Banks of a Feather: The Informational Advantage of Being Alike
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation: Can banks effectively monitor their peers under asymmetric information? Effective peer monitoring matters for functioning interbank markets and, by implication, financial markets and the transmission of monetary policy. If banks monitor effectively, central banks can stay in a “night-watchman” role (Goodfriend and King 1988); if they systematically fail to identify solvent counterparties, central banks should be more active (Freixas and Jorge 2008). The paper argues that PORTFOLIO SIMILARITY between two banks is the key to their reciprocal monitoring ability: a lender uses private information about its own loan portfolio to assess the quality of a peer’s portfolio, so it is better informed the more similar the two exposures.
Data and setup: Quarterly bilateral bank-to-bank and bank-to-firm exposures from the German credit register, 2009-2018, covering 2,054 lending and 2,035 borrowing banks, balanced into 2,644,640 lender-borrower-quarter combinations; 701,533 true credit relations (102,044 within the same banking network, 2,087 within the same holding company). Interbank exposure represents 21% of German banks’ total borrowing and 20% of total lending; ~1.4 trillion euros average quarterly exposure by end-2018. The authors build three novel measures: (1) Portfolio quality = 1 minus the exposure-weighted average probability of default (PD) from proprietary supervisory filings (a forward-looking, private quality proxy); (2) Portfolio opacity = exposure-weighted standard deviation of PDs different banks assign to the same borrower (peers’ disagreement); (3) Portfolio similarity = cosine similarity of two banks’ exposure vectors across 10 industries (WZ 73 one-digit) and 9 regions (first zip digit). Estimation uses a Heckman (1977) two-step sample selection model: a Probit selection equation for the extensive margin (whether a credit relation exists) and an OLS outcome equation for the intensive margin (percentage change in bilateral exposure), with lagged credit relation as exclusion restriction, plus lender, borrower and quarter-year fixed effects. Independent variables are standardized.
Main findings (signs, magnitudes, scope): Portfolio quality validation - it negatively and significantly predicts next-quarter NPL ratios up to 2 years ahead, explaining 16-17% of cross-sectional NPL variation and 71-77% with fixed effects. For the AVERAGE bank, lending does NOT respond to borrower Portfolio quality (coefficients negative, mostly insignificant), but DOES respond to the backward-looking NPL ratio: a one-SD higher borrower NPL ratio lowers the probability of receiving a loan by 118 basis points (vs. unconditional 26.53%) and reduces amounts by 133-236 bp (avg. quarterly change 1.46%). Higher borrower Portfolio opacity reduces lending (extensive -38 bp; intensive -57 to -111 bp). The key result: interacting similarity with quality reverses this for similar pairs. For HIGH-similarity pairs (3 SD above mean), a one-SD increase in borrower Portfolio quality raises matching probability by 50 bp and lending by 408 bp; a deterioration cuts lending by 348-368 bp (avg. change between similar banks 10.95%). For LOW-similarity pairs, higher Portfolio quality LOWERS lending (matching -80 bp; amount -563 bp), and lending rises after quality deteriorates (370/342 bp), which Section 6 shows is a demand effect. The NPL-ratio response vanishes for similar pairs. Portfolio similarity itself raises lending: one-SD more sectoral similarity raises intensive-margin lending ~100-259 bp, regional similarity ~84-114 bp - jointly comparable in magnitude to relationship lending, the strongest known predictor. For opaque borrowers, high-similarity lenders lend MORE (extensive +23 bp; intensive +129 to +162 bp). A variance decomposition (Lemmon et al. 2008 ANCOVA) finds common/bank-pair characteristics explain 98.0% of extensive-margin variation and 18.9% of intensive-margin variation; lender, borrower and market characteristics explain only 1.2/0.8/0.1% (extensive) and 35.6/44.2/9.1% (intensive). Implication: peer monitoring works, but only among similar banks; this raises interbank efficiency at the cost of higher systemic risk and too-interconnected-to-fail concerns.
Layer 2: Deep Dive
What is the identification strategy and what are the main threats to it?
