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Article

Quantitative Analysis of ESG Information Value and Policy Uncertainty

Doctorate Program in Intelligent Banking and Finance, CTBC Business School, Tainan 709, Taiwan
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Author to whom correspondence should be addressed.
Sustainability 2025, 17(2), 496; https://doi.org/10.3390/su17020496
Submission received: 26 November 2024 / Revised: 2 January 2025 / Accepted: 7 January 2025 / Published: 10 January 2025

Abstract

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This study examines the impact of ESG rating disclosures on the stock performance of highly rated semiconductor companies in Taiwan from 2017 to 2023. The findings reveal significant abnormal returns surrounding ESG rating releases, with positive returns before the event reflecting investor optimism and negative returns afterward indicating market reassessment. The analysis highlights varied effects of ESG dimensions: environmental performance benefits lower-performing firms, social initiatives show negative impacts on high-performing firms, and governance practices demonstrate both short-term costs and long-term benefits. Policy continuity emerges as a critical factor in moderating the financial impacts of ESG performance. Stable and supportive policies enhance the positive effects of ESG initiatives, while inconsistent frameworks exacerbate inefficiencies. These results emphasize the importance of aligning ESG strategies with consistent policy environments to maximize shareholder wealth, offering valuable insights for investors, corporate managers, and policymakers.

1. Introduction

Environmental pollution, resource depletion, ecological imbalance, and other environmental issues are increasingly becoming global economic and political problems that concern human survival and social development. To achieve sustainable economic development, it is necessary to strengthen environmental protection. International organizations, government agencies, and academic institutions are exploring various ways to achieve sustainable development, especially in mitigating environmental problems and addressing climate change. Sustainable development has not only become an important component of government governance and corporate development but also a focus of academic attention [1]. In corporate development, environmental, social responsibility, and governance (ESG) are increasingly becoming a consensus [2]. ESG is an extension and enrichment of concepts such as green economy, corporate social responsibility, and responsible investment, and is also an important standard for measuring the level of sustainable development in today’s international community.
The European ESG market has shown significant growth in recent years, becoming a leading region for ESG investment in the world. According to research by [3], the European ESG-oriented asset management market has grown by 172% since 2021 and is expected to grow further by 50% in 2026, showing investors’ strong demand for sustainable development, especially in the environment (progress has been made in CO2 emissions and optimization of the energy structure), society (education spending and digitalization) and governance (rule of law and transparency). In addition, ref. [4] pointed out that ESG investment has begun to show significant performance in the European market since 2014; in particular, governance has become an important factor affecting investment performance, while social indicators have gradually gained attention. Overall, the continued growth of the European ESG market reflects policy support and investors’ confidence in sustainable finance, indicating that Europe will maintain its leading position in global ESG investment in the coming years.
Compared to their peers, institutional investors, asset management companies, financial institutions, and other stakeholders are increasingly relying on ESG reports and indices to evaluate and measure company performance over time. In recent years, ESG analysis has become an important part of the investment process, as there is growing concern about investing in the sustainability and social impact of companies or enterprises. Compared with traditional stock indices, ESG indices screen and select their components based on social responsibility criteria.
The increasingly important role of ESG investment has given rise to many studies on socially responsible investment (SRI), analyzing whether ESG indices outperform traditional indices [5] and affect the performance of financial firms [6] or the degree of market efficiency [7]. However, it is sometimes difficult to judge which investments are or should be considered “socially responsible”. Definitions vary, and the term “social responsibility” is often used interchangeably with alternative labels such as green investing, sustainable investing, ethical investing, and impact investing. In general, socially responsible companies are willing to provide more transparent reporting; this implies higher costs for collecting, compiling, disclosing, publishing, and verifying information according to ESG criteria, and should also lead to lower information asymmetry and higher market efficiency.
Some past studies have shown that if companies focus too much on ESG and adopt this series of criteria in investment decisions, it may limit the scope of investment and reduce corporate earnings [8]. This study uses the TESG release dates over the years as the main axis and classifies the historical rating rankings of listed companies in Taiwan’s semiconductor industry based on high ratings of A and A+ grades, to explore whether the release dates of historical rating rankings will generate abnormal returns in stock price fluctuations, thereby providing relevant investment advice to investors.
This study aims to examine whether abnormal returns occur for highly rated companies following the release of ESG rating reports over the years. Additionally, it explores the impact of the three ESG dimensions—environmental, social, and governance—while incorporating geopolitical risk variables to analyze how these factors collectively influence market reactions.

