Front. Energy Res.Frontiers in Energy ResearchFront. Energy Res.2296-598XFrontiers Media S.A.122924410.3389/fenrg.2023.1229244Energy ResearchOriginal ResearchRETRACTED: Effects of green credit policy on the risk of stock price crashLiu et al.10.3389/fenrg.2023.1229244LiuMeng1DaiBin1*LiuYiran21Sichuan International Studies University, Chongqing, China2University of International Business and Economics, Beijing, China
Edited by:Michal Jasinski, Wrocław University of Science and Technology, Poland
Reviewed by:Najabat Ali, Hamdard University Islamabad, Pakistan
Vidya C. T., Centre for Economic and Social Studies (CESS), India
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Green credit policy (GCP) is a specific instrument for the credit resource allocation dimension in the financial sector, and stock price crashes are an important manifestation of financial market risks that cannot be ignored. However, there are gaps in existing research on how green credit policies affect the stock price crash risk (SPCR). Using the Green Credit Guidelines as a quasi-natural experiment, this paper examines the impact of green credit policies on SPCR of heavily polluting firms. It confirms the crash risk is significantly increased for heavily polluting enterprises, mainly due to facing greater financing pressure; and that corporate governance mechanisms reduce its impact, finding that firms with higher analyst attention, higher levels of independent directors, and higher shares held by institutional investors. The effect between GCP and SPCR is not significant for companies with higher analyst attention, higher levels of independent directors, and higher shareholdings of institutional investors. At the same time, it is less significant in regions with high level of financial development. These results of this paper not only enrich the literature in green credit-related fields, but also provide a reference value for understanding the implementation effect of GCP in China to the stock price crash in the capital market.
For a long time, the dual pressure of environment and economic growth has caused widespread concern from all walks of life. Since 2012, the CBRC has published the Green Credit Guidelines, indicating that the role of financial institutions in improving environmental governance is widely recognized. In order to adher a green and low-carbon economy, Green credit can guide enterprises to implement green production as much as possible through reasonable allocation of credit resources. To date, scholars with an interest in GCP have examined the policy effects of green credit at a different angle. Li et al. (2016) conducted a study how GCP affected the business performance of commercial banks, and the positive and negative effects did not reach a consensus, which was confirmed by Zhang et al. (2021) as well. On the other hand, Sun and Shi (2019) discussed the positive effects of GCP on corporate R&D innovation. Lian (2015) pointed out that GCP can lower a company’s financing costs. According to Cai et al. (2019), GCP reduced the pollution emissions of heavy polluters. From the analysis of Li et al. (2020), GCP encourage the modernization of industrial institutions through “incentive” and “push” mechanisms at the macro level. Ma (2016) claimed the effect of green credit on the regional economic quality growth. Shen and Ma (2014) examined the ability of GCP to mitigate the conflict between environmental protection and “maximizing GDP performance”. King and Levine (1993) confirmed that green credit can effectly lower credit risk and enhance business performance from the perspective of banks. In contrast, Biswas (2011) and Luo et al. (2017) hold the opposite view, stating that banks lack incentives to implement green credit and that firms do not invest more in environmental protection.
Inevitably, the possibility of a stock market crash poses a threat to the financial market’s stability.In order to support the healthy development of the financial market, it is essential to effectively suppress SPCR. This becomes the top priority for financial market governance. Quan et al. (2016) pointed out that China’s capital market is imperfectand that the stock market is more prone to “crash and fall” than other countries. Allen and Faulhaber (1989) explained that managers tends to conceal negative news in order to raise more capital, which increases SPCR in the aspectof information asymmetry, named “Information hiding hypothesis”. It proves that investors have difficulty in grasping accurate corporate operating conditions due to the asymmetry of information held inside and outside the company. According to the principal-agent theory, managers has motivation to conceal negative corporate information due to personal reputation, compensation, and promotion considerations. However, once the accumulation of negative news reaches a certain threshold, same as “paper cannot cover fire”, then all of negative information instantly floods the market, causing a drastic impact on the stock price, which is more likely to generate a stock price crash (Kothari et al., 2009). Academics have examined the variables impacting SPCR from different perspectives. Ye et al. (2015) examined the ability of internal control toreduce the risk ofstock price crash Robin and Zhang (2015) examined that auditor industry expertise has a significant reducing effect on a company’s SPCR. Wang et al. (2014) discussed the effects of investor protection and institutional investors on SPCR. By analyzing the US capital market, Crane et al. (2017) confirmed that institutional investor network connectivity enhances corporate governance effectiveness. Xu et al. (2013) examined how analyst concern how it affects stock price crash. In terms of informal institutions, Zeng and Wei (2017) explored their impact on stock price crashes from a religious standpoint.
From a principal-agent theory perspective, information asymmetry arises when companies possess superior knowledge regarding their operational activities and potential environmental risks. Managers may engage in opportunistic behavior, such as concealing adverse information about their company’s environmental performance, to retain access to favorable green credit terms. This information asymmetry exacerbates the risk of stock price crashes, as investors may remain unaware of the true environmental risks and consequently misjudge the company’s financial soundness. Moreover, information asymmetry extends beyond the principal-agent relationship to encompass the relationship between companies and investors. Investors rely on publicly available information, financial reports, and disclosures to make well-informed investment decisions. However, if companies manipulate or withhold information concerning their environmental impact or adherence to green credit policies, it can distort perceptions of the company’s value and financial stability. The lack of transparency and provision of accurate information significantly heightens the likelihood of stock price crashes as concealed environmental risks eventually come to light.
As the most directly affected heavy polluters, their business decisions are bound to be influenced by the GCP. Heavy polluters would face more severe pressure on debt financing due to the progressively strict and standard requirements of green credit policies. Typically, Implementing green credit inevitably affect the credit environment of heavy polluters, reduce the scope of debt financing, and raise the corporate credit risk. At the same time, in profitability and safety considerations, investors will also be more cautious, increasing the cost of debt for companies. Consequently, a share price crash is a more likely to occur as the rise in corporate risk.Corporate governance is a crucial instrument to mitigate information asymmetry, and is closely related to SPCR. Different internal and external corporate governance factors mitigate SPCR by influencing management mindset, monitoring of executives, information disclosure, performance pressure, and the financing constraint environment, which in turn affects firms' behavior of hiding negative news.
