Dynamic effect of Bitcoin, fintech and artificial intelligence stocks on eco-friendly assets, Islamic stocks and conventional financial markets: Another look using quantile-based approaches

Emmanuel Abakah, Aviral Tiwari, Sudeshna Ghosh and Buhari Doğan ()

Technological Forecasting and Social Change, 2023, vol. 192, issue C

Abstract: Against the milieu of rapidly growing investment in technologically induced assets, this study examines the investment role of Bitcoin, fintech, and artificial intelligence (AI) stocks in relation to major environmentally friendly assets (green bonds, sustainable investments, and clean energy), Islamic stocks, and conventional financial markets using quantile-based approaches. To this end, we specifically examine the distributional and directional predictability between the returns of fintech, Bitcoin, and AI and various markets using the nonparametric causality-in-quantiles method and the cross-quantilogram correlation method respectively. We use daily data spanning March 9, 2018 to January 27, 2021. In terms of the distributional predictability of fintech, Bitcoin, and AI in relation to the traditional markets, Islamic stocks, clean energy stocks, and sustainable investments, we find strong evidence of causal asymmetry across quantiles and strong variations across markets. Likewise, findings in terms of directional predictability between the returns of fintech, Bitcoin, and AI and various markets infer that Islamic stocks act as a good hedge against Bitcoin. The S&P Treasury Bond and S&P Green Bond are also perfect hedges for fintech stocks, while S&P Global Clean Energy is a perfect hedge for AI stocks in terms of long-term dynamics.

Keywords: Fintech; Bitcoin; Artificial intelligence; Predictability; Causality in quantiles; Cross-quantilogram correlation (search for similar items in EconPapers)
JEL-codes: C22 G11 G17 G32 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:192:y:2023:i:c:s0040162523002512

DOI: 10.1016/j.techfore.2023.122566

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