Do Active Sustainable Equity Funds Outperform Their Passive Peers? Evidence from the COVID-19 Pandemic

Abstract

Sustainable investing has grown rapidly, but it remains unclear whether actively managed sustainable funds outperform passive ones. This study compares the performance of high-sustainable active U.S. equity mutual funds and their index peers from September 2018 to April 2022, dividing the period into pre-crash, crash, and post-crash phases around the COVID-19 market downturn. On average, both active and index funds underperform, with the sharpest losses occurring during the crash. High-sustainable funds outperform low-sustainable ones, particularly during the crash. However, high-sustainable active funds do not outperform their passive counterparts in any period. These results suggest that active management does not offer greater downside protection and raise questions about the higher fees typically charged by actively managed sustainable funds.

1. Introduction

Sustainable investing has grown rapidly in recent years1, driven by rising awareness of environmental, social, and governance (ESG) risks and a growing desire among investors to align portfolios with long-term societal goals. Mutual funds with explicit sustainability mandates have attracted billions in inflows; however, relatively few studies have systematically assessed their performance or directly compared active and passive sustainable strategies2.

The U.S. equity mutual fund market provides an important setting for this analysis. U.S. equity funds constitute the largest segment of the global mutual fund industry, with trillions of dollars in assets under management. Over the past decade, index funds have grown rapidly and now account for nearly half of equity fund assets, intensifying the debate over whether investors benefit from paying higher fees for active management. Sustainable funds, both active and passive, have expanded significantly within this landscape, reflecting strong investor demand but also raising questions about performance, resilience, and costs.

The motivation for this study stems from the ongoing debate over the value of active management. While prior work has shown that some sustainable active funds performed relatively well during the COVID-19 market crash (e.g., Pastor & Vorsatz, 2020), there has been little systematic comparison with sustainable index funds. If active managers truly provide downside protection, this advantage should be observable relative to passive sustainable strategies, not only compared to the market as a whole.

The objective of this paper is therefore to evaluate whether actively managed sustainable funds deliver superior performance compared to their passive counterparts. Using Morningstar’s Sustainability Globe Ratings, we examine U.S. equity mutual funds between September 2018 and April 2022, covering the pre-crash, crash, and recovery phases around COVID-19. We employ standard factor models and control for fund characteristics to test whether active sustainable funds outperform index-based sustainable funds.

Funds in our sample underperform on average, generating monthly returns between −0.37% and −0.25% over the full sample period. Losses are most pronounced during the COVID market crash (February–March 2020), when returns drop to between −2.01% and −1.20%. Their performance improves after the crash, but they still underperform (with returns between −0.44% and−0.29%).

When we separate active from passive funds, we find that overall performance is largely driven by the active funds. They generate returns between −0.38% and −0.26% per month over the whole period, with steeper losses during the crash, with returns between −2.05% and −1.24% per month. Index funds also incur losses in our sample, but to a lesser extent, with returns between −0.29% and −0.15% per month for the entire period, and between −1.55% and −0.79% during the crash. Like the active funds, their performance improves after the crash. They still underperform (with returns between −0.23% and −0.19%) but by less compared to the active funds (with returns between −0.46% and −0.30%).

Next, we examine the factors contributing to the fund performance and find that high-sustainable funds outperform low-sustainable funds. They generate additional returns between 0.11% and 0.33% per month over the whole sample period. This outperformance increases significantly to between 0.43% and 0.98% per month during the crash. These results are similar to those of Pastor and Vorsatz (2020)3, who find that most active funds perform poorly during the COVID-19 crash, except for the active sustainable funds. These also support Moskowitz (2000) and Glode (2011), who argue that investors may be willing to accept lower average returns in exchange for the potential insurance-like benefits that active management can provide during market downturns.

We examine this further by testing whether active sustainable funds outperform their passive (index) sustainable counterparts. Using a combined sample of active and index funds, we find that active sustainable funds do not outperform sustainable index funds in any period—pre-crash, crash, or post-crash. We then divide the sample into high-sustainable and low-sustainable funds and find no evidence that high-sustainable active funds outperform their high-sustainable index peers. Likewise, low-sustainable active funds do not outperform low-sustainable index funds. These results add more insights into the conclusion in Pastor and Vorsatz (2020) that active sustainable funds outperform during the crash and, by extension, support Moskowitz (2000) and Glode (2011). Our findings suggest that even when active sustainable funds perform well, they do not outperform comparable sustainable index funds.

Moskowitz (2000) and Glode (2011) argue that investors may be willing to accept lower average returns in exchange for downside protection that active management can provide. If true, this rationale would offer a compelling justification for the continued existence of the active management industry despite its aggregate underperformance. Pastor and Vorsatz (2020) evaluate this proposition in the context of the COVID-19 market crash—a period of extreme volatility and uncertainty. They find that while most active funds perform poorly, a subset of sustainable active funds deliver relatively strong returns, suggesting that such funds may offer advantages during crises. In this study, we revisit and extend this line of inquiry using a longer and more comprehensive dataset covering the period from September 2018 to April 2022.

This paper contributes to the literature on sustainable investing by providing the first comprehensive comparison of actively managed and index-based sustainable mutual funds over the COVID-19 period, using a dataset spanning September 2018 to April 2022. This longer horizon allows us to evaluate performance across the pre-crash, crash, and recovery phases, extending beyond the shorter windows in prior studies. Leveraging Morningstar’s Sustainability Globe Ratings, we directly compare active and passive strategies within high- and low-sustainability categories. Our results show that while high-sustainability funds outperform low-sustainability funds, active management does not deliver additional value relative to passive peers. This challenges the view that active funds offer distinctive downside protection and informs the broader debate on the economic value of active management in ESG investing.

In conclusion, we extend prior evidence on sustainable fund performance by showing that the benefits of sustainability are not driven by active management. High-sustainability funds outperform low-sustainability ones, particularly during the crash, but this advantage accrues equally to passive funds. Actively managed sustainable funds do not outperform index-based strategies in any period, suggesting that investors can achieve sustainability goals more cost-effectively through passive vehicles. By disentangling sustainability effects from managerial style, our findings highlight the limits of active management in ESG investing and underscore the importance of cost considerations in portfolio design.

The remainder of the paper is organized as follows. Section 2 surveys the literature; Section 3 develops the hypothesis; Section 4 describes the data and methodology used in our study; Section 5 presents the empirical results; Section 6 discusses robustness tests, and Section 7 concludes.

2. Literature Review

Research on sustainable mutual funds has expanded considerably in recent years, with growing attention to their performance, risk characteristics, and resilience during periods of market stress. Early studies established mixed evidence on whether ESG integration enhances returns (Renneboog et al., 2008; Statman & Glushkov, 2009). More recent work has focused on fund performance during crises, methodological advances in ESG measurement, and the comparative value of active versus passive management.

