Adverse Selection in Corporate Loan Markets: Beyhaghi, Fracassi & Weitzner (2026)

What this is. The paper’s core results, datasets, and theory: enough to know what it found without reading all 46 pages. To replicate or extend it, read the full source at the original DOI (paywalled).

TL;DR

Using the Federal Reserve’s confidential Y-14Q supervisory data on corporate loans (21,924 loans, 2014Q4–2019Q4, 23 large bank holding companies), the paper documents that markets with more banks have higher interest rates, riskier borrowers (higher bank-assessed probability of default), and larger loan volume. These patterns contradict standard competition models but are consistent with adverse selection: riskier borrowers find it easier to obtain financing when more banks compete, creating a winner’s curse for lenders. The paper constructs a novel markup measure by orthogonalizing the interest rate to the bank’s internal risk assessment, finds markups also rise with the number of banks, and shows that firms staying with their existing bank pay 9 bp higher markups, consistent with information holdup. A GSIB capital-surcharge shock confirms the channel: counties with more GSIBs in 2015 saw falling interest rates and borrower risk after surcharges reduced adverse selection.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Bank risk assessments strongly predict loan interest rates and loan performance; after controlling for PD and LGD, interest rates are orthogonal to future performance	Table III, p. 251; Table IV, p. 254	PD coef 0.077*** (t=4.66), LGD coef 0.003*** (t=4.37); adj. R² rises from 0.52 to 0.55; interest-rate coef on nonperformance drops from 0.527*** to 0.101 (insig) when PD/LGD added
R2	More banks in a county raises interest rates (adverse selection reverses standard competition effect)	Table V, p. 255	coef 0.013*** (t=6.32) in col.(2); 1-SD increase (~6 banks) ≈ +7 bp vs. average credit spread ~150 bp; nonmonotonic U-shape (Fig. 2, p. 256): rates first fall then rise with bank count
R3	More banks raises borrower risk (higher bank-assessed PD, conditional on observables)	Table VI, p. 257	coef 0.011*** (t=4.97) in col.(2); PD increases monotonically with bank count (Fig. 3, p. 258); average PD = 134 bp, effect ≈ +1.1 bp per additional bank
R4	More banks raises total lending volume (consistent with adverse selection and competition alike, but combined with R2–R3 supports adverse selection)	Table VII, p. 258	coef 0.140*** (t=14.66) on log volume; 1 additional bank ≈ +14% volume; monotonically increasing (Fig. 4, p. 259)
R5	Novel markup measure also rises with number of banks, consistent with adverse-selection-driven market power	Table IX, p. 265	coef 0.012*** (t=5.84) in col.(2); markups U-shaped in bank count (Fig. 8, p. 266)
R6	Adverse selection effects are concentrated in low-tangibility firms, for which private information problems are most severe	Table X, pp. 267–268	Interest rate coef: high-tangibility 0.003 (insig), low-tangibility 0.013*** (t=3.77); PD coef: high-tangibility 0.000 (insig), low-tangibility 0.014** (t=2.47); markup: high-tangibility insig, low-tangibility 0.011***
R7	Firms rarely switch banks (~75% stay with existing bank, roughly constant across market structures), consistent with information holdup	Fig. 6, p. 262	Stay Bank rate ~75% across all bank-count bins; small economically insignificant reduction (Fig. 7, p. 263)
R8	Firms staying with their existing bank pay higher markups (information rent extraction); holdup attenuated by prior multi-bank borrowing	Table XI, p. 271	Stay Bank coef 0.090*** (t=3.64) in col.(2); interaction Stay Bank × N. of Prior Lenders: −0.052*** (t=3.50)
R9	GSIB capital surcharges (2016) reduced the number of banks, interest rates, and borrower risk in high-GSIB counties; markup direction consistent but not significant	Table XII (reduced-form DiD), p. 273; Table XIII (2SLS), p. 278	Reduced form: Post × N. of GSIBs(2015) coefs: N. banks −0.201** (t=2.44), log volume −0.084*** (t=3.16), interest rate −0.031* (t=1.96), PD −0.069* (t=1.68); 2SLS: interest rate +0.179* (t=1.85), PD +0.396* (t=1.66) per additional bank

Overall (paper’s conclusion). Standard competition models fail to capture the corporate loan market: more banks lead to riskier borrowers and higher rates, not lower. Adverse selection gives informed incumbent banks market power, which they exploit via information rents on repeat borrowers. Antitrust policies that increase the number of banks in local loan markets may raise rates and borrower risk as an unintended consequence.

Theory / model

The paper has no original structural model. It tests predictions derived from Broecker (1990) and related adverse-selection models (Riordan 1995, Marquez 2002, Dell’Ariccia and Marquez 2006). In these models, when lenders cannot observe whether a borrower has been previously rejected by another bank, they face a winner’s curse: the pool of applicants worsens as the number of banks in the market increases, because lower-quality borrowers have more chances to find an approving lender. Two related mechanisms reinforce each other (p. 246):

1. As the number of banks rises, individual banks become more concerned that applicants have been screened and rejected elsewhere, raising the expected risk of any given applicant and thus the required interest rate.
2. More banks makes it harder for any one bank to infer whether a borrower has been rejected (Marquez 2002), further worsening adverse selection.

