Abstract
The green bond (GB) is a new financial product in the green finance field that has recently become a corporate social responsibility (CSR) tool for organizations. Previous studies show that high-CSR firms receive more trust from shareholders during a financial crisis. This paper aims to assess the stock performance of publicly listed Chinese companies that issued GBs during the COVID-19 pandemic. The bond sample covers 2016–2019 and consists of 67 listed issuers. The paper uses the event study method based on the market and Fama-French (1993) three-factor models. Our results show that GB issuers exhibited significantly positive cumulative abnormal stock returns on the official announcement dates of the COVID-19 outbreak. The positive cumulative abnormal returns are mainly driven by non-financial GB issuers rather than financial GB issuers. The results reflect the attitudes of investors toward GB-issuing companies primarily in the context of the crisis and contribute to the development of green finance policies.
Keywords: Stock market, COVID-19, Green bond, CSR, Event study
Introduction
Environmental protection has become a global concern. Transitioning to a low-carbon economy is a crucial strategy to combat climate change. Green finance, also called climate finance, is rising to achieve the climate challenge by mobilizing the capital of the financial market for environmentally friendly projects. The green bond (GB) is one of the emerging green financial products and is also a fixed-income asset (Ferrer et al., 2021). To promote the financing and transparency of sustainable development projects, the International Capital Market Association published the Green Bond Principles, according to which GBs are intended to support projects with “environmental benefits.”1 This environmental purpose makes the most significant difference between GBs and conventional bonds. As part of corporate social responsibility (CSR), the company that issues GBs should disclose relevant information to the public to ensure the funds are used for qualified green projects.1 This action can help the issuing company build positive public perception; at the same time, it has to bear a considerable reputation risk stemming from possible violations of green guidelines.
Over the past decades, the GB market has witnessed rapid growth. The worldwide cumulative issuance of GBs grew from $0.8 billion in 2007 to $1 trillion at the end of 2020; the annual growth rate is around 95%.2 At the end of 2020, China approached 1.4 trillion yuan of GBs.3 Corporate bond issuance accounts for the largest share among all entities. Hence, Chinese corporates act as vital participants in the GB market. This market is expected to have tremendous potential thanks to the global mission of CO2 emission reduction expressed by the Paris Agreement, which planned to increase investment in renewable energy by $110 trillion by 2050.4 As the large emitter of greenhouse gases in the world, China actively joined the Paris Agreement. The 1st issuer of GB in China was published in 2015 by the wind energy firm Xinjiang Goldwind Science and Technology.5 So far, China has the largest GB market worldwide. Because of the lack of an international standard for identifying green assets, the definition of “green” varies among countries. Therefore, this paper will only discuss GBs issued by China after 2015 and focus on Chinese listed corporate issuers.
The GB market is a relatively young segment that started growing only in 2015. Academic research about it, therefore, is limited. Most scholars have focused on testing the pricing model of GBs (Hyun et al., 2021; Agliardi & Agliardi, 2021), the hedging effect of GBs compared to other assets (Jin et al., 2020), the link between GBs and other financial assets (Reboredo, 2018; Nguyen et al., 2021), and investors’ perception of “green” (Zerbib, 2019; Sangiorgi & Schopohl, 2021). Considerable research has been undertaken in management and finance to examine the global relationship between CSR and corporate performance. Flammer (2013), for example, argued that corporates’ eco-friendly and CSR activities would have a minimal positive effect on stocks, while eco-harmful activities would have an apparent adverse effect. However, few studies have discussed the stock performance of corporates issuing GBs, especially the impact of GBs on stock performance during the COVID-19 pandemic.
This paper studies the impact of the COVID-19 pandemic on GB issuers using the event study method. To improve the accuracy of event date selection for the pandemic, we set two event dates: December 31, 2019, and March 11, 2020. The market model’s event study reports an upward trend of cumulative abnormal returns (CARs) within the event window [-10, 10] for both event dates. Furthermore, there are significantly large positive CARs from the second day after the announcement to the tenth day. It indicates that the sample GB issuers generally have positive abnormal stock performance either when the pandemic breaks out in China or spreads out worldwide. However, by dividing the sampled companies into financial institutions and non-financial firms, the results of the CARs are different. After the event date, there are significant positive CARs for the non-financial issuers but negative CARs for the financial issuers in two cases. These results suggest that GB issuers in the financial sector are shocked by the COVID-19 crisis, while non-financial companies enjoy positive abnormal stock returns and more trust from investors regardless of whether it is the early or the late stage of the pandemic. To better test the results, the paper uses the Fama-French (1993) three-factor model to estimate the CAR values of the two event dates. Under the Fama-French model, the first event results are the opposite of the results based on the market model, but they are similar for the second event. The opposite results suggest that the positive CARs in the market model may be driven by small or value stocks. To summarize, for the whole sample, the pandemic positively impacts financial GB issuers’ CARs around the event dates. The non-financial issuers have positive CARs, while the financial issuers have negative CARs.
For market participants and policymakers, it is crucial to understand the stock performance of GB issuers during the COVID-19 pandemic. The pandemic tends to negatively impact the global stock market (Erdem, 2020) and positively impact the GB market (Yi et al., 2021; Lee & Lu, 2021) also revealed that CSR companies were less affected by the COVID-19 outbreak than non-CSR companies. The impact of the COVID-19 pandemic on the stock market might be moderated by GB issuance and other CSR activities. This study can help policymakers gauge the effectiveness of climate policies, develop a sustainable financial market, and construct a green financial system. Regarding investors, as a new interdisciplinary financial product, the GB is essential for investment portfolio diversification and hedging capability in the financial field and combating global climate change.
The remainder of this paper is structured as follows. Section 2 reviews the relevant literature and develops our hypotheses. The data concerning the sample and the methodology are presented in Sect. 3. Section 4 discusses the study’s results, and Sect. 5 concludes the paper.
Literature Review
Recent developments in green finance have led to an interest in the GB market. Important topics include the benefits of GBs as a CSR tool, the relationship between GBs and other financial markets, and the impact of the COVID-19 pandemic on the stock market and the GB market. This literature review focuses on these topics in the following subsections.
