An Optimal Portfolio Construction for Asset Management with Back-Test Using PSO Algorithm and PyPortfolioOpt in Indian Stock Market

Authors

  • Nikhitha Pai Research Scholar, Department of MCA, CMR Institute of Technology, VTU-RC, Bangalore, Karnataka -560037, India
  • Ilango V. Professor, Department of MCA, CMR Institute of Technology, VTU –RC, Bangalore, Karnataka – 560 037, India.
  • Nithya B. Associate Professor, Department of MCA, New Horizon College of Engineering, Bangalore-560 103, Karnataka – India.

Keywords:

Artificial Intelligence, Particle Swarm Optimization, Sharpe Ratio, Portfolio Optimization, Back-testing, Simple Moving Average Crossover

Abstract

The paper studies the concept of Portfolio optimization and back-testing in the context of BSE and NSE for a group of selected stocks in FMCG sector. Optimizing a portfolio refers to the process where assets are allocated such that the allocation results in maximum returns with minimum variance. This is also the fundamental theory proposed by Harry Markowitz. An optimal allocation which is mathematically valid can be constructed by a combination of different stocks with varying expected returns and volatilities. A comparative analysis is made in the study with Particle Swarm optimization algorithm used for constructing the optimal Portfolio from the selected sector of stocks firstly and next the same stocks are optimized using PyPortfolioOpt package. Suitable Back-testing is applied and the results evaluated. In Back-testing a predictive model or a strategy is applied to historical data which helps to assess its accuracy. We can consider the back-tested results of the trading system as reliable parameters for future performance based on probability. A comparison of the portfolio optimization using PSO and the package PyPortfolioOpt is done in this paper.

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Published

27.10.2023

How to Cite

Pai, N. ., V., I. ., & B., N. . (2023). An Optimal Portfolio Construction for Asset Management with Back-Test Using PSO Algorithm and PyPortfolioOpt in Indian Stock Market. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 352 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3634

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

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