Sustainable fund performance: Several studies show that ESG-oriented funds have performed competitively with traditional funds. Fang and Parida (2022), Nofsinger and Varma (2014), Dong et al. (2019), and Albuquerque et al. (2020) document that sustainable funds may provide downside protection during turbulent periods. More recently, Ceccarelli et al. (2024) show that the portfolios of low-carbon funds have less exposure to climate change news risks and tend to deliver higher returns when the climate risk factor materializes.

Active versus passive strategies: Prior literature offers evidence that active funds lag passive benchmarks net of fees (Jensen, 1968; Elton et al., 1993; Malkiel, 1995; Gruber, 1996; Carhart, 1997; Wermers, 2000; Pastor & Stambaugh, 2002). Pastor and Vorsatz (2020) show that some active ESG funds outperformed during the COVID-19 crash. However, no prior work has examined whether sustainable active funds outperform sustainable passive funds.

Methodological approaches and ESG measures: Recent work emphasizes the importance of how sustainability is measured. Morningstar’s Sustainability Globe Ratings and Low Carbon Designation have been used in studies such as Hartzmark and Sussman (2019) and more recent work by Fang and Parida (2024), highlighting the role of ESG ratings in shaping investor flows and fund performance. Yet concerns remain about consistency and comparability across rating providers (Berg et al., 2022).

Research gaps: While prior studies document the resilience of ESG funds and explore whether active funds add value, few provide a direct comparison of active versus passive sustainable funds over different market phases. Most analyses focus on short crisis windows or on active funds only, leaving the broader picture incomplete. This gap motivates our study, which examines U.S. equity mutual funds from 2018 to 2022, using Morningstar sustainability ratings to compare the performance of active and passive sustainable funds across the pre-crash, crash, and recovery periods of the COVID-19 pandemic.

3. Hypothesis Development

Building on prior literature and the theoretical framework, we develop two testable hypotheses. Recent evidence (e.g., Ceccarelli et al., 2024; Pastor & Vorsatz, 2020) indicates that sustainable funds may provide downside protection during crises, particularly for firms with stronger ESG profiles. This suggests that funds with higher sustainability ratings should outperform their lower-rated counterparts when markets are under stress:

H1: 

High-sustainability funds outperform low-sustainability funds during the COVID-19 market crash.

Active equity mutual funds generally underperform passive benchmarks once fees are accounted for. The persistence of such a large underperforming industry is puzzling, given the wide availability of lower-cost passive alternatives. One explanation is that investors may accept this underperformance because active funds deliver superior returns in periods that matter most, such as recessions or market downturns (Moskowitz, 2000; Glode, 2011). This motivates a direct test of whether actively managed sustainable funds outperform their passive counterparts during the COVID-19 crash:

H2: 

Actively managed sustainable funds outperform passive sustainable funds during the COVID-19 market crash.

4. Data and Methodology

We collect mutual fund returns, characteristics, categories, and ratings from Morningstar Direct and obtain factor return data from the Kenneth R. French Data Library4. Our sample runs from September 2018 through April 20225, including 2565 U.S. equity mutual funds, of which 2397 are actively managed and 168 are passively managed, resulting in 44,910 fund-month observations.

We include only equity funds, following Pastor and Vorsatz (2020). In 2019, there were USD 21.3 trillion in U.S. mutual fund assets and 53% were in equity funds.6 In 2023, out of the USD 25.5 trillion net assets in U.S. mutual funds, 52% were held by equity funds.7 Given their scale and central role in capital markets, equity funds serve as a natural setting for our analysis.

For funds with multiple share classes, we aggregate total net assets (TNAs) across all share classes. Observations with TNAs below USD 15 million8 are excluded to mitigate the impact of small funds. We compute TNA-weighted averages for key variables, including the expense ratio, turnover ratio, and cash holdings. Fund age is calculated based on the launch date of the oldest share class.

In March 2016, Morningstar launched its first sustainability ratings for mutual funds, offering a standardized measure to help investors assess the environmental, social, and governance (ESG) performance of their fund holdings. The ratings cover more than 20,000 mutual funds and are calculated at the share-class level.

Sustainalytics assigns sustainability scores to individual companies based on their exposure to and management of ESG issues. Each month, Morningstar aggregates these scores by computing a weighted average of a fund’s portfolio holdings, resulting in a sustainability score for each share class. Funds are then ranked within their respective Morningstar Global Category peer groups. Based on their sustainability scores, share classes receive a globe rating on a five-point scale:

• The top 10% receive a rating of five globes and are labeled “High”
• The next 22.5% receive four globes (“Above Average”)
• The middle 35% receive three globes (“Average”)
• The next 22.5% receive two globes (“Below Average”)
• The bottom 10% receive one globe (“Low”)

The introduction of the globe rating offers investors a reliable and transparent tool to evaluate how well funds align with ESG principles. Morningstar describes that the ratings are intended to provide “a reliable, objective way to evaluate how investments are meeting environmental, social, and governance challenges,” helping investors “put their money where their values are.”

To assess fund performance, we calculate risk-adjusted returns (alphas) using three standard asset pricing models: the CAPM (Sharpe, 1964; Lintner, 1965), the Fama–French three-factor model (Fama & French, 1993; hereafter FF3), and the Carhart four-factor model (Carhart, 1997; hereafter Carhart4). Each month, we estimate factor exposures using the prior 12 months of return data and apply these exposures to compute fund-specific alphas (see Equations (1)–(3)).

$$R_{j,t}={\alpha }_j+{\beta }_{1,j}{\mathit{MKTRF}}_t+{\epsilon }_{j,t}$$

(1)

$$R_{j,t}={\alpha }_j+{\beta }_{1,j}{\mathit{MKTRF}}_t+{\beta }_{2,j}{\mathit{SMB}}_t+{\beta }_{3,j}{\mathit{HML}}_t+{\epsilon }_{j,t}$$

(2)

$$R_{j,t}={\alpha }_j+{\beta }_{1,j}{\mathit{MKTRF}}_t+{\beta }_{2,j}{\mathit{SMB}}_t+{\beta }_{3,j}{\mathit{HML}}_t+{\beta }_{4,j}{\mathit{UMD}}_t+{\epsilon }_{j,t}$$

(3)

Regression analyses begin in September 2018, which marks the availability of Morningstar’s Sustainability (Globe) Rating data. The sample is divided into three periods to reflect market conditions around the COVID-19 crash:

• Pre-Crash: September 2018 to January 2020
• Crash: February to March 20209
• Post-Crash: April 2020 to April 2022

We define funds with four or five globes in sustainability rating as high-sustainable funds (High Sustainability = 1) and funds with one or two globes in sustainability rating as low-sustainable funds (HS = 0). Equation (5) is used to evaluate the performance of high-sustainable vs. low-sustainable funds.