1.1. The Impact of ESG Ratings on Company Stock Prices

Past research has shown that changes in ESG ratings, whether upgrades or downgrades, affect abnormal stock returns. Ref. [9] indicates that strong ESG performance can reduce stock price volatility, especially in non-state-owned enterprises and industrial companies. Ref. [8], in a study of the Chinese capital market, found that higher ESG ratings help reduce stock price synchronicity, with this effect further influenced by financial constraints and analyst coverage. Ref. [10] discovered that companies with ESG ratings tend to have higher trading volumes than those without ratings, suggesting that ESG scores are important not only to investors but also to the companies themselves.
High ESG ratings are typically associated with better stock price performance. Ref. [11] found that during the COVID-19 crisis, high-ESG-rated European companies were associated with higher abnormal returns and lower stock volatility, with social scores playing a primary role in this effect [12]. Ref. [13] also found that companies with higher ESG ratings generally achieve higher excess returns and lower volatility. However, in their analysis of companies in the Eurostoxx50 index, they found that these companies’ ESG commitments did not seem to significantly affect their stock performance.
In contrast to high ESG ratings, low ESG ratings are usually associated with higher risk and poorer stock price performance. Ref. [14] found significant negative abnormal returns when ESG ratings decline, negatively impacting company value. Ref. [15] showed that ESG rating downgrades negatively affect stock performance, with an average monthly risk-adjusted return decrease of 1.2%. Furthermore, ref. [16] found that ESG rating disagreement leads to higher market risk premiums and lower stock demand, exacerbating the negative impact on stock pricing.
Ref. [17] demonstrated that ESG rating disagreement is positively correlated with stock returns, indicating that companies with higher ESG rating disagreement have a risk premium, mainly stemming from differences in the environmental dimension. Ref. [17] reached similar conclusions, noting that companies with higher ESG rating disagreement earn a risk premium. Ref. [18] found that changes in ESG ratings create temporary pressure on stock prices, mainly due to individual investors being more sensitive to these rating changes; a one-standard-deviation increase in ESG ratings leads to a decrease in monthly abnormal returns of about 1%.
Ref. [19] showed that ESG plays an important role in credit rating decisions, particularly in stock market returns and credit default swap (CDS) spread changes. They found that ESG considerations are important determinants of stock returns and CDS spreads, with corporate governance being the most crucial factor. However, ESG ratings have a significant impact on companies’ short-term abnormal returns, but this effect gradually weakens in the days following disclosure and becomes negative in the third year. This implies that investors often have a short-term mindset towards ESG ratings, which may undermine companies’ long-term sustainability and growth opportunities.
Research from 2019 to 2023 shows that the impact of ESG ratings on company stock prices is multifaceted, including risk premiums, credit ratings, long-term sustainability, and financial performance. Overall, high ESG ratings are usually associated with lower stock price volatility and better market performance, while low ESG ratings are associated with higher risk premiums and poorer stock price performance. However, this impact may vary depending on market conditions, company characteristics, and investor behavior, and may change over time. Investors and company management need to comprehensively consider the short-term and long-term effects of ESG factors on stock prices.

1.2. The Impact of ESG and Political Risk on Company Stock Prices

Past research has shown that ESG performance and political risk have significant effects on stock prices. Ref. [20] found that higher ESG disclosure can reduce stock price crash risk in the U.S., Europe, and Japan. Ref. [21], a study on Chinese listed companies, reached similar conclusions, showing that good ESG performance reduces the likelihood of stock price crashes, but this positive impact weakens when economic policy uncertainty is higher. Furthermore, research has pointed out that political risk has a significant impact on stock price fluctuations, especially in emerging markets such as Taiwan, where political actions such as political meetings and elections can significantly affect stock market returns [22]. Although existing research has not explicitly detailed the direct relationship between political risk and stock prices in the context of ESG, ref. [21] points out that higher economic policy uncertainty may weaken the mitigating effect of good ESG performance on stock price crash risk. This suggests that political risk may interact with ESG factors to influence stock market performance. Ref. [23]’s research found that higher ESG risk ratings are negatively correlated with stock performance, with stocks with lower ESG risk (“green stocks”) tending to outperform stocks with higher ESG risk, especially during periods of market stress such as the COVID-19 crisis.
Based on past literature, it can be assessed that stronger ESG performance generally reduces stock price volatility and crash risk, but political risk factors such as economic policy uncertainty may weaken this effect. Ref. [24] highlights the importance of risk metrics in assessing systemic risk, consistent with the compounding impact of ESG and political risk on stock prices. Therefore, when assessing stock price impacts, both ESG factors and political risk need to be considered simultaneously.

1.3. Taiwan’s Semiconductor Industry in the Global Supply Chain

Taiwan’s semiconductor industry plays a key role in the global supply chain, especially in the fields of wafer foundry and downstream packaging and testing, where its professional advantages make it an indispensable supplier to the global market. However, this industry structure, which is highly dependent on international customer demand, also makes it particularly vulnerable to international developments. According to the research of [25], the efficiency of information transmission between upstream and downstream of the supply chain has a significant impact on investor confidence and overall supply chain performance. Especially in the context of intensified competition in the global technology industry, TSMC, as a core company in the global logic semiconductor supply chain, has an even more important strategic position [26].
The intensification of geopolitical and economic uncertainties further challenges the development stability of Taiwan’s semiconductor industry. Ref. [27] found that changes in US policies will indirectly impact the operating performance of Taiwan’s semiconductor manufacturers by affecting the procurement strategies of technology giants. In addition, ref. [28] further demonstrated the importance of supply chain integration capabilities and downstream enterprise stability in risk management, especially in the face of rapid changes in the global political and economic situation.
In summary, although Taiwan’s semiconductor industry occupies an important position in the global market due to its technological expertise and supply chain integration capabilities, its foundry-based industrial structure and its downstream location in the supply chain make it particularly vulnerable to international political changes and the impact of US policy uncertainty. As [27] points out, US policy changes have a significant impact on the operations of Taiwanese semiconductor manufacturers by affecting the procurement decisions of technology giants. This structural dependency requires Taiwanese companies to more carefully assess risks and strengthen industry resilience in the face of changes in the global political and economic situation in order to maintain their competitive advantage in the global supply chain. This research finding not only deepens our understanding of the vulnerability of Taiwan’s semiconductor industry, but also provides an important reference for the formulation of industrial policies.

