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- W4366811203 abstract "Portfolio optimization is important in quantitative financial economics. Traditional portfolio optimization models such as Mean Variance Optimization (MVO) only focus on metrics including Mean Return and Variance. However, the definition of optimization objective differs from one investor to another one. Recently, many researches have been done to study multi-objective portfolio optimization with the help of Monte Carlo (MC). Although MC is simple and easy to be implemented, it searches solutions by random simulation, and takes up too much computing time. In this paper, an approach has been proposed for multi-objective portfolio selection through Simulated Annealing (SA), a sophisticated optimization technique. Two major issues of applying SA to multi-objective portfolio optimization are settled in this paper. First, an adaptive algorithm is presented to estimate the range statistics of each metric, because standardizing metrics is the precondition of calculating the cost of objective functions. Second, three types of state perturbation have been presented, because perturbation cannot be made directly in weight space. Experimental results indicate that the cost of the objective function found by our approach is twice more than that of MC." @default.
- W4366811203 created "2023-04-25" @default.
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- W4366811203 date "2023-01-01" @default.
- W4366811203 modified "2023-09-27" @default.
- W4366811203 title "A Simulated Annealing Approach for Multi-objective Portfolio Optimization" @default.
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- W4366811203 doi "https://doi.org/10.1007/978-981-99-0416-7_40" @default.
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