From the perspective of the environmental, social and governance (ESG) mechanism, this study aims to investigate the impact of greenwashing reporting on stock price crash risk (SPCR). Further, we investigate the moderating role of climate risk on this relationship.
Using public companies listed on the Indonesia Stock Exchange from 2019 to 2023, the dataset includes 247 firm-year observations. We conduct an ordinary least squares approach, followed by coarsened exact matching and generalized least squares for the robustness test.
We find that greenwashing moderately affects SPCR, suggesting that companies that amplify their environmental claims are more susceptible to market crashes. The moderating analysis indicates the imperative role of climate risk, implying that greenwashing practices under high climate risk circumstances lead to a higher risk of stock price crashes. Overall, our evidence is consistent with the masking effect of socially responsible information and that ESG greenwashing increases SPCR, which is beneficial for market participants and policymakers by providing a reliable decision-making reference for the high-quality development of Indonesia-listed companies.
Despite the regression results, this study has its own limitation: due to the missing data of some companies when sample selection, greenwashing index cannot be calculated correctly, as it focuses on companies that have both ESG performance and disclosure, which may not fully capture the real greenwashing effect on SPCR. As a result, this paper omits samples with missing data, which affects the overall findings by limiting its ability to fully capture the real impact of greenwashing on SPCR in Indonesia, thereby leaving several shortcomings for future investigation.
The practical implications are twofold. For policymakers, the results highlight the urgency of strengthening ESG reporting standards in Indonesia, moving beyond voluntary disclosure to frameworks aligned with global initiatives such as the EU CSRD or US Securities and Exchange Commission rules. For investors, the evidence suggests caution in interpreting ESG claims at face value and the importance of integrating ESG quality assessments and active engagement into portfolio strategies. In this way, both regulators and investors can reduce information asymmetry and limit systemic risks arising from greenwashing.
The study highlights that misleading sustainability reporting not only threatens financial market stability but also erodes public trust in corporate ESG practices. By exposing the risks of greenwashing, the findings encourage greater transparency and accountability, which can foster more responsible corporate behavior and protect broader societal interests in sustainable development.
We contribute to the literature's discussion on the greenwashing and market crash risk nexus, which is still rarely found in the context of emerging economies. Furthermore, the inclusion of climate risk provides more insightful information for market participants.
1. Introduction
Climate change presents significant issues for global society, requiring actions from both public and private sectors. Policymakers have begun to acknowledge that climate change and environmental degradation represent a significant threat to the future. The 2015 Paris Agreement is the inaugural comprehensive climate accord that expressly acknowledges the necessity of aligning financial flows with a trajectory toward low greenhouse gas emissions and climate-resilient development (Ardia et al., 2023; Chan et al., 2024a, b; Kim and Kim, 2023). The attention to climate change has led enterprises on a global scale to turning their attention towards environmental sustainability through the integration of environmental, social and governance (ESG) criteria in risk management (Cai et al., 2023; Chen and Xie, 2022; Chung et al., 2024).
Following regulators worldwide who initiated various actions to encourage firms to engage in ESG practices, Indonesia regulates this through the Financial Services Authority Regulation (POJK) Number 51/POJK.03/2017 on sustainable finance. This regulation requires financial services institutions (FSIs), issuers and public companies to publish sustainability reports. This sustainability reports then presents a substantial challenge for sustainable investing and raises critical questions about the reliability of these disclosures in guiding investment decisions (Agustin et al., 2025). With investor's raising concern on sustainable investment, enterprises attempt to disclosed their social responsibility following the perspective of asymmetric information and stakeholder theory, where this information will increase firm's stock performance. To facilitate this, the rating agency creates an ESG performance rating to assess the seriousness of the company in implementing its social responsibility actions. Thus, investors then recognize two ESG assessment indicators, namely ESG disclosure and ESG Performance.
Various studies were then conducted to examine the relationship between ESG disclosure and stock performance (Anwer et al., 2023; Cai et al., 2023; Chen and Xie, 2022; Chung et al., 2024; Murata and Hamori, 2021; Silva, 2022; Zhang et al., 2022) as well as ESG performance and stock performance (Agustin et al., 2025; Feng et al., 2022; Hao et al., 2025; Hu et al., 2023; Li et al., 2022; Wang et al., 2023; Wu et al., 2024; Xu et al., 2021). However, existing studies only measure social responsibility information disclosure or ESG information from a single dimension, which is difficult to observe the specific motivation of management to disclose social responsibility information. What's more, corporate ESG disclosure information is different from institutional ESG rating information. In response to investors' strong demand for ESG information, enterprises tend to engage in “greenwashing” practices (Netto et al., 2020). Greenwashing refers to the company's environmental claims which are not substantiated by its actual activities, generating a discrepancy between words and deeds with the aim of misleading investors (Li et al., 2024a, b; Zhang et al., 2022). This practice gained worldwide relevance at the beginning of the 2000s, when several multinational companies were found guilty of hiding their polluting activities behind green advertisements. Since then, greenwashing started being a fundamental topic for researchers and governments began to develop policies to identify and combat its diffusion (Zhang et al., 2022).
Despite of strict disclosure regulations at the global level, such as the EU's Corporate Sustainability Reporting Directive (CSRD) and the US Securities and Exchange Commission (SEC) climate disclosure rules, sustainability reporting requirements in Indonesia remain largely voluntary. Nevertheless, Indonesian firms operating globally, particularly those with business ties to Europe or the USA may be influenced to adopt these standards either voluntarily or in response to expectations from international investors and trading partners. As a result, companies in Indonesia are encouraged to disclose their ESG practices in sustainability reports. From the perspective of sustainable investment, greenwashing increases a company's future stock price crash risk (SPCR) by masking sustainability issues and managerial opportunism, leading to a decline in investor confidence and potentially lower financial performance (Liu et al., 2024a, b; Teti et al., 2024).