2. Literature Review

2.1. The Rise of the ESG Trend and the Impact on Corporate Performance

The relationship between corporate social responsibility and financial performance has always been the focus of academic attention. From [29] shareholder primacy theory to [30] stakeholder theory, academic understanding of corporate responsibility has undergone a fundamental shift. The concept of shared value proposed by [31] breaks the traditional view that social responsibility and economic benefits are in conflict, and it lays a theoretical foundation for the development of ESG. At the same time, the natural-resource-based view (NRBV) proposed by [32] explains the intrinsic connection between environmental responsibility and competitive advantage from a strategic management perspective, providing a systematic theoretical explanation for understanding the value creation mechanism of ESG.
Empirical research provides strong support for the value relevance of ESG. Ref. [33], in a meta-analysis, confirmed for the first time the positive correlation between corporate social performance and financial performance at a large sample level. Ref. [34] further found that excellent ESG performance can significantly reduce a company’s capital constraints, a finding that reveals an important way in which ESG affects corporate value. In terms of methodology, ref. [35] used the regression discontinuity design (RDD) to verify the causal effect of ESG on corporate value in a quasi-experimental environment for the first time, greatly enhancing the credibility of the research conclusions.
In the field of asset pricing, recent research has made important breakthroughs. The theoretical model constructed by [36] explains the formation mechanism of ESG premium from the perspective of equilibrium pricing. This theoretical framework is supported by empirical research, with [37] finding that changes in ESG preferences do lead to systematic changes in the risk premium of sustainable assets. Especially during periods of market volatility, companies with strong ESG performance have demonstrated greater resilience. Ref. [38], in research on the COVID-19 period, showed that companies with high ESG ratings had lower systemic risk exposure.
However, the value effect of ESG is clearly context-dependent. Ref. [39], in a cross-national study, found that differences in legal systems significantly affect the economic consequences of ESG practices. This finding suggests that we need to understand the value creation mechanism of ESG in a broader institutional context. As the standardization of ESG information disclosure has increased, ref. [40] found that the market’s pricing efficiency for ESG information has also significantly improved, which means that ESG factors are becoming an increasingly important dimension in corporate value assessment.

2.2. Policy Continuity, Investment Behavior, and ESG Performance

Policy continuity first has a substantial impact on ESG development by affecting companies’ investment decisions and cost structure. Ref. [41] found through empirical research that for every one-standard-deviation increase in policy uncertainty, corporate capital investment will decrease by 6.4%, and this effect is more significant in policy-sensitive industries. The economic policy uncertainty index (EPU) constructed by [42] further confirms that policy uncertainty significantly affects the long-term investment decisions of enterprises, especially in environmental protection projects that require large irreversible investments.
The ESG performance of companies shows a significant response to changes in the policy environment, which is particularly evident during periods of policy transition. Ref. [43] conducted a survey of institutional investors and found that more than 30% of investors believed that uncertainty in climate policy was the main obstacle to their ESG investment decisions, while the stability of policy support significantly improved their investment confidence. This impact is particularly evident during transitions in the US political cycle. Ref. [44] found that a company’s carbon emission risk exposure significantly affects its financing costs and investment decisions, increasing the cost of equity capital by an average of 58 basis points. The interweaving of political risks and geopolitical risks further affects the ESG development of enterprises. Ref. [45] found through studying the tail risks of carbon emissions that when policy uncertainty and market volatility overlap, companies face higher climate transition risk premiums. This risk is even more pronounced in industries with intensive global supply chains. At the same time, ref. [46] found that the divergence in ESG ratings increased significantly when policy uncertainty was higher, reflecting that the market’s assessment of corporate ESG performance faces greater challenges during policy fluctuations.

3. Research Method

3.1. Research Sample Collection and Data Selection

This study investigates listed companies in Taiwan’s semiconductor industry, using data from the Taiwan Economic Journal (TEJ) database. The research sample includes 199 data points from listed companies between 2017 and 2023. These companies were filtered to focus on those with TESG ratings of A+ and A grades. To verify the announcement dates for these samples, the United Daily News Knowledge Database (UDNdata) was used, while stock price information was sourced from the TEJ database.
This study employs the market model, which assumes a linear relationship between the returns of different companies and the returns of the market portfolio. This model, proposed by [47], is expressed as a linear equation: E ( r i t ) = a i + b i   ( R m t ) + μ i t , where r i t is the return on security i at time t, a i is the intercept term, b i represents the systematic risk of security i, R m t is the market portfolio return at time t, and μ i t is the error term.
To assess the impact of events, this study examines abnormal returns (ARs), which refer to the difference between the actual return and the expected return that would occur in the absence of an event. As noted by [48], examining abnormal returns can help in evaluating the market’s reaction to new information or events, as it highlights the deviation from expected performance. By analyzing AR, one can effectively measure how the market responds to specific events, identifying differences that may arise from investor sentiment or market conditions. This measure is expressed as A R i t = r i t E r i t , where A R i t is the abnormal return for company iii during period ttt and E r i t is the expected return. The average abnormal return (AAR) is then calculated by summing the daily abnormal returns of all event samples and dividing the result by the total number of samples, as shown in the equation A R ¯ t = 1 N i = 1 N A R i t , where A R ¯ t is the average abnormal return on day t and N is the number of samples.
Furthermore, this study considers the cumulative abnormal return (CAR) to understand the cumulative effect of abnormal returns over a specific period. The CAR is obtained by summing the average abnormal returns over the observation period: C A R = 1 N i = 1 N ( A R i t ) . This measure provides insight into the overall impact of the events on the stock returns of the sampled companies.