GB and CSR
The growth of the GB market is in line with the practice of CSR by organizations (Febi et al., 2018). CSR has long been a question of great interest across many fields. Previous research has established that high CSR participation can help the financial performance of corporations. For example, Mumtaz & Yoshino (2021) indicate that initial public offerings by greener firms have better long-term performance. Several researchers (e.g., Heinkel et al., 2001; El Ghoul et al., 2011) suggest that CSR activities can help corporations reduce capital costs.
Furthermore, with the development of green finance, some studies have investigated the link between CSR and GBs. Paranque & Revelli (2019) analyze the ethical value of issuing GBs through qualitative research on their social impacts. Under a transparent green financial regulatory regime, GBs are an effective intermediary for companies to exchange financing for social development (Paranque & Revelli, 2019) and financial benefits (Tang & Zhang, 2020; Flammer, 2018; Agliardi & Agliardi, 2021). For companies, the issuance of GBs positively impacts their credit quality (Agliardi & Agliardi, 2021) and CSR scores (Zhou & Cui, 2019). Regarding institutional investors, survey evidence from Krueger et al., (2020) shows that they consider the climate risk and prefer investment in environmental, social, and governance (ESG) oriented assets and that investors’ sentiments affect GB returns and volatility (Pham & Huynh, 2020; Pineiro-Chousa et al., 2021).
However, the effectiveness of CSR on a corporation’s financial performance has been subject to considerable scrutiny. An asymmetric pattern seems to exist in which firms that increase environmental friendliness do not create shareholder value (Fernando et al., 2017), while eco-harmful activities negatively affect stock values (Flammer, 2013). Investors also avoid eco-friendly companies (Fernando et al., 2017). Similarly, Kruger (2015) proposes a small negative stock response to a firm’s positive CSR news. Cui & Docherty (2020) find that ESG events only impact the stock market in the short term, with no long-term effect, suggesting that “greenness” may have no close relationship with financial outcomes.
The Link Between GBs and Other Financial Assets
Another significant discussion concerning GBs is the link between these bonds and other financial assets over different time horizons (Reboredo, 2018; Reboredo & Ugolini, 2020; Reboredo et al., 2020; Nguyen et al., 2021; Ferrer et al., 2021; Pham, 2021; Liu et al., 2021). This discussion also considers hedging effects and diversification benefits. Reboredo & Ugolini (2020) prove that GBs are closely linked to the currency and fixed-income markets while weakly tied to the energy market. Supporting this statement using different empirical methodologies, Ferrer et al., (2021) find connections among GBs, Treasury bonds, and high-quality corporate bonds, while they find little connection among GBs, stocks, oil, and clean energy markets. Furthermore, Pham (2021) finds that GBs and green stocks are closely linked only during extreme market conditions. In other words, GBs have significant volatility spillovers from other markets. The diversification advantages of GBs with the stock market are confirmed by Reboredo (2018), Reboredo & Ugolini (2020), Reboredo et al., (2020), and Pham (2021). Consequently, GBs are vital hedging instruments in other markets, such as the commodity market (Naeem et al., 2021a) and the energy market (Jin et al., 2021).
For companies, the issuance of GBs positively impacts the issuer’s stock price (Tang & Zhang, 2020; Flammer, 2013; Zhou & Cui, 2019; Baulkaran, 2019) examined the stock reaction to the announcement of GB issuance. The CARs are positive and significant, consistent with Tang and Zhang’s (2020) findings. Wang et al., (2020) also revealed positive announcement stock returns for Chinese GB new issues, consistent with the stakeholder value maximization theory that corporate engagement in sustainable financing practice increases firm value in the long run and thus is favored by shareholders. However, Lebelle et al., (2020) show that the stock market reacts negatively to the announcement of international GB issuances.
The above review shows that GBs bring diversification and value-enhancing benefits to firms. The literature discusses the stock price responses to issuing GBs and the hedging benefits of these bonds. The impact of a GB on a corporation in China, particularly in the context of COVID-19, is unexplored.
The Impact of the COVID-19 Pandemic on the Stock Market and the GB Market
Since the beginning of the COVID-19 health crisis in 2020, researchers have widely investigated this phenomenon. Many scholars have focused on the stock market, while others have concentrated on the pandemic’s global impact (Erdem, 2020; Ramelli & Wagner, 2020; Zhang et al., 2020; Ashraf, 2020; Narayan et al., 2021; Engelhardt et al., 2021a, b). Some researchers have studied specific countries or regions (Al-Awadhi et al., 2020; Rahman et al., 2021; Sun et al., 2021; He et al., 2020; Lee & Lu, 2021). For example, Erdem (2020) and Ashraf (2020) use panel data from several countries to examine the global negative stock influence of COVID-19. Al-Awadhi et al., (2020), Sun et al., (2021), and He et al., (2020) clarify the different consequences of the COVID-19 pandemic on various Chinese industries. Their findings show the overall negative impact of the outbreak on the stock market. However, pharmaceutical, IT, and agricultural companies exhibit resilience during the pandemic.
Regarding the global shock of COVID-19 on the financial market, few studies have investigated how the pandemic influences the effectiveness of the hedging role of GBs. Using the event study method, Yi et al., (2021) found that COVID-19 disrupts Chinese green financing. They find that the COVID-19 pandemic has significantly impacted China’s GB market and greatly increased the CAR of the GBs. Naeem et al., (2021b) show that the GB market is more efficient during the pandemic. Taghizadeh-Hesary et al., (2021) also reveal that GBs in Asia tend to show higher returns than those in Europe and North America.
Based on investors’ pursuit of stability rather than return in times of crisis, some researchers (Lins et al., 2017; Engelhardt et al., 2021a, b) focus on evaluating the financial performance of firms practicing CSR activities during a crisis. Their findings show similar results. Lins et al., (2017) found that high-CSR firms obtained more trust and stock returns even during the financial crisis of 2008–2009. Engelhardt et al., (2021a, b) note the higher stock returns and lower volatility in high-ESG-related European firms during the COVID-19 pandemic. Moreover, Lee & Lu (2021) use the event study approach to compare CSR- and non-CSR-practicing companies’ stock reactions to the COVID-19 outbreak in Taiwan. They indicate that CSR-practicing companies were less affected by the outbreak and that their stock prices recovered faster than non-CSR-practicing companies.