Both mechanisms predict that more banks leads to (i) higher average borrower risk conditional on observables, (ii) higher interest rates, and (iii) higher lending volume (pp. 246-247).

A second class of predictions concerns market power arising from information advantages. Because incumbent banks learn about borrowers through lending relationships, they hold private information that competitors lack (Sharpe 1990, Rajan 1992, Dell’Ariccia and Marquez 2006). This advantage lets them charge above-marginal-cost rates to repeat borrowers who would be pooled with riskier applicants if they tried to switch banks. More banks in the market worsens adverse selection further and thus strengthens the incumbent’s information advantage and holdup capacity (p. 246).

Tested predictions:

1. More banks raises average bank-assessed PD, conditional on observables.
2. More banks raises interest rates (adverse-selection effect dominates competition).
3. More banks raises total lending volume.
4. Markups (interest rates net of risk) rise with bank count.
5. Repeat borrowers pay higher markups; the effect attenuates when borrowers have prior multi-bank relationships that reduce holdup.

The paper provides no welfare ranking and is fully empirical.

Method

The paper applies three estimation strategies. All use OLS (or 2SLS) at the loan level with multi-way fixed effects; no structural model is estimated. The approach builds on panel-regression, difference-in-differences, and instrumental-variables.

Risk-assessment validation (Section II, pp. 250-254). Before testing market-structure predictions, the paper verifies that banks’ internal PD and LGD reports contain genuine private information. This is critical because the markup measure relies entirely on the credibility of these assessments. The validation runs two regressions (equations 1 and 2 in the PDF).

Markup construction (Section IV, p. 264). Markup is not an externally observed price-cost margin; it is constructed residually. Equation (5) regresses the interest rate on PD, LGD, their interaction, loan controls, bank x quarter FEs, and industry x quarter FEs. The residual after partialling out the risk components measures the portion of the interest rate unexplained by the bank’s own assessment of risk. This makes the measure internally consistent: by construction, Table IV shows that interest rates do not predict loan performance once PD and LGD are controlled for, so the residual markup arguably reflects market power rather than unpriced risk.

GSIB DiD/IV (Section VII, pp. 272-278). To address endogeneity of the number of banks, the paper uses a Bartik-style design (Goldsmith-Pinkham, Sorkin, and Swift 2020): the pre-determined number of GSIBs in a county in 2015, interacted with a post-surcharge dummy, instruments for changes in the number of lending banks. Following Favara, Ivanov, and Rezende (2021), the paper treats the GSIB surcharge as a shock to large-bank lending costs and uses it to build the DiD/IV instrument (p. 272). The first stage (column (1) of Table XII) shows that more GSIBs in 2015 predicts a significant drop in the total number of banks after 2016. The IV strategy identifies the adverse-selection channel by exploiting a supply-side cost shock that affected GSIBs differentially across counties.

Empirical specifications

All regressions use loan $l$ in industry $i$ originated by bank $b$ in quarter $t$ as the unit of observation unless noted. Standard errors are clustered by county throughout.

Spec (1): Risk assessment and interest rates (Table III, p. 251)

$$\text{IR}_l = \beta_0 \cdot \text{PD}_l + \beta_1 \cdot \text{LGD}_l + \beta_2 \cdot (\text{PD}_l \times \text{LGD}_l) + \Gamma X_l + \delta_{b,t} + \alpha_{i,t} + u_l \tag{1}$$

• $\text{IR}_l$ is loan interest rate (%);
• $\text{PD}_l$ is bank-assessed probability of default (%);
• $\text{LGD}_l$ is loss given default (%);
• $X_l$ is a vector of loan controls (log maturity, log amount, guarantee dummy, loan purpose FE, loan type FE);
• $\delta_{b,t}$ is bank x quarter FE;
• $\alpha_{i,t}$ is industry x quarter FE.

Produces results R1 (Table III, p. 251).

Spec (2): Risk assessments and loan performance (Table IV, p. 254)

$$y_l = \beta_0 \cdot \text{IR}_l + \beta_1 \cdot \text{PD}_l + \beta_2 \cdot \text{LGD}_l + \beta_3 \cdot (\text{PD}_l \times \text{LGD}_l) + \Gamma X_l + \delta_{b,t} + \alpha_{i,t} + u_l \tag{2}$$

• $y_l$ is either nonperformance (dummy) or realized default (dummy);
• same controls and FEs as spec (1).

Produces results R1 (Table IV, p. 254).

Spec (3): Market structure and interest rates (Table V, p. 255)

$$\text{IR}_l = \beta \cdot \text{NOB}_c + \Gamma_0 X_l + \Gamma_1 Z_{f,t} + \delta_{b,t} + \alpha_{i,t} + u_l \tag{3}$$

• $\text{NOB}_c$ is the number of banks in county $c$;
• $Z_{f,t}$ is firm characteristics (log assets, leverage, tangibility, profitability).

Produces results R2 (Table V, p. 255). The same specification is used with $\text{PD}$ as the dependent variable for R3 (Table VI, p. 257).

