Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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Applications of Markov Chains in Investment Strategies for Nifty Sector Rotation

Authors
S. Kanimozhi1, *, S. V. Manemaran2, K. M. Karuppasamy3
1Department of Mathematics and Statistics, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, 600 073, India
2Department of Mathematics, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, 600 073, India
3Department of Mathematics, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tiruchirappalli, 621 105, India
*Corresponding author. Email: kanimozhipraba2001@gmail.com
Corresponding Author
S. Kanimozhi
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_2How to use a DOI?
Keywords
Investment awareness; sector rotation; Markov Chains; portfolio optimization; Nifty sectors; risk management
Abstract

This study examines sector rotation in the Indian equity market using Markov chain models applied to daily data for Nifty IT, Nifty Bank, Nifty Auto, Nifty Infra, and Nifty Energy over 2019–2023. We evaluate performance through average returns, volatility, and Sharpe ratios, and assess inter-sector relationships via pairwise correlations and multiple correlation coefficients to inform diversification. A discrete-state framework classifies daily returns into Strong Growth (SG), Moderate Growth (MG), Stable (S), Moderate Decline (MD), and Strong Decline (SD). For each sector, we estimate transition probability matrices (TPMs) and steady-state probabilities to characterize persistence and long-run tendencies. Results indicate long-run dominance of the Stable state, with IT and Infrastructure exhibiting favourable risk-adjusted characteristics and Energy providing diversification. The Markov-guided allocation emphasizes IT and Infrastructure, maintains measured exposure to Energy, and holds smaller positions in Auto and Bank. The framework provides a transparent, data-driven approach to sector rotation that explicitly incorporates risk and state persistence.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_2How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - S. Kanimozhi
AU  - S. V. Manemaran
AU  - K. M. Karuppasamy
PY  - 2026
DA  - 2026/04/24
TI  - Applications of Markov Chains in Investment Strategies for Nifty Sector Rotation
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 4
EP  - 17
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_2
DO  - 10.2991/978-94-6239-654-8_2
ID  - Kanimozhi2026
ER  -