Overall, the COVID-19 pandemic appears to hurt stock markets but positively impacts GBs and high-CSR firms. Therefore, the following hypothesis is proposed:
Hypothesis 1
Chinese corporations that issue GBs have positive abnormal stock returns during the COVID-19 pandemic.
Financial vs. Non-financial Firms
Foerster & Sapp (2005) have pointed out that it is common practice in many empirical studies in finance to exclude financial services firms from the samples. The standard argument for this exclusion is that financial services firms differ from typical firms because they tend to have much greater leverage and increased sensitivity to financial risks. For example, Fama & French (1993) explicitly exclude financial firms because of their high leverage. Since the primary purpose of financial institutions issuing GBs is to obtain funding, increase their capital liquidity, and invest in other firms’ projects (Tang & Zhang, 2020), the greenness of the bond project may be reduced, making the market reactions less trustworthy. Therefore, this study separates the non-financial issuers from the financial service issuers to further test whether they have different stock performances.
Taghizadeh-Hesary et al., (2021) pointed out that the Asian GBs market is dominated by the banking sector, representing 60% of all issuance. Given that bonds issued by this sector tend to show lower returns than average, they recommend different tax policies for the bonds issued by the banking sector. Lebelle et al., (2020) split their sample into financial and non-financial GBs. They show that financial GBs issuers experience significantly lower CARs than non-financial corporations in response to the announcement of GB issuances. Considering the lower abnormal returns for financial firms, we propose the following hypothesis.
Hypothesis 2
The positive impact of the COVID-19 pandemic on the stock performance of GB issuers is mainly driven by non-financial firms.
Data and Methodology
Data and Sample
This study employs three data sources: the WIND economic database, the China Stock Market & Accounting Research (CSMAR) database, and the Central University of Finance and Economics (CUFE) database. From the WIND database, we obtained information on all existing GBs in the current Chinese market. This information included bond ID, bond name, issuer, nature of the issuing company (private or state-owned, publicly listed or not), coupon rate, and debt rating. The data includes the GBs issued from mainland China and are not classified by bond types. The Chinese CSI300 stock market index daily returns and the sample companies’ daily stock returns from March 11, 2019, to April 10, 2020, were also obtained from WIND.
From the CSMAR database, we downloaded the sample of 1,607 Chinese GBs issued in mainland China from 2016 to 2019, containing bond ID, issuer name, issuer rating, bond characteristics (e.g., par value, bond nature, and bond credit level), and project investment. The information on GBs from WIND was matched and merged with that from CSMAR, using bond ID to identify the issuers’ attributes and filter out the listed firms. By excluding the private issuers, Hong Kong-listed companies, repeat information, and bonds with missing important information (e.g., issue price, par value, bond credit level), 273 GBs and 67 issuers remained. Of the 67 issuers, 20 are financial institutions, and 47 are public companies in other industries. Finally, the daily Fama-French three-factor indexes are retrieved from the CUFE database.
Methodology
The event study method is a standard research method in finance to test the effect of a specific event or policy by looking at the abnormal performance (Kothari & Warner, 1997). Following Huang and Liu’s (2021) and Tang and Zhang’s (2020) model, we use the event study approach to examine the impact of the COVID-19 pandemic on Chinese GB-issuing companies. The event window selection is variable to test the impact during a limited time interval and border times. Furthermore, the paper use models of abnormal returns to measure the sample’s excess returns within the event window. In order to estimate the expected return (ER) and abnormal return (AR), the market model is used based on the following calculations,
 |
1 |
 |
2 |
 |
3 |
where 
is the expected return of firm 
on the t-th day based on the estimation window. 
indicates the excess return of market index m on the t-th day, and α, β, and µ denote the intercept, the slope of the market model, and a random error term, respectively. In Eq. (3), 
represents the abnormal return of the listed company 
on the t-th day, and 
is the stock return of the firm 
on the t-th day.
CARs are computed as follows:
 |
4 |
 |
5 |
 |
6 |
where 
is the cumulative abnormal return of company 
with the time interval T [t1, t2], from initial moment t1 to final moment t2. In Eq. (5), 
calculates the average abnormal return of all the GB-issuing listed firms on the t-th day, where N is the number of the firms (from 1 to n). Similarly, in Eq. (6), 
is the cumulative average abnormal return of all the companies in the sample within the time interval T on the t-th day.
The table reports descriptive statistics for the sample of 67 Chinese public companies that issued GBs from 2016 to 2019. The dataset of stock closing prices and market stock returns is obtained from the WIND database. Using the event study approach, we select two event dates when t = 0. One was December 31, 2019, when the news of the COVID-19 outbreak in Wuhan was announced. Another event day is March 11, 2020, when the World Health Organization declared the COVID-19 pandemic. The estimation event window is [-200, -20]. The event window is [-10, 10]. The raw returns (
) are calculated from daily stock closing prices. The Chinese CSI300 stock market index return is considered the daily market return (
). The expected return (E(R)) is the expected return within the predicted event window based on the market model. The abnormal returns (AR) equal raw returns minus the expected return based on a market model. The cumulative abnormal returns (CAR) are the sums of each company’s abnormal returns within the event window.
To accurately study the investors’ response to the outbreak, we choose two different news points as event days. On December 31, 2019, it was officially confirmed that the novel virus originated from a Wuhan seafood market. Following this announcement, news of the pandemic attracted significant attention, affecting investors’ attitudes toward the sampled companies. On March 11, 2020, World Health Organization declared a global pandemic outbreak of the novel coronavirus (COVID-19) (Cucinotta & Vanelli, 2020). The event window is ten days before and after the announcement date (t = 0). Specifically, the event window interval T is [-10, 10]. The estimation window is defined as [-200, -20] to avoid confounding effects. Table 1 shows the summary statistics of the stock performance of the Chinese GB issuers in our sample.
