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Topology as information: Network effects in corporate lending

Anna Pirogova, Anna Mancini, Tiziano Squartini, Giulio Cimini

Abstract

A central challenge in financial economics is understanding how credit networks form under informational noise. We introduce the concept of topological capital, arguing that banks increasingly rely on topological certification, interpreting a borrower's connectivity as a primary proxy for creditworthiness. Using a novel dataset of bank-firm relationships manually extracted from Italian financial statements, we implement a multi-stage empirical framework, benchmarking empirical patterns against a maximum-entropy benchmark, to separate the determinants of credit access from those of loan volumes. Our results indicate that network topology systematically outperforms traditional fundamentals. In the link-formation stage, connectivity breeds further connectivity through an amplified preferential attachment mechanism. In the loan-sizing stage, network strength absorbs the explanatory power of balance-sheet metrics, documenting a profound network substitution effect where topological signals effectively replace physical collateral across all corporate segments. For SMEs, we identify a critical signal divergence: reported debt acts as a risk signal, while network footprint serves as market validation. Furthermore, we reveal a diversification paradox: while firms fragment debt to avoid hold-up risks, over-diversification leads to a complexity penalty that stagnates credit depth and inflates systemic Loss Given Default. Ultimately, our findings signal the twilight of the balance sheet as the primary anchor of corporate lending, calling for a shift toward topological macro-prudential supervision to manage vulnerabilities invisible to traditional bilateral indicators.

Topology as information: Network effects in corporate lending

Abstract

A central challenge in financial economics is understanding how credit networks form under informational noise. We introduce the concept of topological capital, arguing that banks increasingly rely on topological certification, interpreting a borrower's connectivity as a primary proxy for creditworthiness. Using a novel dataset of bank-firm relationships manually extracted from Italian financial statements, we implement a multi-stage empirical framework, benchmarking empirical patterns against a maximum-entropy benchmark, to separate the determinants of credit access from those of loan volumes. Our results indicate that network topology systematically outperforms traditional fundamentals. In the link-formation stage, connectivity breeds further connectivity through an amplified preferential attachment mechanism. In the loan-sizing stage, network strength absorbs the explanatory power of balance-sheet metrics, documenting a profound network substitution effect where topological signals effectively replace physical collateral across all corporate segments. For SMEs, we identify a critical signal divergence: reported debt acts as a risk signal, while network footprint serves as market validation. Furthermore, we reveal a diversification paradox: while firms fragment debt to avoid hold-up risks, over-diversification leads to a complexity penalty that stagnates credit depth and inflates systemic Loss Given Default. Ultimately, our findings signal the twilight of the balance sheet as the primary anchor of corporate lending, calling for a shift toward topological macro-prudential supervision to manage vulnerabilities invisible to traditional bilateral indicators.
Paper Structure (16 sections, 13 equations, 3 figures, 7 tables)

This paper contains 16 sections, 13 equations, 3 figures, 7 tables.

Figures (3)

  • Figure 1: Empirical network statistics for the consolidated (top) and unconsolidated (bottom) samples. Panels (A,B,C) refer to bank variables while panels (D,E,F) to firm variables. (A,D) Complementary cumulative distribution function of degrees, showing that bank connectivity is more heterogeneous than firm connectivity. (B,E) Balance sheet strength versus network strength (the solid line marks the identity): for banks, the offset is due to our data collection procedure, not covering all corporate loans of banks. (C,F) Network strength versus degree: size and number of relationships correlate well for banks but only modestly for firms, for which the variability is large for poorly-connected nodes.
  • Figure 2: Empirical VS expected degrees. Panels in the top (bottom) row refer to the network-driven (balance-driven) version of the model, while panels on the left (right) refer to the consolidated (unconsolidated) sample. The comparison is for firms' degrees (Panels A,C,E,G) and banks' degrees (Panels B,D,F,H). Benchmark predictions are obtained as averages over an ensemble of $10^4$ configurations. Gray dots show the raw data, coloured dots and shaded area show the mean and standard deviation of the model degrees over each real degree bin. The dashed line marks the identity.
  • Figure 3: Top panels: diagnostic analysis of residuals from the OLS Model 3 for the consolidated sample. The distribution (left) highlights negative skewness, while scatter plots visualize the relationship between residuals and firm connectivity (center) and size (right). Bottom panels: diagnostic analysis of residuals from the OLS Model 3 for the unconsolidated sample. Note the higher kurtosis ('fat tails') compared to the consolidated sample.