ESG News, Future Cash Flows, and Firm Value: Derrien, Kruger, Landier & Yao (2025)

What this is. The paper’s core results, the valuation model used to decompose ESG shocks into cash-flow and discount-rate components, and the regression specifications behind each finding: enough to know what was found and how, without reading the full 56 pages. To replicate or extend, read the original at doi.org/10.1111/jofi.13498.

TL;DR

Using RepRisk ESG incident data matched to IBES consensus analyst forecasts across 9,737 firms in 49 countries (2008-2019, 81,749 ESG incidents), the paper shows that negative ESG news triggers a significant, approximately parallel downward shift in analyst EPS forecasts over all horizons from one quarter to three years. The revision reflects primarily expected sales declines (anticipated customer withdrawal) rather than higher costs, consistent with the customer-demand channel emphasized in Servaes and Tamayo (2013) and corroborated by retail store evidence in Duan, Li, and Michaely (2024). A dividend discount decomposition following the framework of Hommel, Landier, and Thesmar (2023) shows that forecast revisions can account for essentially all of the negative stock-price reaction to ESG incidents, while implied discount rates do not change significantly. This is consistent with Berk and van Binsbergen (2024), who argue theoretically that ESG divestment has no detectable cost-of-capital effect. The paper uses ESG news events from RepRisk rather than ESG ratings (which suffer from the disagreement documented by Berg, Koelbel, and Rigobon (2022)) because news events provide cleaner identifiable shocks. Glosner (2021) documents that negative ESG shocks predict negative future returns, suggesting underreaction; this paper studies the analyst expectations channel that underlies that pattern. Analysts who downward-adjust EPS forecasts following ESG incidents reduce their forecast errors relative to those who do not, confirming the revisions are rational. The paper also shows that the ESG effect on earnings forecasts persists over longer horizons than effects from other negative events, consistent with the permanent-shock interpretation suggested by Pedersen, Fitzgibbons, and Pomorski (2021) for the cash flow channel of ESG information.

Core results

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

#	Result	Locator	Magnitude
R1	Negative ESG incidents cause significant parallel downward revision in EPS forecasts at all horizons (Q1 through three years); the term structure is approximately flat	Table III Panel A, p. 3512; Figure 2, p. 3521	Q1: -0.142*** (t=-2.09); one-year: -0.130*** (t=-3.08); two-year: -0.148*** (t=-3.76); three-year: -0.157*** (t=-4.18)
R2	ESG incidents cause a significant decline in stock returns and analyst-implied price target revisions of similar magnitude to EPS revisions	Table III Panel A cols. 9-10, p. 3512	PTG: -0.168*** (t=-6.20); Return: -0.177*** (t=-5.08)
R3	Multiple incidents amplify the effect: firms with at least two incidents in months [t-6,t] see EPS declines roughly twice as large as firms with one incident	Table III Panel B, p. 3513	1 incident: -0.001 to -0.119 across horizons; 2+ incidents: -0.113 to -0.277 across horizons
R4	Social incidents have the strongest and most persistent effect on EPS forecasts; environmental incidents are less significant; governance incidents also significant but smaller	Table IV Panels A-C, pp. 3514-3515	S incidents one-year: -0.175*** (t=-4.23); G incidents one-year: -0.150*** (t=-3.13); E incidents one-year: -0.100 (t=-1.70, n.s.)
R5	ESG incidents have a longer-lived term structure than other types of negative corporate events: the three-year impact is 21% higher than the one-year impact, while for other KD negative events the three-year impact is 42% lower than the one-year impact	Figure 2 p. 3521; Table VII p. 3523	Three-year/one-year ratio: 1.21 for ESG vs. 0.58 for average KD events; F-test rejects equal term structures (p < 0.01)
R6	ESG-induced EPS revisions are driven primarily by expected sales declines (not cost increases): sales forecast revisions are consistently negative across all horizons; gross margin revisions are smaller and less significant	Table VIII Panel A, p. 3524-3525	Sales one-year: -0.036*** (t=-3.81); two-year: -0.055*** (t=-4.75); avg. decline ~0.051% per year; gross margin one-year: -0.027** (t=-2.53)
R7	Cash flow changes, not discount rate changes, account for observed firm value declines: a dividend discount decomposition shows the EPS-forecast-implied value change covers the stock return; implied discount rates do not change significantly	Table IX p. 3529	At [t,t+3]: market return -0.30% (t=-1.84), forecast-implied value change -0.41% (t=-2.16); discount rate change -0.01% (t=-0.11, n.s.)
R8	Effect is stronger for smaller firms and stronger (though not significantly so) for B2C industries with high advertising intensity	Table XI p. 3539; Table XII p. 3541	Large-firm interaction coefficient 0.670*** (t=5.39) in EPS regressions, implying small-firm effect roughly 0.67 p.p. larger; B2C interaction negative and economically meaningful at one- and two-year horizons
R9	Realized firm earnings and sales decline after ESG incidents: net income decreases by 8.8% to 11.8% and sales by 1.2% to 4.2% in the year(s) following incidents, confirming analysts are correct	Table XIII Panels A-B, pp. 3543-3544	Earnings [t-1 to t]: -0.088*** (t=-4.79); [t-1 to t+1]: -0.118*** (t=-4.79); Sales [t-1 to t+1]: -0.026*** (t=-6.66)
R10	Analysts who downward-adjust EPS forecasts after ESG incidents reduce their forecast errors: interaction of downward adjustment and ESG incident is negative and significant for annual and two/three-year horizons	Table XIV p. 3547	One-year horizon: -0.002** (t=-2.84); two-year: -0.003*** (t=-3.56); three-year: -0.004*** (t=-3.98)