$${\alpha }_{j,t+1}={\mathit{Constant}}_j+{\beta }_{1,j}\times {\mathit{High}\mathit{Sustainability}}_{j,t}+{\mathit{Controls}}_{j,t}+{\epsilon }_{j,t+1}$$

(4)

To assess whether high-sustainable active funds outperform, we estimate regressions based on Equation (6) using a combined sample of active and index funds. The variable Active is a binary indicator equal to one for actively managed funds and zero otherwise.

$${\alpha }_{j,t+1}={\mathit{Constant}}_j+{\beta }_{1,j}\times {\mathit{High}\mathit{Sustainability}}_{j,t}+{\beta }_{2,j}\times {\mathit{Active}}_{j,t+1}+{\beta }_{3,j}\times {\mathit{High}\mathit{Sustainability}}_{j,t}\times {\mathit{Active}}_{j,t+1}+{\mathit{Controls}}_{j,t}+{\epsilon }_{j,t+1}$$

(5)

We then estimate Equation (7) separately for high-sustainable and low-sustainable funds.

$${\alpha }_{j,t+1}={\mathit{Constant}}_j+{\beta }_{1,j}\times {\mathit{Active}}_{j,t+1}+{\mathit{Controls}}_{j,t}+{\epsilon }_{j,t+1}$$

(6)

Control variables include changes in the Morningstar Star10 rating (See Fang & Parida, 2024; Del Guercio & Tkac, 2008; Ceccarelli et al., 2024)11, log-transformed TNAs and fund age (in months), fund expense ratio, turnover ratio, net cash position, and a dummy variable indicating if the fund has a front load.

Table A1. Variable Definitions.

Variables	Definitions
Monthly CAPM α	A fund’s monthly risk-adjusted return estimated using the CAPM.
Monthly FF3 α	A fund’s monthly risk-adjusted return estimated using the Fama-French three-factor model.
Monthly Carhart α	A fund’s monthly risk-adjusted return estimated using the Carhart four-factor model.
Globe	The Morningstar Sustainability Rating assigned to the fund.
High Sustainability	A dummy variable equal to 1 if the fund has a Mornignstar Sustainabilily/Globe rating of 4 or 5, and 0 if rated 1 or 2.
Star	The fund’s Morningstar Overall Rating.
∆Star	The change in the fund’s Morningstar Star rating.
Total Net Asset (TNA)	The total net assets under management by the fund.
Age	The number of months since the fund’s inception.
Expense Ratio	The fund’s annual expense ratio as a percentage of its total net assets.
Turnover Ratio	The percentage of the fund’s holdings that have been replaced over the past year.
Front Load Dummy	A dummy variable equal to1 if the fund charges a front-end load, and 0 otherwise
Cash Position	The proportion of the fund’s total net assets held in cash.

provides the definitions of the variables. To reduce the impact of extreme observations, all continuous variables are winsorized at the 1st and 99th percentiles. All regressions incorporate a time fixed effect based on year and month, and a style fixed effect based on the Morningstar Global Category. Standard errors are clustered using the classes of a fund defined by CRSP.

Panels A and B of

Table 1. Summary statistics.

Panel A Active Funds
	Mean	P25	Median	P75	Standard Deviation	N
Monthly CAPM α (%)	−0.48	−1.89	−0.30	1.07	2.71	42,098
Monthly Fama–French Three-Factor α (%)	−0.21	−1.48	−0.11	1.11	2.57	42,098
Monthly Carhart Four-Factor α (%)	−0.20	−1.53	−0.09	1.18	2.81	42,098
Globe	3.02	2.00	4.00	4.00	1.36	42,098
High Sustainability	0.50	0.00	1.00	1.00	0.50	42,098
∆Star	0.00	0.00	0.00	0.00	0.39	42,098
TNA ($ billion)	2.54	0.16	0.52	1.86	6.44	42,098
Age (months)	213	102	184	288	146	42,098
Annual Expense Ratio (%)	0.97	0.80	0.99	1.15	0.31	42,098
Annual Turnover Ratio (%)	52	22	39	66	45	42,098
Front Load Dummy	0.44	0.00	0.00	1.00	0.50	42,098
Net Cash Position (%)	1.89	0.36	1.04	2.33	2.62	42,098
Panel B Index Funds
	Mean	P25	Median	P75	Standard Deviation	N
Monthly CAPM α (%)	−0.49	−1.52	−0.19	0.69	2.20	2812
Monthly Fama–French Three-Factor α (%)	−0.14	−0.89	−0.01	0.71	1.86	2812
Monthly Carhart Four-Factor α (%)	−0.19	−0.96	−0.02	0.67	1.99	2812
Globe	2.79	2.00	2.00	4.00	1.15	2812
High Sustainability	0.39	0.00	0.00	1.00	0.49	2812
∆Star	−0.01	0.00	0.00	0.00	0.37	2812
TNA ($ billion)	8.59	0.36	1.37	6.29	15.30	2812
Age (months)	165	103	166	227	78	2812
Annual Expense Ratio (%)	0.38	0.09	0.25	0.51	0.42	2812
Annual Turnover Ratio (%)	36	10	21	38	48	2812
Front Load Dummy	0.22	0.00	0.00	0.00	0.42	2812
Net Cash Position (%)	0.97	0.12	0.43	1.08	1.85	2812

present descriptive statistics for active and index equity mutual funds. Both fund types show negative mean alphas. The average Globe rating is 3.0 for active funds and slightly lower at 2.8 for index funds. Index funds are significantly larger, with an average size of USD 8.6 billion, compared to USD 2.5 billion for active funds. The mean annual expense ratio is higher for active funds at 0.97%, versus 0.38% for index funds. Active funds also have a higher average annual turnover ratio of 52%, compared to 36% for index funds. Additionally, 44% of active funds impose a front load, whereas only 22% of index funds do. Finally, the average net cash position of active funds is roughly double that of index funds.

5. Empirical Analysis

5.1. Fund Performance over Time

Table 2. Fund performance over time.

Panel A All Funds
	Entire Period	Pre-Crash	Crash	Post-Crash
	2018/09–2022/04	2018/09–2020/01	2020/02–2020/03	2020/04–2022/04
Monthly CAPM α (%)	−0.37	−0.30	−2.01	−0.29
Monthly Fama–French Three-Factor α (%)	−0.25	0.10	−1.56	−0.38
Monthly Carhart Four-Factor α (%)	−0.28	0.07	−1.20	−0.44
Panel B Active Funds
	Entire Period	Pre-Crash	Crash	Post-Crash
	2018/09–2022/04	2018/09–2020/01	2020/02–2020/03	2020/04–2022/04
Monthly CAPM α (%)	−0.38	−0.30	−2.05	−0.30
Monthly Fama–French Three-Factor α (%)	−0.26	0.10	−1.62	−0.39
Monthly Carhart Four-Factor α (%)	−0.30	0.08	−1.24	−0.46
Panel C Index Funds
	Entire Period	Pre-Crash	Crash	Post-Crash
	2018/09–2022/04	2018/09–2020/01	2020/02–2020/03	2020/04–2022/04
Monthly CAPM α (%)	−0.29	−0.27	−1.55	−0.19
Monthly Fama–French Three-Factor α (%)	−0.15	0.05	−1.00	−0.23
Monthly Carhart Four-Factor α (%)	−0.16	0.01	−0.79	−0.23

reports the mean alphas across different sample periods: Panel A for all funds, Panel B for active funds, and Panel C for index funds. Funds in our sample underperform on average, with alphas between −0.25% and −0.37% per month over the entire period. Performance deteriorates sharply during the COVID−19 market crash (February–March 2020), with alphas dropping to between −2.01% and −1.20%. This underperformance persists post-crash, though at a much lower level (between −0.44% and −0.29%).

