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2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2018)
Barcelona, Spain
Aug. 28, 2018 to Aug. 31, 2018
ISSN: 2473-9928
ISBN: 978-1-5386-6052-2
pp: 1057-1064
Min-Yuh Day , Department of Information Management, Tamkang University, Taiwan
Jian-Ting Lin , Department of Information Management, Tamkang University, Taiwan
Yuan-Chih Chen , Department of Information Management, Tamkang University, Taiwan
ABSTRACT
With the advent of the artificial intelligence (AI) era, the combination of AI with financial technology (FinTech) has become a development trend in the financial industry. However, deep learning (DL) on the application of automated financial management has been rarely investigated. Thus, this research focuses on the applications of FinTech and DL in asset allocation and aims to optimize investment portfolio. The best investment portfolio in index-based funds based on Taiwan's index-type security investment trust funds are the main investment targets. Time series models for DL, that is, long short-term memory, predict the increase of each investment target and find the best investment portfolio in combination with the relevant asset allocation theory. In this research, we use the Markowitz mean-variance and Black-Litterman models as our asset allocation models for robo-advisor. Results show that the Black-Litterman model has a better accumulated return performance than the Morkowitz model and outperforms other strategies. The Human-Computer Interaction (HCI) dialogue service adopts artificial intelligence markup language (AIML) and a generative model. The main contribution of this paper is that we have developed an integrated knowledge-based and generative-based models for AI conversational robo-advisor.
INDEX TERMS
Artificial Intelligence (AI), Conversational Commerce, Deep Learning, Financial Technology (FinTech), Robo-Advisor
CITATION

M. Day, J. Lin and Y. Chen, "Artificial Intelligence for Conversational Robo-Advisor," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2018, pp. 1057-1064.
doi:10.1109/ASONAM.2018.8508269
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