A Robo-Advisor Design using Multiobjective RankNets with Gated Neural Network Structure

Pei Ying Wang, Chun Shou Liu, Yao Chun Yang, Szu Hao Huang

研究成果: Conference contribution同行評審

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.

原文English
主出版物標題Proceedings - 2019 IEEE International Conference on Agents, ICA 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面77-78
頁數2
ISBN(電子)9781728140261
DOIs
出版狀態Published - 十月 2019
事件2019 IEEE International Conference on Agents, ICA 2019 - Jinan, China
持續時間: 18 十月 201921 十月 2019

出版系列

名字Proceedings - 2019 IEEE International Conference on Agents, ICA 2019

Conference

Conference2019 IEEE International Conference on Agents, ICA 2019
國家China
城市Jinan
期間18/10/1921/10/19

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