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20-11-2019 | Original Article | Issue 6/2020

International Journal of Machine Learning and Cybernetics 6/2020

A self-adaptive preference model based on dynamic feature analysis for interactive portfolio optimization

Journal:
International Journal of Machine Learning and Cybernetics > Issue 6/2020
Authors:
Shicheng Hu, Fang Li, Yang Liu, Song Wang
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Abstract

In financial markets, there are various assets to invest in. Recognizing an investor’s preferences is key to selecting a combination of assets that best serves his or her needs. Considering the mean–variance model for the portfolio optimization problem, this paper proposes an interactive multicriteria decision-making method and explores a self-adaptive preference model based on dynamic feature analysis (denoted RFFS-DT) to capture the decision maker (DM)’s complex preferences in the decision-making process. RFFS-DT recognizes the DM’s preference impact factor and constructs a preference model. To recognize the impact factors of the DM’s preferences, which could change during the decision-making process, three categories of possible features involved in three aspects of the mean–variance model are defined, and a feature selection method based on random forest is designed. Because the DM’s preference structure could be unknown a priori, a decision-tree-based preference model is built and updated adaptively according to the DM’s preference feedback and the selected features. The effectiveness of RFFS-DT for interactive multicriteria decision making is verified by a series of deliberately designed comparative experiments.

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