ESG News Sentiment and Stock Price Reactions: A Comprehensive Investigation via BERT

With the increasing awareness of ethical issues such as environment protection and social care, the conception of ESG has become more and more prominent and urgent, not only in our everyday lives but also on the financial markets. As Van Duuren et al. (2016) and Amel-Zadeh and Serafeim (2018) suggest, ESG is already regarded as one of the important considerations for fund managers. In July 2020, as the booming fast fashion giant Boohoo was accused of using forced labor in a factory in Leicester, its stock price dropped more than 20% in a single day. The stock market reaction shows vividly that besides financial news, ESG news can also be an important factor and price driver on the financial markets, mainly due to their impact on reputation. Good ESG news can generally be indications of pro-ethical corporate behavior and bad ones for the opposite. Thus, the questions of how frequent good and bad news are and to what extent stock prices react to these news, help to clarify whether firms really behave ethically and to which extent the market values the apparent behavior. This article is devoted to an empirical investigation of this matter based on text analytics.

In the past decade, ESG has also become one of the hottest topics in finance literature. However, the research of ESG issues is still in its initial stage. Most ESG studies (e.g. Bennani et al. 2018; Hartzmark and Sussman 2019) rely heavily on ESG data such as different ESG ratings provided by specific ESG data providers, based on their in-house developed methodologies (Fiaschi et al. 2020). As Dorfleitner et al. (2015) suggest, there is an evident lack in the convergence of ESG measurement concepts and the different ratings neither coincide in distribution nor in risk. Therefore, empirical studies focusing on proprietary ESG performance proxies may be subjected to the problem of proxy biases. Also, the low-frequency of those ESG ratings and various rating methodologies make it almost impossible to understand how the market reacts to ESG issues in real time. Most recently, several studies (see e.g. Krüger 2015; Capelle-Blancard and Petit 2019; Taleb et al. 2020; Naumer and Yurtoglu 2022) focus more on frequent ESG information such as ESG events and ESG news. Krüger (2015) finds some evidence that investors may react to ESG events and reveal their possible pricing implications. However, due to the difficulty to process unstructured raw text data, these studies have to acquire ESG events or news data from ESG data providers.¹ The reliance on proprietary datasets may raise the concern that empirical results regarding the impact of ESG news on the financial markets could be sensitive to how data providers collect (e.g., different ESG news coverage) and process ESG news (e.g., different implementation of sentiment analysis). Therefore, despite some efforts being made, whether ESG news, or more specifically instant ESG news, influences financial markets are far from being fully understood.

In this study, we show how a comprehensive ESG news dataset is built upon a vast amount of raw ESG news and how news sentiment is extracted in a transparent way before empirical investigations are conducted. Compared to related studies which often adopt ready-for-use ESG events or news data from data providers, this study builds an ESG news dataset based on raw ESG news published by more than 10,000 news sources on Thomson Reuters Eikon. We introduce the recent development in Nature Language Processing (NLP), i.e. the BERT model, to construct a comprehensive and fresh ESG news dataset from raw ESG news. Moreover, we extract sentiment signals from the unstructured textual data by applying the fined-tuned BERT sentiment classifier, which is considered more accurate than classical sentiment analysis methods such as lexicon-based sentiment analysis (Kotelnikova et al. 2021; Alaparthi and Mishra 2021).

With such a comprehensive dataset including almost all listed stocks with ESG news coverage for the past two years, we conduct for the first time a complete empirical investigation on the impact of instant ESG news on major stock markets. It sheds light on market reactions to instant ESG information. We find that the market responds to ESG news parallel to the news sentiment. The market reacts positively to positive ESG news while negatively to negative ESG news. Yet these reactions appear to be asymmetric. The market reaction to negative ESG news is stronger, compared to positive ESG news. These patterns exist not only on American stock markets, but also on European stock markets. At last, we discover an interesting point regarding the relationship between ESG news shocks and historical ESG records. When investors are confronted with ESG news, they also take the overall ESG performance of the target company into consideration. Companies with a better ESG record suffer less from market value loss due to negative ESG news, while those with a worse ESG performance enjoy more market value gain when facing positive ESG news.

