Multilayer financial similarity networks and systemic firm centrality in China

Abstract Most financial-network studies infer links from market co-movement or observed exposures. We ask a prior measurement question: do the firms identified as central remain central when interfirm proximity is defined in different accounting spaces? Using listed firms from mainland China and Hong Kong over 2006–2023, we construct annual weighted, undirected similarity networks from year-standardized accounting ratios. The design compares a GLOBAL layer based on the full ratio vector, RISK and RETURN layers, and a family of single-ratio networks. Euclidean distances are transformed into continuous similarity weights, and weighted eigenvector centrality measures recursive prominence within each representation. The composite layers display a stable topological ordering: average density is 0.286 in GLOBAL, 0.384 in RISK, and 0.457 in RETURN. Yet cohesion and centrality concentration do not move together. GLOBAL is the least dense composite layer but has the largest top-10 centrality share (11.07%, versus 9.72% in RISK and 9.07% in RETURN). Core membership is also representation-dependent: average annual top-10 overlap is 5.50 firms for GLOBAL-RISK, 1.78 for GLOBAL-RETURN, and 1.33 for RISK-RETURN. Single-ratio networks exhibit still greater topological dispersion. After 2017, RISK and RETURN densities frequently move in opposite directions, indicating that balance-sheet vulnerability and profitability can follow different cross-sectional configurations. The contribution is therefore a result about measurement: graph cohesion, centrality concentration, and core membership are distinct, and each depends on the accounting representation used to construct the network. These networks capture common financial-profile proximity; they do not identify bilateral exposure, causal spillovers, or regulatory contributions to systemic loss. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. About this article Cite this article Yu, Y., Rufé, M.C., Pidelasserra, J.M. et al. Multilayer financial similarity networks and systemic firm centrality in China. Appl Netw Sci (2026). https://doi.org/10.1007/s41109-026-00833-z Received: Accepted: Published: DOI: https://doi.org/10.1007/s41109-026-00833-z