A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices
作者：
Highlights：
• A hybrid supervised semi-supervised model for financial prediction is proposed.
• The model uses both past data of markets being predicted and markets interactions.
• A network construction algorithm for modeling markets interactions is proposed.
• The network is constructed from the outset on the basis of prediction purpose.
• Markets interactions can be more important than past data of markets in prediction.
摘要
•A hybrid supervised semi-supervised model for financial prediction is proposed.•The model uses both past data of markets being predicted and markets interactions.•A network construction algorithm for modeling markets interactions is proposed.•The network is constructed from the outset on the basis of prediction purpose.•Markets interactions can be more important than past data of markets in prediction.
论文关键词：Financial markets prediction,Hybrid machine learning models,Graph algorithms,Semi-supervised learning
论文评审过程：Received 8 October 2017, Revised 20 March 2018, Accepted 21 March 2018, Available online 21 March 2018, Version of Record 24 April 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.03.037