These findings add to the discussion of integrating ESG factors in asset pricing (see e.g. Pedersen et al. 2021). Since ESG issues are found to be perceived seriously by investors, they should be considered and included as important factors in related research. Moreover, the empirical results question the efficiency of financial markets, as systematic arbitrage by closely monitoring ESG news could be viable. Our study also suggests that companies tend to exaggerate their ESG performance (see e.g. Kim and Lyon 2015), which is analogous to the so-called “greenwashing” phenomenon in the specific context of environmental issues. Our data shows that positive ESG news prevails on the market, which might suggest the existence of performance exaggeration regarding ESG issues. Meanwhile, the fact that the overwhelming positive ESG news is still perceived positively suggests that investors might not be able to completely detect the false claim of good ESG performance. Consequently, companies could possibly game the system by releasing more ESG information to their advantage.

The contribution of this study is twofold. First, we show how to apply the BERT model to build our own unique and massive ESG news dataset and judge news sentiment effectively and consistently. Especially, the latest breakthrough in NLP can also contribute to the advancement in financial studies focusing particularly on soft factors and provide a new and better approach in the toolbox of financial researchers to gain deeper insight into their role on the financial markets. Our study contributes to the new stream of studies leveraging the recent development in NLP in ESG related topics (Aue et al. 2022; Sokolov et al. 2021; Mehra et al. 2022; Chava et al. 2021). Second, to the best of our knowledge, we examine the impact of ESG news in a comprehensive and complete framework for the first time. In general, we extract almost every piece of relevant instant ESG news piece for almost all listed equities, and avoid dependence on proprietary datasets and possible biases and errors associated with such tailored datasets. Therefore, the employed instant ESG news dataset is unique and comprehensive compared to other ESG events or news datasets directly sourced from ESG data vendors. The way we build the ESG news dataset enables us to come to more credible conclusions. Even though some earlier studies find that only negative ESG events or news matters (Krüger 2015; Capelle-Blancard and Petit 2019; Cui and Docherty 2020), we find evidence that investors may also value positive ones, albeit to a smaller extent. This finding has the policy implication for companies that it really matters to improve their ESG profile, but not just to avoid negative ESG news. Moreover, this study gives some clues regarding how investors deal with the relationship between newest and past ESG performance, which is rarely touched upon in ESG studies (see e.g. Serafeim and Yoon 2021). Our study suggests that a good long-term ESG profile might serve as a buffer to moderate the impact of short-term ESG news.

The remainder of the paper is organized as follows. In Sect. 2, we discuss basic background information regarding different types of ESG information, especially ESG news. In Sect. 3, we introduce briefly the recent development in NLP, i.e., the BERT model. We review the literature on market reactions to ESG performance and propose hypotheses in Sect. 4. Sect. 5 describes how we build our ESG news dataset step by step. In Sect. 6, we discuss necessary empirical methodological approaches. Sect. 7 presents the empirical results and Sect. 8 concludes.