Based on the aforementioned discussion, the study concentrates on answering three questions: first, how GCP effect on SPCR for companies with heavy pollution; second, whether corporate governance mechanisms such as analyst concerns, institutional investors, and independent directors can effectively mitigate the impact of GCP on SPCR; third, it further examines the effect of different regional financial development levels on the relationship between GCP and SPCR.
Using the heavy-polluting firms listed in Shanghai and Shenzhen from 2009 to 2018 as the research objects, we conducted a quasi-natural experiment based on the implementation of the Green Credit Guidelines issued by the CBRC and utilized a difference-in-differences (DID) model to investigate these effects and mechanisms.This study provides empirical evidence that the implementation of green credit significantly increases the SPCR for heavily polluting companies. Furthermore, it emphasizes the role of corporate governance mechanisms in mitigating this impact. Specifically, this influence is statistically insignificant in firms characterized by broader analyst coverage, a higher proportion of independent directors, and increased institutional ownership. Additionally, the study reveals that this effect is more pronounced in regions with lower levels of financial development.
This study has identified several policy implications: firstly, it is recommended that financial institutions rigorously control credit thresholds while concurrently expanding the coverage of green finance. Moreover, emphasis should be placed on fostering innovation and optimizing green financial products and services to ensure the long-term sustainability and stability of green credit policies. Secondly, considering that the effectiveness of green credit is influenced by corporate governance mechanisms and the level of financial development, relevant policies should take into account regional differences in environmental governance pressure. Designing targeted and differentiated assessment indicators can promote voluntary and proactive implementation of green credit policies by local governments. Lastly, financial institutions and local governments need to dynamically adjust the intensity of punishment and incentive mechanisms to avoid phenomena such as financing difficulties and stock price collapses that contradict the long-term objectives of green credit policies. Actively guiding and supporting heavily polluting enterprises in raising their environmental awareness and strengthening green investment and financing is crucial.
The innovations and contributions of this paper are mainly in two areas: on the one hand, quantitative analysis of the policy effect of green credit implementation is conducted from the perspective of SPCR, thereby providing coping strategies and empirical evidence for heavy-polluting companies to deal with the negative effects caused by green finance; on the other hand, the research on the effect of GCP implementation is enriched from different perspective by combining the heterogeneity of corporate governance mechanisms, and it is found that the negative impact between GCP and SPCR varies by different corporate governance mechanisms, and the heterogeneity of financial regional differences is also need tobe considered, which provides suggestions and references for government regulators to improve relevant corporate governance mechanisms.
The research is divided into six parts: Section 2 presents the theoretical analysis and research hypotheses; Section 3 presents the research design; Section 4 presents the empirical results and analysis; Section 5 presents further heterogeneity analysis; and Section 6 contains the research conclusions and policy implications.
Theoretical analysis and research hypothesis
The study of stock price crash has become a temporary phenomenon in the fields of finance and economics since the global financial crisis. Piotroski et al. (2015) show that how badly stock price crashes have impacted on the steady growth of capital market. Therefore, exploring the effect of various policies on stock price crashes and reducing their risk is a crucial research topic that must be addressed by academics. Due to its own position and salary, management has a tendency to conceal the negative behaviors of the company, resulting in the accumulation of negative news. Once the negative news can no longer be concealed or the cost of continued concealment exceeds that of the announcement, it explodes instantly in the stock market, causing a precipitous decline in the company’s share price, resulting in a stock crash (Kim et at., 2011). Green credit policies also offer potential benefits. The aim of GCP is to achieve environmental governance by using financial instruments to enable efficient allocation of credit resources and encourage green transformation and technological innovation. Zhou et al. (2021) have highlighted that the implementation of green credit can enhance the future performance of financial institutions. By imposing restrictions, there is a reduced risk for commercial banks when making loan decisions. Yao et al. (2021) have demonstrated that governments can regulate environmental risks associated with bank loans through penalty effects. Green credit policies can help mitigate pollution and environmental damage, thereby positively impacting the health and wellbeing of both people and the environment. Additionally, these policies play a crucial role in promoting innovation and driving economic growth within the green economy. However, for the heavily polluting industries, are there any unfavorable effects or ways that can escape the risks brought by the GCP?
Green credit has the potential to affect the stock price crash of heavy-polluting firms several ways. First of all, on the one hand, it improves the green credit standards. Financial institutions will actively corresponding government policy requirements, since the implementation of the GCP will raise the threshold of bank credit. They need to focus on the company’s environmental and social risks, the implementation of more stringent standards for corporate environmental governance and environmental information disclosure, which will inevitably directly affect the credit approval of heavy polluters. The difficulty of credit approval is increased for heavy polluters compared to other firms, which exacerbates the difficult financing dilemma. Lian (2015) pointed out that green credit can restrain the financing problem for heavy polluters. On the other hand, due to the normative nature of GCP, heavy polluters will likely fail to meet the credit criteria and obtain debt financing, especially long-term debt financing. Their financing costs will increase. This is primarily because there is the existence of information asymmetry between the company and the external market. Since banks frequently use industry attributes as the benchmark for credit placement when the banks measure and identify th environmental governance and green production of enterprises, it is difficult for companies to meet the green credit lending criteria and obtain sufficient financial support (Wu et al., 2020).