Breaking down by fund type, the poor performance is primarily driven by active funds, which return −0.26% to −0.38% per month on average and suffer deeper losses (−2.05% to −1.24%) during the crash. Index funds also post losses, but to a lesser extent, with alphas ranging from −0.15% to −0.29% overall, and −1.55% to −0.79% during the crash. Like active funds, their performance improves post-crash, and although they still underperform by between −0.23% and −0.19%, the shortfall is smaller than that of active funds (−0.46% to −0.30%).

5.2. The Impact of Sustainability on Fund Performance

We examine how sustainability affects fund performance (Hypothesis 1) by estimating regressions based on Equation (5), using all the funds across the entire period as well as each subperiod. The results are reported in

Table 3. The impact of sustainability on fund performance.

	Entire Period			Pre-Crash			Crash			Post-Crash
	2018/09–2022/04			2018/09–2020/01			2020/02–2020/03			2020/04–2022/04
	(1)	(2)	(3)	(4)	(5)	(6)	(4)	(5)	(6)	(7)	(8)	(9)
	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α
High	0.33 ***	0.11 ***	0.12 ***	0.24 ***	−0.00	−0.03	0.98 ***	0.43 ***	0.48 ***	0.30 ***	0.13 ***	0.19 ***
Sustainability	(12.35)	(5.10)	(5.70)	(9.36)	(−0.03)	(−1.00)	(9.99)	(3.60)	(4.10)	(7.54)	(4.11)	(5.65)
∆Star	0.08 ***	0.04	0.02	−0.10 ***	−0.02	−0.04	−0.16	−0.15	−0.23	0.31 ***	0.13 ***	0.12 **
	(2.88)	(1.58)	(0.66)	(−3.03)	(−0.53)	(−1.04)	(−1.15)	(−1.09)	(−1.31)	(6.39)	(2.99)	(2.27)
ln(TNA)	0.01	−0.01	−0.01	0.02 **	0.01	0.02 *	0.04	−0.08 *	−0.05	−0.00	−0.01	−0.03 ***
	(0.88)	(−1.00)	(−1.56)	(2.19)	(1.29)	(1.96)	(1.13)	(−1.86)	(−1.28)	(−0.33)	(−1.38)	(−2.69)
ln(Age)	−0.01	−0.02	0.00	−0.04 **	−0.06 ***	−0.06 ***	−0.04	0.11	0.08	0.03	0.01	0.06 **
	(−0.38)	(−0.98)	(0.15)	(−2.16)	(−2.92)	(−3.24)	(−0.59)	(1.23)	(0.95)	(0.89)	(0.43)	(2.08)
Expense	0.04	−0.04	−0.04	0.02	0.03	0.07	0.14	−0.54 **	−0.58 ***	0.06	−0.05	−0.08
Ratio	(0.83)	(−1.20)	(−1.00)	(0.39)	(0.78)	(1.55)	(0.70)	(−2.35)	(−2.83)	(0.78)	(−0.86)	(−1.29)
Turnover	−0.00	−0.00	−0.00 **	−0.00 ***	−0.00 ***	−0.00 ***	−0.00	−0.00	0.00	0.00 **	0.00 **	−0.00
Ratio	(−0.21)	(−1.43)	(−2.51)	(−3.45)	(−5.06)	(−3.79)	(−0.03)	(−0.92)	(0.92)	(2.30)	(2.48)	(−0.96)
Front Load	−0.03	−0.00	0.01	0.01	0.02	0.03	−0.05	−0.10	0.01	−0.05	−0.01	−0.01
Dummy	(−1.14)	(−0.06)	(0.39)	(0.23)	(0.91)	(1.31)	(−0.51)	(−0.85)	(0.05)	(−1.27)	(−0.22)	(−0.27)
Cash	−0.01 **	−0.01 ***	−0.01 **	−0.00	−0.00	−0.00	−0.04 *	−0.03	−0.02	−0.02 **	−0.01 *	−0.01
Position	(−2.52)	(−3.04)	(−2.29)	(−0.28)	(−0.48)	(−0.51)	(−1.71)	(−1.00)	(−0.75)	(−2.04)	(−1.92)	(−1.24)
Constant	−0.83 ***	−0.12	−0.28 ***	−0.51 ***	0.01	−0.14	−1.21 **	−1.00 *	−0.74	−3.09 ***	−1.51 ***	−1.22 ***
	(−6.28)	(−1.23)	(−2.69)	(−4.18)	(0.09)	(−1.25)	(−2.28)	(−1.67)	(−1.40)	(−12.42)	(−8.34)	(−6.21)
N	44,910	44,910	44,910	21,046	21,046	21,046	2588	2588	2588	21,276	21,276	21,276
Adj. R2	0.199	0.146	0.152	0.179	0.150	0.136	0.468	0.295	0.228	0.172	0.131	0.170

Note: High Sustainability coefficients are in percentages; t-statistics in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10%, respectively.

. Over the full sample period, high-sustainable funds outperform low-sustainable funds by between 0.11% and 0.33% per month, suggesting that sustainability characteristics are positively associated with fund performance on average. This outperformance becomes considerably more pronounced during the COVID-19 market crash, reaching between 0.43% and 0.98% per month. Notably, the performance advantage remains significant in the post-crash period—high-sustainability funds continue to generate superior returns, outperforming low-sustainability funds by between 0.13% and 0.30% per month.

Taken together, these results provide consistent evidence that high-sustainable funds consistently outperform the low-sustainable ones, especially during a crisis.

In addition to our main variables of interest, several control variables display significant coefficients that warrant further discussion. These results, while secondary to our focus on sustainability and active versus passive management, are consistent with prior findings in the mutual fund literature.

Morningstar Star Ratings: Changes in Morningstar star ratings are variably significant across periods. This pattern is consistent with prior work showing that star ratings have a strong influence on investor flows but limited predictive power for future abnormal performance (Del Guercio & Tkac, 2008).

