Author: Nimay Shah
Mentor: Dr. Tayyeb Shabbir
Dhirubhai Ambani International School
1. Introduction
Environmental, social and governance (ESG) ratings are a framework to score companies based on three broad criteria. Firstly, the environmental dimension considers a firm’s impact on the natural ecosystem. Secondly, the social dimension evaluates the firm’s relationship with its stakeholders: customers, suppliers and communities. Lastly, governance focuses or the board structure and diversity.
Rating the ethics of a company isn’t a new practice. Before ESG, there was corporate social responsibility (CSR) in the 1990s and even prior to that there was socially responsible investing (SRI) in the 1970s ((Krantz, 2024). But neither CSR nor SRI ever gained the recognition ESG has in the past decade. This can be accounted for due to a key difference in their foundations. Measures like CSR and SRI believed and conveyed that businesses should behave ethically because it is morally the correct thing to do. In contrast, ESG ratings aim to score a company’s sustainability and vulnerability to external factors – variables that link directly to monetary returns. As a result, the rigorous set of variables that ESG evaluates makes it an excellent metric to assess a company’s performance through factors that don’t appear on the traditional financial balance sheets. Hence, more and more investors are starting to incorporate these ratings, along with the financial data, to determine a company’s financial return on investment (ROI).
However, the author’s evaluation of rating methodology of Crisil ESG Ratings & Analytics Ltd, a central ESG provider in India, reveals a crucial flaw in current ESG calculation that significantly hinders ESG’s ability to indicate financial performance. Currently a uniform ESG pillar weightage framework of 40% governance 35% environment and 25% social is used across all sectors. While this may be apt for sustainability indication it hinders financial performance indication as the uniform weightage scheme doesn’t account for sector-specific materiality differences. For example, the environment pillar may be far more important for an energy company then for an information technology, for whom governance may be more important. To address this limitation, this section empirically investigates whether sector-specific ESG pillar weighting systems improves the strength of the relationship between ESG scores, and return on capital Employed (ROCE) and aims to find the most optimum ESG weighting systems for 12 sectors. Thus, the research paper aims to answer the question:
What is the optimal sector-specific weighting system of environmental, social, and governance pillars for explaining variation in ROI, and do these optimized models outperform equal-weight ESG frameworks in the Indian market?
The research paper is structured as follows. It begins with a literature review on the importance of considering a company’s qualitative aspects while investing and studies regarding correlation between ESG and ROI. Followed by this is the development of a sector specific ESG weightage scheme through correlations between ESG and return on capital employed (ROCE). The paper concludes by outlining limitations and identifying avenues for future research.
2. Literature Review
2.1 The Efficiency of Traditional Financial Metrics
Investors have relied on and further developed financial analysis for nearly a century. As a result, most indicators of future growth have already been discovered and are heavily used by investors. The Efficient Market Hypothesis, as developed by Fama (1970) states that in a semi-strong efficient market publicly available financial information is rapidly priced in. Consistent with this, Green, Hand, and Zhang (2013) show that most well-documented return predictors lose statistical significance once widely known, as arbitrage activity erodes whatever informational edge they carried.
These findings collectively suggest that due to most forms of analysis of traditional financial metrics being well known company stocks tend to be priced accordingly. Due to this, it has become increasingly difficult to generate excess returns through these forms of financial analysis and investors must look for other sources of information.
2.2 Unexplained Volatility and the Case for Qualitative Metrics
Traditional financial variables explain less of stock price behavior than is commonly assumed. Roll (1988) documented that financial and industry factors only accounted for a small part of individual stock variation; the remainder being determined by firm specific qualitative factors. This is because financial analysis can’t predict regulatory exposure, reputational events, stakeholder dynamics, and governance failures.
More recent empirical work further proves this trend. Edmans (2011) finds that firms with strong employee satisfaction earn returns of roughly two to three percent annually above market benchmarks and these results persist over time. Similarly, Hartzmark and Sussman (2019) indicate that investor invest more money in response to sustainability signals even in underlying financial variables remain constant. This indicates qualitative firm characteristics influence share prices through channels that conventional financial metrics miss.
