Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Portfolio optimization
Statistical measures
Machine learning
Deep learning
Reinforcement learning
Evolutionary techniques
Quantum computing

Author(s) Name:  Abhishek Gunjan & Siddhartha Bhattacharyya

Publisher name:  Springer

DOI:  10.1007/s10462-022-10273-7

Volume Information:  

Paper Link:   https://link.springer.com/article/10.1007/s10462-022-10273-7