As the interest and demand of stakeholders in ESG issues grows, companies are subject to an increasing amount of ESG reporting guidance or requirements (KPMG 2019). According to the survey conducted by KPMG (2020), the percentage of the biggest companies which report on sustainability has increased from 53% in 2008 to 80% in 2020. Nevertheless, ESG disclosure as a source of ESG information has several obvious drawbacks for stakeholders such as low frequency, lack of credibility, timeliness, and relevance (Maniora 2017). Due to the difficulty to process ESG disclosure directly, stakeholders often rely on a third-party assessment, especially ESG ratings from ESG rating agencies (Berg et al. 2022). They usually apply a qualitative and quantitative methodology to assess corporate ESG performance by constructing ESG rating metrics based on information collected from different sources such as ESG disclosure, ESG news, and questionaries (Escrig-Olmedo et al. 2019; Del Giudice and Rigamonti 2020). However, a few studies raise the concern whether ESG ratings are good proxies of corporate ESG performance (Dorfleitner et al. 2015; Drempetic et al. 2020). Many studies show that ESG rating agencies may fail to measure (Escrig-Olmedo et al. 2019; Drempetic et al. 2020), and disagree on ESG performance (Dorfleitner et al. 2015; Berg et al. 2022; Lopez et al. 2020). Also, the fact that most ESG ratings are updated on a yearly or quarterly basis poses a challenge for tracking corporate ESG performance in time. Even though ESG rating agencies consider various sources of ESG information including high-frequency data such as ESG news (Escrig-Olmedo et al. 2019), they are often embedded in rating scores on a periodic basis and cannot reflect the recent development of ESG performance.

Besides official ESG disclosure and ESG ratings from ESG agencies, ESG events or news can be other important sources of information for investors. In recent years, ESG events data, especially ESG incidents data, becomes more and more popular. For instance, RepRisk’s incidents data is widely used by large investors (Gantchev et al. 2022). Specifically, just like traditional ESG ratings, ESG incidents indicators such as the RepRisk rating measure ESG performance quantitatively based on aggregated negative ESG news information and proprietary process. More generally, the media plays a central role in diffusing information on financial markets and contribute to the efficiency of the stock market by improving the dissemination of information (Peress 2014). On financial terminals such as Thomson Reuters and Bloomberg or main stream websites, news stories related to specific companies, including company ESG news, are updated at lightning speed. If investors care about ESG issues just like traditional financial fundamentals, they could possibly be influenced by reading these ESG news articles. However, unlike ESG ratings as numeric values, ESG news articles from different news sources are unstructured text data which is difficult to quantify. While ESG rating values can be homogeneously interpreted as the overall ESG performance, ESG news cannot be easily standardized and transformed into a common index which is easy to comprehend. Although instant ESG news may be consumed by individual or institutional investors and thus integrated into their investment decision-making process, it is unclear how and to what extent they may react to these instant non-financial information. To answer this question, a comprehensive stream of instant ESG news should be available and processed in a plausible way. Nevertheless, a ready-for-use ESG news dataset is usually not for free and should always be purchased from specific ESG data providers. Earlier related studies adopt such ESG news datasets from several popular ESG data providers such as Ravenpack and Covalence (Capelle-Blancard and Petit 2019; Cui and Docherty 2020). The key problem of this approach is that these proprietary ESG data providers may have different news coverage and textual processing methodologies, which are in most cases not transparent to researchers.

As the need to understand the role of soft factors extracted from unstructured text data on financial markets grows, classical textual analysis has been more commonly adopted in financial studies in recent years (see e.g. Dorfleitner et al. 2016). Despite some preliminary progress, it appears that research with classical textual analysis has reached the stage of stagnation as its benefits appear to have been fully exploited.

Progresses in NLP in the past few years, however, give new hope for further quantification of unstructured text data. Devlin et al. (2018) propose a promising language presentation model, called Bidirectional Encoder Representations from Transformers (BERT). The BERT model is designed to pre-train deep bidirectional textual representation from unlabelled text data. Since its introduction, it has been recognized widely as the state-of-the-art language model in various language tasks. The power of the BERT model originates from several parts. First, the massive size of the BERT model is unprecedented: the base BERT model contains 110 million parameters. Second, its deliberately designed neural networks can grasp the complex relationship among words and sentences. The neutral network architecture of the BERT model is based on several encoder layers of the popular Transformer model proposed by Vaswani et al. (2017), of which the most important part is the so-called self-attention mechanism. Third, the BERT model is pre-trained with unprecedentedly massive text datasets including the BookCorpus and English Wikipedia (Devlin et al. 2018) over two different pre-training tasks.² With such a large training input, the BERT model can be pre-trained to the extent that meaningful word or sentence representations can arise.