Fund Size (TNA): Larger funds show a positive association with performance in the pre-crash period, consistent with economies of scale, lower transaction costs, and greater diversification (Elton et al., 1993; Chen et al., 2004). In the post-crash period, however, the coefficient turns negative, suggesting that very large funds may suffer from diseconomies of scale and reduced flexibility in responding to rapidly changing markets (Pollet & Wilson, 2008).

Fund Age: The effect of the fund age is mixed, consistent with prior literature (Chevalier & Ellison, 1999; Ferreira et al., 2013).

Expense Ratios and Turnover: The results for expense ratios and turnover also vary across periods. While higher expenses and trading activity may reflect active information acquisition and portfolio rebalancing (Wermers, 2000), they appear to weigh on performance in the post-crash environment, where higher costs are not offset by corresponding benefits.

Front-Load Dummy: front-load fees do not contribute positively to performance, aligning with prior studies showing that such fees largely represent distribution costs rather than investment skill (Del Guercio & Reuter, 2014).

Cash Holdings: The coefficient on cash holdings is generally negative, especially during the post-crash recovery, consistent with the notion that excess liquidity drags on returns when markets rebound (Yan, 2006).

These findings suggest that the role of traditional fund characteristics is not stable over time. Their influence on performance depends on prevailing market conditions, which underscores the importance of controlling for them when assessing the role of sustainability and management style.

5.3. The Impact of Sustainability on Fund Performance: Active vs. Index Funds (Pooled Sample)

Next, we study whether high-sustainable active funds outperform the index funds (Hypothesis 2) by running regressions following Equation (6). We present the results in

Table 4. The impact of sustainability on fund performance: Active vs. index funds (pooled sample).

	Entire Period			Pre-Crash			Crash			Post-Crash
	2018/09–2022/04			2018/09–2020/01			2020/02–2020/03			2020/04–2022/04
	(1)	(2)	(3)	(4)	(5)	(6)	(4)	(5)	(6)	(7)	(8)	(9)
	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α
High	0.48 ***	0.09	0.17 ***	0.28 ***	−0.04	−0.04	0.88 ***	0.53	0.25	0.49 ***	0.12	0.29 **
Sustainability	(5.61)	(1.44)	(2.60)	(4.19)	(−0.71)	(−0.75)	(2.59)	(1.38)	(0.68)	(3.20)	(0.97)	(2.14)
Active Fund	0.06	−0.02	0.07	0.07	−0.01	0.02	−0.26	−0.09	−0.18	−0.00	−0.03	0.11
	(1.02)	(−0.34)	(1.57)	(1.36)	(−0.16)	(0.41)	(−1.05)	(−0.35)	(−0.64)	(−0.03)	(−0.37)	(1.43)
High Sustainability	−0.17 *	0.01	−0.05	−0.05	0.05	0.02	0.12	−0.11	0.24	−0.20	0.02	−0.10
×Active Fund	(−1.85)	(0.22)	(−0.78)	(−0.65)	(0.68)	(0.29)	(0.33)	(−0.26)	(0.62)	(−1.24)	(0.12)	(−0.75)
∆Star	0.08 ***	0.04	0.02	−0.10 ***	−0.02	−0.04	−0.16	−0.15	−0.23	0.31 ***	0.13 ***	0.11 **
	(2.87)	(1.58)	(0.66)	(−3.02)	(−0.53)	(−1.04)	(−1.17)	(−1.09)	(−1.32)	(6.37)	(2.99)	(2.26)
ln(TNA)	0.01	−0.01	−0.01	0.02 **	0.01	0.02 *	0.04	−0.07 *	−0.05	−0.00	−0.01	−0.03 ***
	(0.88)	(−0.98)	(−1.60)	(2.12)	(1.27)	(1.93)	(1.17)	(−1.83)	(−1.27)	(−0.29)	(−1.36)	(−2.71)
ln(Age)	−0.01	−0.02	0.00	−0.04 **	−0.06 ***	−0.06 ***	−0.05	0.11	0.07	0.03	0.01	0.06 **
	(−0.33)	(−0.98)	(0.15)	(−2.19)	(−2.94)	(−3.27)	(−0.61)	(1.22)	(0.92)	(0.92)	(0.43)	(2.10)
Expense	0.04	−0.04	−0.05	−0.00	0.03	0.06	0.20	−0.50 *	−0.56 **	0.08	−0.05	−0.10
Ratio	(0.75)	(−1.01)	(−1.25)	(−0.01)	(0.64)	(1.22)	(0.89)	(−1.89)	(−2.40)	(0.90)	(−0.67)	(−1.41)
Turnover	−0.00	−0.00	−0.00 **	−0.00 ***	−0.00 ***	−0.00 ***	0.00	−0.00	0.00	0.00 **	0.00 **	−0.00
Ratio	(−0.25)	(−1.42)	(−2.53)	(−3.46)	(−5.04)	(−3.78)	(0.00)	(−0.91)	(0.93)	(2.28)	(2.48)	(−0.98)
Front Load	−0.03	−0.00	0.01	0.01	0.02	0.03	−0.05	−0.10	0.01	−0.05	−0.01	−0.01
Dummy	(−1.17)	(−0.05)	(0.36)	(0.22)	(0.91)	(1.31)	(−0.49)	(−0.85)	(0.07)	(−1.29)	(−0.21)	(−0.32)
Cash	−0.01 **	−0.01 ***	−0.01 **	−0.00	−0.00	−0.00	−0.04 *	−0.03	−0.02	−0.02 **	−0.01 *	−0.01
Position	(−2.49)	(−3.04)	(−2.30)	(−0.32)	(−0.49)	(−0.53)	(−1.72)	(−1.00)	(−0.76)	(−2.01)	(−1.92)	(−1.24)
Constant	−0.89 ***	−0.11	−0.33 ***	−0.56 ***	0.02	−0.15	−1.04 *	−0.95	−0.61	−3.11 ***	−1.49 ***	−1.30 ***
	(−6.42)	(−1.05)	(−3.05)	(−4.31)	(0.18)	(−1.26)	(−1.89)	(−1.54)	(−1.07)	(−12.03)	(−7.95)	(−6.42)
N	44,910	44,910	44,910	21,046	21,046	21,046	2588	2588	2588	21,276	21,276	21,276
Adj. R2	0.199	0.146	0.152	0.179	0.150	0.136	0.468	0.294	0.227	0.172	0.131	0.170

Note: High Sustainability × Active Fund coefficients are in percentages; t-statistics in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10%, respectively.

. The estimated coefficients on the interaction term High Sustainability × Active are mixed in signs across specifications, but none is positively significant. This indicates that high-sustainable active funds do not outperform their sustainable index counterparts in any period considered.

While sustainability contributes to fund performance in general, our findings suggest that active management does not generate additional value beyond what is already captured by passively managed sustainable index funds. The results indicate that active management fails to deliver greater downside protection and call into question the justification for the higher fees charged by actively managed sustainable funds.

