Losing Control: Griffin, Nini & Smith (2026)

What this is. The paper’s core results, the model it builds on, and the method it contributes with the defining equations: enough to know what it found and how, without reading all 42 pages. To replicate or extend it, read the full source at the original (paywalled).

TL;DR

Using hand-collected SEC filings (10-K/10-Q, 1997-2019) merged with Dealscan and Compustat, the paper extends the violation sample of Nini, Smith, and Sufi (2012) from 1997-2008 through 2019 and adds a structural decomposition framework. It documents that the annual share of U.S. public firms reporting a financial covenant violation fell roughly 70%, from about 18% at the 2001 peak to around 5% by 2012 and below that thereafter. A structural model of optimal covenant design, cast as a medical-diagnostic analogy (true positives, false positives, true negatives, false negatives), decomposes the trend. The dominant driver is a collapse in false-positive violations: lenders set looser covenants, reducing nuisance violations for nondistressed borrowers. The corporate distress rate also fell. The true-positive rate (distressed firms that violate) stayed near 75% through 2011 and only declined modestly after the global financial crisis. Observable loan-market changes (larger borrowers, more investment-grade firms, universal-bank arrangers, the shift from balance-sheet to cash-flow covenants) explain about two-thirds of the overall drop; the remaining third reflects an unexplained shift in lender preferences post-2012 consistent with heightened investor sentiment.

Core results

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

#	Result	Locator	Magnitude
R1	Covenant violation rate fell roughly 70% from 1997 to 2019	Figure 1, p. 378	Peak ~18% in 2001; ~5% by 2012; stable at ~5% through 2019; new-violation rate fell from ~9% to ~2% over same period
R2	Loan covenants became fewer and looser over 1997-2019	Figure 2, p. 380	Average covenants per package: ~2.75 (late 1990s) to ~1.5 (2016); tightest-covenant slack ~3x larger at end of sample vs. late 1990s; ex ante covenant strictness (Murfin measure) fell roughly half
R3	More than half of violations are false positives (waived with no consequential change)	Figure 3, p. 385	True positives 2.5%; false positives 2.9%; false negatives 1.1%; true negatives 93.6% of firm-year observations
R4	False negatives cost lenders 7-11 pps in creditor recovery rates at bankruptcy	Table I, p. 387	No-Violation coefficient: -11.4*** (col. 1), -10.1*** (col. 2), -9.8*** (col. 3), -6.7** (col. 4, with Bank Debt Share); N = 403 corporate defaults from Moody’s URD
R5	FPR fell from ~6% to under 0.5% (a 90% drop), explaining 61% of total violation decline	Table II, Figure 5, pp. 389-391	Period 1 (1997-2003) FPR 5.3%; Period 3 (2012-2019) FPR 0.5%; FPR drop explains 4.6 pps of total 7.6 pp decline
R6	Distress rate decline explains 25% and TPR decline explains 14% of the total drop	Table II Panel B, p. 390-391	Distress rate contributes -1.9 pps; TPR decline (from ~72% to ~52%) contributes -1.1 pps over full period
R7	Preference parameter R nearly doubled then nearly quintupled across periods in structural estimates (roughly tenfold overall), while covenant technology improved modestly	Table III, p. 393	R: 3.10 (1997-2003) to 6.06 (2004-2011) to 30.29 (2012-2019); technology parameter $\mu_D$: 2.21 to 2.68 to 2.65
R8	Observable loan-market changes (borrower size, rating, lender type, covenant type) explain ~52% of FPR decline and ~69% of TPR decline	Table V, p. 402; Table VI, p. 404	Observable characteristics explain 5.1 pp of the 7.6 pp total violation decline (~two-thirds); borrower size and speculative-grade rating are the largest contributors
R9	Unexplained post-2012 drop in TPR is consistent with heightened investor sentiment, not deterioration in covenant technology	Table VII, Figure 10, pp. 405-406	Absent characteristic changes, R rises ~80% (from 3.10 to 5.69) vs. ~880% raw; counterfactual violation rate 4.0% vs. realized 1.4% in Period 3

Overall (paper’s conclusion). The dramatic decline in covenant violations is best attributed to a compositional shift in the public borrower population toward larger, rated firms with transactional lenders who prefer fewer costly renegotiations, with a modest but real residual reflecting looser lender preferences post-GFC. The decline does not primarily signal a deterioration in lenders’ ability to monitor distressed borrowers.

