Relation-aware dynamic attributed graph attention network for stocks recommendation

作者：

Highlights：

• Replacing the task of predicting stock prices and trends in the traditional financial field with a new way of recommending the return ratio of stocks.

• Introducing the graph convolutional network as a guide to the graph attention network for information aggregation, whose attention mechanism is expanded from node features to topology information to make the stock correlation integrated into the message passing of the stock features.

• Applying the factor strategy mechanism into the complex stock network to select the important factor components.

摘要

•Replacing the task of predicting stock prices and trends in the traditional financial field with a new way of recommending the return ratio of stocks.•Introducing the graph convolutional network as a guide to the graph attention network for information aggregation, whose attention mechanism is expanded from node features to topology information to make the stock correlation integrated into the message passing of the stock features.•Applying the factor strategy mechanism into the complex stock network to select the important factor components.

论文关键词：Financial market,Attributed graph attention network,Correlation coefficient,Chinese stock recommendation

论文评审过程：Received 30 July 2020, Revised 20 April 2021, Accepted 14 June 2021, Available online 6 July 2021, Version of Record 24 August 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108119