The second function is signaling. According to Wu et al. (2012), green credit can boost the corporate information transparency and conveysignals of enhanced corporate environmental supervision by strengthening the link between financial institutions and environmental protection departments. As for the heavily polluting companies with poor environmental management, there is no doubt that this policy will send negative signals to the capital market. From a reputation risk perspective, the heavily polluting companies would also be subject to greater public pressure and moral condemnation after the credit policy is formally implemented, and they may even at a higher risk of environmental litigation. Given profitability and safety considerations, the higher the creditors' risk perceptions of heavily polluting firms, the less inclined they are to provide debt capital, resulting in creditors' withdrawal or rejection of debt extensions and lower debt financing. Su and Lian (2018) point out that green credit has both a remarkable financing penalty effect as well as an investment disincentive effect.The financing penalty effect refers to the fact green credit significantly increases total debt cost, then sharply decreases the operating performance for heavily polluting firms. The investment disincentive effect refers to the difficulty for companies to maintain the ability to innovate and transform green due to the lack of capital (Cao et al., 2021). This makes the company fall into the dilemma of “poor environmental management - difficult financing - even more difficult green transformation”, when the heavy polluters cannot obtain sufficient green credit, the lack of capital will make the company unable to maintain stable green innovation, which in turn worsens the problem of poor financing for heavy polluters. In turn, this exacerbates the financing problem of heavy polluters and increases the incentive for their management to conceal negative information in order to avoid financial pressure.
Last but not least, there is a rise in business risk. As GCP will exacerbate the financing constraints for heavy polluters, the company’s operating risks would increased. If the company fails to repay its debts when they are due, this increases the company’s risk of credit default. According to modern contract theory, the increase in project risk will result in a rise in principle-agent costs between the company and its creditors. Due to the increased credit default risk of the heavy polluters, from the perspective of risk compensation, banks as creditors may ask the heavy polluters to pay higher credit rates to compensate for the possible default risk. This undoubtedly again increases the financing constraints and the risk of credit default for heavy polluters. As the Guidelines’ implementation restricts the credit financing for heavily polluting firms, it reduces the amount of credit available for them, which in turn increases the business risks faced by firms (Yang and Zhang, 2022). The risk of a firm’s share price collapsing will unavoidably increase as the firm’s risk increases. Increased financing pressure will make heavily polluting firms more likely to manipulate earnings management and the more careful they will be to disclose bad news to obtain more credit resources (Lu and Zhang, 2014). Coupled with internal and external information asymmetries, firms with greater financing constraints will have more incentives to conceal negative information, and these accumulated negative information will undoubtedly raise the likelihood of SPCR, as well as the risk will increase as a result of a deterioration in corporate information disclosure quality and internal controls because of an increase in financing constraints.
The prevention of corporate SPCR has attracted widespread attention in the financial and finance fields. The primary reason for SPCR is information asymmetry within the enterprises. The possibility that management may intentionally or unintentionally conceal negative news beyond the firm’s carrying capacity, resulting in an instantaneous outbreak (Bleck and Liu, 2007).According to agency theory, it is known that the severity of the agency problem directly affects the cost and behavior of management in hiding negative news. Lin and Zheng. (2016) confirmed that the higheragency cost of a firm, the higher probability that negative news will be concealed and the higher risk that the firm’s share price will crash. Corporate governance, precisely as an effective monitoring mechanism to mitigate agency problems, can increase the of a firm’s information transparency, which will inevitably have an impact on the share price crash. Related scholars have also demonstrated how internal and external corporate governance mechanisms affect firms’ SPCR. Callen and Fan (2012) explored how religious beliefs reduce a firm’s share price crashing by reducing managers’ concealment of negative information. An and Zhang (2013) confirmed institutional investors have an impact on SPCR, as well as Callen and Fang (2012). According to Luo and Du. (2014), there is less risk of a stock price crash for a firm with more attention of the media. Similarly, due to the public pressure of analysts’ attention, Pan et al. (2011) pointed out that analysts' attention compresses the space for heavy polluters to adopt “avoidance” in environmental information disclosure, which in turn reduces SPCR. Independent directors can supervise companies to avoid the reputational and legal hazards they face as much as possible, thereby decreasing SPCR. The following hypotheses are put forth in this paper in light of the above analysis.
Assuming all other factors remain constant, GCP significantly increase SPCR for heavily polluting firms.
Study designSample selection and data sources
This paper analyzes the effect of the GCP implementation on SPCR for heavily polluting firms listed in Shanghai and Shenzhen from 2009 to 2018. In particular, according to the “List of Listed Companies’ Environmental Verification Industry Classification Management” issued by the Ministry of Ecology and Environment (formerly the Ministry of Environmental Protection), the “Guidelines on Environmental Information Disclosure of Listed Companies” (draft for public comment) and the “Guidelines on Industry Classification of Listed Companies” of the Securities and Futures Commission, a listed company is defined as a heavy-polluting enterprise if it belongs to the screened heavy-polluting industries. Meanwhile, this paper defines other industries as non-heavily polluting industries, which are served as the control group in order to bulid a difference-in-differences model with reference to Liu and Liu (2015) 's study. After excluding the samples with abnormal financial data and relevant data missing, a total of 15,199 valid samples were obtained. Additionally, we truncate the main continuous variables by 1% at the top and bottom to eliminate the effect of extreme values. The CSMAR and RESSET databases were consulted for the data related to listed companies.
Description of model and variables
In order to mitigate potential endogeneity concerns, it is possible to conceptualize the green credit policy as a quasi-natural experiment. By treating the implementation of the green credit policy as an exogenous shock independent of firm-specific characteristics and other external factors, the identification strategy can provide more robust estimates of the causal impact on the risk of stock price collapse. This approach helps to alleviate potential biases stemming from endogenous factors and enhances the validity of the research findings. To investigatethe effect of GCP on SPCR of heavily polluting firms, the following DID model is constructed:Ncskews\Duvols=β0+β1∗Treat+β2∗After+β3∗Treat×After+δ∗Controls+ε
Ncskews and Duvols in the model are SPCR variables. According to Xu et al. (2012) and other literature, we use two indicators of negative earnings skew coefficient (Ncskews) and earnings up/down volatility ratio (Duvols) to evaluate SPCR, respectively. The following is the precise calculation method:
First, the following regressions were run using listed company weekly stock returns by year:Ri,t=αi+β1∗Rm,t−2+β2∗Rm,t−1+β3∗Rm,t+β4∗Rm,t+1+β5∗Rm,t+2+εi,twhere Ri,t is the weekly individual stock return of listed company i in week t considering cash dividend reinvestment, and Rm,t is the weekly market return using the market capitalization-weighted average method of liquidity. Also, the lagged and ahead terms of market returns are included in this model for the two periods before and after (Dimson, 1979). Then, based on the residuals εi,t of the aforementioned model, weekly specific return of listed company i is calculated, which is Wi,t= ln (1+εi,t).
