Forecasting stock closing prices with an application to airline company data  

作　　者：Xu Xu Yixiang Zhang Clare Anne McGrory Jinran Wu You-Gan Wang 

机构地区：[1]School of Mathematics and Physics,Wenzhou University,Wenzhou,Zhejiang,325035,China [2]Olin Business School,Washington University in St.Louis,St.Louis,63130,Missouri,USA [3]School of Mathematical Sciences,Queensland University of Technology,Brisbane,Queensland,4001,Australia [4]The Institute for Learning Sciences and Teacher Education,Australian Catholic University,Brisbane,Queensland,4001,Australia

出　　处：《Data Science and Management》2023年第4期239-246,共8页数据科学与管理（英文）

基　　金：This work was supported by the Australian Research Council project(Grant No.:DP160104292);the Zhejiang Province Soft Science Project,the Wenzhou Basic Soft Science Research Key Project(First Batch,NO.7);“Chunhui Program”Collaborative Scientific Research Project(Grant No.:202202004).

摘　　要：Forecasting stock market movements is a challenging task from the practitioners’point of view.We explore how model selection via the least absolute shrinkage and selection operator(LASSO)approach can be better used to forecast stock closing prices using real-world datasets of daily stock closing prices of three major international airlines.Combining the LASSO method with multiple external data sources in our model leads to a robust and efficient method to predict stock behavior.We also compare our approach with ridge,tree,and support vector machine regressions,as well as neural network approaches to model the data.We include lags of each external variable and response variable in the model,resulting in a total of 870 predictor variables.The empirical results indicate that the LASSO-fitted model is the most effective when compared to other approaches we consider.The results show that the closing price of an airline stock is affected by its closing price for the previous days and those of other types of airlines and is significantly correlated with the Shanghai Composite Index for the previous day and 3 days prior.Other influencing factors include the positive impact of the Shanghai Composite Index daily share volume,the negative impact of loan interest rates,the amount of highway passenger and railway freight turnover,etc.

关 键 词：Chinese airlines LASSO Ridge regression Support vector machine regression Forecasting 

分 类 号：F42[经济管理—产业经济]