國
立 政 治 大 學
‧
N a tio na
l C h en chi U ni ve rs it y
5. Conclusion and Discussions
In this article, I examine the empirical impact of ESG engagement on firm risks.
Using a comprehensive sample of U.S. firms from 2004 to 2018, I find a significantly negative association between firm’s ESG disclosure score and one-year-ahead financial risk, measured by total risk, systematic risk, and idiosyncratic risk. Further, I examine the relationship between ESG engagement and downside risk. Downside risk is measured by NCSKEW, KZ index, WW index, and SA index. It shows that the coefficient on ESG disclosure score is significantly negative when WW index and SA index are the downside risk measures. After controlling our sample to a smaller data which firms engaging environment, society, and governance issues simultaneously, the coefficient on ESG disclosure score becomes significant negative in KZ index regression. It implies that ESG engagement can reduce downside risk more efficiently when firms participating in ESG activities comprehensively. However, it is reported that ESG disclosure and NCSKEW are positively correlated. I argue that only when ESG disclosure score is steadily increasing every year can it reduce stock price crash risk. Besides, when ESG database is more completed in the future, future studies can employ alternative risk measures such as bankruptcy (there are only 6 bankruptcy firms in the sample).
My study sheds light on ESG individual components. Comparing 3 individual components, I conclude that E disclosure score and G disclosure score play a more important role of risk reducing. In this study, I obtain ESG data from Bloomberg ESG database. Rather than rating each item according to its performance, Bloomberg collects public resources and gives ESG score by the firms’ ESG disclosure level. Besides, it shows that Bloomberg ESG disclosure score is highly correlated with size factor. To better control size factor when considering the effect of ESG on risks, future research could use alternative ESG data to examine the relationship between ESG score and firm
‧ 國
立 政 治 大 學
‧
N a tio na
l C h en chi U ni ve rs it y
risk.
Last, the link between ESG disclosure score and stock return is still unclear. I expect that ESG engagement could bring higher stock return due to lower downside risk.
However, I find no evidence to support the claim. I argue that there are two opposite effects contradicting each other. One is “ high risk, high return” effect. Under this structure, higher ESG score will cause lower stock return. The other is the transfer effect of ESG from risk to return. Through this channel, ESG engagement is positively correlated with stock return when firms are less subject to downside risk. The overall effect is uncertain. In this context, future research could separate the two effects and better capture the influence of ESG on stock return.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h en chi U ni ve rs it y
References
Albuquerque, R., Koskinen, Y., & Zhang, C. (2018). Corporate social responsibility and firm risk: Theory and empirical evidence. Management Science, forthcoming.
Allayannis, G., Lel, U., & Miller, D. P. (2012). The use of foreign currency derivatives, corporate governance, and firm value around the world. Journal of International Economics, 87, 65-79.
Attig, N., Boubakri, N., El Ghoul, S., & Guedhami, O. (2016). Firm internationalization and corporate social responsibility. Journal of Business Ethics, 134, 171-197.
Becchetti, L., Ciciretti, R., & Hasan, I. (2015). Corporate social responsibility, stakeholder risk, and idiosyncratic volatility. Journal of Corporate Finance, 35, 297-309.
Benlemlih, M., Amama, S., Qiu, Y., Trojanowski, G. (2018). Environmental and social disclosures and firm risk. Journal of Business Ethics, 152, 613-626.
Boutin-Dufresne, F., & Savaria, P. (2004). Corporate social responsibility and financial risk. The Journal of investing, 13, 57-66.
Brammer, S., Brooks, C., & Pavelin, S. (2006). Corporate social performance and stock returns: UK evidence from disaggregate measures. Financial Management, 35, 97-116.
CFA Institute (2015). Environmental, social, and governance issues in investing: A guide for investment professionals.
Chan, C. Y., Chou, D. W., & Lo, H. C. (2016). Do financial constraints matter when firms engage in CSR? North American Journal of Economics and Finance, 39, 241-259.