Second, the following two variables are created based on the particular weekly returns of listed companies (Wi,t): negative return skew coefficient (Ncskews) and return up/down volatility ratio (Duvols).Ncskewsi,t=−nn−13/2∑Wi,t3/n−1n−2Wi,t23/2Duvolsi,t=lnnu−1∑downWi,t2/[nd−1∑upWi,t2]
Where n means numbers of trading weeks per year of listed company i; down and up are the up and down phases based on whether the weekly characteristic return is higher than annual average return; nu (nd) means numbers of weeks when weekly characteristic return of listed company i is higher (lower) than the annual average characteristic return. The larger value of Ncskews, the more severe the degree of negative skewness coefficient. While, a larger value of Duvols indicates that the stock return distribution is more left skewed, as well as the higher SPCR of the firm.
Treat and After are dummy variables. If the listed company is part of the treatment group, which includes heavy polluters, Treat assigns it a value of 1, but if company belong to control group, Treat assigns it a value of 0. After assumes a value of 1 in the year following the Green Credit Guidelines' implementation, otherwise it assumes the value of 0 in all other years. The primary explanatory variable for the study is the interaction term Treat*After, and if interaction term coefficient is significantly positive, which means GCP implementation leads to higher SPCR for heavy polluters.
The control variables in the paper include: firm size (Size), which is the natural logarithm of the firm’s total assets; gearing (Lev), which is the ratio of the firm’s total liabilities to its total assets; two positions in one (Same), which takes the value of 1 if the firm’s chairman and general manager are both the same person, otherwise, it takes the value of 0; majority shareholder ownership (Top), which is measured by the shareholding ratio of the top ten shareholders; return on total assets (Roa), which is the ratio of net profit to total assets; development capability (Tobing), measured by the Tobin’s Q value; institutional shareholding (Inst), the percentage of shares held by institutional investors; number of employees (Employee), the natural logarithm of the number of employees; investment expenditure (Invest), the ratio of fixed assets, intangible assets and other long-term assets paid by the firm, Investment expenses (Invest), measured as cash paid for the construction of fixed assets, intangible assets and other long-term assets plus net cash paid for the acquisition of subsidiaries and other business units minus net cash received from the disposal of fixed assets, intangible assets and other long-term assets deduct net cash received from the disposal of subsidiaries and other business units, and normalized using the total assets of the enterprise; audit quality (Audit), measured using the natural logarithm of audit fees; management Holding, which is the ratio of management’s shareholding, Pay, the ratio of cash paid to and for employees to the number of employees, Separate, Ret, which is the annual return on shares considering reinvestment of cash dividends, and Soe, which is 1 for state-owned enterprises (SOE) and 0 for private firms. Additionally, annual fixed effects and the effects of province, industry are controlled. Table 1 provides the description of primary variables.
Description of primary variables.
Variables
Variable description
Explained variables
Ncskews
Negative return bias coefficients, as specified in model 3)
Duvols
Ratio of upward and downward fluctuations in earnings, as specified in model 4)
Explanatory variables
Treat
Dummy variable, treatment group assignsthe value of 1; which the control group assigns the value of 0
After
Dummy variable with a value of 1 for the year after the implementation of the Green Credit Guidelines; otherwise, it takes the value of 0
Control variables
Size
Enterprise size, taking the value of the natural logarithm of the total assets of the enterprise
Lev
Gearing ratio, the ratio of total liabilities to total assets of an enterprise
Same
Two positions in one, the chairman and general manager by one person to take the value of 1; otherwise, take the value of 0
Top
Shareholding ratio of top ten shareholders
Tobinq
Tobin’s Q value
Roa
Return on total assets, corporate net income divided by total assets
Inst
Shareholding of institutional investors
Employee
Natural logarithm of the number of employees in the company
Invest
Investment expenses, (cash paid to construct fixed assets, intangible assets, and other long-term assets plus net cash paid to acquire subsidiaries and other business units minus net cash recovered from disposal of fixed assets, intangible assets, and other long-term assets deduct net cash received from disposal of subsidiaries and other business units)/total assets
Audit
Natural logarithm of audit fees
Holding
Management shareholding ratio
Pay
Employee compensation, cash paid to and for employees/number of employees
Separate
Separation of powers rate
Ret
Annual individual stock return considering reinvestment of cash dividends
Soe
Nature of property rights, state-owned enterprises take the value of 1; private enterprises take the value of 0
Descriptive statistics of primary variables
As can be seen, the results of descriptive statistics for the primary variables are shown in Table 2. The negative earnings skew coefficient (Ncskews) has a mean value of −0.317, a minimum value of −3.293, and a maximum value of 2.672; the up/down earnings volatility ratio (Duvols) has a mean value of −0.175, a minimum value of −3.145, and a maximum value of 1.909, illustrating that SPCR varies significantly between firms. The mean value of the Treat variable is 0.466, indicating that there is a more balanced sample distribution between treatment and control groups.
Descriptive statistics of primary variables.