Table 1.
Descriptive of chinese GBs issuers
| Variables | N | Mean | StdDev | Min | Max | Skewness | Kurtosis |
|---|---|---|---|---|---|---|---|
|
15,631 | -0.3356 | 2.1765 | -10.1266 | 43.9394 | 0.6659 | 19.1548 |
|
15,631 | 0.0396 | 1.2880 | -7.8809 | 3.8608 | -1.1190 | 10.3250 |
| E(R) | 15,631 | -0.1858 | 1.9751 | -12.8163 | 4.1960 | -2.7517 | 13.0970 |
| AR | 1,386 | -0.0347 | 2.3473 | -10.9260 | 17.1436 | 1.1465 | 11.1253 |
| CAR | 1,386 | -0.5474 | 9.3571 | -23.6497 | 55.5644 | 2.1368 | 8.3158 |
| CAAR | 15,631 | -0.7608 | 12.3881 | -18.5594 | 72.4265 | 3.1129 | 18.2403 |
We used the t-test method to test the event study results, investigating whether the COVID-19 crisis impacted the firms’ stock performance. The calculation is based on the following equations,
 |
7 |
 |
8 |
where S is the standard deviation. Finally, the study focuses on the CAR results of two events to investigate the cumulative stock price influence of the COVID-19 pandemic on Chinese GB issuers in the event window.
Results and Discussions
Main Results
Figure 1 presents the CAR trend for Chinese GB-issuing companies impacted by the COVID-19 pandemic during the event window [-10, 10]. Figure 1 shows a positive and rising trend of CARs of all the companies during the event window of the announcement date, December 31, 2019. The exact values and t-test results are presented in Table 2, which shows that all the companies have significant small positive CARs at the announcement date. From the second to the tenth day after the event date, CARs remain significantly positive. This result suggests that COVID-19 positively impacts the stock performance of Chinese companies that issued GBs.
Fig. 1.
CARs of the first event on December 31, 2019
Table 2.
CARs in the early COVID-19 period (December 31, 2019)
| Event Date: 2019/12/31 | ||
|---|---|---|
| Event window | CAR | t value |
| [-10, 10] | 2.7831*** | 2.9072 |
| [-5, 5] | 1.8259*** | 2.7824 |
| [-4, 4] | 1.3683*** | 3.1704 |
| [-3, 3] | 1.0157*** | 2.6756 |
| [-2, 2] | 0.2171 | 0.7791 |
| [-1, 1] | -0.1902 | -1.0877 |
| [0] | 0.2206* | 1.6857 |
| [-10, 0] | 1.4550*** | 2.8427 |
| [-5, 0] | 0.8354* | 1.9352 |
| [-2, 0] | -0.1755 | -0.8591 |
| [-1, 0] | -0.0606 | -0.3413 |
| [-10, -1] | 1.2343** | 2.3636 |
| [-5, -1] | 0.6147 | 1.3700 |
| [0, 1] | 0.0911 | 0.6251 |
| [0, 2] | 0.6132*** | 2.8399 |
| [0, 3] | 1.2902*** | 3.6384 |
| [0, 4] | 1.7241*** | 4.4815 |
| [0, 5] | 1.2111** | 2.4610 |
| [0, 10] | 1.5488** | 2.1970 |
| [1, 5] | 0.9905** | 2.1339 |
| [1, 10] | 1.3282* | 1.9632 |
The table reports the event study result on the event date, December 31, 2019, when the official news announced the epidemic in Wuhan, China. The event study method utilizes the market model based on the estimation event window [-200, -20] and event window [-10, 10]. The CARs are sums of each GB issuing company’s abnormal returns within the event window. ***-stat. sig. at 1% level. **-stat. sig. at 5% level. *-stat. sig. at 10% level
The second event day was March 11, 2020, when China passed the pandemic’s peak, but World Health Organization (WHO) declared the novel coronavirus (COVID-19) outbreak a global pandemic (Cucinotta & Vanelli, 2020). Figure 2 shows a continuous upward trend of CARs during the event window of the second event date [-10, 10]. Furthermore, the detailed results in Table 3 show significantly positive CAR values lasting from one day after the event to the tenth day. CARs on the event day are also significantly positive. It indicates that all the companies have better stock performance in the late COVID-19 pandemic period than in the early period
Fig. 2.
CARs of the second event on March 11, 2020
Table 3.
CARs in the late COVID-19 period (March 11, 2020)
| Event Date: 2020/3/11 | ||
|---|---|---|
| Event window | CAR | t value |
| [-10, 10] | 9.8649*** | 7.7556 |
| [-5, 5] | 6.0783*** | 7.2432 |
| [-4, 4] | 5.1156*** | 6.9490 |
| [-3, 3] | 4.8605*** | 7.9611 |
| [-2, 2] | 2.1348*** | 3.3409 |
| [-1, 1] | 0.0977 | 0.1772 |
| [0] | 0.8507*** | 3.3875 |
| [-10, 0] | 3.6833*** | 3.4898 |
| [-5, 0] | 0.7136 | 1.2133 |
| [-2, 0] | 1.0540** | 2.3293 |
| [-1, 0] | -0.2516 | -0.5662 |
| [-10, -1] | 2.8325*** | 2.8921 |
| [-5, -1] | -0.1371 | -0.2600 |
| [0, 1] | 1.2000*** | 3.2378 |
| [0, 2] | 1.9315*** | 4.1305 |
| [0, 3] | 4.3786*** | 8.9198 |
| [0, 4] | 5.1299*** | 8.3202 |
| [0, 5] | 6.2154*** | 9.0313 |
| [0, 10] | 7.0323*** | 9.8905 |
| [1, 5] | 5.3647*** | 7.4515 |
| [1, 10] | 6.1816*** | 8.4943 |
The table reports the event study result on the second event date, March 11, 2020, when the WHO declared the COVID-19 pandemic. The event study method utilizes the market model based on the estimation event window [-200, -20] and event window [-10, 10]. The CARs are sums of each GB issuing company’s abnormal returns within the event window. ***-stat. sig. at 1% level. **-stat. sig. at 5% level. *-stat. sig. at 10% level
Analyzing Financial and Non-financial GB-Issuers in the Event Windows
As discussed in Sect. 2.4, financial firms might behave differently from non-financial firms due to their high leverage. To better understand the impact of the COVID-19 pandemic on GB-issuing companies, we divide the 67 firms into financial and non-financial companies to determine whether the company’s financial nature affects the results.
