合适.
Analysis of high frequency financial transactions based on beanplot time series
This paper reconstructs the high-frequency financial trading data of Moutai stock market based on the beanplot time series,extracts the average value of each beanplot,and forms a new time series for empirical analysis.In the empirical analysis,multiple time series models were compared,and two models,ARIMA model and TAR model,were ultimately selected to fit the transaction price.After comparison,it was found that predicting based on the TAR model is more suitable.
beanplot time serieshigh frequency dataARIMA modelTAR modelpredicting
NETL
NSTL
万方数据