Variables
N
mean
std
min
p50
max
Ncskews
15199
−0.317
1.088
−3.293
−0.283
2.672
Duvols
15199
−0.175
0.843
−3.145
−0.123
1.909
Treat
15199
0.466
0.499
0
0
1
After
15199
0.812
0.390
0
1
1
Size
15199
21.97
1.245
17.39
21.80
28.25
Lev
15199
0.416
0.207
0.049
0.406
0.900
Same
15199
0.278
0.448
0
0
1
Top
15199
0.590
0.151
0.230
0.601
0.957
Tobinq
15199
2.696
1.871
0.879
2.091
11.00
Roa
15199
4.101
5.497
−18.62
3.808
20.55
Inst
15199
0.06
0.068
0
0.036
0.317
Employee
15199
7.680
1.216
2.197
7.616
12.62
Invest
15199
3.718
5.275
−4.717
1.468
23.55
Audit
15199
13.52
0.639
9.210
13.46
17.52
Holding
15199
0.147
0.210
0
0.005
0.706
Pay
15199
11.35
0.554
0
11.34
17.20
Separate
15199
5.032
7.718
0
0
28.82
Ret
15199
0.140
0.609
−0.593
−0.036
2.641
Soe
15199
0.352
0.478
0
0
1
Empirical results and analysisGreen credit policy and SPCR
The outcomes to determine the impact of GCP on the risk of heavy polluters' stock prices crash are shown in Table 3. These findings reveal that coefficients of the interaction term Treat*After in columns 1) and 3) are 0.138 and 0.107, respectively, and both are significant at the 1% level, indicating the negative return bias coefficient and the ratio of upward and downward earnings fluctuations of the heavily polluting firms significantly increase after implementation of GCP, thereby SPCR increases. Even after further controlling for firm-level control variables, the coefficient of the interaction term Treat*After remains positive and significant at the 5% level as well as province, industry, and year fixed effects in columns 2) and 4), consistent with previous findings. The above results indicate implementation of GCP has a significant positive impact on SPCR of heavily polluting companies, which leads to higher SPCR for heavy-polluting enterprises and verifies the previous hypothesis.
Green credit policy and SPCR.
VARIABLES
1)
2)
3)
4)
Ncskews
Ncskews
Duvols
Duvols
Treat*After
0.138***
0.086**
0.107***
0.061**
(3.63)
(2.47)
(3.77)
(2.39)
Treat
−0.076**
−0.764***
−0.049**
−0.405***
(-2.34)
(-8.26)
(-2.08)
(-6.29)
After
−0.124***
−1.262***
−0.033
−1.048***
(-4.55)
(-26.16)
(-1.61)
(-27.93)
Size
0.043**
0.006
(2.44)
(0.45)
Lev
−0.108*
−0.131***
(-1.88)
(-3.07)
Same
−0.008
−0.017
(-0.37)
(-1.12)
Top
−0.277***
−0.309***
(-4.35)
(-6.67)
Tobinq
0.002
−0.034***
(0.27)
(-5.20)
Roa
−0.012***
−0.010***
(-6.17)
(-7.12)
Inst
1.768***
1.296***
(14.07)
(13.28)
Employee
−0.004
0.008
(-0.28)
(0.74)
Invest
0.001
0.002
(0.71)
(1.42)
Audit
−0.048**
−0.051***
(-2.30)
(-3.20)
Holding
−0.184***
−0.182***
(-3.40)
(-4.43)
Pay
0.029
0.027**
(1.44)
(1.98)
Separate
−0.001
−0.001
(-1.28)
(-1.33)
Ret
−1.101***
−0.953***
(-50.97)
(-54.45)
Soe
−0.047**
−0.051***
(-2.07)
(-3.03)
Constant
−0.232***
1.306***
−0.165***
1.920***
(-10.11)
(4.33)
(-9.64)
(8.51)
Province FE
no
yes
no
yes
Industry FE
no
yes
no
yes
Year FE
no
yes
no
yes
Observations
15,199
15,199
15,199
15,199
Adj_R2
0.001
0.219
0.001
0.303
Note: t-values adjusted using robust regression and clustering are in parentheses (same later).
Parallel trend test
In this study, we also conducted the following model for parallel trend testing, according to Liu and Qiu (2016) 's study:Ncskews\Duvols=β0+β1×Treat+∑j=20102018φj×yearj+∑j=20102018μj×Treat×yearj+δ×Controls+εwhere yearj is a year dummy variable, and yearj takes the value of 1 if the observation belongs to year j and 0 otherwise. The other variables are defined in line with model 1). Using 2009 as the base period, we examine the trend of SPCR between treatment and control groups from 2010–2018. Figure 1 reports the coefficient estimates and upper and lower 90% confidence intervals for the interaction term Treat*yearj. Prior to the implementation of GCP, coefficients of Treat*year2010 and Treat*year2011 are not significant, indicating that there is relatively consistent and not significantly different between the treatment and control groups for the trend of SPCR, which is passing parallel trend test. Further more, there are mostly significant positive in 2012 and after, indicating that compared to the control group SPCR in the treatment group is significantly higher after implementation of GCP, thereby corroborating the robustness of the previous findings.
Parallel trend test.
Placebo test
Additionally, in order to investigatewhether the treatment effects estimated in the previous section are due to omitted country-industry-time level variables, a placebo test is performed. According to Cai et al. (2016) 's study, a sample of treatment groups are selected randomly for the placebo test. First, we construct the dummy treatment group variable Treatfake by randomly selecting some firms from the sample as dummy heavy polluters and the remaining firms as dummy non-heavy polluters. Then, the placebo test interaction term Treatfake*After. The effect of the placebo test interaction term on the risk of a firm’s stock price crash should not be significant because the treatment group is randomly screened. That is, the placebo coefficient of the placebo test interaction term should not significantly deviate from zero. Conversely, if the coefficients deviate significantly from zero, it implies that there may have been omitted variables in the previous test. Moreover, in order to eliminate the effect of low-probability extreme events, we repeated the preceding technological process 500 times for regression analysis.The computed coefficients for the 500 random sample regression analysis of the placebo interaction term Treatfake*After are shown in Figure 2, along with their kernel density distribution. The effect on the risk of a corporate stock price crash is not significant because the estimated coefficients of the placebo test interaction term Treatfake*After are around zero, regardless of the negative earnings skew coefficient (Ncskews) or the up/down earnings volatility ratio (Duvols) as explanatory variables. In addition, the dashed line in the figure shows the actual estimates of the true treatment effect interaction term Treat*After, which are also larger than majorityof the placebo test estimates. It indicates there is no significant omitted variable bias in the results of the previous tests.
Placebo test.