This pattern along with the previous section show that quantitative financial data is priced quickly and efficiently, while qualitative signals are under processed. That gap is where investors can earn excess returns. ESG frameworks are an excellent cumulation of these qualitative indicators. The question is whether it reports this information in a way that is useful for predicting financial performance or does its goal of reporting sustainability cause it to stray from being an indicator of future financial performance.
2.3 Correlation Between ESG and Financial Performance
There is extensive literature on ESG and financial performance. The broad findings can be summarized by Friede, Busch, and Bassen (2015) review of over 2,000 empirical studies, finding that 90% reported a non-negative relationship between ESG scores and financial performance, with the majority showing a positive one. While this isn’t perfect it shows potential.
The study by Khan, Serafeim, and Yoon (2016) distinguish between ESG factor that are financially material to a given industry and those that are not. Firms outperform their competitors when they score well on the material ESG factors while the immaterial ones show no correlation at all.
These two studies, taken together show that currently ESG scores have not reached their financial performance indication potential, primarily due to composite scores not accounting for differing ESG factor materiality across different firms. This is a result of the uniform ESG weightage scheme across all sectors. Hence, this literature review shows that developing a new sector- specific ESG weightage system, so that the final ESG scores give more importance to the more material factors based on the sector, can make ESG an indicator of future financial performance. Furthermore, this would allow investors to identify companies not valued for their qualitative factors allowing them to earn excess returns.
3. Proposed Sector-Specific Optimized ESG Weighting scheme and Correlation with ROCE
3.1 Methodology
3.11 Variables
| Dependent Variable | Independent Variables |
| ROCE (%) | Environmental, Social and governance pillar scores |
| Composite ESG scores (Crisil methodology and alternative constructed models) |
Table 3.11 Variables Note. Author made
3.12 Data Sourcing and Sector Classification
The study uses ESG scores published by Crisil for the financial years 2023 and 2024. The sample comprises 748 listed Indian companies spanning 12 sectors. The 12 sectors were decided to ensure simplicity as well as homogeneity in regard to main pillar importance in each sector.
The following sector classification has been used:
| Sector / Sub-sector | Description |
| Consumer Discretionary – Auto & Components | Dedicated exclusively to automotive manufacturers and auto-ancillary companies. |
| Consumer Discretionary – Durables, Apparel & Services | A consolidated sub-sector capturing retail, hospitality, consumer electronics, textiles, and apparel. |
| Consumer Staples | Companies providing essential products including food and beverage producers, household goods, and personal products. |
| Communication Services | Companies providing communication networks, telecommunications, and internet services. |
| Energy | Companies engaged in the exploration, production, refining, and marketing of oil, gas, and consumable fuels. |
| Financials | Institutions involved in banking, investment services, asset management, insurance, and non-banking financial companies. |
| Healthcare | Companies operating in pharmaceuticals, biotechnology, healthcare equipment, and healthcare providers. |
| Industrials | Manufacturers and distributors of capital goods, including machinery, aerospace, defense, construction, and heavy engineering firms. |
| Information Technology | Companies offering software services, IT consulting, technological hardware, and digital infrastructure. |
| Materials | Companies involved in the discovery, development, and processing of raw materials. This includes chemicals, construction materials, metals, and mining. |
| Real Estate | Companies engaged in real estate development, property management, and real estate investment trusts. |
| Sector / Sub-sector | Description |
| Utilities | Companies operating infrastructure for the generation, transmission, and distribution of electricity, water, and natural gas. |
Table 3.12 Sector classification Note. Author made
Firm-level ROCE data was collected from two independent, reliable financial databases: “Screener” and “Equitymaster”. This ensured consistency and cross-verification of financial performance data. The ESG data for composite scores and main pillar scores was obtained from Crisil’s archives. Companies with incomplete ESG pillar scores or missing ROCE data were excluded.
3.13 Framework of Quantitative Analysis
The strength of the relationship between ESG scores and ROCE was measured using Spearman’s rank correlation coefficient ( . This is beneficial as outliers are extremely common in sectoral ROCE data, making it important to reduce their impact of empirical results as done by this metric. All correlations were computed on a sector-by-sector basis to account for structural heterogeneity across industries and correlations were reported to an accuracy of two decimal places to enforce accuracy.
