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Demiralay, S. 
ORCID: 0000-0003-2543-7914, Gencer, H.G. and Bayraci, S.,
2021.
How do artificial intelligence and robotics stocks co-move with traditional and alternative assets in the age of the 4th industrial revolution? Implications and insights for the COVID-19 period.
Technological Forecasting and Social Change, 171: 120989.
ISSN 0040-1625
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Abstract
This study investigates the interdependence between AI and Robotics stocks and traditional (including stocks and bonds) and alternative (commodities and cryptocurrencies) assets, employing wavelet coherence analysis in time-frequency space. We further provide a fresh perspective on potential hedging and diversification benefits of AI and Robotics stocks. Overall, our results suggest that co-movements between AI and Robotics stocks and other assets significantly depend on the wavelet decomposition levels, suggesting time-scale-dependent investment benefits. Wavelet coherences and correlations have substantially increased, mostly in the low frequencies, during the COVID-19 pandemic. Government securities exhibit safe haven properties for investors at the highest and lowest scales. Even if cryptocurrencies can provide hedging benefits over the full sample, these benefits seem to be diminished during the COVID-19 period. We observe substantially higher co-movements of AI stocks with the composite stock index, corporate bonds, and commodities at all scales after March 2020, implying that inclusion of these assets in AI and Robotics stock portfolios may not enhance risk-adjusted portfolio performance in times of market turbulence. These results offer potential implications for investors and portfolio managers in terms of hedging/diversification benefits as well as for authorities and policy makers regarding the development of strategies to mitigate financial risk.
| Item Type: | Journal article | ||||||
|---|---|---|---|---|---|---|---|
| Publication Title: | Technological Forecasting and Social Change | ||||||
| Creators: | Demiralay, S., Gencer, H.G. and Bayraci, S. | ||||||
| Publisher: | Elsevier BV | ||||||
| Date: | October 2021 | ||||||
| Volume: | 171 | ||||||
| ISSN: | 0040-1625 | ||||||
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| Divisions: | Schools > Nottingham Business School | ||||||
| Record created by: | Laura Ward | ||||||
| Date Added: | 05 Jul 2021 15:01 | ||||||
| Last Modified: | 28 Dec 2022 03:00 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/43323 |
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