The Value of Bank Lending: Flanagan (2025)

What this is. The paper’s core results, the economic framework it builds on, and the risk-adjustment method it adapts from private equity to bank loans, with the defining equations: enough to know what it found and how, without reading all 45 pages. To replicate or extend it, read the full source at the original.

TL;DR

The paper asks: do banks produce real value through lending, and who captures it? Using a novel dataset of realized cash flows for 8,100+ U.S. syndicated term loans (1992-2014), Flanagan adapts the Gupta and Van Nieuwerburgh (2021) private equity risk-adjustment method to estimate the risk-adjusted profit (RAP) of bank loans relative to public-market benchmarks. Banks earn on average 177 basis points annualized gross risk-adjusted returns, consistent with providing valuable screening and monitoring services. These returns are larger when borrowers face more severe financing frictions and when banks invest more resources in lending services. Bank-level RAP also persists over time, indicating skill heterogeneity. However, once commercial lending expenses (182 bps, primarily staff compensation) are deducted, shareholders receive near-zero net risk-adjusted returns, consistent with competitive equilibrium: the present value of loan cash flows covers lending costs, not shareholder rents.

Core results

Magnitudes and significance are as reported; \* = 10%, \*\* = 5%, \*\*\* = 1%. Locators point into the source PDF (pp. 2017-2061).

#	Result	Locator	Magnitude
R1	Baseline gross RAP: banks earn 177 bps annualized risk-adjusted returns on syndicated loan cash flows	Table II Panel B col (2), p. 2037	RAP = $0.032 per $1 invested; annualized psi = 1.77%*** (SE 0.059)
R2	Higher RAP for financially constrained borrowers: top vs bottom financial-constraint quartile earns 93% higher annualized RAP	Table III, p. 2041	Top-quartile psi = 2.42%***, bottom = 1.26%***; H-L = 1.17%*** (SE 0.22)
R3	Shorter-maturity loans earn 53% higher annualized RAP, consistent with a fixed cost of screening	Table IV, p. 2043	Low-maturity psi = 2.35%***, high-maturity = 1.54%***; H-L = -0.81%*** (SE 0.26)
R4	Loans to high fixed-cost industries earn 44-47% higher RAP, consistent with greater irreversibility requiring more screening	Table IV, p. 2043	High fixed-cost psi = 2.17%***, low = 1.51%***; H-L = 0.66%*** (SE 0.18)
R5	Tighter earnings-based covenants (EBC) earn higher RAP, consistent with monitoring intensity; higher renegotiation probability earns lower RAP (substitution)	Table IV, p. 2043	High EBC tightness psi = 2.44%***, low = 1.66%***; H-L = 0.78%*** (SE 0.25)
R6	Bank-level RAP persists 1-4 years ahead; Dollar Value-Added is a stronger persistence predictor than RAP alone	Table VI, p. 2048	L4.RAP = 0.101*** (SE 0.032); L4.DVA = 0.473*** (SE 0.121); R2 for DVA ~0.30 vs ~0.14 for RAP
R7	Shareholders receive near-zero net risk-adjusted returns after deducting commercial lending expenses of 182 bps	Table VII, p. 2050	Net psi = approx -5 bps, statistically indistinguishable from zero; 113 bps staff comp + 69 bps other expenses
R8	Corporate bond placebo confirms methodology: risk-adjusting corporate bond cash flows yields near-zero RAP, validating the loan estimates	Table VIII Panel B, p. 2053	Corporate bond RAP Ann = 0.015 (SE 0.102), not significant; full-sample risk-free return = 2.77%*** is eliminated by risk adjustment

Overall (paper’s conclusion). Bank loan cash flows have large, economically significant gross risk-adjusted returns that compensate bank employees for their costly screening and monitoring services. The cross-sectional pattern of these returns is consistent with banks providing more valuable intermediation to more financially constrained borrowers. However, shareholders of banks receive approximately zero net risk-adjusted returns from syndicated lending, consistent with competitive provision of lending services and with the model of Philippon (2010). These results help reconcile the empirical literature finding low bank shareholder value (Begenau and Stafford (2019)) with classic theories of banking emphasizing productive lending services (Leland and Pyle (1977), Diamond (1984)).