A python program was developed to generate sector specific main pillar weightage systems that were more optimized ROCE indicators based on Crisil’s individual main pillar scores, environmental, social and governance. For each firm i in sector s, the composite ESG score was defined as:
subject to:
Two sets of weightage systems were generated:
- Unconstrained Model:
The objective of this model was to generate weightage systems that maximized ESG’s correlation with ROCE. No lower bound for a main pillars weightage was set, which means the model could deliver a weightage scheme in which the optimum weightage for a main pillar for a sector is 0%.
- Constrained Model:
The objective of this model remains the same as the unconstrained model but a lower bound for a main pillars optimum weight was set at 15%. By setting this minimum at 15% the conceptual integrity of ESG evaluation is preserved.
For each sector, three correlations were calculated to draw comparisons:
- Crisil’s composite ESG score and ROCE
- Unconstrained optimized ESG score and ROCE
- Constrained optimized ESG score and ROCE
3.2 Baseline: Correlation Under Crisil’s Fixed Weightage Scheme
Crisil’s ESG framework calculates composite scores through a uniform weighting structure across sectors. Governance with the highest weightage (40%), followed by environmental (35%), and lastly social (25%). This allows comparability but assumes that environmental, social, and governance risks affect all industries equally. To test this assumption, sector-wise correlations between ESG scores (under Crisil’s weighting scheme) and ROCE were computed.
| Sector | Mean Correlation of ESG and ROCE |
| Consumer Discretionary – Auto | -0.02 |
| Consumer Discretionary – Durables & Services | 0.26 |
| Communication Services | 0.72 |
| Consumer Staples | 0.30 |
| Energy | 0.31 |
| Financials | -0.09 |
| Healthcare | 0.25 |
| Industrials | 0.31 |
| Information Technology | 0.47 |
| Materials | 0.19 |
| Real Estate | 0.21 |
| Utilities | 0.23 |
Table 3.2 Sector-Wise Correlation Between ESG and ROCE (Crisil Weightage Scheme) Note. Author’s calculations based on raw data from souces:18, 19, 20 and 21
The results demonstrate substantial heterogeneity not only in the magnitude but also in the economic interpretation of the ESG–ROCE relationship across sectors.
Industries such as communication services, informational technology demonstrated strong positive correlations due to the structural importance of governance quality and regulatory compliance (governance and environment have high weightages in Crisil’s weightage scheme). In contrast, financials consumer discretionary – auto (-0.02) show near-zero correlations. This can be accounted to the fact that in financials, ROCE may be influenced more heavily by macroeconomic cycles driven through context rather than ESG scoring differentials. As for auto manufacturing, majority of the companies aren’t automobile brands themselves but brand that produce automobile parts. Due to the massive information asymmetries in India and more lenient production rules these companies tend to a low correlation with ESG in present. Additionally, cyclical demand fluctuations, technological transitions (e.g., EV shifts), commodity price volatility, and high fixed-cost structures materially shape ROCE. For this very reason, it was separated from the consumer discretionary sector to not obscure the correlation of the rest of the companies in that sector.
Relatively moderate positive correlations in energy, industrials, healthcare, consumer staples, consumer discretionary – durables and services, materials, real estate and utilities) suggest partial alignment but also potential miss weighting of pillars. For instance, environmental factors may be economically dominant in energy, but Crisil’s model still gives the environmental pillar the lowest weightage.
Collectively, these patterns indicate Crisil’s weightage scheme is able to predict financial performance more strongly for companies that whose ESG factor materiality aligns with Crisil’s weightage scheme. The findings therefore provide substantive economic reasoning for investigating sector-optimized weighting models rather than relying on a single standardized ESG structure.