The core estimation is a Heckman (1977) two-step sample selection model: a first-stage Probit for the extensive margin (existence of a bilateral credit relation) and a second-stage OLS for the intensive margin (log change in bilateral exposure), with the inverse Mills ratio carried into the second stage. The exclusion restriction is the lagged existence of a credit relation (Credit relation_{i,j,t-1}), which strongly predicts a current relation (first-stage t-statistic 335; t=293 in the similarity specification) because German interbank exposures are long-lived, yet carries no information on whether exposure will rise or fall next quarter. The chief threats are: (1) demand vs. supply confounding - observed lending is equilibrium, so a negative quality-lending link could reflect borrowers’ demand rather than lenders’ screening; addressed in Section 6. (2) Correlated portfolio quality of similar banks - a lender cutting lending in response to its OWN deteriorating portfolio could be misread as a reaction to a similar borrower’s portfolio; addressed via a matched sample in Section 7. The paper also notes both Portfolio quality and NPL series are persistent, so the predictive regressions should be read as ‘gentle evidence,’ not strict causal proof.
How do the authors separate supply effects from demand effects?
They adapt Degryse et al. (2019). They define an adjusted exposure change bounded in [-2,2] (Chodorow-Reich 2014; Davis-Haltiwanger 1992) that captures both margins, then regress it on lending-bank-time fixed effects (proxying supply) and borrowing-bank-class x industry x region x time fixed effects (proxying demand, assuming homogeneous demand across lenders). The estimated lender-time fixed effects, demeaned and aggregated to the borrowing-bank level, give a borrower-specific liquidity-supply shock. Regressing this on borrower Portfolio quality, NPL ratio and opacity shows supply is restricted when quality deteriorates, NPL rises, or opacity increases. This confirms the puzzling positive lending-to-low-quality result for dissimilar pairs is a DEMAND effect: low-quality borrowers, shunned by similar lenders, demand more liquidity and turn to dissimilar lenders. The authors stress this borrower-level approach supports but cannot replace the bank-pair analysis, since it cannot include pair characteristics like similarity.
How do they rule out that lenders are just reacting to their own correlated portfolio quality?
In the full sample, the correlation of Portfolio quality between two above-average-similarity banks is 0.0499 versus only 0.0150 for below-average-similarity pairs. They build a matched subsample (nearest-neighbour matching, assigning each ‘similar’ pair - both similarities above the 75th percentile in 2009Q1 - three ‘dissimilar’ pairs below the 25th percentile with the closest Portfolio-quality correlation) so that within-pair quality correlation is the same for similar and dissimilar pairs, and redefine similarity as binary. If lenders only reacted to their own portfolio, the similarity x quality interaction should vanish in this sample. Instead, the interaction stays positive and mostly significant (and NPL x similarity too); weaker significance in some fixed-effect models reflects the smaller sample, since coefficient sizes are comparable to the main results.
What are the main mechanisms, and how are they distinguished empirically?
Mechanism: information on a peer’s asset quality is private and costly to obtain; a lender proxies a peer’s portfolio quality by the average quality of the industries/regions it lends to, and can do this more cheaply when it already lends to the same industries/regions (similar portfolio). So similar lenders are better informed. Empirically distinguished by: (a) the average bank reacts to the public NPL ratio but not to private Portfolio quality, while similar pairs react strongly to Portfolio quality and barely to NPL - showing similar lenders access private information; (b) the similarity x quality and similarity x opacity interactions; (c) the supply-shock decomposition separating screening from demand; (d) the matched sample ruling out own-portfolio reactions. A competing mechanism, risk shifting (Elliott et al. 2018) - banks deliberately courting correlated counterparties to raise bailout probability - cannot be ruled out and may co-drive preferential lending between similar peers.
What heterogeneity is documented?
(1) By similarity: similar pairs (3 SD above mean) react to forward-looking Portfolio quality and lend more to higher-quality and more-opaque peers; dissimilar pairs (3 SD below mean) react only to the backward-looking NPL ratio and end up lending more to low-quality borrowers via demand. (2) By opacity: lending between similar banks is especially important for opaque borrowers, who otherwise struggle to refinance; opaque banks are shunned by dissimilar lenders and turn to similar ones, while low-quality banks are shunned by similar lenders and turn to dissimilar ones. (3) Sectoral vs. regional similarity: both matter; sectoral similarity tends to have larger intensive-margin effects (e.g., 259 vs. 94 bp in Model 3). (4) Lender’s own quality: lenders cut lending when their own Portfolio quality falls (one-SD drop reduces amounts by 215-226 bp within-bank), consistent with prior work (Acharya-Merrouche 2013).