A comparison of the abnormal performance of GB issuers in the financial and non-financial sectors is presented in Table 4, where the CARs are based on the first event date, December 31, 2019. For non-financial firms, the CARs are negative two days before the event. The value is statistically positive at the 10% level on the announcement day and becomes significantly positive at the 1% level from the second day to the tenth day after the event. It indicates that the pandemic positively impacts the non-financial GB issuers’ stock, and these firms may have a relatively significant hedging effect during the initial stage of the crisis.
Table 4.
CARs of financial and non-financial issuers on December 31, 2019
| Event Date: 2019/12/31 | |||||
|---|---|---|---|---|---|
| Event window | Non-financial | Financial | Difference | ||
| CAR | t value | CAR | t value | ||
| [-10, 10] | 4.0973*** | 3.3604 | -0.6483 | -0.6492 | 4.7456 |
| [-5, 5] | 2.7348*** | 3.3603 | -0.5474 | -0.6467 | 3.2822 |
| [-4, 4] | 1.8601*** | 3.5582 | 0.0840 | 0.1229 | 1.7761 |
| [-3, 3] | 1.4712*** | 3.1717 | -0.1738 | -0.3056 | 1.6450 |
| [-2, 2] | 0.5881 | 1.6516 | -0.7516** | -2.6040 | 1.3397 |
| [-1, 1] | -0.1656 | -0.9766 | -0.2543 | -0.5522 | 0.0887 |
| [0] | 0.2386* | 1.7751 | 0.1738 | 0.5362 | 0.0648 |
| [-10, 0] | 1.3356** | 2.1983 | 1.7666* | 1.8169 | -0.4310 |
| [-5, 0] | 0.9031* | 1.7784 | 0.6585 | 0.7822 | 0.2446 |
| [-2, 0] | -0.0387 | -0.1494 | -0.5326* | -1.8564 | 0.4939 |
| [-1, 0] | -0.0953 | -0.6449 | 0.0300 | 0.0575 | -0.1253 |
| [-10, -1] | 1.0971* | 1.7217 | 1.5927* | 1.7499 | -0.4956 |
| [-5, -1] | 0.6646 | 1.2329 | 0.4846 | 0.5863 | 0.1800 |
| [0, 1] | 0.1683 | 1.0063 | -0.1105 | -0.3729 | 0.2788 |
| [0, 2] | 0.8654*** | 3.2318 | -0.0452 | -0.1488 | 0.9106 |
| [0, 3] | 1.8964*** | 4.2691 | -0.2926 | -0.8766 | 2.1890 |
| [0, 4] | 2.3225*** | 4.7615 | 0.1615 | 0.4439 | 2.1610 |
| [0, 5] | 2.0702*** | 3.3199 | -1.0320** | -2.7987 | 3.1022 |
| [0, 10] | 3.0002*** | 3.4215 | -2.2410*** | -5.6906 | 5.2412 |
| [1, 5] | 1.8316*** | 3.1572 | -1.2058*** | -3.0264 | 3.0374 |
| [1, 10] | 2.7617*** | 3.3082 | -2.4148*** | -6.0568 | 5.1765 |
The table reports the event study results on the event date, December 31, 2019, by dividing the sample companies into financial and non-financial companies. The estimation and event windows are [-200, -20] and [-10, 10], respectively. The CARs are sums of each GB issuing company’s abnormal returns within the event window. ***-stat. sig. at 1% level. **-stat. sig. at 5% level. *-stat. sig. at 10% level
For the financial companies, the CARs are positive but insignificant on the first event day, similar to the non-financial companies’ situation. However, the CARs change to negative values from the second day to the tenth day after the event date. These opposite results reflect the better stock performance of the non-financial companies during the initial COVID-19 outbreak. This result is consistent with the literature that financial firms show lower CARs than non-financial firms in response to GB issuances (Lebelle et al., 2020). It also suggests that investors may have had less trust and confidence in financial GB issuers after the COVID-19 outbreak because financial issuers tend to have high leverage and might dilute the greenness of the bond project. As Tang & Zhang (2020) pointed out, corporate GBs are used to finance issuers’ projects, and project eligibility criteria are specified. In contrast, financial institutions issue GBs to make green loans and invest in other firms to finance other firms’ projects and will only define general criteria for selecting green projects. This practice contributes to further reduction of the greenness of the bond project.
Table 5 reports the comparison for the second event date, March 11, 2020. For the non-financial issuers, the results are similar to those in Table 4. There is a significant positive abnormal stock performance after the announcement day and on the event day. For the financial issuers, there are negative CAR values from the event date to the tenth day after the event, which contrasts with the situation of the non-financial companies. Similar to the first event, the stock price of the financial institution issuers deteriorates after the spread of the COVID-19 outbreak, while the stock performance of the non-financial corporate GB issuers improves. Non-financial companies include firms from multiple sectors, such as manufacturing and energy. After the production of non-financial companies resumes, investors still show higher levels of trust in non-financial GB issuers.
Table 5.