3.2. Hypotheses

The environmental dimension reflects a company’s commitment to sustainable practices, which can enhance operational efficiency and reduce exposure to environmental risks [32]; the social dimension fosters stakeholder trust and strengthens brand reputation [34]; the governance dimension increases transparency and mitigates risks [34]. Despite these contributions, the combined impact of these dimensions on cumulative abnormal returns (CARs) remains underexplored. Based on this, this study proposes the following hypothesis:
H1. 
Environmental (E), social (S), and governance (G) performance significantly influences the wealth effect of high-ESG-rated semiconductor companies.
Policy continuity provides a stable regulatory framework that supports long-term investments, particularly for industries like semiconductors that require substantial capital and alignment with ESG goals. Stable policies reduce uncertainty, enhance investor confidence, and enable companies to effectively implement ESG practices [42,45]. On the other hand, differences in ESG attitudes between governing parties can lead to policy uncertainty, disrupting continuity and creating challenges for companies to align with long-term ESG objectives [44,46]. Such shifts in policy focus may increase financing costs and complicate the evaluation of ESG performance, particularly in industries reliant on stable regulatory environments, such as semiconductors. Despite its importance, the combined impact of policy continuity and ESG performance on the wealth effects of high-ESG-rated semiconductor companies remains underexplored. Based on the above research, this study proposes the following hypothesis:
H2. 
Policy continuity and ESG performance significantly influence the wealth effects of high-ESG-rated semiconductor companies.
Changes in political parties have a direct impact on the stability of policies, especially in the area of ESG-related policies. When a change of political parties occurs, the new government usually adjusts or reshapes the ESG policies of the previous government, resulting in a break in policy continuity. Research by [41] shows that for every one-standard-deviation increase in policy uncertainty, corporate capital investment decreases by 6.4%. This impact is particularly evident in policy-sensitive industries such as semiconductors, which rely on a stable policy environment to support their long-term investment plans.
In addition, policy uncertainty has a significant impact on companies’ investment confidence and financing costs. A survey by [43] showed that more than 30% of institutional investors viewed climate policy uncertainty as the main obstacle to ESG investing. The study [44] further showed that policy uncertainty leads to increased exposure to carbon emission risks, which causes an average increase of 58 basis points in the cost of equity capital for enterprises. More importantly, political party alternation has a profound impact on the market’s ESG evaluation mechanism. Ref. [46] found that when policy uncertainty is high, market disagreement on ESG ratings increases significantly. This suggests that investors face greater challenges in assessing corporate ESG performance, especially when they need to predict the direction of new government policies. Based on the above research, this study proposes the following hypothesis:
H3. 
The interaction between policy continuity and ESG ratings significantly influences the wealth effects of high-ESG-rated semiconductor companies.

3.3. GARCH Risk Adjustment Model

Ref. [49] added the lagged conditional variance to the ARCH model, calling it the Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) model. This model not only captures the characteristics of the ARCH model but also provides more flexibility in setting the conditional variance structure, achieving the principle of parameter simplification. Since the stock return rate generation process is related to time and its unconditional error presents a leptokurtic distribution, the GARCH model is recognized as the most appropriate model for describing the behavior of daily stock price returns; Ref. [50] used the GARCH risk adjustment model to analyze the abnormal returns of financial and non-financial firms after the announcement of mobile payment technology, which showed that the GARCH model can effectively capture market fluctuations.
As the ARCH model’s setting is similar to the autoregressive (AR) form, this parameter setting that only includes the autoregressive part does not meet the assumptions required in time series model setting, and the ARCH lag order may be too long, causing too many parameters and making it difficult to require each to be positive. To achieve the principle of simplification, ref. [23] inserted the lagged conditional variance into the ARCH model, expanding it into the GARCH model. Considering that the GARCH model can effectively describe the volatility clustering characteristics in time series and can more accurately capture the conditional volatility characteristics before and after an event, this study uses the GARCH(1,1) model as the benchmark model for volatility analysis. This choice is consistent with the model specification used by [51] in their event study.
The model structure of GARCH(p,q) is as follows:
ε t Ω t 1 ~ N 0 , h t
h t = α 0 + i = 1 q α i ε t i 2 + j = 1 p β j h t j ,

3.4. Multiple Regression Analysis

This study uses regression analysis to explore the factors affecting abnormal returns in Taiwan’s semiconductor industry due to the release of ESG rating reports. The dependent variable is the average cumulative abnormal return, while the independent variables include the environmental dimension score, social dimension score, and corporate governance dimension score. Two dummy variables P r e s i d e n t 1   a n d   P r e s i d e n t 2 are added to assess policy continuity effects during the President Trump and President Biden administrations. Based on these research variables, the regression model is specified as follows:
C A R i = b 1 E N V + b 2 S O + b 3 G O V + b 4 P r e s i d e n t 1 + b 5 P r e s i d e n t 2 + ε i
While traditional regression provides insights into average relationships, it may not capture the complete picture of how variables interact across different levels of abnormal returns. To address this limitation, this study employs quantile regression, proposed by [52], which extends the concept of conditional quantile functions. Unlike the least squares method which only examines average effects, quantile regression is a non-parametric estimation requiring no specific prior assumptions. It assigns different weights to analyze the relationship between explanatory and explained variables at different quantiles. According to [53], data cutting or grouping can lead to the loss of useful sample information and potential sample selection bias, which can be avoided through quantile regression estimation.

4. Empirical Results

This study mainly uses the event study method to explore the impact of ESG rating releases on highly rated listed companies in Taiwan’s semiconductor industry and explores the excess returns they generate in the stock market. Subsequently, regression analysis is used to analyze the impact of ESG indicators and policy continuity on high-ESG-rated listed companies in Taiwan’s semiconductor industry. Finally, combined with quantile regression, in-depth research is conducted based on different quantiles.

