The Paris Agreement in 2015, ESG factors, and US stock performance
Taylor Doan
1Grace Lepone
2Abstract:
This paper applies an event-study approach to examine the effects of the Paris Agreement in 2015 (PA 2015) and environmental, social, and governance (ESG) factors on stock return in the US. Our finding shows that the PA announcement induced an accumulative abnormal return of long-short ESG portfolios, ranging from 1.15% to 1.32%; however, this effect was not long-lasting. The US’s withdrawal from PA 2015 did not show any impact on either systematic risk or return of our portfolios. In the years following the PA 2015, we observed a significant increase in ESG and environmental ratings and stronger positive correlation between the level of ESG commitment and companies’ future operating performance.
Key words: Paris Agreement, climate policy, environmental regulation, ESG, US stock market.
1. Introduction and Motivation
Climate change has become a growing concern for financial markets. Matos (2020) states that “the ESG issue that gets the most attention from institutional investors is climate change”.
ESG investing has recently attracted the attention of investors due to the urgency of environmental and social issues. A common intuition is that the better ESG performance a company is committed to, the lower risk it faces of being penalized by environmental and social issues. However, the correlation between ESG performance and companies’ financial performance is still inconclusive. Some studies found that corporate social responsibility (CSR) and ESG have positive correlation with stock price and financial performance (Torugsa, O’Donohue, & Hecker, 2012; Tang, Hull, &Rob, 2012; Najah, Sadok El, Omrane, & Jungwon, 2013; H. S. T. Pham & Tran, 2020; Dowell, Hart, & Yeung, 2000; Madhavan, Sobczyk, and Ang 2021). Other studies show a contrasting finding: highly rated ESG stocks underperform low rated ESG stocks (Orlitzky, Schmidt, and Rynes, 2003).
While the correlation between ESG profile and financial performance are still controversial, the climate- related risks and the potential loss of environment-related stranded assets add to the urgency of the research in ESG and climate change. This study will contribute to the growing literature about climate finance by investigating the effects of a climate-related policy, i.e., the Paris Agreement in 2015 on stocks with different level of ESG ratings. The US market is chosen for this study because of its changing commitment to the Paris Agreement. This provides us with a rare opportunity to test the effect of environmental regulation and deregulation on stock market.
To examine if companies with better ESG outperform those with lower ESG performance, we constructed long-short ESG portfolios which long stocks with a high ESG rating and short those with a low ESG rating. Upon each event, we captured the cumulative abnormal return and the change in systematic risk of our long-short ESG-based portfolios by using a standard event study approach.
Apart from third-party ESG and environmental ratings, we measured ESG and environmental performance using a regression model adapted from Lys, Naughton, and Wang (2015). Specifically, we ran regressions of ESG and environmental scores on a set of fundamental variables comprising company size, the level of leverage, cash, and expenses. The residuals obtained from these regressions captured the unexplained or abnormal ESG and environmental performance. Positive (negative) abnormal ESG and environmental performance implies that company has a stronger (weaker) commitment to ESG and environmental responsibility compared to those with similar size, levels of leverage, cash, and expenses.
A company which is highly rated by a third party can still have a negative abnormal ESG and vice versa. Later we show that the portfolios built on abnormal ESG factors outperform the ones based on third party ratings.
Our findings show that our long-short ESG portfolios experienced a statistically significant accumulative abnormal return ranging from
1.15% to 1.32%
after the announcement of the PA 2015.The announcement of the US’s withdrawal from the PA 2015 did not show any impact on either systematic risk or return of our ESG-based and environmental-based portfolios. At a sector level, we found a positive effect of the PA announcement on the Energy and Healthcare ESG-based portfolios’
return. However, the effect of Paris Agreement announcement was not long-lasting, indicating that investors overreacted to this event. We also observed a change in the correlation between the level of ESG commitment and companies’ future operating performance. This correlation did not exist before the Paris Agreement announce, from 2010 to 2014; however, we found strong positive correlation between ESG and future operating performance during the period from 2015 to 2018.
We further examined how companies’ ESG and environmental rating has changed after the PA 2015.
Overall, ESG and environmental ratings have increased over time. However, the increasing rate was statistically significantly higher for the year 2015 when the PA was announced, compared to other years in the studied period. This finding suggests that companies are motivated to improve ESG performance during events which have high visibility and media coverage such as the PA 2015.
This study makes two contributions to the existing literature. First, it provides evidence supporting the link between corporate performance and stock market valuation, confirming that better ESG performance leads to an increase in market valuation when an environmental policy is introduced.
Second, we contribute to the growing literature examining the effect of climate change on financial markets, an urgent but under-researched topic in finance. Most of recent studies in this area investigate the effect of climate-related policies on stock market without differentiating the levels of company’s commitment to ESG and environmental responsibility (Monasterolo, 2020; H. Pham, Nguyen, Ramiah, Saleem, and Moosa, 2019; H. N. A. Pham, Ramiah, and Moosa, 2020; Monasterolo and de Angelis, 2020; Ramiah, Pichelli, and Moosa, 2015; Berkman, Jona, & Soderstrom, 2019). We complemented the study of Ramelli, Wagner, Zeckhauser, & Ziegler (2021) and Bolton & Kacperczyk (2021), but differentiate from these studies by examining different events and using different measures of ESG performance.
The finding of this study raises the awareness of socially and environmentally sensitive investors about the effect of a company’s ESG and environmental ratings on its risks and returns. The finding of stock market reaction to climate-related policies and correlation between ESG and companies’ future performance is also useful for policymakers who want to enlist investors in the fight against climate change.