5.4. The Impact of Sustainability on Fund Performance: Active vs. Index Funds (Separate Samples)

Next, we examine the performance differences between active and index funds within high-sustainable and low-sustainable groups by estimating Equation (7).

Table 5. The impact of sustainability on fund performance: Active vs. index funds (separate samples).

Panel A High-Sustainable Funds

Entire Period

Pre-Crash

Crash

Post-Crash

2018/09–2022/04

2018/09–2020/01

2020/02–2020/03

2020/04–2022/04

(1)

(2)

(3)

(4)

(5)

(6)

(4)

(5)

(6)

(7)

(8)

(9)

CAPM α

FF3 α

Carhart4 α

CAPM α

FF3 α

Carhart4 α

CAPM α

FF3 α

Carhart4 α

CAPM α

FF3 α

Carhart4 α

Active Fund

−0.10

−0.05

−0.03

−0.01

−0.01

−0.01

−0.21

−0.50

−0.06

−0.15

−0.03

−0.03

(−1.11)

(−0.77)

(−0.43)

(−0.19)

(−0.25)

(−0.17)

(−0.58)

(−1.17)

(−0.17)

(−0.97)

(−0.21)

(−0.20)

∆Star

0.02

−0.01

−0.04

−0.14 ***

−0.05

−0.08

−0.05

−0.08

−0.29

0.22 ***

0.04

0.03

(0.54)

(−0.28)

(−0.90)

(−3.10)

(−1.04)

(−1.46)

(−0.24)

(−0.37)

(−1.17)

(3.28)

(0.61)

(0.38)

ln(TNA)

0.02

−0.01

−0.00

0.02 *

0.01

0.01

−0.00

−0.12 *

−0.03

0.02

−0.01

−0.01

(1.51)

(−0.61)

(−0.34)

(1.87)

(1.30)

(1.23)

(−0.00)

(−1.92)

(−0.65)

(1.08)

(−0.81)

(−0.69)

ln(Age)

−0.01

−0.03

−0.01

−0.01

−0.03

−0.03

−0.19 *

−0.11

0.03

0.00

−0.01

0.01

(−0.48)

(−1.45)

(−0.42)

(−0.43)

(−1.16)

(−1.06)

(−1.76)

(−0.88)

(0.28)

(0.07)

(−0.33)

(0.27)

Expense

0.10

0.04

0.04

0.07

0.10

0.11

0.38

0.02

−0.29

0.12

−0.01

0.03

Ratio

(1.27)

(0.61)

(0.64)

(1.17)

(1.47)

(1.54)

(1.06)

(0.06)

(−0.83)

(0.96)

(−0.08)

(0.27)

Turnover

−0.00

−0.00

−0.00

−0.00 ***

−0.00 ***

−0.00 ***

−0.00

−0.00

0.00

0.00

0.00

−0.00

Ratio

(−1.62)

(−1.46)

(−1.56)

(−5.75)

(−5.32)

(−3.90)

(−0.76)

(−0.34)

(1.49)

(1.11)

(1.61)

(−0.38)

Front Load

−0.01

0.03

0.00

0.01

0.02

0.03

−0.09

−0.24

−0.13

−0.01

0.07

0.00

Dummy

(−0.43)

(0.87)

(0.11)

(0.33)

(0.63)

(0.98)

(−0.59)

(−1.41)

(−0.77)

(−0.23)

(1.47)

(0.05)

Cash

−0.02 **

−0.01

−0.01

0.01

0.00

0.01

−0.07 *

−0.04

−0.03

−0.03 **

−0.01

−0.01

Position

(−2.46)

(−1.48)

(−0.77)

(0.88)

(0.58)

(0.72)

(−1.87)

(−0.94)

(−0.95)

(−2.52)

(−1.35)

(−0.56)

Constant

−0.43 ***

0.13

−0.10

−0.18

0.24

0.06

0.63

0.78

−0.14

−2.03 ***

−1.33 ***

−1.10 ***

(−2.64)

(0.84)

(−0.59)

(−1.13)

(1.25)

(0.33)

(0.94)

(0.95)

(−0.20)

(−7.41)

(−5.72)

(−4.27)

N

22,208

22,208

22,208

10,713

10,713

10,713

1242

1242

1242

10,253

10,253

10,253

Adj. R2

0.182

0.186

0.203

0.154

0.210

0.185

0.409

0.333

0.228

0.183

0.174

0.234

Panel B Low-Sustainable Funds

Entire Period

Pre-Crash

Crash

Post-Crash

2018/09–2022/04

2018/09–2020/01

2020/02–2020/03

2020/04–2022/04

(1)

(2)

(3)

(4)

(5)

(6)

(4)

(5)

(6)

(7)

(8)

(9)

CAPM α

FF3 α

Carhart4 α

CAPM α

FF3 α

Carhart4 α

CAPM α

FF3 α

Carhart4 α

CAPM α

FF3 α

Carhart4 α

Active Fund

0.08

0.06

0.13 **

0.08

0.03

0.04

−0.05

0.35

0.03

0.01

0.01

0.16 *

(1.22)

(1.21)

(2.56)

(1.36)

(0.53)

(0.82)

(−0.17)

(1.25)

(0.09)

(0.06)

(0.10)

(1.96)

∆Star

0.14 ***

0.11 ***

0.10 **

−0.05

0.04

0.03

−0.28

−0.21

−0.17

0.36 ***

0.22 ***

0.19 ***

(3.22)

(2.73)

(2.10)

(−0.95)

(0.76)

(0.48)

(−1.49)

(−1.13)

(−0.73)

(5.11)

(3.40)

(2.71)

ln(TNA)

0.01

0.00

−0.01

0.02

0.01

0.02 *

0.05

−0.05

−0.08

−0.01

−0.00

−0.03 **

(0.50)

(0.34)

(−0.95)

(1.33)

(0.96)

(1.88)

(1.06)

(−0.99)

(−1.46)

(−0.38)

(−0.07)

(−1.97)

ln(Age)

−0.01

−0.01

0.01

−0.07 **

−0.08 ***

−0.09 ***

0.07

0.30 **

0.14

0.04

0.02

0.09 **

(−0.29)

(−0.46)

(0.38)

(−2.07)

(−2.73)

(−3.08)

(0.68)

(2.57)

(1.24)

(0.85)

(0.56)

(2.14)

Expense

−0.01

−0.10 *

−0.11 *

−0.07

−0.04

0.01

−0.02

−0.96 ***

−0.80 **

0.07

−0.04

−0.14

Ratio

(−0.11)

(−1.95)

(−1.88)

(−0.90)

(−0.62)

(0.15)

(−0.08)

(−2.99)

(−2.54)

(0.62)

(−0.47)

(−1.54)

