Machine Learning Guided Hedging Strategies for Index ETFs: An Indian Perspective

DOI

10.2991/978-94-6463-896-7_6How to use a DOI?

Keywords

Index ETF; NIFTYBEES; Deep learning; LSTM; CNN; India VIX; Hedging strategies; Ethical investing

Abstract

This study examines the application of machine learning guided hedging strategies in index exchange-traded fund (ETF) investing, focusing on Indian retail investors. Using NIFTYBEES, the country’s first and most liquid ETF tracking the NIFTY 50 and volatility signals from India VIX, we evaluate whether predictive models can enhance passive replication by integrating conditional risk management overlays. First, correlation and cointegration diagnostics confirm NIFTYBEES as a reliable benchmark proxy. Volatility band analysis identifies India VIX thresholds between 14 and 15 as effective indicators of impending market turbulence. Building on this, two deep learning architectures, long short-term memory (LSTM) networks and one-dimensional convolutional neural networks (1D-CNN), are trained on leakage-controlled log-return sequences. The models achieve next-day directional accuracies of 55.9% (LSTM) and 56.7% (CNN), with root mean squared errors (RMSEs) below 0.008. When paired with volatility triggers, the framework enhances downside protection through straddle-based overlays, without diluting the cost efficiency of passive index investing. Beyond empirical validation, the paper engages with ethical considerations, highlighting the importance of transparency, explainability, and investor education in deploying algorithmic finance for retail audiences. The findings suggest that machine learning–assisted hedging provides a viable and ethically sound pathway for improving resilience in index-based investing within emerging markets.

Copyright

© 2025 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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TY  - CONF
AU  - Alan Vellaiparambill
AU  - Natchimuthu Natchimuthu
PY  - 2025
DA  - 2025/11/06
TI  - Machine Learning Guided Hedging Strategies for Index ETFs: An Indian Perspective
BT  - Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025)
PB  - Atlantis Press
SP  - 96
EP  - 113
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-896-7_6
DO  - 10.2991/978-94-6463-896-7_6
ID  - Vellaiparambill2025
ER  -