Given the economic conditions, geopolitical factors and recurring financial crises, a potential danger that may emerge is the risk of SPCR, refers to a condition where the risk of extreme negative values in the distribution of firm specific returns, after adjusting for the return portions that co-move with common factors (Wang et al., 2023). This can impose significant losses on investors. While the volatility risk encompasses both losses and gains, the SPCR refers to the likelihood of incurring huge losses that cannot be diversified away (Fiordelisi et al., 2023; Hao et al., 2025). The condition of a market price crash was experienced by the Indonesia Composite Index several times particularly during the financial crisis such as subprime mortgage in 2008 (Tong and Wei, 2008), the COVID-19 outbreak (Baker et al., 2020; Agustin, 2021), which leading to a trading halt. Recently, Indonesia experienced the market crash during the period of November 2024–March 2025, where the composite stock price had a negative tendency. This continued until the Indonesia Stock Exchange (IDX) had to impose a trading halt on March 19, 2025. The condition partly being triggered by uncertain global tensions and also caused by negative investor sentiment due to local policies considered controversial.
This study examines the relationship between greenwashing and the SPCR, a significant concern as ESG principles increasingly underpin investor decision-making in the stock market (Ferrón-Vílchez et al., 2021; Ghitti et al., 2024; Gregory, 2021; Huang et al., 2022; Li et al., 2022). Apart from studies examined the relationship between stock price crash risk and ESG performance or ESG disclosure, studies which determined the greenwashing practices in this area is scarcely found. Extending the studies by Liu et al. (2024a, b) and Huang et al. (2024) in China, this study focuses on companies in Indonesia's emerging economies that may behave differently from those in developed countries.
In addition to EU CSRD which mandates companies to disclose detailed, standardized information on ESG issues, including climate-related risks, transition plans, the debate raises when the SEC In USA has proposed new climate-related disclosure rules (first issued in March 2022), which would require public companies to provide standardized information on greenhouse gas emissions, climate-related risks and governance of climate issues in their registration statements and periodic reports. A key point of debate is whether firms should be required to disclose Scope 3 emissions (indirect emissions in the supply chain), which many corporations argue is costly and uncertain, while investors and regulators emphasize its importance for assessing climate risks.
On the other hand, some studies showed that there is a significant role of climate risk, in both physical and transition forms, on a company's stock price. Gan et al. (2024) explained that the continuing increase in global temperature could directly impact financial stability, especially regarding the company's stock price, due to disruptions in the supply chain. The findings indicate that physical climate risk has a significant positive effect on the likelihood of SPCR and this effect is more pronounced in countries with low economic development and in countries where disasters frequently occur (Dong and Liu, 2023; Liu et al., 2024a, b; Saqib et al., 2024; Zhao et al., 2024). Moreover, transition climate risk, which arises from changes in policies, regulations and technologies in response to climate change, suggests that companies exposed to changes in policies and regulations tend to face higher SPCR as there may be revaluation from investors (Broeders et al., 2023; Jin et al., 2019; Luo and Zhang, 2020). The imperative literature that supports the conceptual framework and scope of this study is presented in Table 1.
Summary of literature review
| Author | Context | Variable | Key findings |
|---|---|---|---|
| Wang et al. (2023) | A-shares listed on Shanghai and Shenzhen stock exchanges from 2015 to 2020 | Crash Risk Index (CRI) | Stocks with higher ESG ratings experience lower crash risk, consistent with negative net spillover effects |
| ESG rating | |||
| Zhang et al. (2024) | All Chinese public firms from 2008–2023 | Stock Price Crash Risk | Greenwashing will increase a firm's future stock price crash risk and have a greater impact on firms with low transparency and higher greenwashing behavior |
| Greenwashing | |||
| Liu et al. (2024a, b) | Chinese A-share listed companies from 2014 to 2021 | Stock Price Crash Risk | ESG report greenwashing has positive correlation with stock price crash risk as asymmetry information between investors |
| Greenwashing | |||
| Li et al. (2023) | All Chinese firms listed on the Shanghai or Shenzhen stock exchanges from 2013 to 2017 | Return on Assets | Greenwashing shows a positive correlation with financial performance and is difficult to identify due to high asymmetric information in emerging economies |
| Greenwashing | |||
| Li et al. (2022) | Shanghai and Shenzhen A-shares from 2016 to 2020 | Stock Price Crash Risk | Stock prices of companies with high environmental scores have lower crash risk when ESG evaluations are good, so investors' decision-making on environmental performance will pay more attention |
| ESG rating |
Given the growing concern about greenwashing among investors, this study provides timely contributions in several ways. Firstly, this paper will focus on the relationship between the greenwashing effect and SPCR, as prior studies mainly focus on ESG disclosure and information or CSR effects on stock price (Li et al., 2022; Wang et al., 2023; Zhang et al., 2024). By understanding the impact of greenwashing on price crash risk, this study is expected to provide meaningful insights for investors or fund managers in market decision-making. Secondly, this study fills the gap in the literature by adding physical and transition climate risk as moderator variables. Stricter disclosure regimes, such as the EU's CSRD and the US SEC climate disclosure proposals, are directly relevant to this study, as they aim to reduce information asymmetry and curb greenwashing practices. By enhancing the credibility and comparability of ESG reporting, such regulations could mitigate the risk of stock price crashes driven by misleading sustainability disclosures. Lastly, the results of this paper provide insightful information for market participants regarding greenwashing activities that influence investment decisions beyond the firm's performance, particularly concerning Indonesia-listed companies.
