The Stock Market and Bank Risk-Taking: Falato & Scharfstein (2025)

What this is. The paper’s core results, the short-termism mechanism it tests, and the difference-in-differences specifications with their equations: enough to know what it found and how, without reading all 40 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using confidential CAMELS supervisory ratings from the Federal Reserve, the paper shows that banks increase risk after going public relative to a matched control group of banks that filed for an IPO but withdrew it. The baseline difference-in- differences (DD) estimate implies that going public raises the composite CAMELS rating by 0.316 on a 1-5 scale (about half a within-bank standard deviation) and raises the probability of a weak CAMELS rating (3 or above) by 8.8 percentage points, roughly equal to the unconditional sample mean. The increase in risk shows up both in observable balance sheet measures (higher risk-weighted assets, less Tier 1 capital, more volatile liabilities) and in confidential supervisory assessments that investors cannot observe, consistent with the Stein (1989) model of short-termism in which managers boost unobservable hard-to-detect risks to raise short-term earnings and stock prices. The risk increase is larger for banks with higher institutional investor turnover, higher CEO short-term disclosure, and larger equity option grants of shorter duration. Banks that went public before the 2007-2009 financial crisis underperformed significantly during the crisis.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF (page numbers from the journal article).

#	Result	Locator	Magnitude
R1	Going public raises the composite CAMELS rating (higher = riskier) in the IPO sample	Table III Panel A col. (1), p. 3236	DD coefficient = 0.316*** (SE 0.114); ~0.5 within-bank SD
R2	Going public raises the probability of a weak CAMELS rating (3 or above)	Table III Panel A col. (2), p. 3236	DD coefficient = 0.088*** (SE 0.024); equal to unconditional mean probability
R3	2SLS-IV using banking sector stock returns in the two months after announcement as instrument confirms the result	Table III Panel B cols. (1)-(2), p. 3237	2SLS coefficient = 0.343** (SE 0.145) for composite CAMELS; 0.104*** (SE 0.033) for weak CAMELS
R4	Asset risk (CAMELS A rating and STBL loan risk) rises after public transition	Table IV Panel A, p. 3242	A-rating DD = 0.316** (SE 0.167); STBL DD = 0.403*** (SE 0.125) in IPO sample
R5	Financing risk (capital adequacy and liquidity in CAMELS; risk sensitivity) also rises after public transition	Table IV Panel B, p. 3242	C,L-ratings DD = 0.254** (SE 0.115); Risk-rating DD = 0.308*** (SE 0.111) in IPO sample
R6	Observable balance sheet measures confirm risk increase: RWA/A rises and Tier 1 capital falls after IPO	Table V Panels A and B, p. 3244	RWA/A DD = 0.371*** (SE 0.116); Tier 1 capital DD = -0.010*** (SE 0.003); Volatile Liabilities DD = 0.039*** (SE 0.008)
R7	ROE rises 70 bps by quarter +4 but falls 130 bps below pre-transition level by year +4; earnings quality (CAMELS E) deteriorates; discretionary loan loss provisions fall post-IPO	Table VII Panels A and B, pp. 3247-3248	ROE change at t+4 quarters = 0.007** (SE 0.003); at t+16 quarters = -0.013** (SE 0.006); E-rating DD at t+16 = 0.363*** (SE 0.157); LLP/Loans DD = -0.006*** (SE 0.001)
R8	Risk increase is larger for banks with higher institutional investor turnover, more short-term CEO disclosure, higher equity option grant values, and shorter-duration option grants; banks that went public before the crisis underperformed significantly during 2007-2009	Table VIII, p. 3250; Table X Panel B, p. 3253	Triple-DD on CEO short-term disclosure = 0.669** (SE 0.327); institutional investor turnover = 0.071** (SE 0.029); crisis ROE interaction for full sample = -0.028*** (SE 0.002)

Overall (paper’s conclusion). Access to public equity markets causes banks to increase risk. The mechanism is consistent with short-termism: public listing introduces pressure from stock market investors and compensation incentives that lead banks to boost short-run earnings by taking hard-to-observe risks, at the expense of long-run performance. The finding has implications for both compensation regulation and the wisdom of governance reforms that enhance shareholder power, since good governance coupled with stock-market pressure may increase rather than decrease risk in banking.

Theory / model

The paper has no formal structural model. It is organized around the short-termism channel first formalized by Stein (1989). In that model, stock market investors rationally attribute higher current earnings to both a permanent and a transitory shock. Because earnings embed news about long-run value, managers who care about the short-term stock price have incentives to cut hard-to-observe long-run investments and boost short-term earnings, even at the expense of long-run value. In banking, the easiest way to raise short-term earnings is to take more risk: loosen lending standards, increase loan yields, rely on cheaper but less stable wholesale funding. These actions increase current earnings but create future credit and rollover risks.

An alternative behavioral version due to La Porta (1996) holds that investors overextrapolate current earnings, reinforcing the managerial incentive to boost them. In both cases the prediction is the same: banks that place greater weight on short-term stock price performance should take more risk after going public.