Theory / model

The paper has no formal structural model but provides an economic framework (Section I.A, pp. 2025-2027) that motivates the empirical tests and the interpretation of RAP.

Cost of capital decomposition. A firm’s cost of borrowing via a bond market is

$$r_{\text{firm,bond}} = r_f + r_{\text{risk}} + \theta_{\text{bond}},$$

where $r_f$ is the risk-free rate, $r_{\text{risk}}$ is the risk premium in a frictionless market, and $\theta_{\text{bond}}$ is the financing friction specific to the bond market. The cost of a bank loan is

$$r_{\text{firm,loan}} = r_f + r_{\text{risk}} + \theta_{\text{loan}} + \psi,$$

where $\theta_{\text{loan}} \leq \theta_{\text{bond}}$ (banks mitigate financing frictions through information production and monitoring) and $\psi \geq 0$ is the bank’s required return for supplying lending services. The incentive compatibility constraint for banks is

$$\psi - \gamma \geq 0,$$

where $\gamma$ is the cost of compensating loan officers and covering other expenses. In competitive equilibrium $\psi = \gamma$, and the borrower takes a loan if the net cost reduction is positive:

$$(\theta_{\text{bond}} - (\theta_{\text{loan}} + \psi)) \geq 0.$$

Total value decomposition. The total value from mitigated financial frictions decomposes into the present value flowing to the lender and the value flowing to the borrower (pp. 2026-2027). Letting $X_t$ be the outstanding principal balance,

$$\underbrace{\sum_{h=1}^{H} \frac{(\theta_{\text{bond}} - \theta_{\text{loan}}) X_{t-1}}{(1 + r_f + r_{\text{risk}})^h}}_{\text{Total Value}} \approx \underbrace{\sum_{h=1}^{H} \frac{r_{\text{firm,loan}} \cdot X_{h-1} + (X_h - X_{h-1})}{(1 + r_f + r_{\text{risk}} + \theta_{\text{loan}})^h} - X_0}_{\text{Present Value to Lender}} + \underbrace{\sum_{h=1}^{H} \frac{(\theta_{\text{bond}} - (\theta_{\text{loan}} + \psi)) X_{h-1}}{(1 + r_f + r_{\text{risk}})^h}}_{\text{Borrower Value}}.$$

Empirically, the present value to the lender corresponds directly to the RAP estimated in the paper, and is a lower bound on the total social value generated. The annualized version of this present value is the parameter $\psi$ estimated throughout.

Key hypotheses. (i) Classic banking theory predicts higher RAP for more financially constrained borrowers, where screening and monitoring provide greater value. Leland and Pyle (1977), Diamond (1984), and Holmstrom and Tirole (1997) all predict banks create value through information production; here, the incentive compatibility condition $\psi - \gamma \geq 0$ ties the bank’s required spread directly to its lending costs. (ii) Risk-shifting theory predicts the opposite. (iii) Competitive equilibrium predicts near-zero net shareholder returns ($\psi = \gamma$). (iv) Persistence in bank-level RAP, in the spirit of Berk and van Binsbergen (2015) for mutual funds, indicates skill heterogeneity in lending services. Indirect evidence from James (1987) and Berger and Udell (1995) shows borrowers benefit from bank lending through higher stock prices and better loan pricing, consistent with the framework.

Method

The method adapts the Gupta and Van Nieuwerburgh (2021) private equity strip-by-strip risk-adjustment (RAP) methodology to bank loan cash flows, with several extensions. It builds on panel-regression and fama-macbeth for factor selection and inference.

Core pricing equation. Let $R^k_{t+h}$ denote the cumulative return on public security $k$ from $t$ to $t+h$. The no-arbitrage pricing equation for the SDF $M_{t,t+h}$ is

$$\text{E}_t[M_{t,t+h} R^k_{t+h}] = 1. \tag{1}$$

Gupta and Van Nieuwerburgh (2021) estimate the regression

$$X^i_{t+h} = a_{t+h} + b_h R^k_{t+h} + e^i_{t+h}, \tag{2}$$

where $X^i_{t+h}$ is the cash flow to loan portfolio $i$ at horizon $h$, normalized to a $1 investment. The key identification assumption is that $b_h R^k_{t+h}$ spans all priced risk in the cash flows, so residuals are orthogonal to the SDF.