2. Literature review and hypothesis development
2.1 Greenwashing and stock price crash risk
The greenwashing practices by a company arise from the pressure of various stakeholders, such as investors, consumers, governments and even corporate consumers, to disclose the company's environmental performance during aggravated environmental issues (Ghitti et al., 2024; Li et al., 2024a, b; Netto et al., 2020). According to Gregory (2021), greenwashing can be defined as a company's strategic decision to present its products as environmentally friendly to enhance firm value, which is used as a tactic to attract investors and consumers. This definition is also supported by Ferrón-Vílchez et al. (2021), who define greenwashing as the disclosure of environmental information initiated by the company to mislead the public. Research by Cheng et al. (2024) and Netto et al. (2020) also concluded that greenwashing is a selective disclosure in which the firm only provides positive environmental information without disclosing negative performance, thereby lacking transparency about their environmental performance in these aspects.
Agency theory (Jensen and Meckling, 1976), stakeholder theory (Freeman and Phillips, 2002) and signaling theory (Spence, 1973) provide a complementary relationship between greenwashing, SPCR, climate risk and investor sentiment. Agency theory elucidates the conflicts between managers and stakeholders, positing that managers may resort to greenwashing to artificially boost the firm's stock price in the short term by concealing negative information or overstating sustainability claims, thereby heightening the risk of stock price collapses upon the revelation of the truth. In this context, monitoring by large shareholders has long been considered an important governance solution to agency problems (Shleifer and Vishny, 1986). Meanwhile, stakeholder theory suggests that companies face pressure from various stakeholders, including investors, to meet specific objectives. In response, companies may engage in greenwashing activities to satisfy these expectations, leading to reputational damage and contributing to crash risk. Lastly, signaling theory explains how companies may use misleading information to send signals to the public, creating a positive sentiment regarding their environmental efforts without making real changes in their practices.
Empirical research by Liu et al. (2024a, b) found that ESG report greenwashing has a significant positive effect on SPCR, and this increase is clearer in companies that voluntarily disclose their social responsibility information in sustainability reports. Wang et al. (2023) empirically found that companies with low ESG performance or high greenwashing activity tend to experience higher SPCR. These findings also impact companies with high ESG performance, especially those involved in operations that contribute to carbon emissions, as investors become more cautious with their investments. Further studies by Yang et al. (2023) exhibit that environmental information disclosure fails due to companies' greenwashing activity, while genuine environmental information disclosure has a significant positive effect on stock price synchronicity. Many companies disclosing false and low-quality information about their environmental performance disrupt stock price indications. In a recent study, the mechanism test found that companies' greenwashing activities will increase firms' future SPCR and this effect is larger in firms with greater greenwashing behaviors (Zhang et al., 2024).
However, a study by Teti et al. (2024) revealed that greenwashing announcements do not significantly affect the cumulative abnormal returns (CAR) of companies in the days after the release of the news. This result goes against their initial hypothesis and demonstrates that the market does not react to greenwashing announcements by penalizing “liable” companies, but instead maintains the company valuation pre-exposure to the announcement. Agustin et al. (2025) found that environmental performance failed to predict stock returns but can be a strong signal for market volatility. This result supports the study by Johnson and Greenwell (2022), which found that greenwashing does not significantly affect stock market performance. Li et al. (2023) posit that the greenwashing effect on market performance weakened with stringent environmental regulations and reversed with low media favorability, underpinning the need for media coverage on greenwashing issues. Having said that, there are contradictory findings among recent studies on the greenwashing-market performance nexus; we propose the following hypothesis:
Greenwashing increases the level of SPCR.
2.1.1 Climate risk
The significant impacts of climate change arise from physical events, such as floods, hurricanes and rising sea levels, as well as regulatory actions, including climate summits, pledges, policies and solutions. These factors generate considerable public attention in the news and on social media, making it reasonable to anticipate that the resulting physical and transition risks will disrupt financial markets (Fahmy, 2025). The World Economic Forum published the “Global Risk Report 2024,” mentioning that climate risk, especially natural disasters caused by El Niño, will be the most serious risk in the next 10 years due to biodiversity loss and ecosystem collapse that can make a critical change to Earth systems. Climate risk can be identified as the main problem that will have a significant effect on financial markets and economic development (Kim and Kim, 2023). Climate risk can be measured by three types of events, such as natural disasters, global warming and climate policies (Li et al., 2024a, b).
Empirical research by Kim and Kim (2023) explained that a company's climate risk, measured by greenhouse gas emissions and energy consumption in Korea, has a significant positive effect on stock crash risk but will weaken after the introduction of the emission trading scheme (ETS). Similarly, Zhao et al. (2024) found that climate risk, especially natural disasters affecting US companies, has a significant positive effect on SPCR in developing countries' financial markets. In this case, the company may tend to hide bad news after climate risk events occur, leading to crash risk once bad news is released to the public. Also, this effect is more significant in countries with low economic development, high climate risk and poor corporate governance. This finding is also supported by research from Dong and Liu (2023) and Liu et al. (2024a, b), which indicates that climate risk has a significant effect on SPCR with firm characteristics, such as CEO performance, geographic distribution (disaster-prone countries) and effective governance. Companies with higher disclosure of information related to climate risk could reduce SPCR in the future (Lin and Wu, 2023).
