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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Agents, ICA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages77-78
Number of pages2
ISBN (Electronic)9781728140261
DOIs
StatePublished - Oct 2019
Event2019 IEEE International Conference on Agents, ICA 2019 - Jinan, China
Duration: 18 Oct 201921 Oct 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Agents, ICA 2019

Conference

Conference2019 IEEE International Conference on Agents, ICA 2019
CountryChina
CityJinan
Period18/10/1921/10/19

Keywords

  • deep learning
  • learning preferences
  • rankings

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  • Cite this

    Wang, P. Y., Liu, C. S., Yang, Y. C., & Huang, S. H. (2019). A Robo-Advisor Design using Multiobjective RankNets with Gated Neural Network Structure. In Proceedings - 2019 IEEE International Conference on Agents, ICA 2019 (pp. 77-78). [8929188] (Proceedings - 2019 IEEE International Conference on Agents, ICA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AGENTS.2019.8929188