Theory / model

The paper develops a model of optimal financial covenant thresholds as a binary-classification problem (Section II, pp. 381-383). Borrowers are either distressed ($D = 1$) or nondistressed ($D = 0$). The lender observes a financial metric $r$ (e.g., a coverage or leverage ratio) correlated with but not perfectly identifying $D$. A covenant sets a contractual threshold $t$; a violation occurs when $r > t$ (for covenants written so that a higher ratio signals distress). Distressed and nondistressed firms have distributions over $r$:

$$\Pr(r < t \mid D = 1) = F_D(t) \qquad \text{(distressed CDF)}$$ $$\Pr(r < t \mid D = 0) = F_{ND}(t) \qquad \text{(nondistressed CDF)}$$

The false negative rate (FNR) and false positive rate (FPR) follow:

$$\text{FNR}(t) = \Pr(r < t \mid D = 1) = F_D(t)$$ $$\text{FPR}(t) = \Pr(r > t \mid D = 0) = 1 - F_{ND}(t)$$

Because $\text{FNR}(t)$ is increasing in $t$ and $\text{FPR}(t)$ is decreasing in $t$, there is a trade-off: tighter thresholds catch more truly distressed firms (lower FNR, higher $\text{TPR} = 1 - \text{FNR}$) at the cost of more nuisance violations for healthy firms (higher FPR). The ROC curve traces this trade-off; better covenant “technology” shifts the ROC curve further from the 45-degree diagonal, allowing a lower FPR at any given TPR (Figure 4, p. 385).

The optimal threshold minimizes total expected costs of both error types (equation (1), p. 382):

$$\min_t \; (1 - \rho) \cdot \text{FPR}(t) \cdot C_{\text{FP}} \;+\; \rho \cdot \text{FNR}(t) \cdot C_{\text{FN}}$$

where $\rho$ is the unconditional probability of distress and $C_{\text{FP}}$, $C_{\text{FN}}$ are the costs of false positives and false negatives, respectively. The first-order condition identifies the optimal threshold $t^*$:

$$\frac{1 - \rho}{\rho} \cdot \frac{C_{\text{FP}}}{C_{\text{FN}}} = \frac{f_D(t^*)}{f_{ND}(t^*)} \tag{1}$$

The left side is the preference parameter $R = \frac{1-\rho}{\rho} \cdot \frac{C_{\text{FP}}}{C_{\text{FN}}}$, the ratio of expected costs of false positives to false negatives. The right side is the likelihood ratio for the relative probability of violation for a distressed vs. nondistressed borrower at $t^*$. Thus:

• A higher $R$ (more costly false positives relative to false negatives) raises $t^*$, loosening the covenant and reducing both FPR and TPR.
• Better technology (ROC curve farther from 45 degrees) allows a lower FPR without reducing the TPR.
• Lower distress prevalence $\rho$ also raises $R$, loosening covenants.

One covenant-technology channel is the shift from balance-sheet to cash-flow covenants in loan agreements documented by Demerjian (2011), which the paper treats as a change in how well the metric separates distressed from nondistressed borrowers.

The paper estimates parameters by assuming $r \sim N(\mu_D, 1)$ for distressed firms and $r \sim N(0, 1)$ for nondistressed firms, so $\mu_D$ indexes covenant technology (how well the metric separates the two populations), and $R$ is estimated from the observed FPR and TPR via the ROC curve slope (p. 393). As an ex ante measure of covenant tightness the paper adopts the covenant strictness measure of Murfin (2012) together with its Demerjian-Owens update.

Method

The paper builds on probit-regression, roc-curve-analysis, and blinder-oaxaca-decomposition to estimate and decompose structural parameters from realized violation rates.