The rest of this paper will present our methodology, results, and discussion.
2. Methodology
We proposed that the announcement of climate related policy has a different impact on companies with high ESG score as opposed to those with a lower ESG score. To test this hypothesis, we first classified companies into high and low ESG performance. Then, we formed portfolios which long high ESG stocks and short low ESG stocks and applied a standard event study approach to test the effects of our events of interest on the performance of these portfolios. We also applied the same procedure but used environmental score (E score) instead of ESG score to consider the effect of environmental factor. Next, we applied a regression to test the correlation between company future operating performance and its ESG commitment level. Lastly, we further examined how companies’ ESG performance and environmental performance changed after the announcement of the PA. Details of our approach are presented as follows.
2.1.First-stage model to measure abnormal ESG performance
In this study, we used two different measures to gauge companies’ ESG performance: third-party ESG ratings and abnormal ESG component, which was constructed using a regression model adapted from Lys et al. (2015). Specifically, to obtain the abnormal ESG component, we ran regressions of third- party ESG score on a set of fundamental values and controlled for fixed industry effects and fixed year effects. The residuals from these regressions capture the unexplained or abnormal ESG component. The same process was repeated with environmental score (E score) to obtain abnormal environmental component.
The regression is designed as follow:
𝐸𝑆𝐺_𝑟𝑎𝑡𝑖𝑛𝑔𝑖,𝑡= 𝛼 + ∑ 𝛽𝑗𝐹𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙𝑠𝑖,𝑡+ 𝜀𝑖,𝑡, (1) 𝐸_𝑟𝑎𝑡𝑖𝑛𝑔𝑖,𝑡 = 𝛼 + ∑ 𝛽𝑗𝐹𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙𝑠𝑖,𝑡+ 𝜀𝑖,𝑡, (2) where ESG_ratin𝑔𝑖,𝑡 is the ESG rating or ESG score of company i in year t, E_ratin𝑔𝑖,𝑡 is the Environmental rating of company i in year t, 𝐹𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙𝑠𝑖,𝑡 is a set of fundamental values of company i in year t listed in Table 1, ε𝑖,𝑡 is the residual and is assumed to have a zero mean, constant variance, and to be normally distributed. ε𝑖,𝑡 obtained from Equation (1) is the measure of abnormal ESG performance and ε𝑖,𝑡 obtained from Equation (2) is the measure of abnormal environmental performance for company i in year t.
We selected a sample of all companies which had the ESG score and fundamental data available from 2010 to 2020. The set of fundamental variables is described in Table 1.
<Table 1>
2.2.Portfolio approach
To test the effects of climate related policy on companies with different ESG performance levels, we replicated a portfolio approach used by previous studies from Berkman et al. (2019) and Schwert (1981). Because our events of interests affected sample companies at the same time, abnormal return of individual companies are not independent. In this case, as recommended by Schwert (1981), a portfolio approach gives more reliable conclusions than analysing the effect of events on individual companies.
Schwert (1981) recommended to combine potential gainers and potential loser into two separate groups, then studying the difference in return between these two groups. We hypothesized that high ESG stocks are potential gainers and low ESG stocks are potential losers when the PA 2015 was announced and vice versa when the US withdrew from this agreement.
Our ESG portfolios long high ESG performance stocks (stocks in the first quintile) and short low ESG stocks (the fifth quintile) from each sector. We then studied the abnormal return and change in systematic risk of these portfolios around our events of interest, using a standard event-study approach.
We formed four different long-short ESG portfolios, denoting as follows:
- ESG-rating portfolio is the long-short ESG portfolio constructed with ESG ratings from Datastream.
- Env-rating portfolio is based on environment rating from Datastream.
- Abnormal-ESG portfolio is based on abnormal ESG component obtained from regression (1) - Abnormal-Env portfolio is based on abnormal environmental component, or residuals of
regression (2)
2.3.Event-study approach
A standard event-study approach was applied to test our proposal. Our events of interest are:
- The announcement of the Paris Agreement 2015 (PA) on 12/12/2015
- The announcement of potential withdrawal from the PA by the former US president Donald Trump on 01/06/2017
We employed a four-factor model to calculate abnormal return (AR) or alpha of our long-short ESG portfolios. This model allows us to control for the difference in companies’ size, growth rate in our sample and market momentum. We added two dummy variables to represent the event periods. The model is specified as follow:
Ri,t− Rf,t= αi+ βi(Rm,t− Rf,t) + siSMBt+ hiHMLt+ miMMt+ γiPA2015
+ δiWithdrawPA + 𝜀i,t, (3)
where 𝑅𝑖,𝑡 is the arithmetic return of portfolio i on day t, 𝑅𝑓,𝑡 is the risk-free return, 𝑅𝑚,𝑡 is the return of a market index on day t, 𝑆𝑀𝐵𝑡 (Small Minus Big) is the return of a small-capitalization portfolio
minus the return of a large-capitalization portfolio on day t, 𝐻𝑀𝐿𝑡 is the return of a high book-to-price portfolio minus the return of a low book-to-price portfolio on day t; 𝑀𝑀𝑡 is the momentum factor on day t. 𝑃𝐴2015 is a dummy variable which takes a value of 1/3 if day t is one of the 3 trading days after the PA 2015 announcement on 12/12/2015; otherwise, it takes a value of 0. 𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑃𝐴 is a dummy variable which takes a value of 1/3 if day t is one of the 3 trading days after the announcement of potential withdrawal from the PA 2015 by the former US president Donald Trump on 01/06/2017;
otherwise, it takes a value of 0. γ𝑖 and δ𝑖 represent the effect of the PA announcement and the withdrawal announcement on return of portfolio i
After completing the analysis of Equation (3), if we found a statistically significant cumulative abnormal return around an event, we further computed buy and hold return for 12 months after the event to determine whether the effect of the event was long-lasting. Buy and hold return was calculated as follows:
BH𝑅𝑖,τ= ∏τ𝑡=1(1 + 𝑅𝑖,𝑡)− 1, (4) where BH𝑅𝑖,τ is the buy-and-hold return of portfolio i over the period 𝜏, 𝑅𝑖,𝑡 is the return of portfolio i on day t.