The BERT model is a transfer learning framework and its usage is often separated into two stages: pre-training and fine-tuning. Various pre-trained BERT models have been pre-trained on different unlabelled text datasets with different training settings and can be accessed by researchers who seek to quantify textual information for their purposes. They can be applied directly to a wide range of down stream tasks such as text classification, named entity recognization and question answering, and has obtained the best results for many language tasks (Devlin et al. 2018). For a specific language task such as sentiment classification, researchers can continue training a pre-trained BERT model with their own labelled datasets.

After the introduction of the original BERT model (Devlin et al. 2018), some more refined and robust BERT-like models, such as RoBERTa (Liu et al. 2019) and ALBERT (Lan et al. 2019), are proposed based on the basic architecture of the BERT model and achieve better performance by slightly modifying some parts of the model design or the pre-training hyper-parameters. These models are also available to scholars and can be further fine-tuned for different language tasks.³

Several studies explore the application of the BERT model in ESG research. Aue et al. (2022) demonstrate how the BERT model could help predict ESG ratings by extracting signals from ESG news for US companies. Sokolov et al. (2021) also apply the BERT model to extract signals from 1000 tweets to predict ESG scores and show the potential of building an automated ESG scoring system. Mehra et al. (2022) fine-tune an ESG-BERT model which help predict environmental scores by utilizing information from 10K filings. Chava et al. (2021) leverage RoBERTa to classify ESG topics in earning calls and build an ESG dictionary.

In general, the BERT model helps advance the understanding of the impact of ESG issues on financial markets. However, it also has some limitations for ESG research. For instance, even though the BERT model offers very impressive language processing capabilities, its large size (in terms of large number of parameters) leads to very high computing resource demand, which may restrict its application in large scale in ESG research. Moreover, like many other large machine learning models, the BERT model has also interpretability challenges. For ESG research, interpretability is of great importance for stakeholders to trust and use model outputs. Fortunately, the recent development in NLP is quite promising, which may help alleviate these problems.

While numerous studies report a positive relationship between ESG performance and corporate financial performance (Friede et al. 2015), there is less consensus about how investors value ESG performance on the stock markets. Although the investment community considers ESG information during investment decision-making process (Amel-Zadeh and Serafeim 2018; Van Duuren et al. 2016), the role of ESG issues on financial markets is not well understood (Bennani et al. 2018). Pedersen et al. (2021) theoretically propose an ESG-adjusted CAPM and predict that a security with a higher ESG score has a higher demand from ESG investors, which is also supported by the empirical evidence that ESG performance proxies correlate positively with institutional holdings. Hartzmark and Sussman (2019) examine the relationship between the sustainability rating rankings of the US mutual funds and fund flows and present evidence that investors do value sustainability. Regarding the market performance related to ESG investment, Mǎnescu (2011) find that only some ESG attributes, such as community relations, have an impact on stock returns by analyzing a long panel dataset of US firms. Bennani et al. (2018) document that the impact of ESG screening on stock performance is highly time-dependent: they find no evidence of a consistent reward for ESG integration during the 2010–2013 period but a significant excess return for the 2014–2017 period.