Heterogeneity analysisThe impact of analyst concerns
Relevant literature has confirmed that analysts are able to optimize the information environment in which an investor lives and minimize the level of internal and external information asymmetry to maintain stock price stability (Ayres et al., 2019). Analysts mainly influence company stock price crash for two aspects; on the one hand by virtue of their professional ability to secondarily interpret the information disclosed by companies, analysts can timely and accurately uncover deeper information about companies and provide investors with the true value of securities, thereby reducing the level of internal and external information asymmetry, improving market information transparency, and lowering SPCR (Pan et al., 2011). Conversely, due to analysts' attention to firms, it will increase the cost and difficulty for firms to hide bad news, thereby reducing management earnings manipulation and SPCR. According to Zhu and Zhou (2014), there is a correlation between the accuracy of analysts' earnigns forecasts and SPCR. Based on the median level of analyst concern, the sample is split into two different level of analyst concern groups, named high and low groups, and Table 4 presents the regression results. According to the findings, enterprises with low analyst concern have higher SPCR as a result of the adoptionn of a GCP. Conversely, a high level of analyst attention can reduce this effect.
Impact of analysts’ concerns.
VARIABLES
1)
2)
3)
4)
High degree of analytical attention
Low degree of analytical attention
High degree of analytical attention
Low degree of analytical attention
Ncskews
Ncskews
Duvols
Duvols
Treat*After
0.006
0.147***
−0.021
0.121***
(0.11)
(3.03)
(-0.53)
(3.60)
Treat
−0.735***
1.520***
−0.335***
2.063***
(-6.26)
(8.74)
(-4.08)
(16.29)
After
−1.010***
−0.481***
−0.777***
−0.468***
(-15.35)
(-6.60)
(-14.63)
(-8.55)
Size
0.078***
−0.013
0.043**
−0.041**
(3.12)
(-0.51)
(2.24)
(-2.18)
Lev
−0.259***
−0.003
−0.266***
−0.066
(-2.91)
(-0.04)
(-4.08)
(-1.22)
Same
0.014
−0.028
−0.001
−0.032
(0.54)
(-0.94)
(-0.05)
(-1.46)
Top
−0.197**
−0.342***
−0.194***
−0.376***
(-2.20)
(-3.86)
(-2.98)
(-5.87)
Tobinq
−0.012
−0.015
−0.041***
−0.048***
(-1.14)
(-1.40)
(-4.42)
(-5.16)
Roa
−0.007**
−0.015***
−0.005**
−0.013***
(-2.19)
(-5.67)
(-2.06)
(-6.68)
Inst
1.258***
2.122***
0.915***
1.626***
(8.26)
(8.53)
(7.91)
(8.81)
Employee
0.030
−0.033*
0.035**
−0.015
(1.42)
(-1.67)
(2.23)
(-1.03)
Invest
0.003
−0.001
0.003*
0.001
(1.12)
(-0.22)
(1.67)
(0.60)
Audit
−0.106***
−0.009
−0.099***
−0.009
(-3.79)
(-0.27)
(-4.75)
(-0.38)
Holding
−0.059
−0.273***
−0.051
−0.278***
(-0.85)
(-3.30)
(-0.97)
(-4.38)
Pay
0.003
0.051*
0.016
0.040**
(0.09)
(1.88)
(0.79)
(2.19)
Separate
−0.001
−0.001
−0.001
−0.001
(-0.79)
(-0.55)
(-0.69)
(-0.53)
Ret
−1.018***
−1.158***
−0.848***
−1.044***
(-34.79)
(-34.36)
(-36.60)
(-38.85)
Soe
−0.062**
−0.025
−0.067***
−0.030
(-2.02)
(-0.77)
(-2.94)
(-1.27)
Constant
1.243***
−0.209
1.403***
0.137
(2.94)
(-0.43)
(4.43)
(0.37)
Province FE
yes
yes
yes
yes
Industry FE
yes
yes
yes
yes
Year FE
yes
yes
yes
yes
Observations
7,136
8,063
7,136
8,063
Adj_R2
0.233
0.223
0.302
0.320
The role of independent directors
It has long been widely confirmed in academia that independent directors can improve corporate governance and mitigate agency problems between shareholders and management. Du and Yu (2019) confirm that independent directors can rely on their professionalism, independence, and reputation mechanisms to reduce the firms’ SPCR. This is mainly since independent directors can participate directly in corporate governance and supervision and have better access to internal information of the company. Huang et al. (2016) point out that independent directors supervise firms mainly through the reputation mechanism. This enhances the quality of information disclosure. Stock crashes are mainly due to the concentration of negative corporate news exposure. Independent directors can improve the firms’ earnings quality and reduce SPCR by improving the transparency of company’s information disclosure and controlling insiders' interception of personal self-interest. Liang and Zeng (2016) emphasize that independent directors can reduce a enterprise’s SPCR by monitoring the quality of enterprise’s information disclosure and by influencing management to intercept personal self-interest. Therefore, we predict that SPCR would be lower in companies with a higher percentage of independent directors. The results of the test for the impact of independent directors are shown in Table 5, indicating that regression system of Treat*After in columns 2) and 4) is significantly positive at 1% and 5% confidence level, respectively. It indicates that effect of GCP on SPCR is more significant for companies with a low percentage of independent directors, and conversely, a high percentage of independent directors can effectively reduce this impact.
Impact of independent directors.