Cheng, B., Ioannou, I., & Serafeim, G. (2014). Corporate social responsibility and access to finance. Strategic Management Journal, 35, 1-23.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h en chi U ni ve rs it y
Dimson, E., 1979. Risk measurement when shares are subject to infrequent trading.
Journal of Financial Economics, 7, 197-226.
El Ghoul, S., Guedhami, O., & Kim, Y. (2017). Country-level institutions, firm value, and the role of corporate social responsibility initiatives. Journal of International Business Studies, 48, 360-385.
El Ghoul, S., Guedhami, O., Kwok, C., & Mishra, D. (2011). Does corporate social responsibility affect the cost of capital? Journal of Banking & Finance, 35, 2388-2406.
Fatemi, A., Glaum, M. & Kaiser, S. (2018). ESG performance and firm value: The moderating role of disclosure. Global Finance Journal, 28, 45-64.
Friede, G., Busch, T. & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance &
Investment, 5, 210-233.
Godfrey, P. C. (2005). The relationship between corporate philanthropy and shareholder wealth: A risk management perspective. The Academy of Management Review, 30, 777-798.
Hoepner, A. G. F., Oikonomou, I., Sautner, Z., Starks, L. T., & Zhou, X. Y. (2018).
ESG shareholder engagement and downside risk. AFA 2018 paper.
Hong, H., Kubik, J. D., & Scheinkman J. A. (2012). Financial constraints on corporate goodness. Working paper.
Husted, B. W. (2005). Risk management, real options, and corporate social responsibility. Journal of Business Ethics, 60, 175-183.
Hutton, A. P., Marcus, A. J., & Tehranian, H. (2009). Opaque financial reports, R2, and crash risk. Journal of Financial Economics, 94, 67-86.
Jo, H., & Na, H. (2012). Does CSR reduce firm risk? Evidence from controversial industry sectors. Journal of Business Ethics, 110, 441–456.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h en chi U ni ve rs it y
Kim, Y., Li, H., & Li, S. (2014). Corporate social responsibility and stock price crash risk. Journal of Banking & Finance, 43, 1-13.
Makni, R., Francoeur, C., & Bellavance, F. (2009). Causality between corporate social performance and financial performance: Evidence from Canadian firms. Journal of Business Ethics, 89, 409-422.
Oikonomou, I., Brooks, C., & Pavelin, S. (2012). The impact of corporate social performance on financial risk and utility: A longitudinal analysis. Financial Management, 41, 483-515.
Renneboog, L., Horst, J. T., & Zhang, C. (2008). Socially responsible investments:
Institutional aspects, performance, and investor behavior. Journal of Banking &
Finance, 32, 1723-1742.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h en chi U ni ve rs it y
Table 1 Descriptive statistics
Variables Observations Number of firms
mean STD 25th median 75th min max
ESG score 21,426 3,305 17.1325 10.1499 11.16 13.22 16.67 0.88 75.62
E score 4,586 694 21.0431 17.5769 6.20 15.50 34.11 0.78 82.17
S score 12,747 2,012 15.8356 13.1228 8.33 8.77 19.30 1.00 86.67
G score 21,258 3,297 51.2633 5.4911 48.21 51.79 51.79 1.00 85.71
Total risk 21,426 3,305 0.0257 0.0135 0.0162 0.0222 0.0313 0.0081 0.0886
Systematic risk 21,426 3,305 0.0110 0.0072 0.0065 0.0093 0.0135 0.0002 0.0420
Idiosyncratic risk 21,426 3,305 0.0223 0.0129 0.0133 0.0188 0.0272 0.0066 0.0846
NCSKEW 21,426 3,305 0.0855 1.7405 -0.6717 -0.050 0.6213 -6.4945 7.6286
RET 21,426 3,305 0.1433 0.4645 -0.1206 0.1002 0.3344 -0.8011 2.5490
