Green Window Dressing: Parise & Rubin (2025)

What this is. The paper’s core results, the identification design, and the estimating specifications: enough to know what it found and how, without reading all 34 pages. To replicate or extend it, read the full source at the original.

TL;DR

ESG mutual fund managers inflate their portfolio ESG exposure in the 10 days before mandatory quarterly disclosure, only to reverse those positions afterward. The paper calls this “green window dressing.” Three independent empirical tests all point to the same behavior: (1) fund ESG betas (loadings on the Morningstar U.S. Sustainability Total Return Index) rise by 0.12 in the 10 days before filing and revert to prior levels immediately after; (2) funds earn a negative return gap of -1.4 bps per day before disclosure (underperforming the portfolio they are about to report) but a positive gap of +1.0 bp per day after (outperforming the just-disclosed portfolio); and (3) ESG stocks earn cumulative abnormal returns of +0.20% in the three days before quarter-end filings, which reverse completely afterward. Green window dressing positively affects Morningstar sustainability ratings and attracts net fund flows, particularly from institutional investors. The behavior is absent before March 2016 (when Morningstar introduced sustainability ratings) and is not present for passive ESG vehicles, confirming active management is required.

Core results

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

#	Result	Locator	Magnitude
R1	ESG funds increase their ESG beta by 0.12 in the 10 days before mandatory disclosure, reverting to baseline immediately after	Table II, p. 3564	Pre-disclosure: $\overline{\Delta\beta}_{-}^{ESG}$ = 0.120*** (SE 0.044) for 10-day window; post-disclosure: 0.026 (SE 0.050), not significant
R2	The pre-disclosure ESG beta rise corresponds to a 46% increase over the baseline ESG exposure from the prior month (estimated at 0.26)	Table II footnote, p. 3565	$0.12 / 0.26 \approx 46\%$; market beta falls by 0.107*** (SE 0.039) concurrently
R3	Funds earn a negative pre-disclosure return gap of -1.4 bps/day, then a positive post-disclosure gap of +1.0 bp/day, with post-disclosure gap driven by lower ESG beta on realized vs. counterfactual returns	Table IV, p. 3570	Pre: GAP = -0.014*** (SE 0.003); Post: +0.010*** (SE 0.004); post-disclosure ESG beta of actual returns (0.127) is 0.047*** lower than counterfactual ESG beta (0.174)
R4	ESG stocks generate cumulative abnormal returns of +0.20% in the three days before fund portfolio disclosure, which reverse completely after	Figure 4, p. 3575	CARs peak at roughly +0.20% at event time 0 in the [-3, +3] window; decline to near zero by +3 days; 95% CIs reported
R5	A one-standard-deviation increase in pre-disclosure ESG exposure raises the probability of receiving a five-globe Morningstar rating by 2.1 pp (between funds) and 1.5 pp (within fund)	Table VI, p. 3579	2.1 pp (SE 0.009)** without fund FE; 1.5 pp (SE 0.006)*** with fund FE; reduces probability of one-globe rating by 1.1 pp** (without fund FE)
R6	Green window dressers attract significantly more fund flows: a one-standard-deviation increase in pre-disclosure ESG exposure raises three-month net flows by 0.48 pp, driven entirely by institutional-client funds	Table VII, p. 3580	All funds: 0.482** (SE 0.201); Institutional: 0.653** (SE 0.275); Retail: 0.152 (SE 0.358), not significant
R7	High-fee funds are 6.7 pp more likely and low-fee funds are 5.2 pp less likely to window dress; star and laggard funds are each more likely to window dress than mid-ranked funds	Table V, p. 3577	High fees: 0.067*** (SE 0.021); Low fees: -0.052*** (SE 0.013); Star fund: 0.027** (SE 0.011); Laggard: 0.077*** (SE 0.015); PRI signatory: 0.035** (SE 0.014)
R8	Green window dressing is absent before March 2016 (no Morningstar sustainability ratings) and absent for ESG ETFs and index funds; ESG funds also reduce CO2 (pollution) exposure before disclosure	Internet Appendix Tables IA.I (col. 2-4), p. 3565	Pre-2016 ESG beta change: not significant; ESG index funds: no significant increase; pollution factor beta also declines pre-disclosure (Table IA.X)

