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paper:ai_robo_advisor_big_data_analytics_financial_services

AI Robo-Advisor with Big Data Analytics for Financial Services

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文献基本信息

标题

AI Robo-Advisor with Big Data Analytics for Financial Services

作者

  1. Min-Yuh Day
  2. Tun-Kung Cheng
  3. Jheng-Gang Li

出版年份

2018

来源

IEEE

关键词

摘要

Robo-Advisors has been growing attraction from the financial industry for offering financial services by using algorithms and acting as like human advisors to support investors making investment decisions. During the investment planning stage, portfolio optimization plays a crucial role, especially for the medium and long-term investors, in determining the allocation weight of assets to achieve the balance between investors expectation return and risk tolerance. The literature on the topic of portfolio optimization has been offering plenty of theoretical and practical guidance for implementing the theory; however, there is a paucity of studies focusing on the applications which are designed for Robo-Advisors. In this research, we proposed a modular system and focused on integrating big data analysis, deep learning method and the Black-Litterman model to generate asset allocation weight. We developed a portfolio optimization module which takes the information from a variety of sources, such as stocks prices, investor profile and the other alternative data, and used them as input to calculate optimal weights of assets in the portfolio. The module we developed could be used as a sub-system for Robe-Advisors, which offers a customized optimal portfolio based on investors preference.

引用方式

Day, Min-Yuh, Tun-Kung Cheng, and Jheng-Gang Li. “AI Robo-Advisor with Big Data Analytics for Financial Services.” In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1027-1031. IEEE, 2018.

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paper/ai_robo_advisor_big_data_analytics_financial_services.txt · 最后更改: 2023/11/10 12:13 由 127.0.0.1

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