Ln(size) 21,426 3,305 7.2681 2.0285 5.8440 7.2019 8.5974 2.5962 13.1162
MB 21,426 3,305 3.5189 5.7653 1.2327 2.0094 3.6417 0.2413 57.0580
LEV 21,426 3,305 0.5497 0.2457 0.3616 0.5538 0.7496 0.0420 0.9751
ROA 21,426 3,305 0.0033 0.1625 0.0031 0.0286 0.0699 -1.0145 0.3188
KZ index 15,828 2,794 -14.864 50.0759 -10.1909 -2.6651 0.1630 -510.3455 7.4702
WW index 15,828 2,794 -0.2475 0.3379 -0.3995 -0.3170 -0.2179 -0.6057 2.4135
SA index 15,828 2,794 -3.2832 0.4878 -3.8858 -3.2458 -2.8817 -3.9258 -1.9991
This table reports descriptive statistics for all variables of my study. The sample period is from 2004 to 2018. All variables except Bloomberg ESG disclosure score are winsorized at the bottom and top 0.5% levels.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h en chi U ni ve rs it y
Table 2 Pearson Correlation coefficients
No. Variables 1 2 3 4 5 6 7 8 9 10 11 12 13
1 ESG score 1.000
2 Total risk -0.285* 1.000
3 Systematic risk -0.082* 0.481* 1.000 4 Idiosyncratic
risk
-0.301* 0.967* 0.257* 1.000
5 NCSKEW 0.018* -0.018* -0.007 -0.009 1.000
6 KZ index 0.075* -0.066* 0.037* -0.086* 0.018* 1.000
7 WW index -0.190* 0.264* -0.019* 0.304* 0.016* -0.097* 1.000
8 SA index -0.350* 0.404* 0.024* 0.449* -0.006 -0.116* 0.266* 1.000
9 RET -0.006 -0.062* -0.058* -0.050* -0.412* -0.047* -0.050* -0.024* 1.000
10 Ln(size) 0.587* -0.456* 0.090* -0.533* 0.040* 0.152* -0.369* -0.474* -0.002 1.000
11 MB 0.050* 0.015 0.001 0.023* 0.033* -0.094* 0.095* 0.081* -0.016 -0.037* 1.000
12 LEV 0.210* -0.160* 0.009 -0.183* -0.012 0.123* -0.205* -0.194* 0.051* 0.467* 0.255* 1.000
13 ROA 0.146* -0.453* -0.035* -0.493* 0.041* 0.097* -0.256* -0.304* -0.009 0.318* -0.092* 0.025* 1.000 This table reports Pearsoncorrelation coefficients among variables for the 15,828 firm-year observations from 2004 to 2018. * Indicates statistical significance at 5 % level or less.
g
‧
Table 3 The relation between ESG and financial risk
Dependent variables Total risk (×1000) Systematic risk (×1000) Idiosyncratic risk (×1000)
(1) (2) (3) (4) (5) (6)
Observations 21426 21426 21426 21426 21426 21426
R square 0.5434 0.4932 0.6117 0.5833 0.5289 0.4434
This table displays OLS regressions for the sample over the period of 2004–2018. As measures of financial risk (dependent variables), I employ the total risk (Columns 1–2), systematic risk (Columns 3–4), and idiosyncratic risk (Columns 5–6), respectively. All models include industry and time fixed effects. All variables except Bloomberg ESG disclosure score are winsorized at the bottom and top 0.5% levels and all coefficients reported have been multiplied by 1000 due to variable scaling issues. ***, **, and * indicate statistical significance at the 1, 5, and 10 % level, respectively.
‧
Table 4 The relation between ESG and downside risk
Dependent variables NCSKEW KZ index WW index SA index
(1) (2) (3) (4) (5) (6)
Observations 21426 21426 15828 15828 15828 15828
R square 0.0338 0.0317 0.1641 0.1551 0.3493 0.3341
This table displays OLS regressions for the sample over the period of 2004–2018. As measures of downside risk (dependent variables), I employ the NCSKEW (Columns 1–2), KZ index (Columns 3–4), WW index (Column 5) and SA index (Column 6), respectively. All models include industry and time fixed effects. All variables except Bloomberg ESG disclosure score are winsorized at the bottom and top 0.5% levels.