4.1. Comparative Analysis of Abnormal Returns

This study employs the event study methodology to examine the abnormal returns and cumulative abnormal returns generated by high-rated listed companies in Taiwan’s semiconductor industry following the release of ESG ratings and to explore the stock market’s reaction to this information. Table 1, Table 2 and Table 3 use the ESG release date as the event day, analyzing the changes in abnormal returns of company stock prices during different periods before and after the event day.
Figure 1 shows the changing trends of abnormal returns (ARs) and cumulative abnormal returns (CARs) 15 days before and after the event period. The AR curve (red) showed slight fluctuations throughout the event, with values mostly remaining between −0.5 and 1. The CAR curve (blue) begins to rise significantly 5 days before the event, reaches a peak of approximately 2.5–2.7 between 0 and 5 days after the event, and then gradually decreases.
According to Table 1, several significant abnormal return days can be observed before the ESG rating release date. Significant positive abnormal returns are shown ten days before (t = −10), seven days before (t = −7), three days before (t = −3), two days before (t = −2), and one day before (t = −1) the release date.
After the release date, market reactions gradually turn negative. Significant negative abnormal returns appear four days after the event day (t = 4), five days after the release date (t = 5), and ten days after the release date (t = 10).
Table 1 presents the abnormal return rates before and after the ESG rating release date. The empirical results show that the market experiences multiple significant positive abnormal returns before the event day, while significant negative abnormal returns occur after the event day. This further illustrates the short-term market reaction to the release of ESG ratings.
Table 2 shows the changes in cumulative abnormal returns (CARs) before and after the event day. The empirical results reveal that before the event day, there were multiple significant positive cumulative abnormal returns, including five days before the event day (t = −5), four days before (t = −4), three days before (t = −3), two days before (t = −2), and one day before (t = −1). Particularly, two days and one day before the event day, the cumulative abnormal returns reached relative peaks, indicating that the market held a positive attitude towards the upcoming ESG rating event.
After the event day, the market reaction remained positive, with significant positive cumulative abnormal returns from the first day after the event (t = 1) to the fourteenth day (t = 14). This shows that the market’s response to the ESG rating event was not only positive but also persistent. Even in the longer period after the event day (from the tenth to the fourteenth day), the cumulative abnormal returns remained positive, indicating that the market affirmed the long-term impact of the ESG rating report release on the company.
Overall, the cumulative abnormal returns were positive and significant both before and after the event day. This suggests that the market maintained a certain level of confidence in the event over the long term, reflecting investors’ optimistic expectations for the future performance of companies with high ESG ratings.
The Jarque–Bera test results demonstrate that both AR and CAR variables exhibit normal distribution characteristics. As these p-values surpass the conventional significance threshold of 0.05, the null hypothesis of normality cannot be rejected. This indicates that AR and CAR are normally distributed, providing a robust foundation for their use in statistical analyses that rely on the assumption of normality.

