### 1. Introduction

### 2. Literature Review

### 3. Data

### 3.1 Base Sample and Variables

*t*.

*ESG score*(

*E*,

*S*, and

*G score*) represents the fund-level ESG score (environmental, social, and governance score).

*TNA*is the total net asset value of fund

_{i,t}*i*at the end of month

*t*, and

*r*is the fund return at month

_{i,t}*t*.

*Flow*is winsorized at the 0.05% and 99.5% levels. To measure a fund’s performance, we use both raw fund returns and alpha.

*Return*is the average monthly return during the 12-month period, ending at the end of the month.

*Alpha*is estimated based on CAPM, Fama and French’s (1993) three-factor model, or Carhart’s (1997) four-factor model, depending on the model specification.4) Taking Carhart’s (1997) four-factor model as an example,

*Alpha*is calculated as follows:

*r*is the fund’s return in month

_{i,t}*t*,

*r*is the risk-free rate,

_{f , t}*r*is the value-weighted market return, and

_{mkt,t}*SMB*, and

_{t}, HML_{t}*UMD*are the size, value, and momentum factors,5) respectively. For each month

_{t}*t*, we estimate the factor loadings over the prior 36-month rolling window. Next, we compute the monthly abnormal returns using the estimated factor loadings.

*Alpha*is the average abnormal return in the 12-month period ending at the end of the month.

*Rsquare*is estimated from regression model (2); it captures the proportion of the fund’s return explained by Carhart’s (1997) four factors. A lower

*Rsquare*may indicate a greater degree of active management (Amihud and Goyenko, 2013).

*LnTNA*is defined as the natural logarithm of fund TNA in month

*t*.

*LnAge*is defined as the natural logarithm of the number of months observed since the fund’s inception.

*Volatility*is the standard deviation of the monthly fund returns over the previous 12 months.

*Turnover*is the maximum aggregated sales or aggregated purchases of securities divided by the average 12-month fund TNA.

*Expense*is defined as total operating expenses divided by total NAV at the end of the previous month.

*LnNumStock*is the total number of stocks in a fund portfolio.

### 3.2 ESG and non-ESG Funds

##### <Table 1>

### 4. Empirical Results

### 4.1 ESG Score and Fund Characteristics

##### (3)

*ESG score*is the ESG (E, S, and G) score for fund

_{i,t}*i*in month

*t*.

*D*(

*ESG*)

*is a dummy variable equal to one if fund*

_{i}*i*is an ESG fund, and zero otherwise. The control variables include return, flow, size, age, return volatility, turnover, R-square, expenses, and the number of stocks.8) The regressions include the year dummy; the standard errors are adjusted for clustering at the fund level.

##### <Table 2>

##### <Table 3>

### 4.2 Fund Flow-return Relationship

##### (4)

*Flow*is the flow of fund

_{i,t}*i*in month

*t*.

*D*(

*ESG*)

*is a dummy variable equal to one if fund*

_{i}*i*is an ESG fund, and zero otherwise.

*Return*

_{i,t-1}is the average monthly return of fund

*i*over the months

*t*

_{−}12 to

*t*

_{−}1, and

*R*

^{+}and

*R*

^{−}are indicator variables equal to one if

*Return*

_{i,t-1}is non-negative or negative, respectively.

*Controls*

_{i,t-1}is a vector of control variables, including flow, size, age, return volatility, turnover, R-square, expenses, and the number of stocks. The regressions include the year dummy; the standard errors are adjusted for clustering at the fund level.

*R*

^{+}×

*Return*is 1.032 (

*t*-statistic = 4.18) and statistically significant, implying that fund flow is sensitive to positive past returns. However, fund flows are not significantly responsive to negative past returns. When we replace raw returns with alphas based on CAPM, Fama and French’s (1993) three-factor model, and Carhart’s (1997) four-factor model, we find that fund flows are positively associated with both positive and negative past performance. Second, in all specifications, we find that the flow-performance sensitivity is not statistically different from that of conventional funds. For example, in the first column, the coefficients of

*R*

^{+}×

*Return*×

*D*(

*ESG*) and

*R*

^{-}×

*Return*×

*D*(

*ESG*) are 0.187 and -0.050, respectively, but both are statistically insignificant.

##### <Table 4>

*D*(

*ESG*)

_{i,t-1}

*, which is a dummy variable equal to one if an ESG fund has a higher ESG score than the median value for each month*

^{High}*t*-1, or zero otherwise. The first column reports that the coefficient of

*R*

^{+}×

*Return*×

*D*(

*ESG*)

*is -0.781 (*

^{High}*t*-statistic = -1.92) and significant at the 10% level. The results are robust to using individual E and S scores in the second and third columns, respectively. These findings imply that the flow-performance relationship may depend on the level of ESG attributes. ESG funds with higher scores significantly weaken the flow-performance sensitivity.

##### <Table 5>

### 4.3 Fund Performance

##### <Table 6>

*Alpha*is the average abnormal return estimated from CAPM, Fama and French’s (1993) three-factor model, and Carhart’s (1997) four-factor model over the previous 12 months.

_{i,t}*ESG score*

_{i,t-1}indicates the fund ESG score for fund

*i*in month

*t*-1.

*Controls*

_{i,t-1}are the same as in Equation (3). The regressions include the year dummy; the standard errors are adjusted for clustering at the fund level.

*ESG*score is -0.154 (

*t*-statistic = -4.35), which is statistically significant at the 1% level. The coefficients are also statistically significant when using alphas based on Fama and French’s (1993) three-factor model and Carhart’s (1997) four-factor model. However, this is not the case for the matched conventional funds, as the coefficient of is statistically insignificant under all specifications. These findings are consistent with the notion that the ESG screening process may constrain the investment universe, thus decreasing future performance (El Ghoul and Karoui, 2017; Renneboog et al., 2008, 2011). Unlike non-ESG funds, the coefficients of

*LnTNA*and

*LnAge*are significantly negative. These results suggest that larger and older ESG funds are more likely to undergo intensive screening and thus significantly underperform with regard to the matched conventional funds. Moreover, the coefficient of

*LnNumStock*is significantly positive, further supporting the notion that ESG screens narrow the investment universe and decrease future performance. Panel B further reports the results of ESG funds based on the ESG sub-category using the Carhart alpha. The coefficients of

*E score*and

*S score*are negative and statistically significant at the 1% level. However, the coefficient result of the

*G score*is not statistically significant, suggesting that the screening intensity of governance factor in the case of ESG fund is relatively low compared to the environmental or social factors.

##### <Table 7>

*Alpha*is the average abnormal return estimated from Carhart’s (1997) four-factor model over the previous 12 months.

_{i,t}*D*(

*ESG*)

*and*

_{i}*Controls*

_{i,t-1}are defined using Equation (3). Column (1) in <Table 8>, based on the CAPM alpha, shows that fund flows are positively related to future fund performance for all sample funds (Muñoz, 2019; Yoo and Kim, 2012). However, we find no evidence to support that ESG fund investors have better selection skills than non-ESG fund investors. As shown in Column (2), the coefficient of the interaction term between

*Flow*and

*D*(

*ESG*) is insignificant. In Columns (3) to (6), the results are robust with the alphas based on Fama and French’s (1993) three-factor model and Carhart’s (1997) four-factor model. These results suggest that ESG fund investors’ fund-selection skills are not significantly different from those of non-ESG fund investors.