Despite their different perspectives and results, these earlier studies usually adopt some kind of ESG performance proxies provided by ESG data providers such as ESG rating. Very few studies address the question of whether the market reacts to high-frequency news in the field of ESG studies (see e.g. Capelle-Blancard and Petit 2019; Cui and Docherty 2020), despite the existence of a stream of studies investigating ESG events (Flammer 2013; Naughton et al. 2019; Grewal et al. 2021; Krüger 2015) and ESG incidents (Gantchev et al. 2022; Glossner 2021; Derrien et al. 2021).⁴ However, there are a significant number of studies analyzing the relationship between high-frequency financial news and stock markets (Alanyali et al. 2013; Boudoukh et al. 2019). For instance, Alanyali et al. (2013) find that financial news is closely linked to trading movements. Boudoukh et al. (2019) find evidence that there is a close relationship between identified relevant firm-level financial news and stock prices. In particular, the tone of news can be of great importance to investors. Many studies apply semantic analysis to extract sentiment signals in financial news articles and investigate their possible influence. Tetlock (2007) uses a word count program to analyze texts – to investigate the interaction between financial news and the stock market – and observes that the extracted media sentiment predicts stock prices and trading volume. In recent years, the development of machine learning techniques has enabled researchers to investigate the role of news tonality on financial markets in deeper detail. Heston and Sinha (2017) measure news sentiment with proprietary neural network and find that daily financial news can predict stock returns for one to two days. Ke et al. (2019) introduce a supervised learning framework that can extract sentiment information from financial news articles and find that those extracted sentiment signals can predict stock returns to a large extent.

Similarly, instant ESG news as an important source of ESG information for (ESG) investors could possibly influence their investment decisions. Positive (negative) ESG news indicates the marginal improvement (deterioration) of company ESG performance and could be considered by investors in two ways. On the one hand, an improvement (deterioration) of ESG performance may lead to an improvement (deterioration) in corporate financial performance (Friede et al. 2015) and thus have an impact on the stock performance via the incorporation of this positive cash flow news into prices. On the other hand, an improvement (deterioration) of ESG performance may attract (repel) ethical investors who have the incentive to promote ESG development (Pedersen et al. 2021). Therefore, we expect that the market reaction to instant ESG news is closely related to the news sentiment.

However, the market reaction to positive and negative ESG news could be different in terms of scale. Capelle-Blancard and Petit (2019) find that companies facing negative ESG news experience a drop of 0.10% in market value, but gain nothing on average from positive ones. Cui and Docherty (2020) also report that the market does not react to positive ESG news but overreacts to ESG controversies by analyzing ESG news processed by Ravenpack. This could be explained by investors’ concern that companies have the incentive to exaggerate their ESG performance (Yu et al. 2020). With the increasing attention paid to ESG from various stakeholders, some companies find it beneficial to overstate their commitment to ESG topics (Bazillier and Vauday 2009). For instance, “greenwashing”, which describes the intention of companies to label non-green products or practices as green, has been a hot topic in the past two decades (Flammer 2021). Nevertheless, a pretending of unsubstantiated ethical engagement can cause public distrust (Jahdi and Acikdilli 2009). If companies disclose ESG information more frequently or exaggerate their ESG performance, the probability that companies do good to the society decreases or the overall contribution is less valued. Therefore, investors may react less actively to overwhelming positive ESG news. Another explanation can be so-called “negativity bias”, in which the market reacts significantly to negative news but remains relatively calm when good news arrives. In psychology, negativity bias refers to the phenomenon that humans give greater weight to negative events, which is manifested in different ways such as negative potency, steeper negative gradients, negativity dominance, and negative differentiation as described by Rozin and Royzman (2001). Several studies examine this negativity bias on the financial markets. Edmans et al. (2007) observe a strong negative stock market reaction to losses of national sports teams while no evidence of a corresponding reaction to victories. Akhtar et al. (2011) investigate the market responses to consumer sentiment announcements and document the existence of negativity bias on the Australian stock market.

Likewise, it can be expected that the market reactions related to negative and positive ESG news are asymmetric. More precisely, negative ESG news may be perceived more seriously by the market and lead to stronger reactions as compared to positive ESG news. We summarize the hypothesis as follows.

Lastly, we discuss the possible linkage between the historical ESG record and the reaction to instant ESG news. As mentioned above, the ESG score and instant ESG news are two different types of ESG information. The former can be seen as a mid- or long-term ESG record of the company in which all past ESG information is aggregated. As opposed to that, the latter reflects short-term changes of ESG performance. Previous studies indicate that low-frequency ESG performance proxies such as ESG ratings are important to investors (see e.g. Amel-Zadeh and Serafeim 2018; Bennani et al. 2018).