These findings are also closely related to transition climate risk, which arises from changes in policies, regulations and technologies in response to climate change. Companies exposed to policy and regulation changes tend to face higher SPCR, as there may be revaluation from investors (Broeders et al., 2023; Jin et al., 2019; Luo and Zhang, 2020). Further results also support this research, indicating that policy shifts toward a low-carbon economy enhance market scrutiny, which will reduce companies' greenwashing practices due to high market attention with environmental-specific political risk acting as disciplinary action (Rahman et al., 2024). Lastly, when the interaction of transition climate risk occurs, it could weaken the effect of greenwashing on SPCR, as the accumulated negative news has already been priced in when high transition risk and greenwashing have been detected earlier (Dunz et al., 2021). Based on the information on the relationship between climate risk and SPCR, the following hypothesis is proposed as follows:
Climate risk moderates the relationship between greenwashing and SPCR.
3. Sample and methodology
3.1 Data and sample
This study's research sample is conducted with secondary data from multiple reputable sources, which consist of all public companies on the IDX during 2019–2023. These time intervals are selected as the POJK regulation enforced sustainability reports as a mandatory requirement for all public companies in 2019, with the Global Reporting Index (GRI) serving as the main framework for ESG disclosure. Out of 938 listed companies in Indonesia, 94 companies met the sample requirement, having published their ESG performance and disclosure within the 2019–2023 period. These companies were identified through Refinitiv (Thomson Reuters) and Bloomberg, as well as sustainability reports, resulting in a total of 247 observations.
3.2 Variable measurement
Following previous studies, the dependent variable in this study is SPCR, which is measured by two metrics: negative skewness (NSKEW) and down-to-up volatility (DUVOL). The data were collected from www.investing.com by calculating the weekly returns of each company's stock closing price from 2019–2023, respectively.
DUVOL is measured by splitting a company's stock weekly returns within the year into two categories: weekly returns below the annual average (down weeks) and weekly returns above the annual average (up weeks). The standard deviation for each category is calculated separately and DUVOL is measured by taking the natural logarithm of both standard deviation ratios. According to Liu et al. (2024a, b), a higher level of negative skewness and DUVOL calculated from a company's stock weekly returns reflects an increased risk for the stock to experience sharp price drops, even during positive stock price momentum, which could expose it to sudden price declines.
Following the work of Yu et al. (2024), Liu et al. (2024a, b) and Ma et al. (2025), the Greenwashing index is defined by the difference between ESG disclosure and ESG performance (ESG score). The greater the difference between both ESG indexes, the greater the greenwashing carried out by the company (Li et al., 2023; Liu et al., 2024a, b). To obtain the required data for ESG disclosure and ESG performance (ESG score), the GRI was used as the main framework to identify ESG disclosure, with a total of 117 disclosures according to GRI standard 2021, although there are some minor adjustments from the previous version. Each disclosure item that aligns with the GRI standard found in a company's sustainability report is marked with 1 point; otherwise, it is marked as 0, reflecting a binary approach for each GRI indicator, whether it is present or absent. This measurement could verify whether companies listed on the IDX are engaging in greenwashing behavior, provided the ESG disclosure and ESG performance data are met. We use the difference between the industry-standardized ESG information disclosure score and the standardized ESG performance score to determine whether and to what extent a company greenwashes ESG information. The calculation for greenwashing can be explained as follows:
As for the moderating variable, the Climate Risk Index (CRI) uses both physical and transition climate risk from the Factiva database www.policyuncertainty.com. This index, provided by Bua et al. (2024), uses text-based approaches and authoritative sources; the global physical and transition climate risk vocabularies provide fully relevance-ranked phraseologies associated with the two types of risks. Starting from authoritative and scientific texts on climate change, the authors screen and aggregate the content into two documents: a Physical Risk Document (PRD) and a Transition Risk Document (TRD). These documents encompass all the information about climate risks, which used to feed the text-based algorithms. PRD and TRD are converted into numerical vectors using the term frequency-inverse document frequency (tf-idf) method. The independent variable is controlled by several financial variables to ensure that the variables remain constant, such as return on assets (ROA), return on equity (ROE), net profit margin (NPM) and book value (BV). A detailed description of the variables, covering their types, definitions and abbreviations, is presented in Table 2.
Variables definition
| Variable type | Variable name | Symbol | Variable definition |
|---|---|---|---|
| Dependent Variable | Stock Price Crash Risk | NSKEW | Negative skewness coefficient from company's stock weekly returns within a year period |
| DUVOL | Natural logarithm of company's stock weekly returns between below annual average and above annual average | ||
| Independent Variable | Greenwashing Index | Greenwashing | The difference between ESG disclosure and ESG performance (score) |
| Moderating Variable | Physical Climate Risk | PCR | Climate risk driven by extreme weather change that effect physical hazards |
| Transition Climate Risk | TCR | Climate risk driven by indirect impact of policy and regulations change | |
| Control Variables | Return on Assets | ROA | Percentage of net income compared to total assets |
| Return on Equity | ROE | Percentage of net income compared to total equity | |
| Net Profit Margin | NPM | Percentage of company net profit compared to total revenue | |
| Book Value | BV | Net difference between total equity on common shares |
3.3 Model specification
To address the research objectives, we ran three main models. The first model explains the relationship between greenwashing and SPCR without any moderator variables. Meanwhile, models 2 and 3, respectively, explain the roles of climate risk (physical and transition) as moderator variables.
Model 1
Model 2
Model 3
4. Empirical results
4.1 Descriptive statistics
Table 3 presents the descriptive statistics for each variable in this research. The greenwashing variable indicates the reliability of a company's actual performance compared to its ESG report, with an average value of −0.08 and a high standard deviation of 1.16. The negative value in this variable indicates that overall, companies report their actual performance in the ESG section, but the high variance, with a range wide from −2.78 to 3.31, is influenced by differences in environmental impact and reporting standards relative to sector regulations. In terms of SPCR, two main metrics, NSKEW with an average value of 0.244 and a range from −5.102 to 4.103, indicate that some companies may experience relatively balanced weekly returns, while others with left-skewed values will experience stock price crashes. Additionally, DUVOL, with an average value of 0.053, also indicates similar volatility of a company's stock price during down weeks, which is below the annual average. Both NSKEW and DUVOL metrics provide results on how vulnerable a company's stock price is, which is relatively unstable for stakeholders.