VARIABLES
1)
2)
3)
4)
High proportion of independent directors
Low proportion of independent directors
High proportion of independent directors
Low proportion of independent directors
Ncskews
Ncskews
Duvols
Duvols
Treat*After
0.062
0.127***
0.052
0.083**
(1.14)
(2.69)
(1.33)
(2.39)
Treat
−0.830***
0.416***
−0.504***
0.316***
(-6.35)
(3.35)
(-5.56)
(3.48)
After
−1.295***
−1.563***
−1.081***
−1.273***
(-18.07)
(-20.74)
(-19.92)
(-20.93)
Size
0.007
0.077***
−0.016
0.024
(0.27)
(3.23)
(-0.87)
(1.31)
Lev
−0.116
−0.112
−0.144**
−0.132**
(-1.36)
(-1.49)
(-2.31)
(-2.34)
Same
−0.011
−0.008
−0.008
−0.026
(-0.39)
(-0.30)
(-0.39)
(-1.28)
Top
−0.212**
−0.325***
−0.246***
−0.354***
(-2.13)
(-3.89)
(-3.56)
(-5.71)
Tobinq
0.012
−0.013
−0.025***
−0.048***
(1.14)
(-1.34)
(-2.76)
(-5.27)
Roa
−0.016***
−0.008***
−0.012***
−0.008***
(-5.57)
(-3.12)
(-5.94)
(-4.13)
Inst
1.984***
1.580***
1.436***
1.194***
(10.30)
(9.77)
(9.68)
(9.78)
Employee
0.024
−0.032
0.022
−0.005
(1.15)
(-1.60)
(1.47)
(-0.36)
Invest
0.005**
−0.002
0.004**
0.000
(1.98)
(-0.78)
(2.01)
(0.11)
Audit
−0.037
−0.058**
−0.048**
−0.054***
(-1.25)
(-2.07)
(-2.05)
(-2.68)
Holding
−0.203***
−0.190**
−0.226***
−0.165***
(-2.61)
(-2.47)
(-3.92)
(-2.83)
Pay
0.071**
−0.018
0.053***
0.001
(2.32)
(-0.65)
(2.84)
(0.05)
Separate
−0.001
−0.001
−0.002
−0.000
(-0.71)
(-0.79)
(-1.23)
(-0.32)
Ret
−1.142***
−1.060***
−0.978***
−0.926***
(-35.69)
(-36.52)
(-37.72)
(-39.09)
Soe
−0.029
−0.066**
−0.032
−0.069***
(-0.82)
(-2.27)
(-1.24)
(-3.09)
Constant
1.207***
0.344
1.922***
1.340***
(2.74)
(0.86)
(5.96)
(4.37)
Province FE
yes
yes
yes
yes
Industry FE
yes
yes
yes
yes
Year FE
yes
yes
yes
yes
Observations
7,030
8,148
7,030
8,148
Adj_R2
0.228
0.212
0.306
0.301
The impact of institutional investors
There is highly controversial about the relationship between institutional investors and SPCR is. In contrast, institutional investors may act as “market stabilizers”. According to Crane et al. (2017), institutional investors can improve corporate governance and effectively monitor management’s earnings management behavior. Mainly consists of “voting with their feet” and “exit threat”. On the other hand, institutional investors may be a “crash gas pedal”. Cao et al. (2015) hold the opposite view, stating that institutional investors act as “accomplices” for managers to conceal bad news. In order to obtain higher profits, institutional investors may conspire with management to obtain inside information to adjust their positions and profit before the stock price crashs. Table 6 displays regression results of GCP, institutional investors and SPCR. Regardless of Ncskews or Duvols is used as an indicator of SPCR, the impact is not significant among firms with a high percentage of institutional investors shareholding. This finding demonstrates the role of institutional investors to enhance corporate governance and thus reduce the impact as a monitoring mechanism.
Impact of institutional investors.
VARIABLES
1)
2)
3)
4)
High percentage of shares held by institutional investors
Low percentage of shares held by institutional investors
High percentage of shares held by institutional investors
Low percentage of shares held by institutional investors
Ncskews
Ncskews
Duvols
Duvols
Treat*After
0.041
0.105*
0.017
0.088**
(0.90)
(1.94)
(0.49)
(2.28)
Treat
−0.875***
−0.602***
−0.380***
−0.286***
(-6.28)
(-5.03)
(-3.83)
(-3.32)
After
−0.490***
−0.525***
−0.324***
−0.480***
(-7.55)
(-6.77)
(-6.20)
(-8.17)
Size
0.051**
−0.013
0.022
−0.046**
(2.33)
(-0.46)
(1.40)
(-2.14)
Lev
−0.103
−0.071
−0.139**
−0.098
(-1.36)
(-0.85)
(-2.55)
(-1.59)
Same
0.022
−0.032
0.004
−0.034
(0.88)
(-1.05)
(0.22)
(-1.49)
Top
−0.176**
−0.292***
−0.173***
−0.357***
(-2.08)
(-3.09)
(-2.88)
(-5.20)
Tobinq
0.033***
−0.036***
0.005
−0.074***
(3.45)
(-3.04)
(0.60)
(-6.81)
Roa
−0.013***
−0.012***
−0.010***
−0.011***
(-4.71)
(-4.41)
(-5.10)
(-5.30)
Inst
1.177***
7.413***
0.654***
6.190***
(7.16)
(6.32)
(5.37)
(6.91)
Employee
0.016
−0.022
0.018
−0.004
(0.85)
(-1.01)
(1.43)
(-0.25)
Invest
0.003
−0.002
0.004**
−0.001
(1.51)
(-0.63)
(2.41)
(-0.45)
Audit
−0.081***
0.006
−0.075***
−0.006
(-3.11)
(0.18)
(-3.95)
(-0.23)
Holding
−0.150**
−0.178**
−0.101*
−0.218***
(-2.06)
(-2.27)
(-1.83)
(-3.70)
Pay
0.024
0.032
0.020
0.030
(0.85)
(1.06)
(1.22)
(1.34)
Separate
−0.001
−0.001
−0.001
−0.001
(-0.99)
(-0.74)
(-0.50)
(-1.06)
Ret
−1.108***
−1.086***
−0.928***
−0.974***
(-39.31)
(-31.05)
(-41.62)
(-34.09)
Soe
−0.082***
0.002
−0.059***
−0.023
(-2.78)
(0.06)
(-2.82)
(-0.86)
Constant
1.482***
1.852***
1.645***
2.597***
(3.59)
(3.94)
(5.57)
(7.08)
Province FE
yes
yes
yes
yes
Industry FE
yes
yes
yes
yes
Year FE
yes
yes
yes
yes
Observations
7,597
7,602
7,597
7,602
Adj_R2
0.249
0.202
0.332
0.299
Diverse regional financial development levels
The degree of regional financial growth affects the impact of GCP on SPCR. Under typical conditions, the access threshold for corporate finance loans will increase because of GCP. Enterprises will have more access to fianncing when regional financial development is more advanced. Xie and Huang, (2014) emphasize that a healthy financial market can foster a favorable financial ecological environment, which can significantly alleviate the pressure of corporate financing. Firms rely less on bank loans as a source of funding, and the regulatory role for green financial policies is diminished. On the contrary, enterprises confront a more challenging funding environment with a relatively single financing channel in area with less developed financial market, where business rely more on bank credit, therebythe policy effect of green credit should be stronger. Referring to Wang et al. (2016), Table 7 displays regression results by using marketization index which is commonly used by scholars as an indicator of regional financial development level. It shows impact of GCP on SPCR is not significant at financially developed areas. On the other hand, this effect is more significant in areas with a low degree of financial development, verifying the role of regional financial development level.