Overall (paper’s conclusion). Fund managers use quarterly disclosure as a coordination device: by holding greener portfolios only when portfolios are publicly observable, they can earn higher sustainability ratings and attract more capital while holding less constrained, higher-yielding portfolios between disclosures. Green window dressing does not generate CAPM alpha and effectively delegates the ESG constraint to disclosure dates only.

Theory / model

The paper has no formal theoretical model. It offers an economic rationale based on two incentives for fund managers operating under imperfect monitoring.

The identification relies on the fact that Morningstar sustainability ratings are determined primarily by disclosed quarterly portfolio holdings: a fund’s ESG profile is assessed based on the ESG scores of the stocks it holds at the disclosure date. Because funds only need to disclose once per quarter, the mandate to hold ESG stocks is effectively binding four times a year. This asymmetric observability creates an incentive to hold ESG-aligned stocks precisely when positions are observable and to substitute toward higher-yielding non-ESG assets when they are not. Hartzmark and Sussman (2019) document that investor flows strongly respond to Morningstar sustainability ratings, providing the economic motive.

Prior work yields conflicting results. Muñoz, Ortiz, and Vicente (2022) compare end-of-month to quarter-end holdings and find no window dressing, but the paper shows their approach relies on the invalid assumption that voluntarily disclosed month-end holdings are free of strategic manipulation. Kempf and Osthoff (2008) find no green window dressing in the period before Morningstar sustainability ratings; this paper reconciles that finding by showing that the behavior emerged only after March 2016, when Morningstar introduced its globe ratings.

Null hypothesis (eq. 4, p. 3562). The identifying assumption is that in the absence of green window dressing, ESG betas are stable from the pre-event control window to the pre-event window:

$$H_0: \; \Delta\beta_{-,i,e}^{ESG} \equiv \beta_{-,i,e}^{ESG} - \beta_{0,i,e}^{ESG} = 0, \quad \forall \; i = 1, \ldots, N \text{ and } e = 1, \ldots, E \tag{4}$$

Placebo and falsification evidence. Three falsification tests validate the design: (i) random disclosure dates produce no ESG beta increase (Internet Appendix Table IA.1, col. 1); (ii) the period January 2010 to February 2016, before Morningstar sustainability ratings, shows no significant effect (col. 2); and (iii) passive ESG ETFs and index funds, which do not actively manage portfolios, show no pre-disclosure ESG beta increase (cols. 3-4, p. 3565-3566). Together these confirm the behavior is driven by active management responding to the incentive created by Morningstar ratings.

Method

The method applies three independent identification strategies to a sample of 223 U.S.-domiciled ESG active equity mutual funds over March 2016 to December 2022.

Test 1: Factor-loading comparison around disclosure (Section I.B, pp. 3561-3563). This is the central design. The paper builds on panel-regression by estimating fund-level two-factor regressions on symmetric windows around each mandatory quarterly filing date $t_e$. The pre-disclosure regression (eq. 1, p. 3561):

$$R_{i,t} = \alpha_{-,i,e} + \beta_{-,i,e}^{MKT} MKT_t + \beta_{-,i,e}^{ESG} ESG_t + \varepsilon_{i,t}, \quad t \in [t_e - n, \; t_e - 1] \tag{1}$$

and the post-disclosure regression (eq. 2, p. 3561):

$$R_{i,t} = \alpha_{+,i,e} + \beta_{+,i,e}^{MKT} MKT_t + \beta_{+,i,e}^{ESG} ESG_t + \varepsilon_{i,t}, \quad t \in [t_e + 2, \; t_e + n + 1] \tag{2}$$