Descriptive statistics
| Obs | Mean | St.dev | Minimum | Maximum | |
|---|---|---|---|---|---|
| Greenwashing | 247 | −0.080 | 1.160 | −2.784 | 3.313 |
| NSKEW | 247 | 0.244 | 0.945 | −5.102 | 4.103 |
| DUVOL | 247 | 0.053 | 0.023 | 0.016 | 0.134 |
| PCR | 247 | −0.001 | 0.002 | −0.007 | 0.000 |
| TCR | 247 | −0.000 | 0.003 | −0.005 | 0.003 |
| ROE | 247 | 0.247 | 1.414 | −0.756 | 21.972 |
| ROA | 247 | 0.061 | 0.078 | −0.106 | 0.557 |
| NPM | 247 | 0.117 | 0.296 | −3.769 | 0.553 |
| BV | 247 | 3151.650 | 4582.360 | 13.599 | 26865.596 |
Climate risk variables, PCR and TCR, show average values nearing zero with low variance, indicating that listed companies in Indonesia reported low awareness of their exposure to climate risk impacts. This is concerning, as climate changes in Indonesia are relatively high regarding both physical and transition risks associated with policy and regulatory shifts. The under-reporting of climate risk exposure in Indonesia could place both companies and governance in a weak position regarding sustainability.
4.2 Pearson correlation
Table 4 presents the Pearson correlation matrix results for greenwashing, SPCR, physical climate risk, transition climate risk and other control variables. Greenwashing is found to have a significantly positive correlation with both crash risk metrics, although the result is stronger and more significant at the 1% level for NSKEW with a coefficient of 0.171, compared to the DUVOL metric. Furthermore, both crash risk metrics also exhibit significant correlation results with physical climate risk and transition climate risk, at the 10% and 1% significance levels, respectively. Interestingly, while NSKEW shows a marginally significant positive correlation with PCR and a strong correlation result with TCR, DUVOL presents a contrasting correlation result, which suggests that climate risk implications could be different depending on the SPCR metrics. NSKEW captures return asymmetry with the accumulation of bad news over the long term, which can lead to a sudden stock price decline. In contrast, DUVOL captures short-term market fluctuations by comparing volatility during down weeks and up weeks, which may indicate lower stock price volatility despite long-term price declines.
Pearson correlation
| NSKEW | DUVOL | GWI | PCR | TCR | ROE | ROA | NPM | |
|---|---|---|---|---|---|---|---|---|
| DUVOL | 0.112* | 1.000 | ||||||
| (0.078) | ||||||||
| GWI | 0.171*** | 0.002 | 1.000 | |||||
| (0.007) | (0.979) | |||||||
| PCR | 0.114* | −0.417*** | 0.143** | 1.000 | ||||
| (0.074) | (0.000) | (0.025) | ||||||
| TCR | 0.193*** | −0.421*** | 0.346*** | 0.739*** | 1.000 | |||
| (0.002) | (0.000) | (0.000) | (0.000) | |||||
| ROE | −0.037 | 0.026 | −0.046 | 0.048 | 0.076 | 1.000 | ||
| (0.566) | (0.684) | (0.475) | (0.456) | (0.237) | ||||
| ROA | 0.011 | −0.077 | −0.039 | 0.020 | −0.014 | 0.145** | 1.000 | |
| (0.866) | (0.230) | (0.540) | (0.759) | (0.831) | (0.023) | |||
| NPM | −0.069 | −0.162** | −0.037 | 0.054 | 0.024 | 0.028 | 0.260*** | 1.000 |
| (0.283) | (0.011) | (0.558) | (0.402) | (0.705) | (0.663) | (0.000) | ||
| BV | −0.077 | −0.041 | 0.051 | 0.011 | 0.001 | −0.044 | 0.255*** | 0.087 |
| (0.227) | (0.525) | (0.427) | (0.867) | (0.986) | (0.496) | (0.000) | (0.172) |
Note(s): ***, **, * denotes significant at 1%, 5% and 10% level respectively
Additionally, greenwashing shows a significantly positive correlation with both PCR and TCR, indicating that companies facing high-related climate risk may tend to engage in greenwashing practices to protect their stock price. Moreover, physical climate risk and transition climate risk are found to have a strong positive significant correlation, which means companies exposed to one type of climate risk may be likely to encounter the other climate risk as well.
4.3 Main results : regression analysis
Table 5 presents the regression results for all hypotheses around greenwashing, SPCR, physical climate risk and transition climate risk. models (1a) and (1b) show the results of the greenwashing variable on NSKEW and DUVOL proxies with positively significant effects at the 5% and 1% significance levels, respectively. These findings align with previous studies indicating that companies will be prone to experience sudden stock price crashes without notice when greenwashing practices are exposed to the public (Liu et al., 2024a, b; Nguyen et al., 2023; Teti et al., 2024; Silva, 2022). For instance, research by Liu et al. (2024a, b) reported that there was information asymmetry in the company's sustainability report about greenwashing, which will not easily be detected by stakeholders, increasing SPCR. Similar research also found that the impact of greenwashing is more significant on companies with non-transparent governance, especially companies with stronger greenwashing practices (Li et al., 2023). Additionally, Li et al. (2024a, b) found that greenwashing behavior initially induces investors to embrace positive investment ideas in the near term; however, these impacts are not sturdy. Eventually, investors typically shift towards embracing gloomy investment ideas. These findings emphasize the important role of greenwashing reporting in sustainability reports for increasing company transparency and reducing stock price crashes.
