Dynamic Linkages and Asset Allocation Across China’s Industrial Chain in the AI Era
- 1School of Finance, Hebei University of Economics and Business, Shijiazhuang 050062, China
- 2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Abstract
The economic impact of artificial intelligence extends across all stages, including resources, manufacturing, and consumption and services. Against this background, how linkages among the upstream, midstream, and downstream stages of the industrial chain evolve, and what this evolution implies for asset allocation, are important questions for understanding the interaction between technological change and capital markets. Taking the multifactor model as its theoretical starting point, this study uses DCC-ARMA-GARCH to extract dynamic coefficients that measure each stage’s response to AI-related information, employs TVP-VAR to characterize cross-stage linkages, and incorporates linkage information into asset allocation. The results show that responses to AI-related information generally exhibit a linkage pattern in which the upstream stage transmits outward and the midstream stage receives transmissions from the other stages. In contrast, overall return linkages feature the midstream stage as the principal transmitter, whereas transmission roles in the former system vary more across periods. Thus, the financial transmission position of the same industrial stage changes with information content and time. Portfolios constructed from AI-related linkage information produce more balanced cross-stage holdings and higher cumulative returns than covariance-based benchmark portfolios, alongside higher volatility and larger drawdowns. By translating each stage’s dynamic response to AI changes into information about industrial-chain relationships, this study reveals distinctive connections between the industrial chain and AI and offers a new perspective on industrial-chain linkages and their asset allocation implications in the AI era.
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BibTeX
@article{yang2026dynamic,
title = {{Dynamic Linkages and Asset Allocation Across China’s Industrial Chain in the AI Era}},
author = {Xuemei Yang and Wang Gao and Yuanyuan Jiang},
journal = {Mathematics},
year = {2026},
month = nov,
doi = {10.3390/math14214046},
url = {https://www.mdpi.com/2227-7390/14/21/4046},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Dynamic Linkages and Asset Allocation Across China’s Industrial Chain in the AI Era
AU - Xuemei Yang
AU - Wang Gao
AU - Yuanyuan Jiang
PY - 2026
DA - 2026/11/04
JO - Mathematics
DO - 10.3390/math14214046
UR - https://www.mdpi.com/2227-7390/14/21/4046
AB - The economic impact of artificial intelligence extends across all stages, including resources, manufacturing, and consumption and services. Against this background, how linkages among the upstream, midstream, and downstream stages of the industrial chain evolve, and what this evolution implies for asset allocation, are important questions for understanding the interaction between technological change and capital markets. Taking the multifactor model as its theoretical starting point, this study uses DCC-ARMA-GARCH to extract dynamic coefficients that measure each stage’s response to AI-related information, employs TVP-VAR to characterize cross-stage linkages, and incorporates linkage information into asset allocation. The results show that responses to AI-related information generally exhibit a linkage pattern in which the upstream stage transmits outward and the midstream stage receives transmissions from the other stages. In contrast, overall return linkages feature the midstream stage as the principal transmitter, whereas transmission roles in the former system vary more across periods. Thus, the financial transmission position of the same industrial stage changes with information content and time. Portfolios constructed from AI-related linkage information produce more balanced cross-stage holdings and higher cumulative returns than covariance-based benchmark portfolios, alongside higher volatility and larger drawdowns. By translating each stage’s dynamic response to AI changes into information about industrial-chain relationships, this study reveals distinctive connections between the industrial chain and AI and offers a new perspective on industrial-chain linkages and their asset allocation implications in the AI era.
SN - 2227-7390
ER -