Step 1: Structural parameter estimation (Section IV, pp. 392-393). Under the normality assumption, the optimal threshold satisfies $\text{FPR} = 1 - \Phi(t^*)$, so:

$$t^* = \Phi^{-1}(1 - \text{FPR}_t)$$

where $\Phi(\cdot)$ is the standard normal CDF. Given $t^*$, the technology parameter $\mu_D$ is recovered from the TPR:

$$\text{TPR} = 1 - \Phi(t^* - \mu_D) \implies \mu_D = t^* - \Phi^{-1}(1 - \text{TPR})$$

The preference parameter $R$ is then recovered from the likelihood ratio condition of equation (1):

$$R = e^{(t^* - \mu_D / 2) \cdot \mu_D}$$

Standard errors use the delta method, since the parameters are functions of sample proportions with known sampling variances (Table III, p. 393).

Step 2: Violation-rate decomposition (Section III, p. 388). The annual violation rate is decomposed using the identity (equation (2), p. 388):

$$V_t = \rho_t \cdot \text{TPR}_t \;+\; (1 - \rho_t) \cdot \text{FPR}_t \tag{2}$$

• $\rho_t = (\text{FN}_t + \text{TP}_t) / N_t$ is the realized distress rate.
• $\text{TPR}_t = \text{TP}_t / (\text{TP}_t + \text{FN}_t)$.
• $\text{FPR}_t = \text{FP}_t / (\text{FP}_t + \text{TN}_t)$.

The change in violation rate from period $s$ to period $t$ decomposes as:

$$\Delta V_{s,t} = \underbrace{\rho_s \cdot (\Delta\text{TPR})}_{\text{"TPR"}} \;+\; \underbrace{(1 - \rho_s) \cdot (\Delta\text{FPR})}_{\text{"FPR"}} \;+\; \underbrace{(\text{TPR}_t - \text{FPR}_t) \cdot (\Delta\rho)}_{\text{"Distress"}}$$

Step 3: Blinder-Oaxaca decomposition (Section V, pp. 399-402). To separate the portion of the FPR and TPR trends explained by observable characteristics from the unexplained portion, the paper employs the decomposition of Blinder (1973) and Oaxaca (1973). It estimates two probit regressions: one for the FPR (nondistressed firms only) and one for the TPR (distressed firms only). For each, the marginal effects of borrower size, credit rating, lender type, and covenant type are estimated. The Blinder-Oaxaca decomposition of the change in violation rates across periods takes the form (equation (3), p. 402):

$$\bar{V}^t - \bar{V}^s = \underbrace{\bigl[\Phi(\bar{X}^t \hat{\beta}) - \Phi(\bar{X}^s \hat{\beta})\bigr]}_{\text{"Explained"}} \;+\; \underbrace{U}_{\text{"Unexplained"}} \tag{3}$$

• $\Phi$ is the normal CDF.
• $\hat{\beta}$ is the estimated probit coefficient vector from Table IV.
• $X$ is the set of explanatory variables (borrower size, rating, lender type, loan type, covenant type).
• $U$ captures changes in the mapping from $X$ to violation status beyond observable composition shifts. The Yun (2004) method attributes the explained portion to individual characteristics.

Empirical specifications

All main specifications use annual firm-year observations from the Compustat-EDGAR panel (85,876 firm-years) merged with the Dealscan loan sample (17,724 packages) for the FPR/TPR analysis. The Dealscan-to-Compustat merge uses the link file of Chava and Roberts (2008), whose imputed-violation measure the paper also uses as a robustness check.