We also applied the four-factor model to examine the effects of our events of interest on systematic risk of ESG-based portfolios. The model is specified as follows.
Ri,t− Rf,t= αi+ βi(Rm,t− Rf,t) + γiPA2015 ∗ (𝑅𝑚,𝑡− 𝑅𝑓,𝑡) + δiWithdrawPA
∗ (𝑅𝑚,𝑡− 𝑅𝑓,𝑡) + siSMBt+ hiHMLt+ miMMt+ ϵi,t, (5) where 𝑃𝐴2015 takes a value of 1 if day t is one of the 60 trading days after the PA 2015 announcement on 12/12/2015; otherwise, it takes a value of 0. 𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑃𝐴 takes a value of 1 if day t is one of the 60 trading days after the announcement of potential withdrawal from the PA 2015 by the former US president Donald Trump on 01/06/2017; otherwise, it takes a value of 0. γ𝑖 and δ𝑖 represent the effect of the PA announcement and the withdrawal announcement on systematic risk of portfolio i 60 trading days after each event.
2.4.Companies’ ESG performance after the PA 2015
In this subsection, we describe our approach to examine how company ESG performance changed after the PA 2015. In this exercise, we only include companies whose ESG ratings available from 2012 to 2019. We calculated the percentage change in ESG performance (ESG ratings) for each company in each year from 2012 to 2018. Data was winsorised at 5% and 95% to eliminate the effect of outliners.
gESG𝑖,t=ESGrating𝑖,t− ESGrating𝑖,t−1
ESGrating𝑖,t−1 (6)
where gES𝐺𝑖,𝑡 is the ESG performance change or ESG rating change of company i in year t;
ESGratin𝑔𝑖,𝑡 is a third-party ESG rating (ESG score) of company i in year t; ESGratin𝑔𝑖,𝑡−1 is a third- party ESG rating (ESG score) of company i in year t-1
The same formulas were applied to test the change in companies’ environmental ratings.
2.5. ESG and future operating performance
In this subsection, we investigate if ESG commitment correlates with companies’ future operating performance during the sample periods. (Lys et al., 2015) proposed three theories to explain for why companies undertake ESG activities and how ESG correlates to companies’ operating performance in each theory:
- Charity theory: Companies undertake ESG activities due to social responsibility, regardless financial benefits or burden. This theory implies that ESG spending3 does not correlate with future operating performance
- Investment theory: ESG activities are driven by financial benefits. Only ESG projects that bring high net present values are selected. Therefore, ESG spending is positively correlated with future operating performance
- Signalling theory: Companies undertake ESG projects to signal investors about future financial prospects. Managers only decide to invest in ESG when they foresee a reasonable future performance. Like investment theory, signalling theory also results in a positive correction between ESG spending and future operating performance.
(Lys et al., 2015) suggest that investment theory and signalling theory can be distinguished by using optimal ESG component and abnormal ESG component in analysis. To gain insights into companies’
motivation when undertake ESG activities, we follow the method from (Lys et al., 2015), breaking down ESG ratings into two components: optimal ESG component, which is the fitted values obtained from the regression (1) and (2) and abnormal ESG component, which is the residuals or unexplained values obtained from the regression (1) and (2).
The charity theory implies that either optimal ESG or abnormal ESG component correlates to companies’ future performance. Investment theory suggests that optimal ESG component is positively correlated with future performance and abnormal ESG component does not correlate with future performance. Signalling theory has a vice versa implication, optimal ESG component does not correlate with future performance and abnormal ESG component positively correlates with future performance.
3Previous studies suggest that ESG ratings strongly correlate with companies’ spending on ESG activities (Lys et al., 2015) and (Naughton, Wang, & Yeung, 2019)
Return on assets (ROA) and cash flows from operating activities (CFO) scaled by total assets are used to proxy for operating performance. The regressions testing the correlation between future operating performance and ESG spending are specified as follows:
∆𝑅𝑂𝐴𝑖,𝑡+1= 𝑎0+ 𝑎1𝑂𝑝𝑡𝑖𝑚𝑎𝑙𝐸𝑆𝐺𝑖,𝑡+ 𝑎2𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙𝐸𝑆𝐺𝑖,𝑡+ 𝑎3∆𝑅𝑂𝐴𝑖,𝑡+ 𝑎4𝑅𝑂𝐴𝑖,𝑡−1+ 𝜀𝑖,𝑡 (7)
∆𝐶𝐹𝑂𝑖,𝑡+1 = 𝑏0+ 𝑏1𝑂𝑝𝑡𝑖𝑚𝑎𝑙𝐸𝑆𝐺𝑖,𝑡+ 𝑏2𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙𝐸𝑆𝐺𝑖,𝑡+ 𝑏3∆𝐶𝐹𝑂𝑖,𝑡+ 𝑏4𝐶𝐹𝑂𝑖,𝑡−1+ 𝜀𝑖,𝑡 (8) where ∆𝑅𝑂𝐴𝑖,𝑡+1 is the change in ROA of company i in year t+1; ∆𝑅𝑂𝐴𝑖,𝑡 is the change in ROA of company i in year t; 𝑅𝑂𝐴𝑖,𝑡−1 is the ROA of company i in year t-1; ∆𝐶𝐹𝑂𝑖,𝑡+1 is the change in CFO of company i in year t+1; ∆𝐶𝐹𝑂𝑖,𝑡 is the change in CFO of company i in year t; 𝐶𝐹𝑂𝑖,𝑡−1 is the CFO of company i in year t-1; 𝑂𝑝𝑡𝑖𝑚𝑎𝑙𝐸𝑆𝐺𝑖,𝑡 is the optimal ESG component (obtained from the regression (1) and (2)) of company i in year t; 𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙𝐸𝑆𝐺𝑖,𝑡 is the abnormal ESG component of company i in year t.