To model the impact of instant ESG news in light of an existing long-term ESG rating, we propose a simple adaptive model to depict how investors adapt their perception of company ESG performance to the arrival of instant ESG news. Considering the fact that ESG agencies often update their ESG ratings based on the aggregated ESG information since the last evaluation period (e.g. Escrig-Olmedo et al. 2019), we propose a steady adaption to the arrival of ESG news. Let, \(\text{ESG}_{i,t-1}\) denotes the present ESG performance figure, based on past ESG information, while \(\textit{esg}_{i,t}\) measures the additional ESG contribution inherent in the instant news under consideration. We regard \(\textit{esg}_{i,t}\) as exogenous, while its expected value can depend on the company’s past ESG profile to some extent. This is because past ESG ratings may have already embedded some part of future ESG activities, and positive (negative) news is more anticipated for companies with a good (bad) ESG record (Serafeim and Yoon 2021). Also, Glossner (2021) document that companies’ past ESG incident rates, which may already be integrated into ESG ratings, predict more future incidents. The new ESG performance \(\text{ESG}_{i,t}\) then results as the sum of past ESG performance \(\text{ESG}_{i,t-1}\) and the ESG performance change \(\textit{esg}_{i,t}\) due to the news, i.e.: Note that the sign of \(\textit{esg}_{i,t}\) is positive (negative) in case of positive (negative) ESG news, while \(\text{ESG}_{i,t-1}\) can without loss of generality be assumed to lie between 0 and 100, in which 100 (0) describes a perfectly sustainable (unsustainable) company. Furthermore, usually \(\text{ESG}_{i,t}\) is not immediately published by the ESG score provider. However, it can be seen as the theoretical new value for an investor who considers both the old ESG score and the content value of the new instant news.

As for a company with a high ESG score it is less easy to increase its ESG score compared to a company with a low ESG score, we consider the relative ESG performance change Given the same value of \(\textit{esg}_{i,t}\), it is obvious that \(\Delta\text{ESG}_{i,t}\) is higher (lower) for companies with lower \(\text{ESG}_{i,t-1}\) when they encounter positive (negative) ESG news. Consequently, the market may behave differently to the same kind of instant news for companies with different past ESG ratings. If ESG performance enhances value, as claimed by H1, then the relative value can increase much more for a company with a low ESG score, while for a company with an already high ESG score positive and negative instant news with the same absolute value \(| \textit{esg}_{i,t}|\) will yield a lower value change. This view is supported by Glück et al. (2021), who argue that companies with a good ESG profile may face diminishing marginal benefits of ESG performance improvement, which is consistent with the over-investment view proposed by Goss and Roberts (2011). Combining the expectation argumentation that companies with a bad ESG record may enjoy even higher ESG performance increase from good ESG news as such news is less anticipated and more surprising to the market, we can expect stronger market reactions for these companies. However, it is less clear regarding how differently the market may react to bad ESG news for companies with different ESG records. On the one hand, the expectation argument indicates that bad ESG news is less anticipated for companies with a good ESG record and thus \(| \textit{esg}_{i,t}|\) may be higher. On the other hand, it should be noted that companies with a good ESG profile are still perceived as doing relatively well despite the slight downgrade of ESG performance (Glück et al. 2021) due to negative ESG news. Several studies (Lins et al. 2017; Shiu and Yang 2017; Bartov et al. 2021) show that an overall good ESG reputation can alleviate the negative impact of negative ESG events. If the latter aspect outweighs the former, we can expect that the market reacts less strongly to ESG news of companies with a good ESG record. To sum up these considerations, we state our third hypothesis as follows.

