2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization
AbstractPortfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA). In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Eftekharian, S.E.; Shojafar, M.; Shamshirband, S. 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization. Algorithms 2017, 10, 130.
Eftekharian SE, Shojafar M, Shamshirband S. 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization. Algorithms. 2017; 10(4):130.Chicago/Turabian Style
Eftekharian, Seyedeh E.; Shojafar, Mohammad; Shamshirband, Shahaboddin. 2017. "2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization." Algorithms 10, no. 4: 130.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.