Open AccessForecasting leading industry stock prices based on a hybrid time-series forecast model
Author(s) -
MingChe Tsai,
ChingHsue Cheng,
Meei-Ing Tsai,
Huei-Yuan Shiu
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0209922
Subject(s) - computer science , econometrics , time series , stock (firearms) , multivariate statistics , support vector machine , feature selection , regression , regression analysis , model selection , data mining , machine learning , economics , statistics , mathematics , engineering , mechanical engineering
Many different time-series methods have been widely used in forecast stock prices for earning a profit. However, there are still some problems in the previous time series models. To overcome the problems, this paper proposes a hybrid time-series model based on a feature selection method for forecasting the leading industry stock prices. In the proposed model, stepwise regression is first adopted, and multivariate adaptive regression splines and kernel ridge regression are then used to select the key features. Second, this study constructs the forecasting model by a genetic algorithm to optimize the parameters of support vector regression. To evaluate the forecasting performance of the proposed models, this study collects five leading enterprise datasets in different industries from 2003 to 2012. The collected stock prices are employed to verify the proposed model under accuracy. The results show that proposed model is better accuracy than the other listed models, and provide persuasive investment guidance to investors.