Overall (paper’s conclusion). Negative ESG news generates a permanent downward shift in analyst earnings expectations driven primarily by anticipated customer withdrawal (reduced future sales), not by higher costs. This cash flow effect can quantitatively account for most of the negative stock-price response to ESG incidents, while no significant change in cost of capital is detected. The downward revisions are rational: realized earnings and sales drop after ESG incidents, and analysts who revise downward make more accurate forecasts.

Theory / model

The paper has no structural economic model. The theoretical framework is a dividend discount decomposition to separate cash-flow from discount-rate effects of ESG shocks on firm value.

Hypotheses tested. ESG information can affect firm value through two channels: (1) a cash flow channel, where ESG incidents signal lower future earnings because customers avoid firms with poor ESG profiles, or because the firm cannot instantaneously adjust its production technology; and (2) a discount rate channel, where divestment by ESG-conscious investors raises the cost of capital.

Gordon growth-formula pass. As a first pass (Section IV.A, p. 3527), the equity value of firm $i$ at time $t$ follows Gordon’s formula for a growing perpetuity:

$$PV_{it} = \frac{b_i F_t\text{EPS}_{i,t+1}}{r_{it} - g_{it}} \tag{3}$$

where $b_i$ is the payout ratio, $F_t\text{EPS}_{i,t+1}$ is the one-year earnings forecast, $r_{it}$ is the discount rate, and $g_{it}$ is the expected earnings growth rate. The theoretical firm-level return induced by an ESG information shock is

$$\frac{\Delta PV_{it}}{PV_{it}} = \frac{\Delta F_t\text{EPS}_{i,t+1}}{F_t\text{EPS}_{i,t+1}} - \frac{\Delta r_{it} - \Delta g}{r_{it} - g_{it}}$$

Because Table III shows that the ESG impact on LTG is economically and statistically insignificant (column (8)), the growth term drops out and changes in earnings forecasts should equal changes in firm value. The similarity between the EPS revision coefficient and the stock return coefficient (Table III Panel A, cols. (5)-(7) vs. (10)) confirms this.

Discounted dividends decomposition. The more formal approach (Section IV.B, pp. 3527-3528) uses the present value of near-term earnings payouts:

$$\frac{PV_{it}(r_{it})}{b_i} = \frac{F_t\text{EPS}_{i,t+1}}{(1+r_{it})^{\theta_{it}}} + \frac{F_t\text{EPS}_{i,t+2}}{(1+r_{it})^{\theta_{it}+1}} + \frac{F_t\text{EPS}_{i,t+3}}{(1+r_{it})^{\theta_{it}+2}} + \frac{1}{(1+r_{it})^{\theta_{it}+2}} \cdot \frac{(1+g_t)F_t\text{EPS}_{i,t+3}}{r_{it}-g_t} \tag{4}$$

where $\theta_{it}$ is the fraction of the fiscal year remaining, $b_i$ is the rolling-average industry payout ratio, and $g_t$ is the expected long-run nominal GDP growth from macro forecasters. The implied discount rate $r_{it}$ is the solution to

$$PV_{it}(r_{it}) = P_{it} \tag{5}$$

where $P_{it}$ is the observed stock price. The authors compute the forecast- implied value change when EPS forecasts are updated at each post-event window, holding $r_{it}$ fixed, and compare it to the actual market return and to changes in the implied discount rate (Table IX, p. 3529).