3.3 Optimized ESG Weightage Scheme
To test whether sectoral customization improves explanatory power, ESG weights were optimized to maximize correlation with ROCE. A python program was used to come up with unconstrained and constrained weightage schemes to maximize ESG’s correlation with ROCE.
| Sector | Correlation with ROCE | E | S | G |
| Consumer Discretionary – Auto | 0.15 | 0.02 | 0.00 | 0.98 |
| Consumer Discretionary – Durables & Services | 0.30 | 0.10 | 0.18 | 0.72 |
| Communication Services | 0.89 | 0.42 | 0.00 | 0.58 |
| Consumer Staples | 0.45 | 1.00 | 0.00 | 0.00 |
| Energy | 0.50 | 0.02 | 0.10 | 0.88 |
| Financials | 0.14 | 0.00 | 0.00 | 1.00 |
| Healthcare | 0.32 | 0.32 | 0.00 | 0.68 |
| Industrials | 0.33 | 0.56 | 0.00 | 0.44 |
| Information Technology | 0.56 | 0.16 | 0.00 | 0.84 |
| Materials | 0.21 | 0.04 | 0.32 | 0.64 |
| Real Estate | 0.48 | 0.00 | 0.00 | 1.00 |
| Utilities | 0.32 | 0.06 | 0.52 | 0.42 |
Table 3.31 Sector-Wise Unconstrained Optimization System’s Correlation with ROCE Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics
As the results indicate, the unconstrained weighting system results in theory significant jumps in ESG’s correlation with ROCE. However, in practice these could vary significantly based on the data set used and since a lot of weightages for environmental, social or governance pillars are near zero in some sectors this weighting scheme pulls away from the very concept of ESG. Thus, it is of more relevance to determine and the optimized weighting system that is constrained at a minimum of 15% weightage for each main pillar as this keeps the core, multidimensional concept of ESG intact while allowing for a generalization as the most appropriate sector-specific weightage scheme in the Indian Market.
| Sector | Correlation with ROCE | E | S | G | Logical Justification of Weightage |
| Consumer Discretionary – Auto | 0.07 | 0.15 | 0.15 | 0.70 | Due to local sector factors such as lenient regulation, informational asymmetries environment and social factors have a smaller effect on financial performance. Governance quality directly influencing capital allocation efficiency and long-term strategic execution making it important for this sector. |
| Consumer Discretionary – Durables & Services | 0.30 | 0.17 | 0.15 | 0.68 | While environmental and social factors matter, due to local informational asymmetries and lenient regulation board quality and managerial discipline primarily influence ROCE outcomes. |
| Communication Services | 0.75 | 0.37 | 0.15 | 0.48 | This sector faces regulatory scrutiny, data governance challenges, and infrastructure sustainability concerns hence governance, and environmental factors jointly influence operational stability and capital productivity. |
| Consumer Staples | 0.42 | 0.67 | 0.17 | 0.16 | Supply chain sustainability, resource use, packaging waste, and emissions materially affect margins and regulatory exposure (this sector has stricter regulation). |
| Energy | 0.44 | 0.15 | 0.17 | 0.68 | Energy firms operate under intense regulatory oversight and capital discipline requirements. Governance frameworks strongly determine project selection, risk control, and capital deployment effectiveness. |
| Financials | 0.05 | 0.15 | 0.15 | 0.70 | Financial institutions are governance-centric entities where risk management, board oversight, and regulatory compliance directly determine capital efficiency and stability. Additionally, the money focused ideology of most of these firms’ consumer’s leads them to be less aware of the firm’s social and environmental practices. |
| Healthcare | 0.28 | 0.29 | 0.15 | 0.56 | Governance influences research allocation and quality of service, while environmental factors relate to regulatory compliance. |
| Industrials | 0.32 | 0.41 | 0.23 | 0.36 | Environmental exposure through manufacturing processes and emissions significantly affects operational costs. Governance remains important, but environmental efficiency drives margins. |
| Information Technology | 0.54 | 0.15 | 0.17 | 0.68 | Governance and strategic management of intellectual capital, cybersecurity, and data oversight critically affect long-term profitability and investor confidence. |
| Materials | 0.21 | 0.17 | 0.33 | 0.50 | Extractive and production activities create environmental and stakeholder risks. Social relations to operate near communities and governance to guide the company jointly influence capital efficiency. |
| Real Estate | 0.36 | 0.15 | 0.15 | 0.70 | Capital structure, project selection, regulatory compliance, and board oversight largely determine asset returns, making governance central to ROCE performance. Additionally, highly competitive price centric real estate market in India leads to a lower influence of social and environmental practices in most consumers’ eyes. |
| Utilities | 0.31 | 0.15 | 0.39 | 0.46 | Utilities operate under public accountability and regulatory pricing structures. Social obligations and governance oversight influence operational continuity and financial stability. |
Table 3.32 Sector-Wise Constrained Optimization System (Minimum 15%) Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics
Under the constrained optimization system, communication services record the strongest correlation under the constrained framework, reflecting a very tight alignment between ESG performance and capital efficiency. Information technology and energy also demonstrate relatively strong positive associations due to the government-dominant allocations (G ≈ 0.68) that influence oversight, capital discipline, and regulatory management. Moderate correlations are observed in consumer staples, real estate, industrials, and utilities as while the optimization of weightages accounts for sector materiality ESG factors there are sector specific aspect in each that are not accounted by ESG factors.