Creditor recovery rate regression (R4, Table I, p. 387).

$$\text{RecoveryRate}_i = \alpha + \beta \cdot \text{NoViolation}_i + \gamma' X_i + \epsilon_i$$

• $\text{RecoveryRate}_i$: firm-level par value-weighted average recovery rate (Moody’s URD).
• $\text{NoViolation}_i$: indicator equal to 1 if the firm did not report a covenant violation in the year before bankruptcy.
• $X_i$: controls including operating cash flow/assets, debt/assets, interest expense/assets, net worth/assets, current ratio, market-to-book, cash/assets, and (in column 4) bank debt share.
• Specification: OLS, year and industry fixed effects (columns 2-4), standard errors in parentheses.
• Sample: 403 corporate bankruptcies (Moody’s URD, 1997-2020). Winsorized at 1/99%.

Probit regressions for FPR and TPR determinants (R8, Table IV, p. 401).

$$V_i = \Phi\!\left(\alpha + \beta_1 \cdot \text{BorrowerSize}_i + \beta_2 \cdot \text{SpeculativeRating}_i + \beta_3 \cdot \text{InvestmentRating}_i + \beta_4 \cdot \text{UniversalBank}_i + \beta_5 \cdot \text{InstitutionalLoan}_i + \beta_6 \cdot \text{BalanceSheetCovenant}_i + \gamma' \cdot \text{IndustryFE}_i + \epsilon_i\right)$$

• $V_i$: covenant violation indicator (1 = violation).
• Two separate regressions: (i) nondistressed firms only (FPR regression, N = 29,719 full sample; period sub-samples 5,792 / 6,715 / 7,500) and (ii) distressed firms only (TPR regression, N = 1,102 full sample; period sub-samples 968 / … / 763).
• Specification: probit, Fama-French 12-industry fixed effects, standard errors via delta-method for marginal effects. Coefficients reported as estimated marginal effects.
• $\text{BorrowerSize}$: log(total assets, real 2000 dollars). Ratings and lender/covenant-type indicators are as described in Section V.

Blinder-Oaxaca decomposition (R8, Table V, p. 402). Based on probit coefficients from Table IV and period-specific sample means. The explained portion equals the predicted change due to shifts in the distribution of $X$ using a constant coefficient vector; the unexplained portion is the residual. Standard errors via delta method.

Datasets used

Dataset	Role in paper	Wiki page
Compustat (WRDS)	Firm financials, firm-year sample construction (85,876 firm-years, 9,618 firms, 1997-2019)	WRDS (licensed)
SEC EDGAR (10-K and 10-Q filings)	Hand-collected covenant violation disclosures; loan amendment exhibits	EDGAR
Dealscan (WRDS)	Loan package and covenant data; covenant strictness measure; lender characteristics (17,724 packages, 5,258 firms)	DealScan (licensed)
CRSP (WRDS)	Stock price and shares outstanding for sample filters and controls	WRDS (licensed)
Moody’s Ultimate Recovery Database (URD)	Creditor recovery rates for 403 corporate defaults, 1997-2020	Moody’s URD (licensed)
Audit Analytics	Bankruptcy filings cross-check for false-negative identification	no page yet
UCLA-LoPucki Bankruptcy Research Database	Bankruptcy filing dates for false-negative classification	LoPucki BRD

Sample: 85,876 firm-year observations (Compustat-EDGAR); 17,724 loan packages (Dealscan); 403 bankruptcies with recovery data (Moody’s URD).

When to read the full paper

Read the original if you are: replicating the violation-coding procedure (the text-search and manual inspection process from SEC filings); extending the structural covenant-design model; analyzing the Blinder-Oaxaca decomposition in detail; or checking the Internet Appendix robustness results (alternative distress measures, ratio-manipulation tests, loan amendment trends). The locators above point to the exact table. For “what did this paper find,” the table above is sufficient.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(1), February 2026, pp. 371-412. (c) 2025 the American Finance Association. This distillation was extracted by an LLM on 2026-05-31 and is not human-verified or independently reproduced. The paper is paywalled; only the extract (core results, datasets, theory) is reproduced here under fair-use principles.

Griffin, Thomas P., Greg Nini, and David C. Smith. “Losing Control? The Two-Decade Decline in Loan Covenant Violations.” The Journal of Finance 81, no. 1 (February 2026): 371-412. DOI: 10.1111/jofi.70005. Extract-only; no PDF hosted here.