Loan benchmark funds. Because loan principal balances amortize (more than 80% repaid within 4 years, Figure 3, p. 2032), the paper instruments public security returns $R^k_{t+h}$ with the loan’s outstanding balance $z^i_t$, defining instrumented returns $\tilde{R}^i_{t+h} = R_{t+h} z^i_t$. Their price satisfies

$$\text{E}_t[M_{t,t+h} \tilde{R}^i_{t+h}] = \text{E}_t[M_{t,t+h} R_{t+h} z^i_t] = z^i_t. \tag{3}$$

Two types of benchmark funds implement this (similar in spirit to the benchmark fund construction in Korteweg and Nagel (2016) for venture capital): a rollover investment benchmark $\bar{F}^{i,k}_{t+h}$ (pays out the change in loan balance and reinvests the rest each period, price = $1 by equation (4)) and a gain investment benchmark $\bar{G}^{i,k}_{t+h}$ (goes long risky asset and short risk-free bond, accumulates compounded returns, winds down with the loan balance, price = $0 by equation (5)). The main regression is then

$$X^i_{t+h} = a_{t+h} + \sum_{k=1}^K \left[ b^k \bar{F}^{i,k}_{t+h} + c^k_h \bar{G}^{i,k}_{t+h} \right] + e^i_{t+h}. \tag{6}$$

RAP estimation. The unconditional mean RAP is

$$\widehat{RAP} = \text{E}[\text{E}_t[\sum_{h=1}^H M_{t,t+h} X^i_{t+h}]] - 1 = \frac{1}{N}\sum_{i=1}^N [\sum_{h=1}^H P^{\$}_{t,h} \hat{a}_{t+h} + \sum_{k=1}^K \hat{b}^k + \sum_{h=1}^H P^{\$}_{t,h} \hat{e}^i_{t+h}] - 1. \tag{8}$$

The annualized risk-adjusted return is

$$\hat{\psi} = \frac{\widehat{RAP}}{\text{WAL}}, \tag{9}$$

where WAL is the weighted-average life of the outstanding loan balance. Inference uses a nonparametric block bootstrap (100 replications) following Driessen, Lin, and Phalippou (2012).

Risk factors. The baseline model uses: a risk-free floating-rate rollover benchmark (analogous to a LIBOR-linked bond), a Term factor (10-year Treasury returns), BBB-rated corporate bond returns, HY-rated corporate bond returns, and CRSP value-weighted stock returns plus the bottom-quintile size portfolio. The model’s $R^2 > 0.99$ (Table II Panel A, p. 2037), indicating benchmark funds span nearly all variation in loan cash flows.

Noninterest expense hedonic regression. To estimate net risk-adjusted returns to shareholders, the paper estimates commercial loan expense ratios using the approach of Hanson et al. (2015). For FRY-9C bank holding companies (1994-2014), the pooled regression is

$$\frac{\text{Expense}_{it}}{\text{Asset}_{it}} = a + \sum_{k=1}^K b^{(k)} \frac{\text{Asset}^{(k)}_{it}}{\text{Asset}_{it}} + \sum_{j=1}^J c^{(j)} \frac{\text{Deposit}^{(j)}_{it}}{\text{Asset}_{it}} + dX_{it} + e_{it}, \tag{10}$$

estimated with Fama-MacBeth (1973) cross-sectional regressions and Newey-West (1) standard errors. The commercial loan expense ratio is the predicted value for a hypothetical bank investing only in commercial loans with wholesale funding.

Empirical specifications

All regressions use quarterly loan cash flows normalized to a $1 investment. The main estimating equation is (6) above, estimated by OLS with nonparametric block bootstrap inference.

Baseline (R1). Equation (6) is estimated on 259,300 quarterly observations of the full loan sample (8,125 loans, 1992Q3-2014Q1). Factors: Rf (floating-rate rollover), Term, BBB, HY, Stock. Risk-adjusted returns are computed via equation (8), annualized by WAL (equation (9)). The model achieves $R^2 = 0.991$ (Table II Panel A), confirming near-complete spanning.

