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

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.