標題: A Robo-Advisor Design using Multiobjective RankNets with Gated Neural Network Structure
作者: Wang, Pei-Ying
Liu, Chun-Shou
Yang, Yao-Chun
Huang, Szu-Hao
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: learning preferences;rankings;deep learning
公開日期: 1-Jan-2019
摘要: With rapid developments in deep learning and financial technology, a customized robo-advisory service based on novel artificial intelligence techniques has been widely adopted to realize financial inclusion. This study proposes a novel robo-advisor system that integrates trend prediction, portfolio management, and a recommendation mechanism. A gated neural network structure combining three multiobjective RankNet kernels could rank target financial products and recommend the top-n securities to investors. The gated neural network learns to choose or weigh each RankNet for incorporating the most important partial network inputs, such as earnings per share, market index, and hidden information from the time series. Experimental results indicate that the recommendation results of our proposed robo-advisor based on a gated neural network and multiobjective RankNets can outperform existing models.
URI: http://hdl.handle.net/11536/154274
ISBN: 978-1-7281-4026-1
期刊: 2019 IEEE INTERNATIONAL CONFERENCE ON AGENTS (ICA)
起始頁: 77
結束頁: 78
Appears in Collections:Conferences Paper