***, **, and * indicate statistical significance at the 1, 5, and 10 % level, respectively.
‧
Table 5 The relation between ESG individual components and financial risk
Dependent variables Total risk (×1000) Systematic risk (×1000)
(1) (2) (3) (4) (5) (6) (7) (8)
‧
Dependent variables Idiosyncratic risk (×1000)
(9) (10) (11) (12)
Observations 4586 12747 21258 4476
R square 0.4915 0.4431 0.4368 0.4981
This table displays OLS regressions for the sample over the period of 2004–2018. As measures of financial risk (dependent variables), I employ the total risk (Columns 1–4), systematic risk (Columns 5–8), and idiosyncratic risk (Columns 9–12), respectively. All models include industry and time fixed effects. All variables except Bloomberg ESG disclosure score are winsorized at the bottom and top 0.5% levels and all coefficients reported have been multiplied by 1000 due to variable scaling issues. ***, **, and * indicate statistical significance at the 1, 5, and 10 % level, respectively.
‧
Table 6 The relation between ESG individual components and downside riskDependent variables NCSKEW KZ index
(1) (2) (3) (4) (5) (6) (7) (8)
‧
Dependent variables WW index SA index
(9) (10) (11) (12) (13) (14) (15) (16)
This table displays OLS regressions for the sample over the period of 2004–2018. As measures of downside risk (dependent variables), I employ the NCSKEW (Columns 1–4), KZ index (Columns 5–8), WW index (Column 9-12) and SA index (Column 13-16), respectively. All models include industry and time fixed effects. All variables except Bloomberg ESG disclosure score are winsorized at the bottom and top 0.5% levels.
***, **, and * indicate statistical significance at the 1, 5, and 10 % level, respectively.
‧
Table 7 The relation between ESG individual components and downside risk (small sample) Dependent variables NCSKEW(1) (2) (3) (4) (5) (6)
‧
Dependent variables KZ
(7) (8) (9) (10) (11) (12)
This table displays OLS regressions for the sample over the period of 2004–2018. The dependent variable is NCSKEW in columns (1) to (6) while the dependent variable is KZ index in columns (7) to (12). All models include industry and time fixed effects. All variables except Bloomberg ESG disclosure score are winsorized at the bottom and top 0.5%
levels.
***, **, and * indicate statistical significance at the 1, 5, and 10 % level, respectively.
‧
Table 8 The relation between ESG and stock returnDependent variables RET (×1000)
(1) (2) (3) (4)
Panel A. Total risk as a measure of financial risk Intercept -153.0000** Score* financial risk 10.6900
(0.37)
Observations 20681 4548 12428 20521
R square 0.1981 0.3151 0.2314 0.1982
‧
Dependent variables RET (×1000)
(5) (6) (7) (8)
Panel B. Systematic risk as a measure of financial risk Intercept -163.1800** Financial risk 1786.4400**
(1.96) Score* financial risk -67.1900
(-1.58)
Observations 20681 4548 12428 20521
R square 0.1982 0.3157 0.2315 0.1982
(continued)
‧
Dependent variables RET (×1000)
(9) (10) (11) (12)
Panel C. Idiosyncratic risk as a measure of financial risk Intercept -145.9100** Score* financial risk 45.6100
(1.27)
Observations 20681 4548 12428 20521
R square 0.1981 0.31 0.2314 0.1983
This table displays OLS regressions for the sample over the period of 2004–2018. The independent variable of financial risk is total risk, systematic risk, and idiosyncratic risk in panel A, B, and C, respectively. All models include industry and time fixed effects. All variables except Bloomberg ESG disclosure score are winsorized at the bottom and top 0.5% levels and all coefficients reported have been multiplied by 1000 due to variable scaling issues.
***, **, and * indicate statistical significance at the 1, 5, and 10 % level, respectively.