∆𝑅𝑂𝐴𝑖,𝑡, 𝑅𝑂𝐴𝑖,𝑡−1, ∆𝐶𝐹𝑂𝑖,𝑡, and 𝐶𝐹𝑂𝑖,𝑡−1 are included to control for mean reversion of the change in ROA and CFO at time t+1
We propose that the correlation between ESG spending and future operating performance, if it exits, varies over time. The strength of this this correlation depends on how ESG commitment is regulated and the level of pressure from public and governments. We ran this test on two period samples, before the Paris Agreement (from 2010 to 2014) and after the Paris Agreement (from 2015 to 2018) to test if the correlation between ESG spending and future operating performance exists and if the Paris Agreement affected this relationship. This test also provides us with some insights into why companies undertook ESG activities during the sample periods, or which theory, charity, investment, or signalling theory is the best to explain this.
3. Data
Data in this study is from the New York Stock Exchange (NYSE) and the National Association of Securities Dealer Automated Quotation (NASDAQ) exchange. Trading data and ESG ratings are all collected from Datastream. To mitigate data quality issues, anomalies such as a daily return of greater than 100% or a drop in value of more than 50% are removed as most of these cases are caused by errors in the data. Furthermore, while selecting stocks to construct long-short ESG portfolios, we only included stocks whose number of observations is at least two thirds of the total number of trading days in the testing period.
4. Results
We summarize our results into four subsections. The first subsection presents the first-stage regression result, which was used to measure abnormal ESG performance and abnormal environmental performance. The second subsection shows results of our main analysis: how the announcement of PA
2015 and the US’s withdrawal from PA in 2017 affected the return and systematic risk of our long-short ESG portfolios. The third subsection displays our finding about company ESG performance and environmental performance before and after the announcement of the PA 2015. Last, we revealed the correlation between the level of ESG commitment and companies’ future operating performance.
4.1.The first-stage regression result - Determinants of abnormal ESG performance and abnormal environmental performance
The results of the Equation (1) and (2) are presented in Table 2. Including the fixed year effect shows improvement to the model. Specifically, we found that the years from 2015 to 2020 have a statistically significantly positive impact on the ESG ratings. This result suggests that ESG rating may increase linearly with time.
Most fundamental variables have positive impacts on ESG ratings and environmental ratings, except for leverage level which shows negative correlation. This result is consistent with findings from previous studies from Lys et al. (2015) and Naughton et al. (2019)4. A higher level of asset turnover, cash, cash flow from operating, market to book ratio, profit margin, and a bigger size are positively correlated with a company’s ESG rating and environmental ratings. A higher ESG rating and higher environmental score are associated with a lower leverage level.
<Insert Table 2>
4.2.The effects of PA 2015 and US’s withdrawal from the PA 2015 on companies’ value and systematic risk
4.2.1. Portfolios’ return
The result in Table 3 shows that our ESG-based and environmental-based portfolios experienced positive accumulative abnormal return around the announcement of the PA 2015, except for the ESG- rating portfolio. The abnormal-Env portfolio showed the highest abnormal return among our four portfolios, 1.32%. However, only the results of abnormal-ESG and abnormal-Env portfolios are statistically significant. This result is consistent with our prediction that investors reacted positively to the news about PA 2015 and reward companies with abnormal ESG and environmental performance.
The abnormal ESG and environmental performance have a stronger positive correlation with abnormal return induced by the PA in 2015 than the third-party ratings do.
We applied the same analysis to each following sector: Energy, Healthcare, Technology, Basic Materials, Industrials, Consumer Cyclical, Consumer Non-cyclical, Financials, Real Estate, and
4 Note that we did not include advertising cost and litigation cost as specified by (Lys et al., 2015) and (Naughton et al., 2019) due to missing data. Datastream only reports advertising cost and litigation cost if they are presented in the financial statements.
Missing data cannot be assumed as zero spending. Sometimes, these costs are included in the operating expense item; therefore, we included operating expense in our model instead of advertising cost and litigation cost.
Utilities. Results are reported in Appendix A. The effect of the Paris Agreement is mixed: positive effect on return of Industrials, Energy, Consumer non-cyclicals, Financials portfolios, and negative effect on Healthcare portfolio. The US’s withdrawal from PA 2015 did not affect our sector-level portfolios.
