Portfolio optimization using simulated annealing and quantum-inspired simulated annealing: A comparative study
Evolutionary algorithm; Portfolio optimization; Quantum-inspired metaheuristic; Quantum-inspired simulated annealing; Simulated annealing
Portfolio optimization has been a highly studied problem in financial investment expert systems. The nonlinear constraint portfolio optimization problem cannot be efficiently solved using traditional approaches. This chapter presents a metaheuristic approach to portfolio optimization using simulated annealing (SA). Experiments have been conducted on over 10 years of NASDAQ stock price data. This first-of-its-kind effort is also made to implement the quantum-inspired version of SA (QiSA) for portfolio optimization, and the results are compared with the classical approach. The optimization parameters are chosen using sensitivity analysis, and the results are compared using different statistical measures. Preliminary results show that the QiSA approach is very promising and faster than SA when applied to the portfolio optimization domain. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications, pp. 213-243.
Gunjan A., Christ (Deemed to be University), Bengaluru, India; Bhattacharyya S., VSB Technical University of Ostrava, Ostrava, Czech Republic, Algebra University, Zagreb, Croatia