Predicting short-term stock prices using ensemble methods and online data sources
作者：
Highlights：
• A financial expert system for predicting the monthly stock price.
• “Knowledge base” captures the impact of traditional and online sources.
• The “engine” uses four ensemble machine learning methods.
• The error for most ensembles considered is < 1%.
• The system is hosted online and freely available.
摘要
•A financial expert system for predicting the monthly stock price.•“Knowledge base” captures the impact of traditional and online sources.•The “engine” uses four ensemble machine learning methods.•The error for most ensembles considered is < 1%.•The system is hosted online and freely available.
论文关键词：Big data,Ensembles,Google trends,R programming,Sentiment analysis,Wikipedia
论文评审过程：Received 18 October 2017, Revised 18 December 2017, Accepted 7 June 2018, Available online 15 June 2018, Version of Record 27 July 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.06.016