CARs of financial and non-financial issuers on March 11, 2020
| Event Date: 2020/3/11 | |||||
|---|---|---|---|---|---|
| Event window | Non-financial | Financial | Difference | ||
| CAR | t value | CAR | t value | ||
| [-10, 10] | 9.8649*** | 5.5450 | 3.0089*** | 4.0464 | 6.8560 |
| [-5, 5] | 6.0783*** | 5.7358 | -3.6570** | -2.8551 | 9.7353 |
| [-4, 4] | 5.1156*** | 5.4645 | 0.5231 | 0.5042 | 4.5925 |
| [-3, 3] | 4.8605*** | 6.3724 | -0.4650 | -0.4667 | 5.3255 |
| [-2, 2] | 2.1348*** | 2.7596 | -0.1097 | -0.1099 | 2.2445 |
| [-1, 1] | 0.0977 | 0.1304 | -2.9611*** | -5.2061 | 3.0588 |
| [0] | 0.8507** | 2.5134 | -0.7679*** | -3.3633 | 1.6186 |
| [-10, 0] | 3.6833** | 2.4865 | 5.1526*** | 10.0655 | -1.4693 |
| [-5, 0] | 0.7136 | 0.9101 | 0.4193 | 0.5871 | 0.2943 |
| [-2, 0] | 1.0540* | 1.7452 | -0.5529 | -1.0102 | 1.6069 |
| [-1, 0] | -0.2516 | -0.4142 | -2.8407*** | -6.7480 | 2.5891 |
| [-10, -1] | 2.8325** | 2.0550 | 5.9205*** | 11.4508 | -3.0880 |
| [-5, -1] | -0.1371 | -0.1968 | 1.1872* | 1.7249 | -1.3243 |
| [0, 1] | 1.2000** | 2.3936 | -0.8883** | -2.2435 | 2.0883 |
| [0, 2] | 1.9315*** | 3.5518 | -0.3247 | -0.4180 | 2.2562 |
| [0, 3] | 4.3786*** | 7.5678 | -0.8127 | -0.9525 | 5.1913 |
| [0, 4] | 5.1299*** | 6.7319 | -1.7444* | -1.8099 | 6.8743 |
| [0, 5] | 6.2154*** | 7.4058 | -4.8442*** | -4.1359 | 11.0596 |
| [0, 10] | 7.0323*** | 7.2811 | -2.9115*** | -4.0171 | 9.9438 |
| [1, 5] | 5.3647*** | 5.9481 | -4.0763*** | -3.8596 | 9.4410 |
| [1, 10] | 6.1816*** | 6.1548 | -2.1436*** | -3.3038 | 8.3252 |
The table reports the event study results on the event date, December 31, 2020, by dividing the sample companies into financial and non-financial companies. The estimation and event windows are [-200, -20] and [-10, 10], respectively. The CARs are sums of each GB issuing company’s abnormal returns within the event window. ***-stat. sig. at 1% level. **-stat. sig. at 5% level. *-stat. sig. at 10% level
Alternative Models
To conduct robustness checks, we choose the Fama-French three-factor model (Fama & French, 1993). We used this as an alternative method to reestimate expected stock returns and obtain new CAR values. Doing so allowed us to investigate whether the conclusions of the event studies based on two different return models were the same. Using the Fama-French three-factor model, the abnormal return is calculated as follows:
 |
9 |
 |
10 |
 |
11 |
where 
, 
, and 
are estimated by regressing company i’s returns on three factors—daily market excess returns, firm size factor, and book-to-market—in the estimation windows [-200, -20]. 
indicates the excess return of the market index. 
and 
are the high-minus-low book-to-market and the small-minus-big size factors on day t, respectively. Fama & French (1993) sort firms into six portfolios based on large/small market-capitalization and large/intermediate/small book-to-market ratios. HML is the difference between the simple average of the returns on the two high book-to-market portfolios and the average of the returns on the two low book-to-market portfolios. SMB is the difference between the simple average of the returns on the three small-capitalization portfolios and the simple average of the returns on the three big-capitalization portfolios. AR1 is the abnormal return based on Fama-French three-factor model. CARs are calculated based on Eq. (4) in the methodology section.
Table 6 presents the Fama-French CAR values for the first event date, December 31, 2019, and compares the results for the financial institutions and non-financial firms. Based on the Fama-French three-factor model, the CAR results for the first event are very different from those obtained with the market model. In this case, all the firms suffer a significantly negative shock after the COVID-19 outbreak. Specifically, under the Fama-French model, the financial institution issuers exhibit significantly positive abnormal stock returns, while the non-financial corporate issuers have significantly negative abnormal stock returns. After adding more risk factors, the sampled companies have different benchmarks when estimating the expected returns. Thus, the outperformance of CARs in the market model might be driven by size or value effects (driven by small stocks or high book-to-market stocks) and disappears once the additional risk factors are considered.
Table 6.
