Is Shariah compliance a buffer against market shocks?

Purpose This study aims to investigate the comparative performance and safe haven hedging properties of Shariah-compliant stock indices and the conventional NIFTY 50 index in India, focusing on their stability and risk-adjusted returns during market crises, particularly in the post-COVID scenario. Design/methodology/approach The authors use traditional asset pricing models, including the capital asset pricing model, Fama-French three-factor model and Carhart four-factor model, to assess the risk-adjusted returns of the indices. To address the limitations of fixed factor sensitivities inherent in these models, the authors use the Mamba selective state-space model (a deep-learning framework) that captures dynamic market conditions. Wavelet quantile correlation (WQC) to trace the safe haven hedging properties. The analysis is based on daily return data from January 2015 to March 2024. Findings Traditional models tend to overestimate the risk-adjusted returns of Shariah-compliant indices. In contrast, the Mamba model provides a more accurate assessment, exposing that Islamic indices exhibit enhanced stability, especially in the post-COVID period, but do not significantly outperform the NIFTY 50 benchmark. The close alignment of risk-adjusted returns between Shariah indices and the conventional market highlights the Mamba model’s superior predictive accuracy, further confirmed with robustness tests. WQC findings show that NIFTY50 SHARIAH offers strong safe haven behavior, especially during market downturns and across all time horizons. Research limitations/implications Although the Mamba model offers improved predictive accuracy, this study is limited to the Indian market and a specific timeframe. Future research could explore other emerging markets and extend the methodology to different asset classes. Practical implications The findings provide valuable insights for investors seeking stable investment options during market crises and encourage the adoption of advanced modeling techniques, such as deep learning, for more precise portfolio evaluations and risk assessments. The strong safe haven and hedging role of NIFTY50 SHARIAH highlights its usefulness for investors aiming to protect their portfolios across short, medium and long-term market downturns. Originality/value To the best of the authors’ knowledge, this study is among the first to integrate a deep-learning-based approach into asset pricing for emerging markets, offering a perspective on the performance evaluation of Shariah-compliant investments under dynamic market conditions. Furthermore, our study is the first in using WQC to detect the hedging capability of Shariah-compliant investments.