4.2. Results from Multiple Regression and Quantile Regression Analysis

The selection of the event window (−3, 2) is supported by multiple theoretical foundations. As detailed in Ref. [54], the Efficient Market Hypothesis suggests that price discovery of market information is a dynamic process, while ref. [55] Information Asymmetry Theory emphasizes that professional assessments by third-party rating agencies effectively reduce information gaps among market participants, providing theoretical underpinnings for analyzing the market effects of ESG ratings. This window design also aligns with [56] event study methodology, particularly in controlling external interference factors. The empirical analysis adopts a two-stage strategy: first examining market reactions of high-rated listed companies in Taiwan’s semiconductor industry through event study methodology and then employing multiple regression models to investigate influencing factors. In the regression model specification, cumulative abnormal returns (CARs) serve as the dependent variable, combined with ESG’s three dimensional scores as independent variables, and U.S. Presidential terms (Trump and Biden) are incorporated as policy continuity variables to comprehensively evaluate the market impact mechanism of ESG information disclosure.
Table 3 analysis indicates that the environmental (ENV), social (SO), and governance (GOV) dimensions impact cumulative abnormal returns (CARs) differently across performance levels. Environmental performance benefits firms with lower CAR, particularly at the 0.05 quantile, by enhancing efficiency and reducing risks. Social performance shows a stronger negative impact at higher CAR levels, suggesting that its costs may outweigh financial benefits for high-performing firms. Governance has a negative effect on low-performing firms but positively influences high-performing firms, reflecting its dual role in imposing initial costs and fostering long-term value creation.
The results support H1, demonstrating that ESG dimensions have varying impacts on CAR depending on performance levels, with ENV aiding low-performing firms, SO imposing costs on high-performing firms, and GOV benefiting well-performing firms.
In Table 4, the impact of ESG dimensions and policy continuity on cumulative abnormal returns (CARs) for high-ESG-rated semiconductor companies is analyzed. Panel A shows that environmental (ENV), social (SO), and governance (GOV) dimensions affect CAR differently across quantiles. EN benefits low-performing firms but has diminishing or negative effects on high-performing firms, reflecting a mixed impact across performance levels. SO consistently shows negative impacts, particularly at higher quantiles, highlighting the financial costs of social initiatives.
Governance (GOV) demonstrates a significant influence, particularly at lower quantiles. At the 0.05 quantile, GOV has a negative and significant impact, suggesting that governance practices may impose short-term costs or inefficiencies on low-performing firms. Policy continuity under President 1 and President 2 also has a significant impact on CAR. While ESG initiatives expanded during both administrations, differences in policy focus led to notable variations in their effects. President 2 prioritized broader ESG integration, resulting in stronger positive effects, particularly for high-performing firms, demonstrating a strategic alignment with their long-term objectives.
Panel B aggregates ESG dimensions and emphasizes policy continuity. ESG performance negatively impacts CAR at higher quantiles, suggesting that the costs of ESG initiatives might outweigh their financial benefits for top-performing firms. Policy continuity, however, consistently shows positive effects, with President 2 having a more pronounced impact. This underscores the importance of stable regulatory environments in fostering investor confidence and supporting the long-term implementation of ESG strategies.
The findings align with H2; while both presidencies facilitated the growth of ESG practices, their differing policy priorities led to significant variations in CAR.
In Table 5 the analysis examines the relationship between ESG performance, policy continuity, and cumulative abnormal returns (CARs) under two presidencies, highlighting the moderating effect of party rotation on ESG outcomes.
Panel A shows that ESG performance positively impacts CAR; however, the interaction between ESG and President 1’s policies shows a significant negative effect. This indicates that while ESG initiatives were implemented, President 1’s administration did not strongly support ESG practices, leading to inefficiencies and higher costs for firms, especially those with stronger financial performance. The lack of consistent ESG-oriented policy support contributed to a more pronounced negative impact.
In Panel B, ESG performance continues to positively influence CAR, and the interaction term also shows a negative effect. However, the magnitude of this negative impact is smaller compared to President 1. This reflects President 2’s stronger support for ESG practices, which mitigated some of the inefficiencies observed during President 1’s tenure. The more favorable policy environment under President 2 allowed firms to integrate ESG practices with less financial strain, resulting in a reduced negative effect on CAR.
Findings support H3, showing that party rotation and differing policy priorities significantly influence the financial outcomes of ESG performance. President 1’s administration, with less focus on ESG promotion, amplified the negative financial effects of ESG implementation. In contrast, President 2’s more ESG-supportive policies reduced the negative impact.
The inclusion of dummy variables for President 1 and President 2, along with their interaction terms with ESG, introduced high multicollinearity, as reflected in VIF values exceeding acceptable thresholds. This multicollinearity arises from the mathematical dependency between the dummy variables and their interaction terms, as well as the mutually exclusive nature of the dummy variables. High multicollinearity inflates standard errors, potentially reducing the statistical significance of these coefficients and complicating the interpretation of their independent effects.
For example, the interaction terms ESG × President 1 and ESG × President 2 aim to capture the moderating effects of each presidency on ESG performance. However, the dependency between these interaction terms and the dummy variables (President 1 and President 2) creates structural correlations. Despite these challenges, the variables were retained due to their theoretical importance in capturing differences in political environments and their moderating effects on ESG performance. Removing these variables would undermine the model’s ability to analyze the interplay between political leadership, policy continuity, and ESG practices, which is essential to this study’s objectives.
This study underscores the significant role of policy continuity in moderating the relationship between ESG performance and cumulative abnormal returns (CARs). Under President 1, the limited policy emphasis on ESG resulted in inefficiencies and higher costs for firms, particularly those with strong financial performance, amplifying the negative interaction effect between ESG and policy. Conversely, President 2’s administration demonstrated stronger alignment with ESG principles, reducing inefficiencies and mitigating the negative financial impact, albeit without fully eliminating it.
ESG initiatives are shown to enhance firm value; their effectiveness is contingent on a supportive political and regulatory environment. The divergence in policy priorities between the two administrations illustrates how shifts in political leadership can influence the market’s evaluation of ESG efforts, emphasizing the necessity of consistent and strategically aligned policies to optimize the wealth effects of ESG performance. This contributes to the broader discourse on the interplay between political structures, regulatory stability, and corporate sustainability initiatives.

5. Conclusions

This study investigates the impact of ESG ratings on shareholder wealth in Taiwan’s semiconductor industry by analyzing the abnormal returns and cumulative abnormal returns (CARs) of high-rated ESG companies following the release of ESG reports. The findings reveal that ESG ratings significantly influence market reactions, with positive abnormal returns observed prior to the report release, reflecting optimistic investor expectations, and negative returns after the release, suggesting a reassessment of new information. These results highlight the growing importance of ESG ratings in shaping shareholder wealth in this critical industry.
The analysis of individual ESG dimensions uncovers nuanced impacts. Cross-sectional regression indicates a significant negative correlation between the social dimension and shareholder wealth, reflecting investor concerns about balancing social responsibility with shareholder interests. This highlights the importance of strategically aligning ESG initiatives with both stakeholder expectations and shareholder value creation.
Policy continuity plays a crucial role in moderating the financial effects of ESG ratings. Under President 1, limited support for ESG initiatives led to inefficiencies, particularly for high-performing firms. In contrast, President 2’s ESG-focused policies provided a more stable and supportive environment, enhancing the positive impacts of ESG performance. These findings emphasize that stable and consistent policy environments amplify the wealth effects of ESG initiatives, making policy continuity a critical consideration for companies and investors.
In conclusion, this study demonstrates the significant influence of ESG ratings and policy continuity on shareholder wealth in Taiwan’s semiconductor industry. By bridging the gap between ESG practices, market responses, and policy environments, this research provides actionable insights for corporate managers and investors. Future studies could expand this analysis to other industries and explore the interaction between ESG performance and diverse policy contexts, contributing to a deeper understanding of sustainable finance and shareholder wealth creation.