What robustness checks and additional analyses are run?
(1) Multiple fixed-effect layers: cross-section, lender/borrower fixed effects, and added quarter-year fixed effects (Models 1-4 across tables). (2) Control set: lagged Capital ratio, Liquidity ratio, ROA, Loans-to-assets, Size, relationship lending and reverse relationship lending over an 8-quarter window, difference in liquidity surplus, same-network and same-holding-company dummies. (3) Supply-vs-demand decomposition (Section 6). (4) Matched-sample analysis breaking the quality correlation (Section 7). (5) Validation of Portfolio quality via NPL-predictive regressions and a panel Granger causality test (Juodis et al. 2021; Half-Panel Jackknife Wald > 300; Dumitrescu-Hurlin Z < -50), significant 5-50 quarters ahead. (6) Two-digit WZ 73 industry classification (100 industries) in Appendix B. (7) Variance decomposition (ANCOVA, Type III sums of squares) quantifying explanatory power.
How does this paper relate to and differ from closely related prior work?
It extends peer-monitoring literature (Goodfriend-King 1988; Rochet-Tirole 1996; Flannery-Sorescu 1996; Furfine 2001) by showing that even among banks, the more similar the lender, the better its monitoring - identifying Perignon et al. (2018)’s ‘informed lenders’ as similar-portfolio banks. Versus relationship-lending work (Affinito 2012; Braeuning-Fecht 2017; Cocco et al. 2009), it shows that with a similar portfolio NO long-standing relationship is needed to obtain quality information, and that similarity mitigates opaque banks’ hampered access on top of relationships. It augments lender/borrower/market-characteristic studies by adding dyadic (common) covariates. Unlike prior work using aggregate bank-level ratios, CDS spreads, or rating-agency disagreement, it uses granular real-exposure data and proprietary supervisory PDs to measure private quality and peer-perceived opacity directly. It links to systemic-risk/contagion literature (Allen-Gale 2000; Fecht et al. 2011; Elliott et al. 2018), showing banks over-expose to similar counterparties despite indirect-contagion risk, surfacing an efficiency-vs-systemic-risk trade-off akin to focus-vs-diversification in Acharya et al. (2006).
What are the policy implications and their scope conditions?
Peer monitoring is real but partial: only similar banks effectively screen on private, forward-looking quality, while others fall back on inferior public proxies (NPL ratios). This bears on the central-bank ’night-watchman vs. active’ debate - because monitoring fails for dissimilar pairs, a purely hands-off stance may be insufficient. The headline trade-off: stronger lending between similar banks raises interbank informational efficiency and monitoring, but the above-average direct exposure between similar (correlated) banks multiplies systemic risk and too-interconnected-to-fail concerns, and reflects a lack of diversification. Scope conditions: results are specific to the German banking system (2009-2018), a tiered market dominated by private, savings, and cooperative banks with mostly long-term interbank loans (45% over a year, only 15% overnight); the data lack interest rates, so the analysis covers quantities/existence of lending, not prices; effects are estimated on bank-pairs that lent at least once; and the supply-identification assumes homogeneous borrower demand across lenders.
What are the key caveats the authors themselves flag?
(1) No interest-rate data, so price effects of similarity, quality and opacity are untested. (2) Portfolio quality and NPL series are persistent, so the forward-looking predictive evidence is ‘gentle,’ not definitive. (3) The supply-shock approach gives borrower-level (not pair-level) shocks and cannot incorporate similarity. (4) Risk shifting cannot be ruled out as a co-driver of preferential lending between similar peers. (5) Portfolio quality is built using the median PD across IRB banks, excluding borrowers exposed only to Standardised-Approach banks. (6) The balanced sample includes only pairs that lent at least once, ignoring pairs that could theoretically but realistically would not lend (consistent with tiered-market evidence).