<Insert Table 3>
As we found statistically significant cumulative abnormal return after the PA announcement, we further tested the buy and hold return of our long-short ESG portfolios. The results are reported in Table 4 and visualized in Figure 1. The buy and hold return of our portfolios were all positive for the period seven months after the announcement of the PA 2015. From the eighth month after the PA 2015 announcement, buy and hold return of portfolios based on third-party ESG and environmental rating were negative while return of the portfolios based on our abnormal ESG and environmental measure were positive. This result suggests that the positive effect of the PA announcement was long-lasting only for our abnormal ESG and abnormal environmental portfolios. A similar reversed return for a 12- month period was also observed at sector level as shown in Figure 2
<Insert Figure 1>
<Insert Table 4>
<Insert Figure 2>
4.2.2. Systematic risk
Table 5 presents the estimation of Equation (5), which measures the events’ effect on the systematic risk of our portfolios. The result suggests that both events, the PA 2015 announcement and the US’s withdrawal from this agreement, did not impact the systematic risk of our long-short ESG portfolios, except for the Env-rating portfolio, which showed a statistically significant decrease in its systematic risk 60 days after the PA 2015 announcement. At a sector level, we observed a reduce in systematic risk of the long-short ESG portfolios in Technology, Real Estate, Utilities and an increase in systematic risk of Financials portfolio. The US’s withdrawal did not affect the systematic risk of our industry portfolios. Results are presented in Appendix B.
<Insert Table 5>
4.3.The Paris Agreement 2015 and companies’ ESG commitment
Figure 3 displays the density of ESG rating data from 2010 to 2019. During the period from 2010 to 2014, the ESG data distribution showed very little change. From the year 2015, the height of the density chart started increasing, indicating an increase in the number of rated companies. Meanwhile, the average ESG ratings gradually reduced. This is due to the fact that newly added companies usually have lower ESG and environmental ratings than existing companies. We also observed a similar pattern for environmental ratings.
<Insert Figure 3>
After removing all the newly-added companies from 2012 to 2018, we recalculated the statistics of our ESG and environmental rating datasets and reported these statistics in Table 6.
The result reveals that companies which had ESG ratings reported continuously from 2012 to 2018 showed a statistically significant improvement in both ESG and environmental ratings over time. T-test results are also reported in Table 6. Figure 4 visualizes the distribution of ESG data, and Figure 5 displays the change in ESG and environmental ratings, excluding the newly-added companies. The distribution chart gradually shifted to the right, indicating an increase in ESG rating.
<Insert Table 6>
<Insert Figure 4>
<Insert Figure 5>
After the announcement of the PA 2015, i.e., the year 2015, average ESG ratings increased dramatically by 13.83%. This result is consistent with our conjecture that companies improved their ESG and environmental performance after the PA announcement. One may argue that the PA was announced on 12/12/2015; therefore, companies’ reaction to the PA announcement, if there is any, should be accounted in the ESG ratings in the following year, i.e., 2016. We have reasons to believe that companies’ reaction to the PA announcement is reflected in the ESG ratings in 2015. The ESG rating agency, Refinitive, indicated in their methodology that “in most cases, reported ESG data is updated once a year in line with companies’ own ESG disclosure”. The PA was announced on 12/12/2015 and most US companies publish their annual financial statements in the first quarter of the next year.
Therefore, companies could enrich their self-disclosure of ESG activities which was published in 2016 but disclosed information of activities in 2015.
4.4. ESG and future operating performance
Table 7 presents the correlation between ESG ratings and future operating performance for two periods, before the Paris Agreement, from 2010 to 2014, and after the Paris Agreement, from 2015 to 2018.
Panel A shows that during the period from 2010 to 2014, ESG ratings were not correlated with companies’ future operating performance, i.e., ROA and CFO. The coefficients of optimal ESG, abnormal ESG, and ESG score (ESG score = optimal ESG + abnormal ESG) are all close to 0. This result supports the charity theory: companies undertake ESG activities due to social commitment instead of financial benefit.
The result for the period from 2015 to 2018 illustrates that both components, optimal ESG and abnormal ESG, have positive effects on future operating performance. This result supports both investment theory
and signalling theory: Companies selectively invested in ESG projects that bring financial benefits and ESG activities are used to signal the management’s confidence about companies’ future performance.
<Insert Table 7>
5. Robustness Test
We conducted the following robustness tests:
Event window [0, +10]: We tested Equation (3) using a window of 10 trading days after the event instead of 3 like in the main test. The result is reported in Panel A of Table 8. The PA 2015 announcement induced cumulative abnormal return ranging from 2.03% to 2.7%. The US’s withdrawal from the PA 2015 did not show any impact on our portfolios.
Market model: We applied a market model instead of the four-factor model to test the effects of our events of interests on ESG-based portfolio return. The results reported in Panel B of Table 8 are consistent with our results using the four-factor model.
Change in systematic risk 30 days after events: We tested the change in systematic risk 30 days after each event. The results reported in Panel C of Table 8 show that our events of interest did not have any impact on systematic risk of ESG-based and environmental-based portfolio.
<Insert Table 8>
6. Discussion
6.1.Events’ effect on ESG-based and environmental-based portfolio
The focus of our study is to examine if the announcement of the PA 2015 and the US’s withdrawal from this agreement created or destroyed value for companies with high level of ESG commitment compared to those with low commitment level. We achieved the answer for this question by first constructing ESG-based portfolios which long stocks with high ESG performance and short those with low performance. Then, we studied the change in market value and systematic risk of these portfolios in accordance with our events of interest.
We found that the PA 2015 announcement induced positive abnormal return for some of our long-short ESG portfolios, indicating that the value of companies with high level of ESG commitment increased, in relation to those with lower commitment level. The implication here is that the value of companies with high ESG and environmental performance increased, in relation to low ESG and environmental rating ones after the PA announcement.