Author Contributions

Conceptualization, M.-F.L. and K.-H.S.; methodology, Y.-H.W.; software, F.-M.L.; validation, K.-H.S., Y.-H.W. and F.-M.L.; formal analysis, M.-F.L.; investigation, F.-M.L.; resources, Y.-H.W.; data curation, M.-F.L.; writing—original draft preparation, F.-M.L.; writing—review and editing, Y.-H.W. and F.-M.L.; visualization, M.-F.L.; supervision, K.-H.S.; project administration, K.-H.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. AR and CAR trend chart for ESG rating release date.
Figure 1. AR and CAR trend chart for ESG rating release date.
Sustainability 17 00496 g001
Table 1. AR of high-rated ESG report release dates in Taiwan’s semiconductor industry.
Table 1. AR of high-rated ESG report release dates in Taiwan’s semiconductor industry.
PeriodARPeriodAR
−150.13410.223
−14−0.0112−0.082
−13−0.01330.053
−12−0.0334−0.687 ***
−11−0.04450.419 **
−100.541 ***6−0.058
−9−0.16970.012
−8−0.05480.202
−70.354 **9−0.252
−60.02010−0.303 *
−50.19511−0.164
−40.15412−0.223
−30.828 ***13−0.086
−20.438 ***140.014
−10.392 **15−0.264
0−0.150
Note: *** indicates significance level at 1%. ** indicates significance level at 5%. * indicates significance level at 10%. Unit: percentage (%).
Table 2. CAR of high-rated ESG report release dates in Taiwan’s semiconductor industry.
Table 2. CAR of high-rated ESG report release dates in Taiwan’s semiconductor industry.
PeriodCARPeriodCAR
−150.13412.805 ***
−140.12222.723 ***
−130.10932.775 ***
−120.07642.089 ***
−110.03252.508 ***
−100.57362.449 ***
−90.40472.461 ***
−80.35182.663 ***
−70.70592.411 ***
−60.725102.108 **
−50.920 *111.945 **
−41.074121.722 **
−31.902 ***131.636 *
−22.340 ***141.650 *
−12.732 ***151.386
02.582 ***
Note: *** indicates significance level at 1%. ** indicates significance level at 5%. * indicates significance level at 10%. Unit: percentage (%).
Table 3. Regression analysis of high-rated ESG report release dates in Taiwan’s semiconductor industry. The table presents the regression analysis results of cumulative abnormal returns (CARs), with the model specified as C A R i = a + b 1 E N V + b 2 S O + b 3 G O V + ε i , where CAR is the cumulative abnormal return calculated over the event window (−3, 2), and ENV, SO, and GOV represent the environmental, social, and governance dimensions of the ESG framework, respectively.
Table 3. Regression analysis of high-rated ESG report release dates in Taiwan’s semiconductor industry. The table presents the regression analysis results of cumulative abnormal returns (CARs), with the model specified as C A R i = a + b 1 E N V + b 2 S O + b 3 G O V + ε i , where CAR is the cumulative abnormal return calculated over the event window (−3, 2), and ENV, SO, and GOV represent the environmental, social, and governance dimensions of the ESG framework, respectively.
VariablesOLSQuantile
0.050.250.500.050.95
const5.312−18.116 *−5.15910.084 *29.538 ***12.584
ENV0.0290.151 **0.0370.040−0.038−0.017
SO−0.105 **0.041−0.023−0.099 **−0.186 ***−0.181 ***
GOV0.025−0.0090.041−0.073−0.134 *0.173 **
Note: *** indicates significance level at 1%. ** indicates significance level at 5%. * indicates significance level at 10%. Variance inflation factors (VIFs) for all variables were below 5, indicating no significant multicollinearity issues. Unit: percentage (%).
Table 4. The impact of ESG dimensions and policy continuity. Panel A: Regression analysis of ESG dimensions and policy continuity. The table presents the regression analysis results of cumulative abnormal returns (CARs), with the model specified as C A R i = b 1 E N V + b 2 S O + b 3 G O V + b 4 P r e s i d e n t 1 + b 5 P r e s i d e n t 2 + ε i . In this model, CAR is the cumulative abnormal return calculated over the event window (−3, 2), while ENV, SO, and GOV represent the environmental, social, and governance dimensions of the ESG framework, respectively. The variables P r e s i d e n t 1 and P r e s i d e n t 2 correspond to different presidential terms, with the timeline defined starting from the election day of each president rather than the inauguration day. Panel B: Regression analysis of ESG composite scores and policy continuity. The table presents the results of regression of cumulative abnormal returns (CARs), modeled as C A R i = b 1 E S G + b 2 P r e s i d e n t 1 + b 3 P r e s i d e n t 2 + ε i . CAR represents the cumulative abnormal return over the event window (−3, 2), while ESG is a single variable capturing the combined effects of environmental, social, and governance factors. P e r s i d e n t 1 and P e r s i d e n t 2 denote the influence of different presidential terms, with the timeline beginning on the election day rather than the inauguration day.
Table 4. The impact of ESG dimensions and policy continuity. Panel A: Regression analysis of ESG dimensions and policy continuity. The table presents the regression analysis results of cumulative abnormal returns (CARs), with the model specified as C A R i = b 1 E N V + b 2 S O + b 3 G O V + b 4 P r e s i d e n t 1 + b 5 P r e s i d e n t 2 + ε i . In this model, CAR is the cumulative abnormal return calculated over the event window (−3, 2), while ENV, SO, and GOV represent the environmental, social, and governance dimensions of the ESG framework, respectively. The variables P r e s i d e n t 1 and P r e s i d e n t 2 correspond to different presidential terms, with the timeline defined starting from the election day of each president rather than the inauguration day. Panel B: Regression analysis of ESG composite scores and policy continuity. The table presents the results of regression of cumulative abnormal returns (CARs), modeled as C A R i = b 1 E S G + b 2 P r e s i d e n t 1 + b 3 P r e s i d e n t 2 + ε i . CAR represents the cumulative abnormal return over the event window (−3, 2), while ESG is a single variable capturing the combined effects of environmental, social, and governance factors. P e r s i d e n t 1 and P e r s i d e n t 2 denote the influence of different presidential terms, with the timeline beginning on the election day rather than the inauguration day.