Regression results
| (1a) | (2a) | (3a) | (1b) | (2b) | (3b) | |
|---|---|---|---|---|---|---|
| NSKEW | NSKEW | NSKEW | DUVOL | DUVOL | DUVOL | |
| GWI | 0.112** | 0.160** | 0.091 | 0.004*** | 0.003** | 0.003** |
| (1.99) | (2.43) | (1.55) | (3.22) | (2.49) | (2.57) | |
| PCR | −1.4e+03 | 72.039*** | ||||
| (−1.47) | (3.68) | |||||
| TCR | 69.861* | −3.054*** | ||||
| (1.89) | (−3.96) | |||||
| GWI*PCR | 33.746 | −0.250 | ||||
| (1.40) | (−0.49) | |||||
| GWI*TCR | −27.907 | −0.877* | ||||
| (−1.30) | (−1.97) | |||||
| ROE | −0.033 | −0.032 | −0.036 | 0.001 | 0.001 | 0.001 |
| (−0.79) | (−0.76) | (−0.86) | (1.30) | (1.29) | (1.20) | |
| ROA | 0.788 | 0.807 | 0.812 | −0.007 | −0.008 | −0.007 |
| (0.96) | (0.98) | (0.99) | (−0.43) | (−0.44) | (−0.39) | |
| NPM | −0.250 | −0.243 | −0.259 | −0.010** | −0.011** | −0.011** |
| (−1.21) | (−1.19) | (−1.26) | (−2.43) | (−2.43) | (−2.51) | |
| BV | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 |
| (−1.45) | (−1.47) | (−1.43) | (−0.46) | (−0.45) | (−0.44) | |
| _cons | 0.029 | 0.520*** | 0.275** | 0.060*** | 0.037*** | 0.053*** |
| (0.16) | (2.85) | (2.24) | (15.81) | (9.61) | (20.87) | |
| Firm FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| r2 | 0.088 | 0.096 | 0.095 | 0.295 | 0.296 | 0.307 |
| r2_a | 0.054 | 0.058 | 0.057 | 0.269 | 0.266 | 0.277 |
| N | 247 | 247 | 247 | 247 | 247 | 247 |
Note(s): ***, **, * denotes significant at 1%, 5% and 10% level respectively; Standard error in parentheses
DUVOL, which captures the relative volatility of returns in negative versus positive weeks, shows higher sensitivity to greenwashing. This outcome can be explained by the fact that once misleading sustainability claims are questioned or exposed, investors react swiftly, producing sharper fluctuations in “down weeks.” In contrast, NSKEW reflects the gradual accumulation of hidden negative information and its eventual release. While this mechanism is consistent with the theory of bad-news hoarding, our findings imply that investors in the Indonesian market may be quicker to adjust when sustainability misrepresentation is suspected, thereby amplifying short-term downside volatility. Thus, although both proxies confirm the risk-enhancing effect of greenwashing, the stronger results in DUVOL highlight that the market's immediate reaction to greenwashing disclosures dominates over the longer-term build up of asymmetric information.
Model (2a) and (2b) with physical climate risk as a moderator variable shows that the interaction effect of greenwashing and PCR is not statistically significant, with a positive coefficient. Although physical climate risk shows a direct effect on SPCR with DUVOL metrics, this does not alter the greenwashing impact on SPCR. These findings indicate that companies facing physical climate risk are more vulnerable to experiencing SPCR, which is consistent with prior studies that emphasized environmental hazards from GHG emissions as climate physical risk to SPCR (Kim and Kim, 2023). Prior studies by Nguyen et al. (2023) found that companies participating in green practices based on environmental sustainability are less likely to experience SPCR, especially during periods of high climate risk.
Referring to models (3a) and (3b), transition climate risk as a moderating variable shows a negative significant effect on the interaction effect of greenwashing and transition climate risk. This implies that high transition climate risk can weaken the initially positive impact of greenwashing on SPCR. This effect may occur due to an increase in scrutiny and awareness, as investors become more sensitive to a company's actual ESG performance in response to policy shifts towards a low-carbon economy. Consequently, companies are faced with greater pressure to maintain transparency in ESG reporting, driven by heightened market attention and scrutiny over climate-related disclosures (Dong and Liu, 2023; Dunz et al., 2021; Gan et al., 2024).
5. Additional test
5.1 Alternative measure of greenwashing index
Du (2015) and Teti et al. (2024) define greenwashing using a dummy variable, where a value of 0 indicates firms that do not engage in greenwashing and a value of 1 indicates firms that do. The use of this dummy variable serves as a signal of potential overclaims made by companies in disclosing their sustainability practices. This study adopts a similar measurement technique, assigning a value of 1 when a firm's index is greater than 0 and a value of 0 when the index is less than 0.
Table 6 presents the regression analysis results using the Greenwashing Index in the form of a dummy variable. The findings are consistent with the baseline regression, showing that greenwashing significantly increases SPCR, particularly when measured by the DUVOL proxy. Similarly, the analysis of the moderating variable reveals that the interaction between greenwashing and transition climate risk yields a negative coefficient, indicating that higher transition climate risk can weaken the relationship between greenwashing and SPCR.