Regional financial development effect.
VARIABLES
1)
2)
3)
4)
Higher degree of financial development
Low level of financial development
Higher degree of financial development
Low level of financial development
Ncskews
Ncskews
Duvols
Duvols
Treat*After
0.062
0.105**
0.048
0.066*
(1.15)
(2.24)
(1.25)
(1.90)
Treat
−0.544**
0.748***
−0.543***
0.446**
(-2.17)
(2.92)
(-6.14)
(1.97)
After
−1.138***
−0.558***
−0.959***
−0.420***
(-15.87)
(-7.96)
(-18.25)
(-7.59)
Size
0.067**
0.010
0.026
−0.022
(2.55)
(0.42)
(1.33)
(-1.16)
Lev
−0.271***
0.038
−0.251***
−0.016
(-3.41)
(0.47)
(-4.27)
(-0.26)
Same
−0.010
−0.005
−0.021
−0.010
(-0.33)
(-0.17)
(-0.94)
(-0.50)
Top
−0.311***
−0.264***
−0.340***
−0.289***
(-3.42)
(-3.01)
(-5.19)
(-4.51)
Tobinq
0.008
−0.007
−0.029***
−0.040***
(0.74)
(-0.64)
(-3.26)
(-4.27)
Roa
−0.014***
−0.010***
−0.011***
−0.009***
(-5.05)
(-3.76)
(-5.57)
(-4.56)
Inst
1.913***
1.729***
1.447***
1.230***
(10.60)
(9.93)
(10.27)
(9.16)
Employee
0.008
−0.006
0.018
0.004
(0.37)
(-0.33)
(1.09)
(0.30)
Invest
−0.000
0.002
0.001
0.002
(-0.08)
(0.98)
(0.42)
(1.47)
Audit
−0.076**
−0.018
−0.078***
−0.022
(-2.46)
(-0.64)
(-3.38)
(-1.01)
Holding
−0.194**
−0.149*
−0.212***
−0.128**
(-2.52)
(-1.93)
(-3.65)
(-2.20)
Pay
0.051*
0.006
0.045**
0.007
(1.80)
(0.21)
(2.20)
(0.41)
Separate
−0.003**
−0.000
−0.003**
0.000
(-2.03)
(-0.02)
(-2.15)
(0.04)
Ret
−1.067***
−1.139***
−0.942***
−0.968***
(-34.31)
(-37.25)
(-38.38)
(-38.49)
Soe
−0.046
−0.047
−0.068***
−0.036
(-1.39)
(-1.55)
(-2.71)
(-1.60)
Constant
0.526
0.595
1.640***
1.606***
(1.13)
(1.19)
(5.19)
(4.22)
Province FE
yes
yes
yes
yes
Industry FE
yes
yes
yes
yes
Year FE
yes
yes
yes
yes
Observations
7,208
7,991
7,208
7,991
Adj_R2
0.219
0.224
0.306
0.305
Conclusion and insight
GCP is a significant financial instrument for government to stimulate the green development of companies, but also effect operating decisions and financing constraints of enterprises by promoting green transformation and development, especially heavily polluting companies. Green Credit Guidelines used in this essay to demonstrate its impact on SPCR of heavy-polluting companies. The main results of the paper are: 1) The GCP implementation increases SPCR for heavy-polluting enterprises. 2) The above impact is constrained by degree of corporate governance, and the effect is not significant in enterprises with higher analyst attention, higher percentage of institutional investors' shareholding, and higher proportion of independent directors. 3) The aforementioned effectsare also constrained by the degree of regional financial development, where GCP increase SPCR in heavily polluting firms in regions with low regional financial development, while the effects are discernible in regions with low regional financial development.
Green finance aims to guarantee implementation of transformation and upgrading of enterprises, especially heavily-polluting enterprises, in order to improve the rational allocation of green resources. However, GCP may have certain limitations or even adverse effects on the heavily polluting enterprises in the concrete implementation, such as the increased SPCR.
These results of the paper have significant implications for the improvement of the green credit system as well as for the managers of heavy polluters. On the one hand, GCP may aggravate the financing constraints of heavy-polluting firms, resulting in companies falling into a vicious circle of “difficulty in financing–information disclosure quality declined - increased risk of share price crash”, which defeats the original purpose of green credit for environmental management. Therefore, financial institutions and local governments must modulate the strength and standards of policy implementation in a timely manner, prevent “one-size-fits-all” GCP, support and guide heavy polluters to actively engage in green transformation and strengthen companies' green awareness. On the other hand, GCP is likely to be a challenge and an opportunity for heavy polluters, as a result, they should focus on their own sustainable development. In order to gain the favor of green financial resources, enterprises should carry out green innovation, eliminate equipment with more serious environmental pollution, enhance the environmental information disclosure standard and carry out green transformation and upgrading. Only when enterprises enforce green transformation and financial institutions collaborate, can we truly promote ecological civilization and high-quality development of economic growth.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.gov.cn/gzdt/2008-07/07/content_1038083.htm.
Author contributions
Conceptualization, ML and BD; methodology, ML, BD, and YL; formal analysis, YL; investigation, BD; resources, ML; writing—original draft preparation, ML; writing—review and editing, ML and BD; visualization, YL; supervision, BD. All authors contributed to the article and approved the submitted version.
Funding
This research was supported by The National Social Science Fund of China (Project Number 19BGL059).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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