Panel A
VariablesOLSQuantile
0.050.250.500.050.95
ENV−0.0270.133 ***−0.008−0.032−0.077 **−0.035
SO−0.151 **0.033 ***−0.045−0.105 *−0.240 ***−0.231 *
GOV−0.032−0.089 ***−0.007−0.095−0.0830.105
P r e s i d e n t 1 14.613 ***−14.536 ***1.84516.121 *30.339 ***20.920
P r e s i d e n t 2 17.228 ***−10.033 ***3.16318.048 *33.738 ***22.476
Panel B
VariablesOLSQuantile
0.050.250.500.050.95
ESG−0.295 ***0.077−0.057−0.247 **−0.562 ***−0.666 ***
P r e s i d e n t 1 20.157 ***−14.6941.46316.753 **40.918 ***53.729 ***
P r e s i d e n t 2 22.947 ***−9.9123.04219.112 **44.973 ***57.213 ***
Note: *** indicates significance level at 1%. ** indicates significance level at 5%. * indicates significance level at 10%. Variance inflation factors (VIFs) for all variables were below 5, indicating no significant multicollinearity issues. Unit: percentage (%).
Table 5. Regression analysis of ESG performance and policy continuity with interaction. The table presents the results of regression and quantile regression analyses of cumulative abnormal returns (CARs), modeled as C A R i = b 1 E S G + b 2 P r e s i d e n t 1 + b 3 E S G X P r e s i d e n t 1 + ε i in Panel A and C A R i = b 1 E S G + b 2 P r e s i d e n t 2 + b 3 E S G X P r e s i d e n t 2 + ε i in Panel B. In both panels, CAR represents the cumulative abnormal return over the event window (−3, 2), and ESG is a composite variable capturing the combined performance in environmental, social, and governance dimensions. In Panel A, P r e s i d e n t 1 reflects the effects associated with the first presidential term, while in Panel B, P r e s i d e n t 2 corresponds to the second presidential term. The interaction terms E S G X P r e s i d e n t 1 and E S G X P r e s i d e n t 2 examine how the influence of ESG performance on CAR varies depending on the political and economic context of each presidential term. These terms highlight whether market reactions to ESG initiatives are amplified or mitigated under different administrations, with timelines defined from the election day of each president.
Table 5. Regression analysis of ESG performance and policy continuity with interaction. The table presents the results of regression and quantile regression analyses of cumulative abnormal returns (CARs), modeled as C A R i = b 1 E S G + b 2 P r e s i d e n t 1 + b 3 E S G X P r e s i d e n t 1 + ε i in Panel A and C A R i = b 1 E S G + b 2 P r e s i d e n t 2 + b 3 E S G X P r e s i d e n t 2 + ε i in Panel B. In both panels, CAR represents the cumulative abnormal return over the event window (−3, 2), and ESG is a composite variable capturing the combined performance in environmental, social, and governance dimensions. In Panel A, P r e s i d e n t 1 reflects the effects associated with the first presidential term, while in Panel B, P r e s i d e n t 2 corresponds to the second presidential term. The interaction terms E S G X P r e s i d e n t 1 and E S G X P r e s i d e n t 2 examine how the influence of ESG performance on CAR varies depending on the political and economic context of each presidential term. These terms highlight whether market reactions to ESG initiatives are amplified or mitigated under different administrations, with timelines defined from the election day of each president.
Panel A: Interaction Between ESG Performance and President 1
VariablesOLSQuantile
0.050.250.500.050.95
ESG0.033 ***−0.063 ***−0.014 ***0.019 **0.085 ***0.178 ***
P e r s i d e n t 1 19.757−50.194 ***−14.577 *23.89242.497 ***86.358 ***
E S G X P e r s i d e n t 1 −0.3220.715 ***0.198−0.372−0.670 ***−1.354 ***
PanelB: Interaction Between ESG Performance and President 2
VariablesOLSQuantile
0.050.250.500.050.95
ESG0.011−0.157 ***−0.034 ***0.0000.055 ***0.149 ***
P e r s i d e n t 2 23.105 ***5.9969.25219.112 **44.489 ***37.936 ***
E S G X P e r s i d e n t 2 −0.308 **0.008−0.115−0.247 **−0.611 ***−0.532 ***
Note: *** indicates significance level at 1%. ** indicates significance level at 5%. * indicates significance level at 10%. Unit: percentage (%).
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Lee, M.-F.; Shih, K.-H.; Wang, Y.-H.; Lai, F.-M. Quantitative Analysis of ESG Information Value and Policy Uncertainty. Sustainability 2025, 17, 496. https://doi.org/10.3390/su17020496

AMA Style

Lee M-F, Shih K-H, Wang Y-H, Lai F-M. Quantitative Analysis of ESG Information Value and Policy Uncertainty. Sustainability. 2025; 17(2):496. https://doi.org/10.3390/su17020496

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Lee, Ming-Fang, Kuang-Hsun Shih, Yi-Hsien Wang, and Fu-Ming Lai. 2025. "Quantitative Analysis of ESG Information Value and Policy Uncertainty" Sustainability 17, no. 2: 496. https://doi.org/10.3390/su17020496

APA Style

Lee, M.-F., Shih, K.-H., Wang, Y.-H., & Lai, F.-M. (2025). Quantitative Analysis of ESG Information Value and Policy Uncertainty. Sustainability, 17(2), 496. https://doi.org/10.3390/su17020496

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