Fama-French CARs on December 31, 2019
| Event Date: 2019/12/31 | ||||||
|---|---|---|---|---|---|---|
| Event window | All companies | Non-financial | Financial | |||
| CAR | t value | CAR | t value | CAR | t value | |
| [-10, 10] | -5.6778*** | -6.6533 | -5.6778*** | -5.0131 | -4.3197*** | -4.1698 |
| [-5, 5] | -5.7793*** | -9.4432 | -5.7793*** | -7.3421 | -1.9438** | -2.3555 |
| [-4, 4] | -2.8272*** | -6.7031 | -2.8272*** | -5.3985 | -2.2468*** | -3.2427 |
| [-3, 3] | -3.3614*** | -8.3588 | -3.3614*** | -6.8045 | -2.3031*** | -3.6186 |
| [-2, 2] | -1.5800*** | -5.4908 | -1.5800*** | -4.3873 | -1.418*** | -4.1644 |
| [-1, 1] | -1.4064*** | -7.1920 | -1.4064*** | -7.4386 | -0.8139 | -1.6614 |
| [0] | -0.2952** | -2.2645 | -0.2952** | -2.1418 | 0.1704 | 0.5692 |
| [-10, 0] | -2.3477*** | -4.3402 | -2.3477*** | -3.9391 | -4.136*** | -3.5648 |
| [-5, 0] | -2.3215*** | -5.5133 | -2.3215*** | -4.7203 | -4.152*** | -5.0353 |
| [-2, 0] | -0.4468* | -1.8957 | -0.4468* | -1.6991 | -4.4139*** | -11.439 |
| [-1, 0] | -0.2749 | -1.3912 | -0.2749* | -1.7748 | -3.0383*** | -5.4515 |
| [-10, -1] | -2.0524*** | -3.7832 | -2.0524*** | -3.2655 | -4.3064*** | -4.0284 |
| [-5, -1] | -2.0262*** | -4.6384 | -2.0262*** | -3.8639 | -4.3224*** | -5.372 |
| [0, 1] | -1.4267*** | -8.9261 | -1.4267*** | -8.212 | 2.3949*** | 6.836 |
| [0, 2] | -1.4284*** | -6.3254 | -1.4284*** | -5.2719 | 3.1663*** | 7.5524 |
| [0, 3] | -2.2872*** | -6.6641 | -2.2872*** | -5.2414 | 3.2849*** | 6.5506 |
| [0, 4] | -1.7902*** | -5.2114 | -1.7902*** | -4.1084 | 3.6366*** | 6.7631 |
| [0, 5] | -3.7531*** | -8.3245 | -3.7531*** | -6.4253 | 2.3786*** | 4.1057 |
| [0, 10] | -3.6253*** | -6.0883 | -3.6253*** | -4.6396 | -0.0133 | -0.0212 |
| [1, 5] | -3.4579*** | -8.1581 | -3.4579*** | -6.4627 | 2.2082*** | 3.6392 |
| [1, 10] | -3.3301*** | -5.7843 | -3.3301*** | -4.4678 | -0.1837 | -0.2801 |
The table reports the event study results on the event date, December 31, 2019, using the Fama-French three-factor model. The estimation and event window are [-200, -20] and [-10, 10], respectively. The CARs are sums of each GB issuing company’s abnormal returns within the event window. ***-stat. sig. at 1% level. **-stat. sig. at 5% level. *-stat. sig. at 10% level
The Fama-French reestimation results for the second event date, March 11, 2020, are presented in Table 7. They are similar to the market model for the pandemic mitigation scenario. All the firms show significantly positive abnormal returns after the event date, with financial firms being significantly negatively affected on the fourth and fifth day after the event. Therefore, the positive abnormal returns are more robust during the late than the early COVID-19 periods.
Table 7.
Fama-French CARs on March 11, 2020
| Event Date: 2020/3/11 | ||||||
|---|---|---|---|---|---|---|
| Event window | All companies | Non-financial | Financial | |||
| CAR | t value | CAR | t value | CAR | t value | |
| [-10, 10] | 4.3361*** | 3.5675 | 4.3361** | 2.5405 | 3.3070*** | 4.9569 |
| [-5, 5] | 2.8168*** | 3.5616 | 2.8168*** | 2.7780 | -2.5859** | -2.4947 |
| [-4, 4] | 2.5350*** | 3.6002 | 2.5350*** | 2.8389 | 1.2344 | 1.3504 |
| [-3, 3] | 2.3789*** | 4.2613 | 2.3789*** | 3.3931 | -0.5077 | -0.5861 |
| [-2, 2] | 1.6355*** | 2.6603 | 1.6355** | 2.1906 | -0.1638 | -0.1759 |
| [-1, 1] | 0.1988 | 0.3641 | 0.1988 | 0.2676 | -2.6417*** | -4.8281 |
| [0] | 0.4863* | 1.9661 | 0.4863 | 1.4589 | -0.8493*** | -3.6697 |
| [-10, 0] | -0.3714 | -0.358 | -0.3714 | -0.2561 | 5.3835*** | 8.5929 |
| [-5, 0] | -0.8609 | -1.5427 | -0.8609 | -1.1589 | 1.9257** | 2.7765 |
| [-2, 0] | 0.7287 | 1.6534 | 0.7287 | 1.2357 | -0.5557 | -1.0570 |
| [-1, 0] | -0.0181 | -0.0412 | -0.0181 | -0.0300 | -2.7113*** | -6.6197 |
| [-10, -1] | -0.8577 | -0.8885 | -0.8577 | -0.6344 | 6.2328*** | 10.0401 |
| [-5, -1] | -1.3472** | -2.6349 | -1.3472** | -2.0158 | 2.7751*** | 4.1289 |
| [0, 1] | 0.7032* | 1.9442 | 0.7032 | 1.4337 | -0.7797** | -2.0930 |
| [0, 2] | 1.3932*** | 3.0835 | 1.3932** | 2.6439 | -0.4574 | -0.6260 |
| [0, 3] | 2.6076*** | 5.6863 | 2.6076*** | 4.7763 | -1.2946 | -1.6864 |
| [0, 4] | 3.0756*** | 5.2334 | 3.0756*** | 4.2011 | -2.0195** | -2.3036 |
| [0, 5] | 4.1640*** | 6.3018 | 4.1640*** | 5.0746 | -5.3609*** | -5.0889 |
| [0, 10] | 5.1938*** | 7.4264 | 5.1938*** | 5.4444 | -2.9258*** | -4.1981 |
| [1, 5] | 3.6777*** | 5.2341 | 3.6777*** | 4.1062 | -4.5116*** | -4.7723 |
| [1, 10] | 4.7075*** | 6.4990 | 4.7075*** | 4.6930 | -2.0765*** | -3.3645 |
The table reports the event study results on the event date, March 11, 2020, using the Fama-French three-factor model. The estimation and event window are [-200, -20] and [-10, 10], respectively. The CARs are sums of each GB issuing company’s abnormal returns within the event window. ***-stat. sig. at 1% level. **-stat. sig. at 5% level. *-stat. sig. at 10% level
Although most event studies utilized the market model and the Fama-French model to estimate the expected return and subsequent abnormal return (Kothari & Warner, 1997; Lebelle et al., 2020), the market model used for computing abnormal returns could have been adjusted by adding another exogenous variable to account for global shocks if any. Since the United States dominates the international financial market, we adopted S&P 500 index returns as our exogenous variable. After adding this additional risk factor, our results are shown in Table A1. The CARs based on [-10, 10] event days around March 11, 2020, show significantly positive numbers for all the companies and the non-financial companies but a negative and insignificant number for the financial companies. Such an observation is consistent with our results for the market model and the Fama-French model.
