Implementation of machine learning in $$\ell _{\infty }$$ ℓ ∞ -based sparse Sharpe ratio portfolio optimization: a case study on Indian stock market

Jyotirmayee Behera () and Pankaj Kumar ()
Additional contact information
Jyotirmayee Behera: SRM Institute of Science and Technology
Pankaj Kumar: SRM Institute of Science and Technology

Operational Research, 2024, vol. 24, issue 4, No 10, 26 pages

Abstract: Abstract Constructing the optimal portfolio by determining and selecting the best combinations of multiple portfolios is computationally challenging due to its exponential complexity. This paper considers the above issue and demonstrates an efficient portfolio selection method based on the sparse minimax Sharpe ratio model involving pre-selected stocks by an unsupervised machine learning approach. Different clustering techniques, such as k-means, fuzzy c-means, and ward linkage, have been used to cluster the stock market data into a finite number of clusters created based on their return rates and related risk levels. Several validity indices have been applied to arrive at the most appropriate number of groups to opt into the portfolio. Further, the sparse minimax Sharpe ratio model is implemented for the selection of the most efficient portfolio. Finally, the efficacy of the developed technique is justified and validated by illustrating a numerical example based on the historical dataset taken from the Bombay stock exchange (BSE), India.

Keywords: Portfolio optimization; Clustering; Minimax risk measure; Sparse portfolio; Sharpe ratio (search for similar items in EconPapers)
JEL-codes: C61 G11 G12 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12351-024-00867-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:24:y:2024:i:4:d:10.1007_s12351-024-00867-0

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-024-00867-0

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().