where $R_{i,t}$ is the daily return of fund $i$ on day $t$, $MKT_t$ is the Kenneth French daily market return, and $ESG_t$ is the return on the Morningstar U.S. Sustainability Total Return Index. Both regressions exclude the disclosure date itself and the following trading day. The control window is the entire second month of the fiscal quarter, excluding its first and last trading days (eq. 3, p. 3562):

$$R_{i,t} = \alpha_{0,i,e} + \beta_{0,i,e}^{MKT} MKT_t + \beta_{0,i,e}^{ESG} ESG_t + \varepsilon_{i,t}, \quad t \in \mathcal{T}_{0,e} \tag{3}$$

The test statistic is the cross-fund, cross-event average change in ESG beta (eq. 5, p. 3563):

$$\overline{\Delta\hat{\beta}_{-}^{ESG}} \equiv \frac{1}{N \cdot E} \sum_{i=1}^{N} \sum_{e=1}^{E} \Delta\hat{\beta}_{-,i,e}^{ESG} \tag{5}$$

where $\Delta\hat{\beta}_{-,i,e}^{ESG} \equiv \hat{\beta}_{-,i,e}^{ESG} - \hat{\beta}_{0,i,e}^{ESG}$. Inference uses a wild bootstrap (Internet Appendix Section I) to account for residual cross-sectional correlation.

The baseline uses $n = 10$ trading days; robustness uses $n = 5$ and $n = 15$.

Test 2: Return gap (Section II.B, pp. 3569-3572). Building on Kacperczyk, Sialm, and Zheng (2008) and the portfolio-disclosure timing design of Agarwal, Gay, and Ling (2014), the daily return gap for fund $i$ is (eq. 7, p. 3570):

$$GAP_{i,t} = R_{i,t} - \underbrace{\bigl(R_{i,t}^H - EXP_{i,t}\bigr)}_{R_{i,t}^C} \tag{7}$$

where $R_{i,t}$ is the net-of-fees realized fund return, $R_{i,t}^H$ is the return on a hypothetical buy-and-hold portfolio invested in the disclosed quarter-end positions, and $EXP_{i,t}$ are fund fees. A fund that holds its disclosed positions exactly earns $GAP_{i,t} = 0$; negative values indicate pre-disclosure positioning away from what will be reported.

Test 3: ESG stock event study (Section II.C, pp. 3574-3575). The paper estimates cumulative abnormal returns on ESG stocks around the four annual filing dates (March 31, June 30, September 30, December 31). Abnormal returns use a market model estimated on a 100-day window ending 50 days before each event; CARs are computed in the [-3, +3] day window. The equally-weighted portfolio covers all ESG-eligible stocks disclosed by sample funds.

Trading cost estimation (Section II.A, pp. 3566-3569). Trading costs are measured using the Abdi and Ranaldo (2017) “CHL” two-day corrected effective spread (eq. 6, p. 3566):

$$\hat{\kappa}_{two\text{-}day,j,t} = \frac{1}{D_t} \sum_{d=1}^{D_t} \hat{\kappa}_{j,d}, \quad \hat{\kappa}_{j,d} = \sqrt{\max\!\bigl\{4(\text{cls}_{j,d} - \text{mid}_{j,d})(\text{cls}_{j,d} - \text{mid}_{j,d+1}),\, 0\bigr\}} \tag{6}$$

High-ESG stocks have average effective spreads of 0.86% versus 0.98% for non-high-ESG stocks, making them about 12% cheaper to trade.

Empirical specifications

All main results use OLS on the panel of 223 ESG active equity mutual funds over March 2016 to December 2022 (223 funds, 5,793 non-ESG funds available as comparison group; Table I, p. 3560). The identifying variation is temporal (within-fund, across-event), not cross-sectional.

Factor-loading specification (R1, R2, R8). Separate time-series regressions (eqs. 1-3) are estimated per fund per event. The cross-event average change (eq. 5) is the test statistic. Bootstrap inference accounts for cross-sectional correlation. Sample: 4,063 fund-event observations (Panel A, Table II, p. 3564). Standard errors in parentheses from the wild bootstrap.