In Bolton, Scheinkman & Xiong (2006), equity compensation also leads to short-termism because managers exploit the market’s overvaluation of short-term performance. Earlier cross-sectional work by Kwan (2004) and Nichols, Wahlen & Wieland (2009) finds no significant risk differences between public and private banks, but that work relies on ex-post performance measures rather than ex-ante supervisory CAMELS ratings, which may explain why it misses the effect documented here (p. 3252).

The two key empirical predictions are:

1. Going public raises ex-ante supervisory risk (CAMELS), especially on dimensions that investors cannot easily observe.
2. The risk increase should be larger for banks more subject to short-term pressure (higher institutional investor turnover, higher CEO short-term disclosure, more short-duration equity compensation) and should boost short-run ROE at the cost of long-run underperformance (p. 3225).

Identification strategy. The concern is that IPOs are endogenous: banks may go public in response to growth opportunities that are also correlated with a riskier environment. The paper addresses this by using a difference-in-differences (DD) design in which the control group is banks that announced but then withdrew their IPO filings (following Bernstein (2015) and Seru (2014)). The idea is that both groups intended to go public for the same reasons, so comparing within-bank changes in risk for treated banks to those of control banks differences out the selection concern. The paper verifies that treated and control banks have parallel pre-trends in CAMELS and are balanced on all observable characteristics except size (p. 3233, Table II). As a further check, the paper instruments IPO completion with S&P banking sector index returns in the two months after the announcement (Bernstein (2015) instrument): deals announced when bank stocks are doing poorly are less likely to be completed.

Method

The paper applies three main estimators. It builds on difference-in-differences as the primary causal design, instrumental-variables (2SLS) for robustness, and panel-regression with matching for sensitivity.

Baseline DD estimator (equation 1, p. 3232). For each bank $i$ and year-quarter $t$, the estimating equation is:

$$\text{RISK}_{it} = \beta_1 \times \text{After}_{it} + \beta_2 \times \text{After}_{it} \times \text{Treatment}_i + \gamma \times Z_{it} + \mu_t + \alpha_i + \epsilon_{it} \tag{1}$$

where $\text{RISK}$ is the composite CAMELS rating (1-5) or the weak CAMELS indicator; $\text{After}_{it}$ equals one for all bank-quarters after the IPO announcement date; $\text{Treatment}_i$ equals one for banks that completed the IPO (zero for withdrawn filers); $Z_{it}$ is bank size (log total assets); $\mu_t$ is year-quarter fixed effects; and $\alpha_i$ is bank fixed effects. Standard errors are clustered at the BHC level. The coefficient of interest is $\beta_2$, the difference-in-differences estimate.

2SLS-IV estimator (equations 2 and 3, p. 3238). To address residual selection concerns, the paper instruments deal completion with banking sector stock returns in the two months after the announcement. The second-stage is:

$$\text{RISK}_i^{\text{Post}} = \beta_1 \widehat{\text{CompletedIPO}}_i + \gamma_1 \text{RISK}_i^{\text{Pre}} + \gamma_2 Z_i + \mu_t + \epsilon_i \tag{2}$$

where $\text{RISK}_i^{\text{Post}}$ is the average risk proxy after the announcement and $\text{RISK}_i^{\text{Pre}}$ is the pre-announcement average. The first stage is:

$$\text{CompletedIPO}_i = \beta_2 \cdot S\&P\text{BankReturns}_i + \gamma_3 \text{RISK}_i^{\text{Pre}} + \gamma_4 Z_i + \mu_t + \epsilon_i \tag{3}$$

where $S\&P\text{BankReturns}_i$ is the S&P bank index return in the two months after the announcement. The exclusion restriction is that these short-window returns are uncorrelated with longer-term bank-specific risk decisions.

Triple-DD estimator (cross-sectional heterogeneity, p. 3250). To test whether the risk increase is greater for banks with stronger short-term incentives, the paper estimates:

$$\text{RISK}_{it} = \beta_1 \text{After}_{it} + \beta_2 \text{After}_{it} \times \text{Treatment}_i + \beta_3 \text{After}_{it} \times \text{Treatment}_i \times X_i + \beta_4 \text{After}_{it} \times X_i + \gamma Z_{it} + \gamma_1 \text{After}_{it} \times Z_{it} + \mu_t + \alpha_i + \varepsilon_{it}$$

where $X_i$ is the cumulative density of a short-termism proxy (institutional investor turnover, CEO short-term disclosure, equity option value or duration). The term $\text{After}_{it} \times X_i$ cannot be identified from $\text{After}_{it} \times \text{Treatment}_i \times X_i$ because $X_i$ does not vary within private banks, so it drops out of the estimation.

Empirical specifications

All main regressions use quarterly Call Report and supervisory data for U.S. commercial banks held by BHCs, 1990-2012, restricted to a 10-year pre-crisis window (1997-2006) for baseline tests. Standard errors are clustered at the BHC level. The key specifications and their links to the core results are:

• Baseline DD on composite CAMELS and weak CAMELS (R1, R2). Spec (1) in Table III, Panel A: equation (1) above with bank FE, year-quarter FE, supervisor FE, and log total assets; IPO sample of 406 completed and 122 withdrawn banks; 8,237 bank-quarter observations.

