Portfolio formation and optimization with continuous realignment: A suggested method for choosing the best portfolio of stocks using variable length NSGA-II
作者：
Highlights：
• Vertical and Horizontal Clustering using Multi-Objective Optimization algorithm.
• Designing clustering algorithm to have a sufficiently diversified number of stocks.
• Determining Quarter-wise Weights of the Constituent Stocks for each Portfolio.
• Comparing each Portfolio Returns with Nifty Returns as the benchmark.
• Implemented robust cross over and mutation methods in variable length NSGA-II.
摘要
•Vertical and Horizontal Clustering using Multi-Objective Optimization algorithm.•Designing clustering algorithm to have a sufficiently diversified number of stocks.•Determining Quarter-wise Weights of the Constituent Stocks for each Portfolio.•Comparing each Portfolio Returns with Nifty Returns as the benchmark.•Implemented robust cross over and mutation methods in variable length NSGA-II.
论文关键词：Vertical and horizontal clustering,Markowitz principle,Dynamic realignment,Benchmark return,Variable length NSGA-II,Realistic genetic operator
论文评审过程：Received 30 November 2020, Revised 28 July 2021, Accepted 4 August 2021, Available online 16 August 2021, Version of Record 19 August 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115732