Dummy greenwash – alternative measure of greenwashing index
| (1a) | (2a) | (3a) | (1b) | (2b) | (3b) | |
|---|---|---|---|---|---|---|
| NSKEW | NSKEW | NSKEW | DUVOL | DUVOL | DUVOL | |
| GWI_D | 0.164 | 0.209 | 0.123 | 0.005* | 0.004* | 0.004* |
| (1.36) | (1.52) | (1.02) | (1.86) | (1.87) | (1.77) | |
| PCR | −2.0e+03* | 58.039*** | ||||
| (−1.71) | (2.82) | |||||
| GWI*PCR | 17.550 | −0.621 | ||||
| (0.72) | (−1.44) | |||||
| TCR | 84.962* | −2.534*** | ||||
| (1.82) | (−3.26) | |||||
| GWI*TCR | −31.319 | −1.016** | ||||
| (−1.25) | (−2.12) | |||||
| (−3.06) | (−2.98) | (−3.07) | (4.31) | (4.10) | (3.73) | |
| ROA | 0.740 | 0.740 | 0.776 | −0.010 | −0.010 | −0.008 |
| (1.00) | (0.99) | (1.05) | (−0.51) | (−0.52) | (−0.47) | |
| NPM | −0.266 | −0.267 | −0.273 | −0.011** | −0.011* | −0.011* |
| (−1.12) | (−1.13) | (−1.12) | (−1.97) | (−1.96) | (−1.96) | |
| BV | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 |
| (−1.54) | (−1.50) | (−1.57) | (−0.21) | (−0.28) | (−0.27) | |
| _cons | −0.127 | 0.493** | 0.195 | 0.055*** | 0.037*** | 0.051*** |
| (−0.46) | (2.10) | (1.10) | (14.75) | (6.37) | (13.75) | |
| Firm FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| r2 | 0.080 | 0.082 | 0.089 | 0.272 | 0.277 | 0.288 |
| r2_a | 0.045 | 0.043 | 0.050 | 0.244 | 0.246 | 0.258 |
| N | 247 | 247 | 247 | 247 | 247 | 247 |
5.2 Robustness test – coarsened exact matching (CEM) analysis
Following the baseline regression results in Table 5, the robustness test is conducted with coarsened exact matching (CEM) presented in Table 7, aimed at addressing potential sample bias and further examining the relationship between greenwashing, SPCR, physical climate risk and transition climate risk. In this robustness test, the dummy greenwashing sample is used to distinguish companies with high and low levels for testing the moderating variable. The robustness test found consistent results with the regression test in Table 5, indicating that greenwashing is positively significant at the 5% level for NSKEW. However, for DUVOL, the results were inconsistent with the regression results, where greenwashing becomes statistically insignificant. For Model (2a), the moderating physical climate risk variable and its interaction effect confirmed consistent results with SPCR at the significant 5% level. Moreover, the interaction variable in the robustness test became positively significant, supporting the earlier hypothesis, although this was not found in the earlier regression results.
Robustness test – CEM results
| (1a) | (2a) | (3a) | (1b) | (2b) | (3b) | |
|---|---|---|---|---|---|---|
| NSKEW | NSKEW | NSKEW | DUVOL | DUVOL | DUVOL | |
| GWI | 0.143** | −0.000 | ||||
| (2.218) | (−0.242) | |||||
| d_GWI | 0.328** | 0.068 | −0.001 | 0.004 | ||
| (2.206) | (0.561) | (−0.265) | (1.242) | |||
| PCR | 2.267 | −3.500*** | ||||
| (0.059) | (−5.039) | |||||
| TCR | 83.042** | −2.979*** | ||||
| (2.105) | (−3.711) | |||||
| c.d_GWI#c.PCR | 129.136** | −1.601 | ||||
| (2.475) | (−1.422) | |||||
| c.d_GWI#c.TCR | −27.831 | −1.675 | ||||
| (−0.555) | (−1.609) | |||||
| ROE | 0.229 | 0.092 | 0.151 | 0.002 | 0.005 | 0.003 |
| (1.096) | (0.457) | (0.734) | (0.330) | (0.652) | (0.479) | |
| ROA | 0.589 | 0.894 | 0.866 | −0.009 | −0.016 | −0.015 |
| (0.554) | (0.815) | (0.811) | (−0.362) | (−0.641) | (−0.594) | |
| NPM | −0.856* | −0.968* | −0.994* | −0.035*** | −0.030*** | −0.033*** |
| (−1.663) | (−1.907) | (−1.807) | (−2.987) | (−3.108) | (−3.502) | |
| BV | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 |
| (−1.646) | (−1.564) | (−1.435) | (−0.585) | (−0.558) | (−0.619) | |
| _cons | 0.358*** | 0.276* | 0.366** | 0.058*** | 0.052*** | 0.055*** |
| (3.273) | (1.843) | (2.565) | (22.957) | (16.573) | (16.585) | |
| r2 | 0.061 | 0.075 | 0.076 | 0.068 | 0.236 | 0.261 |
| r2_a | 0.041 | 0.048 | 0.048 | 0.048 | 0.213 | 0.239 |
| N | 243 | 243 | 243 | 243 | 243 | 243 |
In Model (3a), with transition climate risk as a moderator variable, the results are still consistent with earlier regression results, where both greenwashing and the interaction variable are not significant, while the moderating effect remains positively significant at the 5% level. Along with that, models (1b), (2b) and (3b) with the DUVOL metric only found a direct effect of the moderating variable on SPCR, which is inconsistent with prior regression results.
5.3 Robustness test – generalized least squares analysis
To ensure the robustness of the main findings, this study further employs the generalized least squares (GLS) estimation technique. GLS is particularly suitable in the presence of heteroskedasticity and autocorrelation, which are common characteristics in panel data involving financial performance and market risk. By allowing for panel-specific error structures, GLS provides more efficient and reliable estimates compared to ordinary least squares (OLS), especially when the classical assumptions of homoskedastic and uncorrelated errors are violated. The use of GLS in this context serves as a robustness check to examine whether the observed relationship between greenwashing and SPCR remains consistent when the potential issues of heteroskedasticity across firms and autocorrelation over time are addressed (Greene, 2003; Wooldridge, 2010). As presented in Table 8, the GLS results are consistent with the baseline regression, indicating that greenwashing increases SPCR.
