A Self-Learning Based Preference Model for Portfolio Optimization

Shicheng Hu, Danping Li, Junmin Jia and Yang Liu
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Shicheng Hu: School of Economics & Management, Harbin Institute of Technology, Harbin 150001, China
Danping Li: School of Economics & Management, Harbin Institute of Technology, Harbin 150001, China
Junmin Jia: School of Economics & Management, Harbin Institute of Technology, Harbin 150001, China
Yang Liu: School of Computer Science & Technology, Harbin Institute of Technology, Harbin 150001, China

Mathematics, 2021, vol. 9, issue 20, 1-17

Abstract: An investment in a portfolio can not only guarantee returns but can also effectively control risk factors. Portfolio optimization is a multi-objective optimization problem. In order to better assist a decision maker to obtain his/her preferred investment solution, an interactive multi-criterion decision making system (MV-IMCDM) is designed for the Mean-Variance (MV) model of the portfolio optimization problem. Considering the flexibility requirement of a preference model that provides a guiding role in MV-IMCDM, a self-learning based preference model DT-PM (decision tree-preference model) is constructed. Compared with the present function based preference model, the DT-PM fully considers a decision maker’s bounded rationality. It does not require an assumption that the decision maker’s preference structure and preference change are known a priori and can be automatically generated and completely updated by learning from the decision maker’s preference feedback. Experimental results of a comparison show that, in the case that the decision maker’s preference structure and preference change are unknown a priori, the performances of guidance and fitness of the DT-PM are remarkably superior to function based preference models; in the case that the decision maker’s preference structure is known a priori, the performances of guidance and fitness of the DT-PM is approximated to the predefined function based model. It can be concluded that the DT-PM can agree with the preference ambiguity and the variability of a decision maker with bounded rationality and be applied more widely in a real decision system.

Keywords: multiple criteria decision; preference model; expert systems; portfolio; risk assessment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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