A brief review of portfolio optimization techniques

被引：48

作者：

Gunjan, Abhishek ^{[1
]}

Bhattacharyya, Siddhartha ^{[2
]}

机构：

[1] Christ Deemed Be Univ, Bangalore, Karnataka, India

[2] Rajnagar Mahavidyalaya, Birbhum, India

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2023年 / 56卷 / 05期

关键词：

Portfolio optimization; Statistical measures; Machine learning; Deep learning; Reinforcement learning; Evolutionary techniques; Quantum computing; INSPIRED TABU SEARCH; MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS; TRACKING ERROR MINIMIZATION; PARTICLE SWARM OPTIMIZATION; ANT COLONY OPTIMIZATION; VALUE-AT-RISK; GENETIC ALGORITHM; TRADING SYSTEM; SUPPORT; MANAGEMENT;

D O I：

10.1007/s10462-022-10273-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Portfolio optimization has always been a challenging proposition in finance and management. Portfolio optimization facilitates in selection of portfolios in a volatile market situation. In this paper, different classical, statistical and intelligent approaches employed for portfolio optimization and management are reviewed. A brief study is performed to understand why portfolio is important for any organization and how recent advances in machine learning and artificial intelligence can help portfolio managers to take right decisions regarding allotment of portfolios. A comparative study of different techniques, first of its kind, is presented in this paper. An effort is also made to compile classical, intelligent, and quantum-inspired techniques that can be employed in portfolio optimization.

引用

页码：3847 / 3886

页数：40

共 186 条

[1] Continuous control with Stacked Deep Dynamic Recurrent Reinforcement Learning for portfolio optimization [J].