However, financials and consumer discretionary – auto continue to exhibit weak correlations despite governance-heavy weightings (0.70 in both sectors). This suggests that even when pillar emphasis is adjusted to reflect sector logic, ESG scores may not translate strongly into ROCE variation due to reasons explained in detail in 3.2 which ESG factors simply can’t account for. ESG performance may influence long-term strategic positioning or risk mitigation in these sectors.
These findings indicate that while sector-specific optimization improves statistical alignment in several industries with most demonstration a strong or moderate correlation it cannot uniformly overcome structural characteristics that weaken the ESG–ROCE transmission mechanism. The constrained framework proposed therefore demonstrates the strengths and limits of ESG factor’s ability to indicate financial performance: its predictive power is considerable when firms are affected by the qualitative factors ESG reports but gets weaker when financial outcomes are driven primarily by macroeconomic or capital-cycle forces.
3.4 Comparative Performance: Crisil vs Proposed Sector-Specific Weighting Scheme
| Sector | Crisil’s Correlation with ROCE | Proposed Weightage Schemes’ Correlation with ROCE | Improvement (%) |
| Consumer Discretionary – Auto | -0.02 | 0.07 | 450.00 |
| Financials | -0.09 | 0.05 | 155.56 |
| Real Estate | 0.21 | 0.36 | 71.43 |
| Energy | 0.31 | 0.44 | 41.94 |
| Consumer Staples | 0.30 | 0.42 | 40.00 |
| Utilities | 0.23 | 0.31 | 34.78 |
| Consumer Discretionary – Durables & Services | 0.26 | 0.30 | 15.38 |
| Information Technology | 0.47 | 0.54 | 14.89 |
| Healthcare | 0.25 | 0.28 | 12.0 |
| Materials | 0.19 | 0.21 | 10.53 |
| Communication Services | 0.72 | 0.75 | 4.17 |
| Industrials | 0.31 | 0.32 | 3.23 |
Table 3.4 Comparative Performance: Crisi’s Weightage scheme vs Author’s Proposed Weightage Scheme Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics
As indicated by the data, applying sector specific weightages drastically improves the correlation between ESG and ROCE across most sectors. The most significant improvement is seen in real estate (consumer discretionary – auto and financials have high percentage improvement but correlation is still near-zero) as the proposed weightage scheme gives allows for governance to drive the composite score as in India it is the most material to financial performance. Energy Consumer Staples and Utilities have also shown strong improvements due to improved sector-specific materiality considerations by the proposed weightage scheme. Moderate improvements can be seen in Consumer Discretionary – Durables & Services, Information Technology, Healthcare, Materials. Only two sectors Communication Services and Industrials showed small improvements as for both Crisil’s weightage scheme fairly rated these companies through their weightage system leaving little room for improvement.
Taken together the table demonstrates the need for sector-specific weightage schemes for ESG calculation. The proposed weightage scheme gives importance to ESG pillars based on their sector specific materiality to financial performance allowing for ESG to become a substantial indicator of financial performance.
3.5 How to Calculate ESG Scores for Multi-Sectorial Companies.
The weightage schemes this research paper has presented makes it clear as to the method used to calculate the most reliable ESG ratings for most companies. However, there are certain companies who don’t entirely fit into one sector. An example of this is reliance limited.