Spec (4): Market structure and loan volume (Table VII, p. 258)

$$\text{Volume}_{c,t} = \beta_0 \cdot \text{NOB}_c + \Gamma_0 X_{c,t} + \delta_t + u_{c,t} \tag{4}$$

• $\text{Volume}_{c,t}$ is log total dollar loan volume (or log total number of loans) in county $c$ in quarter $t$;
• $X_{c,t}$ is county-level controls (log population density, log wages, log financial industry wages, log population);
• $\delta_t$ is year-quarter FE.

Produces results R4 (Table VII, p. 258). The same specification with blanket lien as the outcome produces collateral results (Table VIII, p. 261).

Spec (5): Market structure and markups (Table IX, p. 265)

$$\text{IR}_l = \beta_0 \cdot \text{NOB}_c + \beta_1 \cdot \text{PD}_l + \beta_2 \cdot \text{LGD}_l + \beta_3 \cdot (\text{PD}_l \times \text{LGD}_l) + \Gamma_0 X_l + \Gamma_1 Z_{f,t} + \delta_{b,t} + \alpha_{i,t} + u_l \tag{5}$$

This is spec (3) augmented with the risk measures as controls; the coefficient on $\text{NOB}_c$ now captures the markup effect (interest rate variation beyond risk). Produces results R5 (Table IX, p. 265).

Spec (6): Switching banks and markups (Table XI, p. 271)

$$\text{IR}_l = \beta_0 \cdot \text{Stay Bank}_l + \beta_1 \cdot \text{PD}_l + \beta_2 \cdot \text{LGD}_l + \beta_3 \cdot (\text{PD}_l \times \text{LGD}_l) + \Gamma_0 X_l + \Gamma_1 Z_{f,t} + \delta_{b,t} + \alpha_{i,t} + \lambda_{c,t} + u_l \tag{6}$$

• $\text{Stay Bank}_l$ equals one when the borrower stays with an existing bank;
• $\lambda_{c,t}$ is county x quarter FE (additional to bank x quarter FE);
• Sample restricted to firms with more than one loan.

Produces results R8 (Table XI, p. 271).

Spec (7): GSIB surcharges - DiD reduced form and 2SLS (Tables XII-XIII, pp. 273-278)

$$y_{t,c} = \beta_0 + \beta_1 \cdot (\text{NOG2015}_c \times \text{Post}_t) + \Gamma_0 X_l + \Gamma_2 Z_{f,t} + \delta_{b,t} + \gamma_{b,c} + \alpha_{i,t} + u_{t,c} \tag{7}$$

• $\text{NOG2015}_c$ is the number of GSIBs in county $c$ in 2015;
• $\text{Post}_t$ equals one for 2016 and later;
• $\gamma_{b,c}$ is bank x county FE;
• $y_{t,c}$ is in turns: number of banks, log loan volume, interest rate, PD, or markup.
• Sample period: 2014Q4-2019Q4 except column (1) of Table XII which uses 2015Q1-2019Q4 for the annual bank-count series.
• In the 2SLS version (Table XIII, p. 278), $\text{NOG2015}_c \times \text{Post}_t$ is used as an instrument for the annual number of banks in the county.

Produces results R9.

Datasets used

Dataset	Role in paper	Wiki page
Federal Reserve Y-14Q (Schedule H.1)	Confidential supervisory loan-level data: interest rates, PD, LGD, loan characteristics, firm financials, 2014Q4-2019Q4, 21,924 loans from 23 BHCs	FR Y-14Q (confidential)
FDIC Summary of Deposits	Alternative county-level bank count (branches), corroboration of main measure	FDIC Summary of Deposits
Bureau of Labor Statistics (BLS)	County-level wage and financial-industry wages	BLS
U.S. Census Bureau	County-level population estimates	Census
Zillow	County-level residential rent (robustness)	no page yet

Sample: 21,924 new corporate loans (private, nonsyndicated borrowers) originated 2014Q4-2019Q4 by 23 U.S. bank holding companies. Median firm assets $23.6 mm, median revenue $46 mm; median interest rate 3.66% (~150 bp credit spread).

When to read the full paper

Read the source at doi.org/10.1111/jofi.70011 if you are: replicating or extending the markup methodology (orthogonalizing interest rates to bank internal risk assessments); studying adverse selection identification in loan markets; using the GSIB surcharge as an instrument for bank presence; or auditing a specific coefficient. The locators above point to the exact table or figure.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(1), February 2026, pp. 239–284. DOI: 10.1111/jofi.70011. © 2025 American Finance Association. Paywalled; Wiley VOR licence (no CC grant). The Federal Reserve co-author’s contribution carries a U.S. Government public-domain notice under 17 U.S.C. §105, which does not extend to the full article.

This distillation was extracted by an LLM on 2026-05-31 and is not human-verified or independently reproduced. Extract-only: no PDF is hosted here.

Beyhaghi, Mehdi, Cesare Fracassi, and Gregory Weitzner. “Adverse Selection in Corporate Loan Markets.” The Journal of Finance 81, no. 1 (February 2026): 239–284. DOI: 10.1111/jofi.70011.