Conclusion
GB is an emerging financial product related to CSR and green finance. China has the biggest GB market in the world. The impact of GB issuances and CSR activities on company share prices has been widely researched but remains controversial. Several studies investigate this topic in the context of the COVID-19 pandemic, but there is still a gap in research on the pandemic’s impact on the stock performance of the companies that have issued GBs. This study fills this gap by exploring such an impact on Chinese GB issuers.
We select two event days and discuss the stock price performance of 67 Chinese GB-issuing firms during the pandemic using an event study methodology. Furthermore, we divide the sample into financial and non-financial companies, and we use the market model and the Fama-French three-factor model to compare the estimated abnormal return results. Our findings show that (1) at the early period of the pandemic, the two return models present different results, suggesting that the outperformance in the market model is mainly driven by small or value stocks. (2) at the late period of the pandemic, the two return models present similar results, with GB issuers having significantly positive abnormal stock returns and non-financial issuers performing better.
The different results for the non-financial and the financial companies are consistent with Sun et al., (2021) and He et al., (2020), who indicate that different industries are affected by the COVID-19 pandemic to different degrees. However, this study only distinguishes between the financial and non-financial sectors because of its small sample size. Future research could discuss whether companies issuing GBs have different stock market performances across various industries. Furthermore, this study employs the market and Fama-French three-factor models in the event study methodology. Future research could consider the results of other models, such as the four-factor model and the generalized autoregressive conditional heteroskedasticity model.
Acknowledgements
This work was supported by the National Natural Science Foundation of China [No. 71772111], the Student Partnering with Faculty Research Program of Wenzhou-Kean University [No. WKUSPF202222], and the Internal Research Support Program of Wenzhou-Kean University [No. IRSPG202206].
Appendix
Table A1.
Adjusted Market Model CARs on March 11, 2020
| Event Date: 2020/3/11 | ||||||
|---|---|---|---|---|---|---|
| Event window | All companies | Non-financial | Financial | |||
| CAR | t value | CAR | t value | CAR | t value | |
| [-10, 10] | 2.8551** | 2.5338 | 2.8551* | 1.8194 | -0.5347 | -0.8199 |
| [-5, 5] | -1.8907** | -2.6163 | -1.8907* | -1.9727 | -9.7520*** | -11.3658 |
| [-4, 4] | -1.5201** | -2.3504 | -1.5201* | -1.8120 | -4.5731*** | -5.5696 |
| [-3, 3] | -2.7517*** | -4.8575 | -2.7517*** | -3.8403 | -6.8436*** | -7.6581 |
| [-2, 2] | -2.4366*** | -4.3795 | -2.4366*** | -3.5471 | -3.8229*** | -4.9124 |
| [-1, 1] | 0.7692 | 1.4259 | 0.7692 | 1.0472 | -1.7969*** | -3.2759 |
| [0] | 0.5065** | 2.0199 | 0.5065 | 1.5104 | -0.9392*** | -3.8562 |
| [-10, 0] | 3.3196*** | 3.3377 | 3.3196** | 2.388 | 6.5774*** | 12.3916 |
| [-5, 0] | 0.6095 | 1.0651 | 0.6095 | 0.8086 | 1.2933* | 1.7309 |
| [-2, 0] | -0.1398 | -0.3229 | -0.1398 | -0.2423 | -1.2405** | -2.3536 |
| [-1, 0] | 0.3600 | 0.8037 | 0.3600 | 0.5875 | -1.9147*** | -4.5503 |
| [-10, -1] | 2.8130*** | 3.0327 | 2.8130** | 2.1693 | 7.5165*** | 12.6637 |
| [-5, -1] | 0.1030 | 0.1982 | 0.1030 | 0.1534 | 2.2325*** | 2.9423 |
| [0, 1] | 0.9157** | 2.5904 | 0.9157* | 1.9103 | -0.8214** | -2.2058 |
| [0, 2] | -1.7903*** | -4.3088 | -1.7903*** | -3.6219 | -3.5216*** | -5.6654 |
| [0, 3] | -1.0775** | -2.3616 | -1.0775* | -1.9114 | -5.5441*** | -7.9918 |
| [0, 4] | -2.1638*** | -3.9330 | -2.1638*** | -3.1108 | -8.1481*** | -10.3352 |
| [0, 5] | -1.9938*** | -3.1506 | -1.9938** | -2.5083 | -11.9846*** | -12.1342 |
| [0, 10] | 0.0421 | 0.0601 | 0.0421 | 0.0451 | -8.0512*** | -9.7888 |
| [1, 5] | -2.5003*** | -3.7023 | -2.5003*** | -2.8549 | -11.0454*** | -13.0885 |
| [1, 10] | -0.4644 | -0.6461 | -0.4644 | -0.4729 | -7.1120*** | -9.9187 |
The table reports the event study results on the event date, March 11, 2020, using an adjusted market model. The estimation and event window are [-200, -20] and [-10, 10], respectively. The CARs are sums of each GB issuing company’s abnormal returns within the event window. ***-stat. sig. at 1% level. **-stat. sig. at 5% level. *-stat. sig. at 10% level
Declaration
Conflict of Interest
The authors have no potential conflicts of interest to declare.
Footnotes
“Green Bond Principles,” the International Capital Market Association, June 2021, https://www.icmagroup.org/sustainable-finance/the-principles-guidelines-and-handbooks/green-bond-principles-gbp/.
“Explaining green bonds,” Climate Bonds Initiative, https://www.climatebonds.net/market/explaining-green-bonds.
“Green bonds: China’s green bond market 2020 analysis briefing,” International Institute of Green Finance, Central University of Finance and Economics, January 20, 2021, http://iigf.cufe.edu.cn/info/1012/3676.htm.
“Paris Agreement,” United Nations Climate Change, https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement.
Umesh Desai, “China’s first green bond to spur interest for future deals,” Reuters, July 20, 2015, https://www.reuters.com/article/china-greenbond/chinas-first-green-bond-to-spur-interest-for-future-deals-idUSL4N0ZW4XN20150720.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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