For a multi-sector company like Reliance Industries Limited, the ESG score should be a weighted sum of the scores calculated using each sector’s optimal weightage.
The General Formula:
Where:
- Wsector is the percentage of revenue from that sector
- ESGsector is the ESG score calculated using the optimal weights for that specific sector.
4. Strengths and Limitations of the Quantitative Analysis
This study has several methodological strengths. Data used is from reputable sources: Screener, Equitymaster and Crisil Ratings and Analytics. Furthermore, the study only uses data available to everyone publicly which makes this highly relevant for retail investors and smaller firms, not just larger corporations. The clear segregation into 12 sectors makes using the proposed weightage scheme straightforward and the system also accounts for multi-sectoral companies. Additionally, the use of ROCE as the dependent variable strengthens the analysis by focusing on operational capital efficiency instead of market-based performance. This aligns ESG evaluation with fundamental financial productivity. Statistically, the Spearman’s correlation was used to reduce weightage of outliers as when data is limited outliers could significantly harm reliability of the conclusion. Lastly, the implementation of both unconstrained and constrained optimization frameworks allows for differentiation between purely statistical maximization and theoretically grounded calibration.
However, certain limitations must be acknowledged. The study is conducted over a limited period, due to lack of publicly available ESG data in India. This potentially restricts insights into the stability of optimized weightages overtime and across economic cycles. Secondly, ESG scores are published annually as an aggregate which doesn’t account for intra-year variation in sustainability performance that could have affected ROCE. Sizes of sector-level datasets may have also affected overall results. Most importantly, the dataset is exclusively composed of Indian companies. As a results, the proposed weightage scheme is only directly applicable to India and potentially other economies at similar stages of development. This is because the sector-specific weightage system gives importance to the pillars based on their materiality due to the Indian context such as local information asymmetries and the tendency of Indian government to impose regulation. For example, it is likely that a highly developed country has low information asymmetry regarding business practices (between businesses and consumers), whereas in an underdeveloped or develop this asymmetry may be higher. Therefore, the optimized weightage schemes identified in this study are most appropriate for India or countries with comparable institutional and developmental characteristics. Due to this same concept, these ESG weightage systems are also only relevant in India while it remains in its current broad development stage.
5. Concluding Remarks
This research has examined the evolving role of ESG as an indicator of corporate financial performance within the Indian context. While frameworks like those provided by Crisil have significantly improved the accessibility of sustainability data, this study identifies a critical gap in the application of uniform, non-sector-specific weighting systems. Such models fail to capture the unique operational parameters and types of risks inherent to different industries, hence diluting the correlation between ESG performance and financial returns.
Through empirical analysis and quantitative modeling, this paper produces a sector-specific weighting scheme, tailored to the environmental, social, and governance priorities of specific industries, that serves as a far more robust indicator of ROCE. By improving the framework of existing ESG practices as financial performance indicators this paper aims for ESG ratings to be more widely used alongside financial metrics while predicting the future share performance of a firm.
The proposed framework also carries broader implications for markets and society. By providing a stronger and more consistent correlation between ESG performance and financial returns, this framework increases the likelihood that sustainability considerations will be integrated into investment decision-making. This could lead to qualitative factors regarding companies’ sustainability and ethics also being priced in (as financial metrics are now) in the long run. Once this has happened more companies are likely to make most decisions keeping consequences regarding sustainability and ethics in mind with the profit-maximizing goal of improving their stock price. This will result in huge positive externalities as more firms demonstrate responsible environmental stewardship, ethical governance structures, and socially sustainable practices. In the long run, such alignment between financial incentives and sustainability outcomes has the potential to reinforce ethical corporate conduct and hence have considerable benefits to society from an economic and social perspective.
6. Future Scope for Research
The author intends to pursue further research and publication in the areas outlined below, building upon the analytical framework developed in this study.
1) Cross-Country Optimal Weightage Comparative Studies: Applying this optimization framework across multiple countries at varying stages of development would determine whether sectoral ESG relevance is development dependent. It would provide insights into how differing governments’ regulation tendencies and information asymmetries across the development spectrum affect the ideal ESG weightages for financial performance indication. Moreover, conclusions from such a study could be significant as it could lead to a significant alteration in the methodology of major multinational ESG providers like MSCI transforming the ESG landscape completely and leading to much higher correlations between ESG and ROI globally.