In this study, we examine the pricing mechanism of ESG news on the major stock markets. We show how the newest development in NLP can be applied in understanding the market reactions to instant ESG news. Instead of directly adopting a proprietary ESG news dataset from ESG data providers, we construct our sample by extracting raw ESG news from Thomson Reuters Eikon and clean the news data in a consistent way. Based on a pre-trained sentence-BERT model, we are able to remove fuzzy duplicate or stale news and retain only fresh and unique ESG news to a large extent. This procedure makes sure that we have a unique and fresh news dataset while enjoying the wide coverage of ESG news from all over the world. Moreover, we fine-tune an ESG news sentiment classifier based on the BERT-like language model and achieve relatively good predictive performance. We apply it to judge the sentiment of ESG news instead of using classical lexicon-based sentiment analysis methods.

We find that the impact of ESG news is closely related to the ESG news sentiment. However, the market reactions to positive and negative ESG news are asymmetric. Positive ESG news has positive influence on the stock price while negative ESG news has stronger and negative influence on stock performance. This indicates that positive ESG news may add some value to the firm while negative ESG news do harm to the firm value to a considerable extent. Moreover, the historical ESG image of a company may influence the impact of ESG news on stock markets. More specifically, the market reaction to negative ESG news is related to the ESG record of the company. If the company had a good ESG record in the past, the negative influence of negative ESG news could be dampened and less severe. In contrast, if the company had a bad ESG record, the market reacts more favorably to marginal improvement of ESG performance.

This study has clear research implications for other financial studies. We show how the recent development in NLP could possibly facilitate and advance the research on ESG topics in different ways. The proposed text processing methodologies can also be applied in related studies, especially those investigating the role of non-financial factors. We focus specifically on the possible pricing effect of instant ESG news and provide new insight on how the market reacts to instant ESG news on the major stock markets. The empirical findings suggest the importance of ESG issues on the financial markets. Investors may incorporate daily ESG information into their investment decisions, instead of merely depending on company ESG disclosure and ESG ratings from agencies. Therefore, more attention should be given to more frequent ESG information such as instant ESG news in order to better understand the role of ESG issues.

The practical implications of this study are obvious and straightforward. Firstly, we show the importance of timely tracking of instant ESG news for investors. Investors can monitor real-time ESG news and incorporate this information in a timely manner into investment practice. Secondly, companies should not only avoid negative ESG news, but also work on improving their ESG performance since positive ESG news is also valued by investors. Moreover, companies should build up their own media monitoring system as part of their investor-relationship management, so as to build a better ESG image and avoid any misunderstandings with investors and the general public. At last, for related policy makers, our study indicates the possibility of ESG performance fraud or exaggeration in ESG news. Regulations or policies that can detect or increase the cost of such behavior should be considered and implemented. One possible countermeasure is the establishment of a third-party reviewing system in which independent external reviewers validate and evaluate ESG news on a regular basis. Moreover, the advancement in NLP could also be applied to alleviate the problem. By constructing an ESG news dataset with a label indicating the authenticity of the news, a classifier can be be trained to detect fraud or exaggeration.

We are also aware of the limitations of this study and thus provide some future research directions. First, despite the relatively good sentiment classification result, our sentiment classifier is trained on a labelled dataset pre-processed by a third party and thus its validity is restrained by the given training dataset. A better (but more costly and complicated) solution would be constructing a sentiment-labelled dataset by designing an experiment in which participants are asked to read company ESG news and evaluate the news sentiment. By controlling the ESG news to be analyzed in this way, it may become possible to identify those positive ESG news items that elicit similarly strong reactions as the negative news (and which are masked by the many irrelevant positive ESG news items in the present study). Second, we do not differentiate various types of ESG news in this study and may not know whether investors may perceive them differently. For example, whether sustainability issues are financially material has a significant impact on the firm value (Khan et al. 2016). It would be interesting to integrate the financial materiality aspect into the pricing implication analysis of ESG news. Finally, the ESG appetite of institutional and individual investors, or investors from different countries may be different. Consequently, different groups of investors may react differently to ESG news. Therefore, to understand their behavior more comprehensively, more research effort is needed.