Return gap specification (R3). OLS of $GAP_{i,t}$ on event-time indicators, separately for the 10-day pre-disclosure window $[-10, -1]$ and post-disclosure window $[2, 11]$. Bootstrapped standard errors. 23,320 fund-day observations. ESG beta comparison: two-factor model on $[2, 11]$ post-disclosure window, comparing betas on realized and counterfactual returns (2,332 observations, Table IV, p. 3570).

Rating specification (R5). Linear probability model regressing $I(\text{Five globes})_{t+2}$ and $I(\text{One globe})_{t+2}$ on standardized $\Delta\hat{\beta}_{-,i,t}^{ESG}$:

• Column (1): time fixed effects only; column (2): time + fund fixed effects.
• Standard errors clustered at fund level. 2,656 observations (Table VI, p. 3579).

Fund characteristics and window dressing propensity (R7). Linear probability model regressing $\text{Window dresser}_{t+1}$ on fund characteristics (fees, past performance decile, size, disclosure frequency, PRI status, retail investor share). Time fixed effects throughout. Standard errors clustered at fund level. 3,519 observations (Table V, p. 3577).

Fund flow specification (R6). OLS of three-month net flows (in %) on standardized $\Delta\hat{\beta}_{-,i}^{ESG}$, fund size, family size, expense ratio, and lagged performance. Time fixed effects. Split by retail vs. institutional clientele (50% retail asset cutoff). 3,795 observations (Table VII, p. 3580).

Datasets used

Dataset	Role in paper	Wiki page
CRSP mutual fund data (returns, portfolio holdings, TNA, turnover)	Fund returns, portfolio holdings, quarterly disclosure events, fund characteristics	WRDS / CRSP (licensed)
Morningstar sustainability ratings and fund identifiers	ESG fund classification, five-globe sustainability rating outcome variable, fund category	No page yet
Morningstar U.S. Sustainability Total Return Index (MSEGUSTU)	ESG factor ($ESG_t$) in two-factor fund-return regressions	No page yet
Trucost CO2 emissions	Pollution factor (robustness: pollution exposure before disclosure, Table IA.X)	Trucost (licensed)
Kenneth French data library	Daily market factor ($MKT_t$), Fama-French factors in robustness tests	Ken French library
SEC EDGAR N-PORT filings	Mandated quarterly portfolio disclosure dates	SEC EDGAR

Sample: 223 ESG active equity mutual funds, March 2016 to December 2022. Comparison group: 5,793 non-ESG U.S. domestic equity mutual funds (Internet Appendix Section III). Portfolio holdings matched with CRSP daily stock returns for 89% of fund assets (median fund, p. 3570 fn.18).

When to read the full paper

Use the original if you are: replicating the bootstrap inference procedure (Internet Appendix Section I); applying trade imputation to recover intra-quarter holdings (Internet Appendix Section II, using the Bongaerts, van Brakel, and van Dijk (2024) methodology); testing for green window dressing in non-ESG funds (Internet Appendix Table IA.XVII); studying the asymmetry in ESG beta changes for small vs. large funds (Section IV.C, Table IA.XII); or studying broader ESG fund performance using the Fama and French (1993) five-factor model (Internet Appendix Table IA.XVI). Locators above point to the exact tables and figures.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6). This distillation was extracted by an LLM on 2026-06-03 and is not human-verified or independently reproduced. The CC BY-NC 4.0 licence permits sharing and adaptation for non-commercial purposes; the verbatim PDF is not hosted here.

Citation. Parise, Gianpaolo, and Mirco Rubin. “Green Window Dressing.” The Journal of Finance 80, no. 6 (December 2025): 3555-3588. DOI: 10.1111/jofi.13499. © 2025 The Author(s). Licensed under CC BY-NC 4.0. This page is an extract by the Institute for Automated Research: core results and specifications summarized; changes were made.