Financial constraints (R2). Loans are sorted into four quartile buckets by the first principal component of four financial-constraint indicators (log firm size with negative sign, log firm age with negative sign, Firm Unrated indicator, Firm Issued Bond indicator with negative sign). Equation (6) is estimated separately for each bucket. Differences in bucket-level annualized RAP test whether banks earn more when borrowers face more severe frictions (Table III, p. 2041). The H-L spread is bootstrapped across the two buckets.

Screening, monitoring, renegotiation (R3-R5). Loans are sorted by: (i) contractual maturity (above/below bottom and top quartile), (ii) average industry fixed costs (SG&A from Compustat, two-digit SIC), (iii) predicted ex ante renegotiation probability (Prob(Reneg)) using a linear probability model on loan characteristics, and (iv) EBC tightness (Murfin (2012) measure, following Kermani and Ma (2020)). Equation (6) is estimated separately for each half and differences are bootstrapped (Table IV, p. 2043).

Bank-level persistence (R6). Bank-level portfolios aggregate individual loan cash flows weighted by lead-lender retention fraction. Equation (7) (the conditional version of equation (6)) yields a time series of bank-level risk-adjusted returns. Panel regressions with quarter-time fixed effects regress these on lags L4, L8, L12, L16 (in quarters), with standard errors clustered by bank (Table VI, p. 2048). Dollar Value-Added is RAP times portfolio size.

Net shareholder returns (R7). Commercial lending expense ratios from equation (10) are subtracted from gross annualized RAP to obtain net shareholder risk-adjusted returns (Table VII, p. 2050; Table IA.XXIV in Internet Appendix for bank-level results).

Placebo validation (R8). Equation (6) is applied to corporate bond cash flows (fixed-rate, maturities up to 8 years, from Mergent FISD with TRACE transaction prices). Near-zero bond RAP validates the spanning assumption (Table VIII, p. 2053).

Datasets used

Dataset	Role in paper	Wiki page
Dealscan (Refinitiv Loan Connector)	Loan originations, amortization schedules, interest rate spreads, covenants, lead lender retention; primary source for loan cash flow construction	DealScan (licensed)
Compustat (via Chava-Roberts 2008 linking file)	Borrower financial characteristics (firm size, age, fixed costs, financial constraints); performance-pricing covenant ratios	WRDS / Compustat (licensed)
CRSP	Stock return factors for benchmark fund construction; value-weighted market return; size quintile portfolio	WRDS / CRSP (licensed)
SEC EDGAR (10-K, 10-Q, 8-K filings, web-scraped)	Loan prepayment and refinancing dates not in Dealscan; identifies 94% of prepayment dates accurately	SEC EDGAR
FRY-9C bank holding company data	Bank-level expense decomposition (noninterest expense, compensation, other); bank financial constraint measures	no page yet
Corporate bankruptcy databases (UCLA, 8-K)	Default identification and recovery rate estimation (industry-by-year LGD from Moody’s Annual Default Report)	no page yet
Mergent FISD / TRACE	Corporate bond cash flows and transaction prices for placebo validation test	no page yet
Federal Reserve / Treasury (yield curve)	Risk-free ZCB term structure for discounting; 3-month T-bill rate as risk-free proxy	no page yet
Moody’s Annual Default Report	Industry-by-year loss-given-default (LGD) estimates for loan recovery rates	no page yet

Sample: 8,125 senior floating-rate term loans to U.S. public borrowers, originated 1992Q3-2014Q1. Security returns data cover 1992Q3-2021Q2. Bank-level tests include 31 banks with at least 40 quarters of data.

When to read the full paper

Read the original if you are: building or testing a risk-adjustment methodology for non-traded private credit instruments; studying the distribution of value added between bank shareholders, employees, and borrowers; extending the cross-sectional tests to other loan types (mortgages, small business); or applying the hedonic expense regression of Hanson et al. (2015) to decompose lending costs. The internet appendix contains extensive robustness tests, the loan-matching algorithm, and the simulation exercise.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(4). This distillation was extracted by an LLM on 2026-06-05 and is not human-verified or independently reproduced. Licensed CC BY-NC 4.0; the verbatim PDF is not hosted here.

Attribution (CC BY-NC 4.0). Flanagan, Thomas. “The Value of Bank Lending.” The Journal of Finance 80, no. 4 (August 2025): 2017-2061. DOI: 10.1111/jofi.13465. © 2025 The Author(s). Licensed under CC BY-NC 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.