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Financial risk and investment in artificial intelligence in the USA: a fresh evidence from quantile wavelet regression and quantile wavelet correlation tests

Derviş Kırıkkaleli et al.

The Journal of Risk Finance2025https://doi.org/10.1108/jrf-08-2024-0245article
AJG 1ABDC B
Weight
0.48

What the paper says

Purpose The present study aims to explore the co-movement between artificial intelligence (AI) investment and financial risk for the case of the USA using the most recently available dataset from 2012 to 2022. Design/methodology/approach The present study used the quantile in an augmented Dickey–Fuller (ADF) unit root, wavelet quantile regression (WQR) and wavelet quantile correlation (WQC) tests to capture and obtain information regarding time series variables at different quantiles and different time periods. Findings In the USA, financial stability and investments in AI are positively correlated at various quantiles and time periods. Since AI investment is positively correlated with financial stability, US policymakers should continue to support it. Originality/value Despite AI’s importance, comprehensive empirical research on this topic has not been conducted in the USA. A novel empirical approach is utilized in this study to fill a gap in the empirical literature for the USA.

5 citations

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https://doi.org/https://doi.org/10.1108/jrf-08-2024-0245

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@article{derviş2025,
  title        = {{Financial risk and investment in artificial intelligence in the USA: a fresh evidence from quantile wavelet regression and quantile wavelet correlation tests}},
  author       = {Derviş Kırıkkaleli et al.},
  journal      = {The Journal of Risk Finance},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1108/jrf-08-2024-0245},
}

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Financial risk and investment in artificial intelligence in the USA: a fresh evidence from quantile wavelet regression and quantile wavelet correlation tests

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Evidence weight

0.48

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.63 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.