Method

The paper applies a standard panel fixed-effects framework augmented with an event-study design for the valuation decomposition. It does not propose a new method. The approach builds on panel-regression and event-study.

Baseline analyst reaction regression. For each forecast horizon $h$, equation (1) (p. 3509) is estimated:

$$\frac{\Delta F_t\text{EPS}_{i,t+h}}{\text{abs}(F_{t-1}\text{EPS}_{i,t+h})} = \alpha + \beta \, \mathbf{1}[\text{ESG incidents in } [t-6,t]] + \gamma_{\text{Country}\times\text{Industry}\times t} + \sigma_i + \epsilon_{i,t} \tag{1}$$

The dependent variable is the month-over-month change in consensus EPS forecast scaled by the absolute value of the prior month’s consensus forecast. The main independent variable is an indicator equal to one if RepRisk reports at least one ESG incident in the six months prior to month $t$. The specification includes firm fixed effects ($\sigma_i$) and industry-by-country- by-month fixed effects ($\gamma_{\text{Country}\times\text{Industry}\times t}$). Standard errors are double-clustered at the firm and month levels.

Mechanism regressions. The same specification as equation (1) is estimated replacing the EPS forecast change with the change in consensus sales forecasts ($\frac{\Delta F_t\text{Sales}_{i,t+h}}{F_{t-1}\text{Sales}_{i,t+h}}$) and the change in gross margin forecasts ($\frac{\Delta F_t\text{GrossMargin}_{i,t+h}}{F_{t-1}\text{GrossMargin}_{i,t+h}}$) to separate the sales from the cost channel (Table VIII, p. 3524).

Term structure comparison. To compare ESG incidents with other negative corporate events, equation (2) (p. 3521) is estimated pooling horizons and testing whether the slope of the term structure differs:

$$\frac{\Delta F_t\text{EPS}_{i,t+h}}{\text{abs}(F_{t-1}\text{EPS}_{i,t+h})} = \alpha + \beta \, \mathbf{1}[\text{ESG incidents in } [t-6,t]] + \eta \, \mathbf{1}[\text{KD Negative Events in } [t-6,t]] + \gamma_{\text{Country}\times\text{Industry}\times t} + \sigma_i + \epsilon_{i,t} \tag{2}$$

Event-study for the valuation decomposition. Equation (6) (p. 3528) estimates how the forecast-implied value change, actual return, and implied discount rate evolve over months $s = 0, 1, \ldots, 6$ following an ESG event month:

$$y_{t,t+s} = \alpha + \beta \, \mathbf{1}[\text{ESG incidents in month } t] + \gamma_{\text{Country}\times\text{Industry}\times t} + \text{Controls} + \epsilon_{i,t} \tag{6}$$

Controls include firm size and book-to-market quintile dummies. Standard errors are double-clustered at the firm and month level.

Realized fundamentals regression. To test whether analysts are correct, equation (7) (p. 3538) uses annual data:

$$\frac{Y_{i,t+h} - Y_{i,t-1}}{Y_{i,t-1}} = \alpha + \beta \, \mathbf{1}[\text{ESG incidents between year } t-1 \text{ and } t] + \gamma_{\text{Country}\times\text{Industry}\times t} + \sigma_i + \epsilon_{i,t} \tag{7}$$

where $Y_{i,t}$ denotes realized annual earnings, sales, or gross margin.

Analyst accuracy regression. Equation (8) (p. 3545) uses the analyst-firm panel to compare forecast accuracy for analysts who do versus do not downward- adjust following ESG incidents:

$$\frac{|FEPS_{i,e,j,t} - EPS_{i,e}| - |FEPS_{i,e,j,t-1} - EPS_{i,e}|}{|EPS_{i,e}|} = \alpha + \eta \, \text{DownwardAdj}_{i,e,j,t} + \beta \, \text{DownwardAdj}_{i,e,j,t} \times \mathbf{1}[\text{ESG incidents of firm } i \text{ in } [t-6,t]] + \gamma_{i,e,t} + \epsilon_{i,e,j,t} \tag{8}$$

where $FEPS_{i,e,j,t}$ is analyst $j$‘s EPS forecast for firm $i$ earnings announcement $e$ in month $t$, and $\gamma_{i,e,t}$ indicates firm-by-earnings- announcement-by-month fixed effects.