2) Multi-Year Longitudinal Analysis Across Economic Cycles: Extending this study over a larger time frame once there is Indian ESG data available over more years would make this system far more reliable and would also allow for insights into how the optimum ESG weightages change across sectors through economic cycles.
3) High-Frequency or Quarterly ESG Data: If quarterly ESG metrics become available in India, studies on how ESG shocks influence ROCE within shorter intervals. This would be useful to determine the speed of ESG-financial transmission mechanisms which would be useful for investors as it will indicate the average time they have to act on information reported by ESG.
4) ESG in Stress Periods: Since ESG is primarily an indicator of sustainability, such a study would indicate whether firms with higher ESG ratings are able to outperform competitors during stress periods.
References
“Crisil ESG Ratings & Analytics Limited.” CRISIL, 2021, www.crisilesg.com/en/home.html.
“Crisil ESG Ratings & Analytics Ltd Gets SEBI Nod to Offer ESG Ratings.” CRISIL, 2024, www.crisilratings.com/en/home/newsroom/press-releases/2024/04/crisil-esg-ratings-and-analytics-limited-gets-sebi-nod-to-offer-esg-ratings.html.
Edmans, Alex. “Does the Stock Market Fully Value Intangibles? Employee Satisfaction and Equity Prices.” Journal of Financial Economics, vol. 101, no. 3, 2011, pp. 621-640.
Equitymaster – Get Unbiased Equity Research on Indian Stocks and Share Market Updates for Value Investing in India. Equitymaster, 2025, www.equitymaster.com.
“ESG Ratings 2023.” CRISIL, 2023, www.crisilesg.com/en/home/esg-ratings/historical-ratings/esg-ratings-2023.html.
“ESG Ratings Criteria and Methodology.” CRISIL, 2025, www.crisilratings.com/content/dam/crisilesg/criteria-and-methodology-for-crisil-esg-ratings/archives/criteria-and-methodology-for-esg-ratings-feb-2025.pdf.
“ESG Ratings List 2024.” CRISIL, 2024, www.crisilesg.com/en/home/esg-ratings/historical-ratings/esg-ratings-2024.html.
Fama, Eugene F. “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance, vol. 25, no. 2, 1970, pp. 383-417.
Friede, Gunnar, et al. “ESG and Financial Performance: Aggregated Evidence from More than 2000 Empirical Studies.” Journal of Sustainable Finance & Investment, vol. 5, no. 4, 2015, pp. 210-233.
Green, Jeremiah, et al. “The Supraview of Return Predictive Signals.” Review of Accounting Studies, vol. 18, no. 3, 2013, pp. 692-730.
Hartzmark, Samuel M., and Abigail B. Sussman. “Do Investors Value Sustainability? A Natural Experiment Examining Ranking and Fund Flows.” The Journal of Finance, vol. 74, no. 6, 2019, pp. 2789-2837.
Khan, Mozaffar, et al. “Corporate Sustainability: First Evidence on Materiality.” The Accounting Review, vol. 91, no. 6, 2016, pp. 1697-1724.
Krantz, Tim. “The History of Environmental Social and Governance (ESG).” IBM, 8 Feb. 2024, www.ibm.com/think/topics/environmental-social-and-governance-history.
Roll, Richard. “R2.” The Journal of Finance, vol. 43, no. 3, 1988, pp. 541-566.
“Stock Screener and Fundamental Analysis Tool for Indian Stocks – Screener.” Screener, www.screener.in.
About the author
Nimay Shah
Nimay is an IB Diploma student with a keen interest in finance, economics, and quantitative research. His work focuses on applying mathematical and data-driven methods to improve investment analysis, particularly in sustainable finance and ESG.
Through this research, Nimay aims to bridge the gap between academic theory and practical investing by developing more effective approaches to evaluating corporate performance. He hopes to pursue business, finance, or economics at university and continue exploring the intersection of data, markets, and sustainability.