Multilayer financial similarity networks and systemic firm centrality in China

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.