• Subcomponent supervisory ratings (R4, R5). Table IV Panels A and B: same specification (1) applied to the A rating (asset quality), STBL loan risk rating, C and L combined (capital adequacy and liquidity), and the risk sensitivity rating. Identifies which dimensions of the CAMELS deteriorate.

• Balance sheet DD (R6). Table V: equation (1) with RWA/A, residential real estate loans to total loans, Tier 1 capital ratio, and volatile liabilities as outcomes. Confirms risk increase is visible in observable data.

• Robustness via controlling for observables (Table VI). Adds the balance sheet risk measures as controls to the supervisory rating regressions; the treatment effect remains significant, confirming that hidden risk (unobservable to investors) is the source of the CAMELS deterioration.

• Performance dynamics (R7, Table VII). Calendar-time specification: $Y_{t+N} - Y_{t-1} = \beta_1 \text{Treatment}_i + \gamma Z_{it} + \mu_t + \alpha_i + \varepsilon_{it}$ tracking the change in quarterly ROE and the E rating at horizons N = 1, 4, 8, and 16 quarters post-announcement; and DD regressions of discretionary LLP/loans, loan loss provisions to delinquencies, IBES long-term EPS growth forecast, and earnings restatements.

• Triple-DD on mechanism (R8, Table VIII). Interacts the treatment effect with CEO short-term disclosure (Brochet, Loumioti & Serafeim (2015) frequency- of-short-term-horizon-words measure from earnings calls and 10-K MD&A sections), institutional investor turnover, equity option B-S value, and option grant duration.

• M&A robustness (Table III Panel A cols. 3-4). Replicates the main result using the Completed M&As Sample (1,631 banks, 10,312 observations), where treatment is acquisition by a publicly traded BHC and control is other acquisitions that do not change ownership status. Coefficients are smaller (0.098*** for composite CAMELS, M&A sample) but strongly significant.

• Broader cross-section and financial crisis (Table X). OLS and FE regressions of CAMELS on a public BHC dummy in the merged BHC-Commercial Bank Sample restricted to the pre-crisis window (90,733 bank-quarter observations); Public BHC dummy coefficient = 0.089*** (FE, p. 3253). Triple-DD on crisis ROE: After*Treatment*Crisis = -0.028*** (full IPO sample, p. 3253).

Datasets used

Dataset	Role in paper	Wiki page
NIC (National Information Center), Federal Reserve	Confidential CAMELS supervisory ratings (composite and components); BHC ownership and public listing history	no page yet
STBL (Survey of Terms of Business Lending), Federal Reserve	Confidential loan-level risk ratings for C&I loans (1 to 5 scale); 1997-2012; used by DellAriccia, Laeven & Suarez (2017) and others	no page yet
Call Reports (Reports of Condition and Income), FDIC/Federal Reserve	Balance sheet variables: total assets, RWA, Tier 1 capital, deposits, loans, volatile liabilities; 1990-2012	no page yet
SDC New Issues / S&P Capital IQ / SNL Financial Capital Offerings	Lists of completed and withdrawn bank IPO filings; 1990-2012	no page yet
CRSP-FRB Link (New York Fed)	Stock market listing history for BHC public/private status	WRDS / CRSP (licensed)
IBES (Institutional Brokers’ Estimate System)	Equity analysts’ consensus long-term EPS growth forecasts for newly public banks	I/B/E/S (licensed)
Thomson-Reuters Institutional Holdings (13F)	Institutional investor portfolio turnover; 1990-2012	Thomson 13F (s34) (licensed)
Riskmetrics / Capital IQ	Employee stock option grant data (B-S value, duration)	no page yet

Sample: 178,980 bank-quarter observations for 7,166 (3,251) unique banks (BHCs); IPO identification sample 17,754 bank-quarter observations for 528 unique commercial banks (276 BHCs), 1990-2012; baseline tests use the 10-year window 1997-2006 (8,237 observations, Table I p. 3231). CAMELS ratings are quarterly.

When to read the full paper

Use the original if you are: studying bank risk regulation and the role of stock market incentives; replicating the DD design with confidential supervisory data (the Internet Appendix has additional robustness tables and placebo tests); building on the short-termism mechanism in nonbank financial intermediaries; or extending the analysis to international banking systems or post-crisis periods. The locators above point to the exact tables.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6), December 2025, pp. 3223-3261. DOI: 10.1111/jofi.13502. Paywalled; Wiley standard terms-of-use (not CC). This distillation was extracted by an LLM on 2026-06-03 and is not human-verified or independently reproduced.

Falato, Antonio, and David Scharfstein. “The Stock Market and Bank Risk-Taking.” The Journal of Finance 80, no. 6 (December 2025): 3223-3261. DOI: 10.1111/jofi.13502. Extract-only: no redistribution of the verbatim article.