Empirical specifications

All regressions use panel data at the firm-month level (or firm-year for the realized-fundamentals tests). The headline specifications are:

• EPS/Sales/GrossMargin forecast regressions (R1, R3, R4, R5, R6, R8): Equation (1) with firm FE and industry-by-country-by-month FE, double- clustered standard errors, horizon $h$ = Q1, Q2, Q3, Q4, one-year, two-year, three-year. Sample: 2008-2019, global, 9,737 firms in 49 countries. EPS observations: 2,976,889; sales: 2,831,931; gross margin: 1,442,110. The indicator for ESG incidents is cumulated over months $[t-6,t]$ (the main specification; Internet Appendix Tables IA.III-IV show robustness to $[t-3,t]$ and $[t-9,t]$). The ESG incident variable has 10.44% of firm-months with at least one incident (6.57% exactly one, 3.87% at least two).

• Term structure test (R5): Equation (2) pools one-, two-, and three-year horizons; the F-test in columns (4)-(5) of Table VII (p. 3523) rejects equal slopes for ESG vs. average KD events (p < 0.01).

• Valuation decomposition event study (R7): Equation (6), US firms only (needed for the payout-ratio and growth rate computation). Table IX (p. 3529) reports cumulative event-window coefficients for windows $[t,t]$ through $[t,t+6]$. Standard errors double-clustered by firm and month.

• Realized outcomes (R9): Equation (7) at the firm-year level, annual data, firm FE and country-by-industry-by-year FE. Dependent variable is percentage change in earnings, sales, or gross margin over one- and two-year windows.

• Analyst accuracy (R10): Equation (8), analyst-firm panel. Firm-by- earnings-announcement-by-month FE. Observations: 2.6M (Q1) to 3.2M (three years). Results are negative and significant for annual and longer horizons.

Datasets used

Dataset	Role in paper	Wiki page
RepRisk ESG incident data	Main independent variable: daily negative ESG incidents at firm level, 2007-2019, 28 ESG issue categories, novelty/severity/reach scores	RepRisk (licensed)
IBES consensus analyst forecasts	Dependent variable: EPS, sales, gross margin, LTG, and price target consensus forecasts at firm-month level, quarterly and annual horizons	I/B/E/S (licensed)
CRSP / Compustat (via WRDS)	Stock returns (daily), firm fundamentals (annual), book-to-market, market cap, payout ratios	WRDS (licensed)
Capital IQ Key Developments	Comparison non-ESG corporate events (153 types); identify 33 types with significant negative EPS impact	No page yet
Refinitiv (Asset4), Morningstar Sustainalytics, MSCI ESG scores	Validation that RepRisk incidents correlate with major ESG ratings (Appendix)	No page yet

Sample: January 2008 to December 2019 (monthly). Final sample: 744,858 unique firm-month observations; 9,737 firms in 49 countries; 81,749 ESG incidents. EPS forecasts: 2,976,889 observations; sales: 2,831,931; LTG: 253,735; PTG: 688,899.

When to read the full paper

Use the original if you are: studying the cash-flow versus discount-rate channel for ESG-related firm value changes; working with RepRisk data and need to understand its term structure and heterogeneity properties; building models of ESG-driven analyst expectations; or extending the analysis to additional horizons, industries, or event types beyond the 33 KD categories examined. The tables above provide exact locators for each result.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6), December 2025. This distillation was extracted by an LLM on 2026-06-03 and is not human-verified or independently reproduced. The paper is paywalled; only extracts are reproduced here.

Derrien, Francois, Philipp Kruger, Augustin Landier, and Tianhao Yao. “ESG News, Future Cash Flows, and Firm Value.” The Journal of Finance 80, no. 6 (December 2025): 3499-3554. DOI: 10.1111/jofi.13498. Published by Wiley on behalf of the American Finance Association. All rights reserved. Extract-only reproduction.