Turnover

0.00

−0.00

−0.00 *

−0.00

−0.00 **

−0.00

0.00

−0.00

0.00

0.00 *

0.00 *

−0.00

Ratio

(0.72)

(−0.46)

(−1.91)

(−1.13)

(−2.12)

(−1.48)

(0.18)

(−0.87)

(0.15)

(1.72)

(1.78)

(−0.90)

Front Load

−0.04

−0.02

0.02

0.00

0.02

0.03

−0.05

−0.00

0.10

−0.09

−0.07

−0.02

Dummy

(−0.99)

(−0.70)

(0.59)

(0.05)

(0.61)

(0.75)

(−0.36)

(−0.00)

(0.58)

(−1.37)

(−1.36)

(−0.35)

Cash

−0.01

−0.01 **

−0.01 **

−0.01

−0.01

−0.01

−0.02

−0.01

0.01

−0.01

−0.01

−0.02

Position

(−1.57)

(−2.32)

(−2.06)

(−0.81)

(−0.64)

(−0.85)

(−0.49)

(−0.33)

(0.18)

(−0.80)

(−1.08)

(−1.21)

Constant

−1.01 ***

−0.42 ***

−0.57 ***

−0.59 ***

−0.11

−0.28 *

−1.75 *

−1.95 **

−0.89

−4.33 ***

−1.91 ***

−1.56 ***

(−4.94)

(−2.87)

(−3.74)

(−3.07)

(−0.62)

(−1.66)

(−1.93)

(−2.40)

(−1.03)

(−10.64)

(−6.61)

(−4.99)

N

22,702

22,702

22,702

10,333

10,333

10,333

1346

1346

1346

11,023

11,023

11,023

Adj. R2

0.243

0.132

0.129

0.211

0.113

0.113

0.504

0.265

0.238

0.209

0.115

0.133

Note: High Sustainability coefficients are in percentages; t-statistics in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10%, respectively.

presents the results, with Panel A for high-sustainable funds and Panel B for low-sustainable funds.

For the high-sustainability group, the evidence indicates that active funds do not outperform their index counterparts in either the full sample period or any of the subperiods, suggesting that active management does not add value beyond what is already embedded in sustainable index strategies. A similar pattern emerges within the low-sustainability group. Active funds generally fail to deliver superior returns relative to their index peers during all sample periods, including the crash, when active managers are expected to demonstrate greater skill in navigating market turmoil.

6. Robustness Tests

We conduct several robustness checks to validate our findings in Table 3 and Table 4. The results are presented in

Table 6. Robustness tests.

Panel A AUM ≥ USD 30 million
Panel A1
	Entire Period			Pre-Crash			Crash			Post-Crash
	2018/09–2022/04			2018/09–2020/01			2020/02–2020/03			2020/04–2022/04
	(1)	(2)	(3)	(4)	(5)	(6)	(4)	(5)	(6)	(7)	(8)	(9)
	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α
High Sustainability	0.33 ***	0.11 ***	0.13 ***	0.24 ***	−0.00	−0.02	0.99 ***	0.43 ***	0.50 ***	0.31 ***	0.14 ***	0.20 ***
	(12.41)	(5.20)	(5.94)	(9.38)	(−0.06)	(−0.92)	(9.91)	(3.54)	(4.27)	(7.52)	(4.23)	(5.72)
N	43,571	43,571	43,571	20,394	20,394	20,394	2513	2513	2513	20,664	20,664	20,664
Adj. R2	0.199	0.147	0.153	0.179	0.149	0.136	0.468	0.299	0.233	0.172	0.132	0.172
Panel A2
	Entire Period			Pre-Crash			Crash			Post-Crash
	2018/09–2022/04			2018/09–2020/01			2020/02–2020/03			2020/04–2022/04
	(1)	(2)	(3)	(4)	(5)	(6)	(4)	(5)	(6)	(7)	(8)	(9)
	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α
High Sustainability	0.48 ***	0.09	0.17 **	0.28 ***	−0.05	−0.05	0.89 ***	0.54	0.26	0.46 ***	0.10	0.27 *
	(5.45)	(1.35)	(2.47)	(4.18)	(−0.77)	(−0.81)	(2.62)	(1.41)	(0.71)	(2.97)	(0.83)	(1.94)
Active Fund	0.05	−0.01	0.06	0.08	0.00	0.03	−0.26	−0.09	−0.18	−0.02	−0.04	0.08
	(0.91)	(−0.33)	(1.38)	(1.51)	(0.06)	(0.58)	(−1.07)	(−0.35)	(−0.63)	(−0.19)	(−0.54)	(1.07)
High Sustainability	−0.15 *	0.02	−0.04	−0.04	0.05	0.02	0.12	−0.12	0.26	−0.16	0.04	−0.07
×Active Fund	(−1.67)	(0.33)	(−0.57)	(−0.60)	(0.72)	(0.36)	(0.34)	(−0.29)	(0.65)	(−1.01)	(0.29)	(−0.52)
N	43,571	43,571	43,571	20,394	20,394	20,394	2513	2513	2513	20,664	20,664	20,664
Adj. R2	0.199	0.147	0.153	0.179	0.149	0.136	0.468	0.299	0.232	0.172	0.132	0.171
Panel B Control for Star
Panel A1
	Entire Period			Pre-Crash			Crash			Post-Crash
	2018/09–2022/04			2018/09–2020/01			2020/02–2020/03			2020/04–2022/04
	(1)	(2)	(3)	(4)	(5)	(6)	(4)	(5)	(6)	(7)	(8)	(9)
	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α
High Sustainability	0.24 ***	0.05 **	0.07 ***	0.20 ***	−0.03	−0.05 **	0.73 ***	0.42 ***	0.36 ***	0.17 ***	0.03	0.11 ***
	(9.49)	(2.45)	(3.07)	(8.54)	(−1.04)	(−2.15)	(7.29)	(3.43)	(2.94)	(4.06)	(0.96)	(3.06)
N	45,010	45,010	45,010	21,097	21,097	21,097	2588	2588	2588	21,325	21,325	21,325
Adj. R2	0.209	0.151	0.157	0.188	0.154	0.141	0.484	0.296	0.230	0.182	0.138	0.173
Panel A2
	Entire Period			Pre-Crash			Crash			Post-Crash
	2018/09–2022/04			2018/09–2020/01			2020/02–2020/03			2020/04–2022/04
	(1)	(2)	(3)	(4)	(5)	(6)	(4)	(5)	(6)	(7)	(8)	(9)
	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α
High Sustainability	0.40 ***	0.03	0.11 *	0.23 ***	−0.08	−0.09	0.73 **	0.53	0.19	0.36 **	0.02	0.20
	(4.38)	(0.48)	(1.67)	(3.18)	(−1.32)	(−1.43)	(2.35)	(1.37)	(0.52)	(2.17)	(0.13)	(1.48)
Active Fund	0.11 *	0.02	0.10 **	0.11 **	0.02	0.05	−0.12	−0.10	−0.13	0.05	0.01	0.14 *
	(1.87)	(0.38)	(2.34)	(2.00)	(0.31)	(1.00)	(−0.51)	(−0.38)	(−0.46)	(0.49)	(0.15)	(1.86)
High Sustainability	−0.17 *	0.02	−0.05	−0.04	0.06	0.04	0.00	−0.11	0.18	−0.20	0.02	−0.11
×Active Fund	(−1.78)	(0.28)	(−0.74)	(−0.49)	(0.90)	(0.54)	(0.01)	(−0.27)	(0.47)	(−1.19)	(0.12)	(−0.75)
N	45,010	45,010	45,010	21,097	21,097	21,097	2588	2588	2588	21,325	21,325	21,325
Adj. R2	0.209	0.151	0.157	0.188	0.154	0.141	0.483	0.295	0.230	0.182	0.137	0.173
Panel C Crash Period: 2020/02–2020/03
Panel A1
	Entire Period			Pre-Crash			Crash			Post-Crash
	2018/09–2022/04			2018/09–2020/01			2020/02–2020/04			2020/04–2022/04
	(1)	(2)	(3)	(4)	(5)	(6)	(4)	(5)	(6)	(7)	(8)	(9)
	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α
High Sustainability	0.33 ***	0.11 ***	0.12 ***	0.24 ***	−0.00	−0.03	0.94 ***	0.29 ***	0.32 ***	0.27 ***	0.14 ***	0.21 ***
	(12.35)	(5.10)	(5.70)	(9.36)	(−0.03)	(−1.00)	(9.70)	(3.45)	(3.89)	(6.86)	(4.17)	(5.84)
N	44,910	44,910	44,910	21,046	21,046	21,046	3859	3859	3859	20,005	20,005	20,005
Adj. R2	0.199	0.146	0.152	0.179	0.150	0.136	0.387	0.158	0.137	0.159	0.134	0.179
Panel A2
	Entire Period			Pre-Crash			Crash			Post-Crash
	2018/09–2022/04			2018/09–2020/01			2020/02–2020/04			2020/04–2022/04
	(1)	(2)	(3)	(4)	(5)	(6)	(4)	(5)	(6)	(7)	(8)	(9)
	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α	CAPM α	FF3 α	Carhart4 α
High Sustainability	0.48 ***	0.09	0.17 ***	0.28 ***	−0.04	−0.04	0.83 ***	0.56 **	0.37 *	0.44 ***	0.07	0.26 *
	(5.61)	(1.44)	(2.60)	(4.19)	(−0.71)	(−0.75)	(2.68)	(2.20)	(1.65)	(2.97)	(0.55)	(1.80)
Active Fund	0.06	−0.02	0.07	0.07	−0.01	0.02	−0.46 *	−0.12	−0.14	0.06	−0.00	0.14 *
	(1.02)	(−0.34)	(1.57)	(1.36)	(−0.16)	(0.41)	(−1.80)	(−0.64)	(−0.82)	(0.64)	(−0.01)	(1.66)
High Sustainability	−0.17 *	0.01	−0.05	−0.05	0.05	0.02	0.13	−0.28	−0.05	−0.18	0.08	−0.06
×Active Fund	(−1.85)	(0.22)	(−0.78)	(−0.65)	(0.68)	(0.29)	(0.41)	(−1.05)	(−0.21)	(−1.17)	(0.56)	(−0.38)
N	44,910	44,910	44,910	21,046	21,046	21,046	3859	3859	3859	20,005	20,005	20,005
Adj. R2	0.199	0.146	0.152	0.179	0.150	0.136	0.387	0.158	0.136	0.159	0.134	0.179