This finding is explained by the adjustment in market behaviour in relation to investor’s preference for high ESG stocks after the announcement of the PA in 2015. During a period when “market prices have
been adjusting to a new equilibrium that reflects ESG considerations”, investors’ preference for highly rated ESG stocks increases. As a result, “the discount rate for highly rated ESG companies will fall and the discount rate for low rated ESG companies will rise”, therefore, “highly rated ESG stocks will increase and the relative prices of low ESG stocks will fall” or “highly rated ESG stocks will outperform the low ESG stocks” (Cornell, 2021).
(Ramelli et al., 2021) found that following the 2020 US election, with the prospect that the US would resume its commitment to the PA 2015, companies with better environmental ratings experienced an increase in market value. Our study complements the study of Ramelli et al., (2021) and confirms that the PA 2015 has positive effect on the value of companies with high ESG and environmental ratings.
However, we found that a buy-and-hold strategy after the PA event resulted in negative returns for the long-short portfolios based on third-party ratings. Some of the industries such as Healthcare, Financials, and Technology also experienced this stock price reversed return, indicating that investors might overreacted to the news about Paris Agreement.
In contrast with the PA announcement, the US’s withdrawal from PA 2015 did not induce abnormal return to our ESG-based and environmental-based portfolios. There are several possible explanations for this finding:
- The withdrawal from the PA 2015 did not induce additional environmental costs for either highly rated or low rated ESG groups.
- The withdrawal from the PA 2015 did not affect the stock market due to the commitment of shareholders, other stakeholders, and local councils to tackle climate change regardless of the federal decision to withdraw from the PA in 2015 (Berkman et al., 2019)
- Lastly, it is possible that the event was expected; therefore, no abnormal return was detected for both highly rated and low rated ESG stocks. It was quite predictable that Trump was shifting his focus away from environmental area, so when he finally withdrew the PA, the market was not shocked by that. The effect of the withdrawal from the PA on the stock market, if there is any, may be accounted for in the stock price movement before the announcement on the 1st of June 2017.
Overall, our events of interest did not affect the systematic risk of our ESG and environmental-based portfolios, except for the environmental-rating portfolio, which showed a reduction in its systematic risk after the PA announcement. Previous studies suggest that the sensitivity of systematic risk to climate-related policies varies by sector and industry. For example, alternative energy, automotive and parts, and the mining industry increased in the short-term while systematic risks of beverages, health care and industrial transportation declined (Ramiah, Martin, and Moosa (2013)). In Germany, basic resources and consumer discretionary experienced an increase in systematic risk (beta), while automotive experienced a decrease in beta (H. Pham et al., 2019). Our portfolios are sector-wide and
includes ten sectors: Energy, Healthcare, Technology, Basic Materials, Industrials, Consumer Cyclical, Consumer Non-cyclical, Financials, Real Estate, and Utilities. Therefore, we attribute the mild effect of our events of interest on our portfolios’ systematic risk to the variation of reaction by sector. Some sectors may experience an increase in beta while others may decrease or have no change. In a non- tabled analysis, only ESG-based portfolios in Energy and Healthcare were affected by the PA 2015 announcement. The implication here is that ESG-oriented investors can incorporate ESG factors into their investment and still achieve diversification. Investing in stocks with high ESG and environmental ratings in varied industries is another option for ESG-oriented investors, apart from investing in high ESG industries only. By expressing preference for high ESG stocks in different sectors, investors encourage companies to improve ESG and environmental management, even in “brown” industries such as oil and gas. The implication for management level is that companies can implement eco-friendly business strategies or improve environmental management without any financial sacrifice in terms of risk or return as suggested by (Humphrey, Lee, & Shen, 2012).
6.2.ESG and environmental ratings after the PA 2015
Our first discovery when examining the ESG ratings over time is that more companies had ESG and environmental ratings reported after the PA in 2015. This finding suggests that the PA 2015 motivated companies to disclose more ESG information and caused ESG rating agencies to enhance their data coverage. The increase in ESG data coverage was likely also affected by the growth of ESG investing and increased media attention to ESG issues, especially after the PA announcement.
Overall, ESG and environmental ratings have increased over time; however, significant improvement was found shortly after the PA. Then, after the announcement of the US’ withdrawal from PA 2015, the rate of increase of ESG score dropped back to a similar level as it was before the PA. This finding raises a question about companies’ motivation and strategies in ESG management. There is an implication for policymakers about the effectiveness of climate-related policies on companies ESG and environmental performance: companies are motivated to improve ESG performance during events which have high visibility and media coverage such as the PA 2015.
6.3.ESG commitment and future operating performance
The correlation between the level of ESG spending and future operating performance varies overtime.
We found that before the Paris Agreement, from 2010 to 2014, this correlation did not exist. This can be explained by the charity theory that companies undertaking ESG activities for non-pecuniary reasons. Another possible explanation is that companies did consider the financial benefits; however, ESG projects turned out ineffective. Hence, the future operating performance was not affected.
After the Paris Agreement in 2015, companies’ ESG ratings (both optimal and abnormal ESG component) and their future operating performance were positively correlated. Previous studies suggest several channels to explain how investing in ESG could financially benefit companies. For example:
- CSR builds trust between a companies and its stakeholders, hence improves customers loyalty, employees’ productivity, and financial credits (Lins, Servaes, & Tamayo, 2017).
- High ESG and environmental ratings are usually associated with good environmental management and lower risk of negative regulatory and legislative actions (Dowell et al., 2000).
- The PA 2015 induced opportunities to increase revenue for “green” companies. The PA 2015 caught a lot of media attention and was shown to eventually affect consumer behaviour. With the increase in awareness of climate change and environmental issues, demand for environmental-friendly products also surged. As a result, consumers will tend to favour companies with good environmental practices, which might lead to an increase in revenue and future cashflows.