Generalized least squares results
| (1a) | (2a) | (3a) | (1b) | (2b) | (3b) | |
|---|---|---|---|---|---|---|
| NSKEW | NSKEW | NSKEW | DUVOL3 | DUVOL3 | DUVOL3 | |
| GWI | 0.111** | 0.160** | 0.091 | 0.004*** | 0.003*** | 0.003*** |
| (2.05) | (2.53) | (1.61) | (3.26) | (2.60) | (2.60) | |
| ROE | −0.033 | −0.032 | −0.036 | 0.001 | 0.001 | 0.001 |
| (−0.80) | (−0.78) | (−0.87) | (1.32) | (1.22) | (1.22) | |
| ROA | 0.792 | 0.812 | 0.813 | −0.008 | −0.007 | −0.007 |
| (0.98) | (1.01) | (1.01) | (−0.45) | (−0.42) | (−0.42) | |
| NPM | −0.249 | −0.242 | −0.258 | −0.010** | −0.011** | −0.011** |
| (−1.24) | (−1.21) | (−1.29) | (−2.45) | (−2.54) | (−2.54) | |
| BV | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 | −0.000 |
| (−1.47) | (−1.50) | (−1.46) | (−0.46) | (−0.45) | (−0.45) | |
| PCR | −1.4e+03 | |||||
| (−1.52) | ||||||
| GW*PCR | 33.839 | |||||
| (1.47) | ||||||
| TCR | 72.272** | −2.969*** | −2.969*** | |||
| (2.00) | (−3.89) | (−3.89) | ||||
| GW*TCR | −26.142 | −0.838* | −0.838* | |||
| (−1.27) | (−1.93) | (−1.93) | ||||
| _cons | 0.019 | 0.507*** | 0.264** | 0.060*** | 0.053*** | 0.053*** |
| (0.11) | (2.82) | (2.21) | (15.98) | (20.98) | (20.98) | |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| N | 247 | 247 | 247 | 247 | 247 | 247 |
Note(s): t statistics in parentheses
*p < 0.1, **p < 0.05, ***p < 0.01
6. Conclusions, implications and limitations
As the uncertainty level keeps growing in the financial market, causing the overall market to become more volatile and unpredictable, these uncertainty factors have recently forced companies to conceal all their negative information in response to the increasing scrutiny over sustainability, especially environmental claims. In the case where all negative information is revealed to the public, it will result in a market crash, which starts from an extreme downward movement of stock prices. Based on the regression test results and supported by robustness tests, it was found that the greenwashing variable has a positively significant effect on SPCR. When investors find out that the company's actual performance differs from its ESG disclosure report and engages in greenwashing practices, it will lead to a sudden stock price decline, as investors will withdraw all their investment funds. Furthermore, the physical climate risk moderating the greenwashing variable also found a positively significant result on SPCR, as proved by the robustness test results. However, greenwashing with the moderating transition climate risk variable only found a positively significant effect with DUVOL metrics.
Our findings confirm that greenwashing significantly increases SPCR, with stronger effects on DUVOL than NSKEW. This suggests that, while bad-news hoarding explains the long-term mechanism, the Indonesian market is especially sensitive to short-term volatility once sustainability misrepresentation is suspected. The results support information asymmetry perspectives, showing how misleading ESG disclosure erode market confidence and accelerate downside reactions. From a theoretical perspective, the findings align with several key frameworks. Agency theory suggests managers may engage in greenwashing to protect their own interests by concealing negative information, but this behavior ultimately harms shareholders when the truth emerges. Stakeholder theory is reinforced by the evidence that misleading ESG practices undermine trust among investors, regulators and the public, creating reputational and financial penalties. Signaling theory further explains the mechanism: sustainability reports are intended as positive signals of firm quality, yet when those signals are deceptive, they lose credibility and heighten crash risk once exposed. The moderating role of transition climate risk emphasizes how institutional and regulatory pressures make these signals more heavily scrutinized, sharpening market reactions.
The practical implications are twofold. For policymakers, the results highlight the urgency of strengthening ESG reporting standards in Indonesia, moving beyond voluntary disclosure to frameworks aligned with global initiatives such as the EU CSRD or US SEC rules. For investors, the evidence suggests caution in interpreting ESG claims at face value and the importance of integrating ESG quality assessments and active engagement into portfolio strategies. In this way, both regulators and investors can reduce information asymmetry and limit systemic risks arising from greenwashing.
Despite the robustness of the regression results, this study has certain limitations. The greenwashing index employed relies on the availability of both ESG performance and disclosure data, leading to the omission of firms with incomplete information. Recent literature emphasizes that greenwashing is a multidimensional phenomenon encompassing selective disclosure, symbolic actions and the gap between apparent and real sustainability performance. By relying solely on quantitative ESG scores, the index may overlook subtler forms of greenwashing, such as misleading textual self-representation, excessive green marketing or strategic virtue signaling. To address these shortcomings, future studies could incorporate alternative approaches, such as text-based content analysis of sustainability reports, sentiment analysis or AI-driven classification of corporate disclosures (Dorfleitner and Utz, 2024). Mixed-method designs, including qualitative case studies or interviews with stakeholders, may also help reveal the nuanced motivations behind greenwashing and strengthen the interpretation of its effects on SPCR.
I would like to express my sincere gratitude to Universitas Internasional Batam for the generous grant support, which has been instrumental in advancing this work.