Note: High Sustainability coefficients are in percentages; t-statistics in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10%, respectively.

.

6.1. Asset Under Management > USD 30 Million

In our main regressions, we restrict the sample to funds with total net assets of at least USD 15 million. As a robustness check, we apply alternative thresholds, and the results remain similar to the ones in Table 3 and Table 4. Panel A reports the findings using a USD 30 million threshold, with A1 and A2 reproducing Table 3 and Table 4, respectively. Over the full sample period, high-sustainable funds outperform low-sustainable funds by between 0.11% and 0.33% per month, as shown in Panel A1. This outperformance becomes more pronounced, to between 0.43% and 0.99% per month during the COVID-19 market crash and remains significant in the post-crash period, between 0.14% and 0.31% per month. In Panel A2, the coefficients on the interaction term High Sustainability × Active are mixed in terms of signs, but none are positively significant, indicating that high-sustainable active funds do not outperform their sustainable index counterparts in any period.

6.2. Controlling for Morningstar Star Ratings

In our baseline models, we control for changes (innovations) in Morningstar star ratings. As an alternative, we follow Pastor and Vorsatz (2020) and control for the level of star ratings instead. The results remain robust, as shown in Panel B. As reported in Panel B1, high-sustainability funds, including both active and passive, generally outperform their low-sustainability counterparts—the outperformance ranges from 5 to 24 basis points over the full sample period, with the effect being most pronounced during market crashes and remaining statistically meaningful in the post-crash period. The insignificant coefficients on the interaction term in Panel B2 suggest that high-sustainable active funds do not outperform their sustainable passive counterparts.

6.3. Alternative Definition for Crash Period

In our main analysis, we define the crash period as February–March 2020 (see Bae et al., 2021), as the market started recovering in April 2020. Pastor and Vorsatz (2020) define the crisis period as the ten-week period between 20 February and 30 April 2020. We perform a robustness test by defining the market crash period as February–April 2020. The results are reported in Panel C echo our conclusions in Table 3 and Table 4—high-sustainability funds outperform their low-sustainability counterparts, but high-sustainable active funds do not outperform their sustainable passive peers.

7. Conclusions

This paper examines the relative performance of active and passive sustainable mutual funds from September 2018 to April 2022, a period of heightened climate risk awareness and market volatility. Consistent with prior studies, we find that high-sustainability funds outperform low-sustainability funds, particularly during the COVID-19 crash. However, this outperformance is not driven by active management. In none of the periods—pre-crash, during the crash, or post-crash—do actively managed sustainable funds outperform their passive peers.

These findings challenge the view that active sustainable funds offer superior protection during market downturns and suggest that passive strategies can provide more cost-effective exposure to sustainability. Our results contribute to the broader debate over the value of active fund management and underscore the importance of distinguishing between management styles within the sustainable investing segment.

This study has some limitations. Our sample is restricted to U.S. equity funds from 2018–2022, and the results may not generalize to other markets, asset classes, or time periods. We also rely on Morningstar Globe Ratings as our primary sustainability measure. Future research could explore larger datasets, international funds, and alternative ESG metrics to further test the robustness of our findings.