According to (Lys et al., 2015), the positive correlation between the abnormal ESG component and future operating performance implies that companies improve ESG ratings not only to signal their social and environmental commitment to issues, but also to signal their confidence about future financial performance.
Although the mechanisms how ESG activities and future financial performance interact with each other are still inclusive, the positive correlation between them in the recent years is good news for policymakers and stakeholders that pursuing sustainable targets.
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Attached Tables
Table 1. Fundamental variables description
This table describes the fundamental variables included in Equation (1) and Equation (2), which are used to gauge the abnormal ESG and environmental performance.
𝐸𝑆𝐺_𝑟𝑎𝑡𝑖𝑛𝑔𝑖,𝑡= 𝛼 + 𝛽1𝐹𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙𝑠𝑖,𝑡+ 𝜖𝑖,𝑡, 𝐸_𝑟𝑎𝑡𝑖𝑛𝑔𝑖,𝑡= 𝛼 + 𝛽1𝐹𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙𝑠𝑖,𝑡+ 𝜖𝑖,𝑡,
where 𝐸𝑆𝐺_𝑟𝑎𝑡𝑖𝑛𝑔𝑖,𝑡 is the ESG rating or ESG score of company i in year t, 𝐸_𝑟𝑎𝑡𝑖𝑛𝑔𝑖,𝑡 is the Environmental rating or E score of company i in year t, 𝐹𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙𝑠𝑖,𝑡 is a set of fundamental values of company i in year t, 𝜖𝑖,𝑡 is the residual and is assumed to have a zero mean, constant variance, and to be normally distributed. 𝜖𝑖,𝑡 obtained from Equation (1) is the measure of abnormal ESG performance and 𝜖𝑖,𝑡 obtained from Equation (2) is the measure of abnormal environmental performance for company i in year t.
Variables Description
EXPEND Operating expense divided by net sales
ATO Net sales divided by total assets
CASH Cash divided by total assets
CFO Cash flow from operations divided by total assets LEVERAGE Total of debt and current liabilities divided by total assets
MTB Market-to-Book ratio
PM Income before extraordinary items divided by net sales
SIZE Natural logarithm of total assets
GOV Governance rating (included in Equation (2) only)
Table 2. Regression of ESG ratings and environmental ratings on fundamental variables
This table presents the results of Equation (1) and Equation (2). Column [1] and column [2] are from the model including the fixed year effects and column [3] and column [4] are results of the model excluding the fixed year effects. ***, **, and * indicate statistical significance at 1%, 5%, and 10% level, respectively.
ESG E score ESG E score
[1] [2] [3] [4]
EXPEND 13.95*** 18.53*** 13.01*** 18.22***
ATO 4.19*** 1.86** 4.36*** 1.90**
CASH 2.79 7.03** 2.87 7.18**
CFO 5.66 14.70** 7.18 14.32**
LEVERAGE -0.04*** -0.05*** -0.05*** -0.05***
MTB 1.38*** 1.99*** 1.52*** 2.17***
PM 14.30*** 21.04*** 14.52*** 20.95***
SIZE 5.83*** 10.17*** 6.19*** 10.28***
GOV 0.3*** 0.29***
Industry fixed effects Yes Yes Yes Yes
Year fixed effects No No Yes Yes
Adjusted R squared 25.60% 50.05% 26.32% 50.33%
Observations 6303 6197 6303 6197
Table 3. Estimation of events’ effect on portfolios’ return using four factor model
This table presents the results of Equation (3):
𝑅𝑖,𝑡− 𝑅𝑓,𝑡= 𝛼𝑖+ 𝛽𝑖(𝑅𝑚,𝑡− 𝑅𝑓,𝑡) + 𝑠𝑖𝑆𝑀𝐵𝑡+ ℎ𝑖𝐻𝑀𝐿𝑡+ 𝑚𝑖𝑀𝑀𝑡+ 𝛾𝑖𝑃𝐴2015 + 𝛿𝑖𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑃𝐴 + 𝜖𝑖,𝑡,
where 𝑅𝑖,𝑡 is the arithmetic return of portfolio i on day t, 𝑅𝑓,𝑡 is the risk-free return, 𝑅𝑚,𝑡 is the return of a market index on day t, 𝑆𝑀𝐵𝑡 is the Small Minus Big factor on day t, 𝐻𝑀𝐿𝑡 is the High Minus Low factor on day t; 𝑀𝑀𝑡 is the momentum factor on day t. 𝑃𝐴2015 is a dummy variable which takes a value of 1/3 if day t is one of the 3 trading days after the PA 2015 announcement on 12/12/2015; otherwise, it takes a value of 0. 𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑃𝐴 is a dummy variable which takes a value of 1/3 if day t is one of the 3 trading days after the announcement of potential withdrawal from the PA 2015 by the former US president Donald Trump on 01/06/2017; otherwise, it takes a value of 0. 𝛾𝑖 and 𝛿𝑖 represent the effect of the PA announcement and the withdrawal announcement on return of portfolio i. ***, **, and * indicate statistical significance at 1%, 5%, and 10%
level, respectively.
