The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
A Global Version of Samuelson's Dictum
Open accessAbstract
Samuelson’s Dictum refers to the conjecture that there is more informational inefficiency at the aggregate stock market level than at the individual stock level. Our paper recasts it in a global setup: there should be more informational inefficiency at the global level than at the country level. We find that sovereign CDS spreads can predict future stock market index returns, GDP, and PMI of their underlying countries. Consistent with the global version of Samuelson’s Dictum, the predictive power for both stock returns and macro variables is almost entirely from the global, rather than country-specific, information from the sovereign CDS market. (JEL G12, G14, G17)
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“Such information inefficiency is likely to be stronger in the aggregate stock market (Xiao, Yan, and Zhang, 2022).”
From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Xiao, Yan, and Zhang, 2022
Citations
Cites
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Research works printed but not linked: 6
- Chang Jeffery, 2021, Journal of Financial Economics. https://www. sciencedirect.com/science/article/abs/pii/S0304405X21003500.
- Gârleanu Nicolae, 2021, The Review of Asset Pricing Studies. https://doi.org/10/1093/rapstu/raab020.
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Cited by
Ripples into waves: Trade networks, economic activity, and asset prices
Chang, J., Du, H., Lou, D., & Polk, C. (2021) · Journal of Financial Economics · doi:10.1016/j.jfineco.2021.08.005 · Cited by 39
ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?Preprint
Chen, J., Tang, G., Zhou, G., & Zhu, W. Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4660148 · Cited by 30
Under-reaction in the sovereign CDS market
Wang, X., Xiao, Y., Yan, H., & Zhang, J. (2021) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2021.106191 · Cited by 7
Self-citationChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?
Chen, J., Tang, G., Zhou, G., & Zhu, W. (2025) · arXiv · doi:10.48550/arxiv.2502.10008 · Cited by 6
Penayo, B., Pribičević, V., & Novák, A. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113193 · Cited by 4
Metadata
- Authors
- Xiao, Y., Yan, H., & Zhang, J.
- Affiliations
- Capital University of Economics and Business (CN)
- DePaul University (US)
- Chinese University of Hong Kong, Shenzhen (CN)
- Venue
- American Economic Review: Insights
- Publisher
- American Economic Association
- Published
- 2022-06-01
- Volume
- 4
- Issue
- 2
- Pages
- 239-254
- ISSN
- 2640-205X (ISSN-L)
- 2640-2068
- OpenAlex
- W4281841999
- Cited by
- 8
- References
- 25
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 1
- FWCI
- 0.92
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- American Economic Review Insights (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Credit Risk and Financial Regulations
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Keywords
- information efficiency, sovereign credit default swaps, credit default swap spreads, stock index returns, international stock markets, macroeconomic forecasting, GDP forecasting
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
