Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks
Abstract
Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic approach to designing portfolios using two algorithms, the critical line algorithm, and the hierarchical risk parity algorithm on eight sectors of the Indian stock market. While the portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31, 2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The backtesting results of the portfolios indicate while the performance of the CLA algorithm is superior on the training data, the HRP algorithm has outperformed the CLA algorithm on the test data.
Keywords
Cite
@article{arxiv.2202.02728,
title = {Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks},
author = {Jaydip Sen and Sidra Mehtab and Abhishek Dutta and Saikat Mondal},
journal= {arXiv preprint arXiv:2202.02728},
year = {2022}
}
Comments
The is the preprint version of our published paper listed in the IEEE Xplore. The final paper is published in the Proceedings of the IEEE International Conference on Decision Aid Sciences and Applications, pp. 668-675, December 7-8, 2021, Bahrain. The preprint consists of 8 pages and it contains 32 figures and 9 tables