ESG-rating portfolio Env-rating portfolio abnormal-ESG portfolio abnormal-Env portfolio
[1] [2] [3] [4]
Intercept -0.0016*** -0.0017*** -0.0017*** -0.0016***
Rm - Rf -0.0005** -0.0006*** -0.0009*** -0.0002
SMB -0.0029*** -0.0053*** 0.0003 0.0007**
HML -0.0006** 0.0031*** 0.001** 0.0015***
MM -0.0001 -0.0001 -0.0006** -0.0005**
PA2015 -0.0024 0.0079 0.0115* 0.0132**
WithdrawPA -0.0033 -0.0008 -0.0047 -0.0049
Adj R squared 19% 46.76% 5.76% 9.84%
Obs. 652 652 652 652
Table 4. Buy and hold return after the PA announcement
This table presents the buy and hold abnormal return of our ESG-based and environmental-based portfolios after the PA announcement. The buy and hold return is calculated as described in Equation (4): BH𝑅𝑖,τ= ∏τ𝑡=1(1 + 𝑅𝑖,𝑡)− 1 where BH𝑅𝑖,τ is the buy-and-hold return of portfolio i over the period 𝜏 and 𝑅𝑖,𝑡 is the return of portfolio i on day t.
Period (months)
ESG-rating portfolio [1]
Env-rating portfolio [2]
Abnormal-ESG portfolio [3]
Abnormal-Env portfolio [4]
1 1.21% 3.43% 1.65% 1.43%
2 1.91% 6.85% 2.86% 1.42%
3 -0.09% 3.14% 2.31% 2.23%
4 1.93% 2.29% 1.82% 1.40%
5 2.33% 3.24% 3.50% 4.62%
6 1.48% 0.68% 3.38% 2.46%
7 2.09% 0.67% 4.82% 3.89%
8 1.01% -0.21% 2.13% 1.40%
9 -0.20% -1.28% 1.36% 1.17%
10 0.94% -0.68% 0.98% 0.95%
11 -1.90% -3.18% 3.23% 3.56%
12 -1.55% -0.82% 0.93% 2.33%
Table 5. Estimation of events’ effect on portfolios’ systematic risk
This table presents the results of the Equation (5):
𝑅𝑖,𝑡− 𝑅𝑓,𝑡= 𝛼𝑖+ 𝛽𝑖(𝑅𝑚,𝑡− 𝑅𝑓,𝑡) + 𝛾𝑖𝑃𝐴2015 ∗ (𝑅𝑚,𝑡− 𝑅𝑓,𝑡) + 𝛿𝑖𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑃𝐴 ∗ (𝑅𝑚,𝑡− 𝑅𝑓,𝑡) + 𝑠𝑖𝑆𝑀𝐵𝑡 + ℎ𝑖𝐻𝑀𝐿𝑡+ 𝑚𝑖𝑀𝑀𝑡+ 𝜖𝑖,𝑡,
where 𝑃𝐴2015 takes a value of 1 if day t is one of the 60 trading days after the PA 2015 announcement on 12/12/2015;
otherwise, it takes a value of 0. 𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑃𝐴 takes a value of 1 if day t is one of the 60 trading days after the announcement of potential withdrawal from the PA 2015 by the former US president Donald Trump on 01/06/2017; otherwise, it takes a value of 0. 𝛾𝑖 and 𝛿𝑖 represent the effect of the PA announcement and the withdrawal announcement on systematic risk of portfolio i 60 trading days after each event. ***, **, and * indicate statistical significance at 1%, 5%, and 10% level, respectively.
ESG-rating portfolio
Env-rating portfolio
Abnormal-ESG portfolio
Abnormal-Env portfolio
[1] [2] [3] [4]
Intercept -0.0016*** -0.0017*** -0.0016*** -0.0016***
Rm - Rf -0.0006** -0.0004** -0.0009*** -0.0002
SMB -0.0029*** -0.0053*** 0.0003 0.0007**
HML -0.0006** 0.0031*** 0.001** 0.0016***
MM -0.0001 -0.0001 -0.0005** -0.0004**
PA2015*(Rm - Rf) 0.0001 -0.0007* 0.0003 0.0005
WithdrawPA*(Rm-Rf) 0.0002 -0.0003 -0.0007 -0.0004
Adj R squared 19% 46.90% 5.42% 9.27%
Obs. 652 652 652 652
Table 6. Statistics of ESG ratings and Environmental ratings
This table presents the statistics of ESG and environmental ratings. Data includes only companies which had ESG ratings reported continuously from 2012 to 2018. ***, **, and * indicate statistical significance at 1%, 5%, and 10% level, respectively.
2012 2013 2014 2015 2016 2017 2018
Panel A: ESG ratings
Obs. 805 805 805 805 805 805 805
Max 91.31 89.4 89.42 89.65 91.09 90.74 92.03
Min 5.65 5.18 7.88 7.21 9.85 6.92 10.01
Mean 42 41.64 42.51 47.24 49.38 50.69 50.88
Median 40.09 39.99 39.2 45.12 48.17 50.3 49.68
Std. 17.97 17.36 17.21 17.49 17.19 17.2 17.01
Change (%) 1.03%* 5.33%*** 13.83%*** 6.45%*** 3.66%*** 1.58%**
p 0.0962 <0.0001 <0.0001 <0.0001 <0.0001 0.0025
Panel B: Environmental ratings
Obs. 626 626 626 626 626 626 626
Max 98.52 98.51 98.55 98.3 97.78 98.2 99.1
Min 0.51 0.21 0.04 0.1 0.48 0.24 0
Mean 45.93 46.25 47.51 50.14 51.96 53.44 55.33
Median 45.39 46.05 49.68 51.82 55.03 56.32 58.95
Std. 27.13 27.13 26.67 26.45 26.17 25.94 25.55
Change (%) 6.92%** 22.25%** 20.22%*** 52.08%** 18.16%** 16.92%**
p 0.0397 0.0025 <0.0001 0.0276 0.0014 0.0077
