ESG disclosure and portfolio returns: evidence from the Indian equity market

Introduction

The contemporary investment landscape creates a demand for transparent sustainability reporting. This mandate for accountability came after “UN defined Principles of Responsible Investment (UNPRI)” were introduced in 2005, where the structural definitions for “environmental, social, and governance (ESG)” ratings are developed. The movement spread across 50 countries and 1,400 firms. The development of modern business ethics traces back to Brundtland Report, of World Commission on Environment and Development (1987), which transform the abstract concept of sustainability into a structured corporate framework. Implementation of these measures serves as a catalyst for green innovation (Xu et al., 2021) while significantly reducing the cost of debt (Atif and Ali, 2021), improving access to equity markets (Bodhanwala and Bodhanwala, 2018), facilitating capital investment (Cupertino et al., 2019), enhancing institutional reputation (Nirino et al., 2021) and finally improving the performance of the organization (Bhaskaran et al., 2020).

ESG measures company’s ethical health, moving beyond mere compliance for value creation and stakeholder trust. Firms that incorporate ESG are better equipped to navigate regulatory shifts and make use of resources effectively. That means being sustainable is no longer the right thing to do but it is also necessary for companies to survive in the long term by gaining competitive edge. Although ESG disclosures are broadly recognized as a crucial measure of sustainability, a unified standard for such reporting is yet to be established. Various rating agencies construct ESG disclosure metrics using different methodologies which can potentially hinder investor’s ability to make informed decisions (Matos et al., 2020). Increasingly, portfolio managers are compelling stakeholders to adopt the “Triple Bottom Line (TBL)” framework. As established by Elkinjton (1998) and furthered by Hammer and Pivo (2017), this approach emphasizes the broader spectrum of value creation by organizations beyond financial metrics to include economic, environmental and social contributions. It is also known as “3 Ps (People, Planet, Profit)” approach.

Extensive research has established the significance of several key determinants in explaining cross-sectional stock returns, most notably size (Banz, 1981), price-to-earnings ratios (Basu, 1983), leverage (Bhandari, 1988), book-to-market equity (Fama and French, 1993), human capital (Jagannathan et al., 1998), profitability (Novy-Marx, 2013), and investment factors (Aharoni et al., 2013); while neglecting the role of sustainable and responsible investing, such as ESG disclosure. According to Maiti (2021), the changing landscape of risk determinants has led to decline in the relevance of traditional asset pricing models. It further suggests that ESG considerations will play a dominant role in forthcoming investment paradigms.

The “Climate Change Performance Index (CCPI)” functions as a prominent benchmark for assessing national climate action, with India ranking 8th among over 60 countries in 2023. This has urged investors to adopt sustainable investment approach driven by regulatory requirements, reputational considerations, and anticipation of superior long-term returns. Consequently, it leads to a discussion whether ESG factors can help the investors to earn superior returns if they include it in their investment decision making. Further, whether incorporating ESG as an additional factor in established risk models can offer any additional explanatory power remains ongoing and globally debatable topic.

In this study we have considered firms listed within the Indian equity market, as Indian economy is one of the rapidly emerging economies owing to many reasons including economic growth, inflows of foreign investment, and policy changes. The emergence of digital trading platforms, along with the availability of cost-efficient index funds and ETFs, has broadened market participation among investors. As per PWC (2021), India sustainability journey began in 2009 with the introduction of the “National Voluntary Guidelines (NVGs).” It gained significant momentum in 2012, 2015, and 2019 as SEBI directed the submission of “Business Responsibility Reports (BRR)” for the leading 100, 500, and 1,000 listed companies, respectively, based on their “market capitalization (MC).” In 2021, SEBI mandated publishing the “Business Responsibility and Sustainability Reporting (BRSR)” report which is a more stringent reporting framework that incorporates a wider range of ESG indicators, reflecting India’s progress toward strengthening disclosure standards with regards to sustainability. We are strongly motivated to examine the impact of this sustainability reporting on the portfolio returns in Indian equity market considering the country progress toward it.

The remainder of this manuscript is organized as follows: A detailed literature review on ESG and asset pricing is provided in the second section. The third section describes about the data employed in the study along with the data sources. The methodological approach used to construct the portfolios and model estimation is explained in the fourth section. The fifth section presents and discusses the empirical findings of the study. Finally, the last section summarizes the key findings of the study and highlights their practical implications.

Review of literature

The association between ESG disclosure and portfolio performance is widely reported which is characterized by high level of ESG integration within developed market. Existing literature indicates that firms exhibiting weak “governance (Gov)” structures, engaging in “environmentally (Env)” unsustainable practices, or neglecting “social (Soc)” responsibility are more susceptible to heightened risk exposures (Díaz et al., 2021; Pineau et al., 2022).

Accordingly, higher ESG disclosure indicate that a firm is better positioned to manage adverse events effectively, suggesting that ESG disclosure can serve as a proxy for the enterprise ability to mitigate risks affecting its stakeholders. Portfolios characterized by strong ESG disclosure tends to achieve superior returns, as they are more likely to attract capital from Env-sensitive investors, mitigating exposure towards regulatory and compliance-related penalties, fostering positive relations within the communities in which they operate (Zehir and Aybars, 2020). Cupertino et al. (2019), demonstrate that superior ESG disclosure particularly higher Env and Soc dimensions exert positive influence on firm’s investment decision by considering US manufacturing companies. Further, they suggest that ESG neutralize the adverse effects of financialization on capital accumulation and promotes sustained value creation.

According to Pástor et al. (2021) investors with sustainability preferences are inclined to accept lower expected returns by paying a premium for Env responsible firms, which reduces the firm’s cost of capital and leads to negative CAPM alphas. At equilibrium, such stocks yield returns below their expected levels, reflecting the hedging benefits they provide against ESG risks. Nonetheless, during favorable market conditions these firms tend to generate superior returns. Thus, as the proportion of Soc responsible investors rises, firms with high ESG disclosure are anticipated to exhibit stronger performance than low ESG firms. Similarly, Aureli et al. (2020) demonstrates that ESG disclosures exert a meaningful impact on the valuation of firms listed on the Dow Jones Sustainability Index, driven by heightened investor interest in sustainability reporting. Likewise, Alsayegh et al. (2020) discovered that ESG disclosures positively impact the economic performance of firms in Asian context. Their results indicate that firms characterized by Env responsibility, Soc commitment, and strong Gov structures achieve superior sustainability outcomes and are better positioned to manage future risks.

Bamahros et al. (2022) suggest that the disclosure of complete ESG initiatives signals a firm’s commitment to societal welfare, thereby enhancing its perceived legitimacy among stakeholders. This improved perception is reflected in stock price indicating a positive long-term valuation effect of ESG transparency. Furthermore, the sustained alignment between stakeholders and management helps reduce information asymmetry between firms and society, as highlighted by Sinha Ray and Goel (2023). Investors might continue to retain holdings in firms with high ESG ratings even when these stocks appear overpriced or are subject to adverse information. Conversely, investors with strong ESG preferences may deliberately avoid firms with poor ESG disclosure, despite their potential to generate superior future returns (Cao et al., 2023; Riedl and Smeets, 2017). They further posit that sustainable investors are ready to accept lower expected returns from ESG-oriented funds while simultaneously incurring higher management fees. This behavior reflects a deliberate willingness to sacrifice financial performance in favor of aligning investments with ethical and sustainability objectives.

Lööf et al. (2022) found that firms with superior ESG disclosure are less vulnerable to downside risk despite exhibiting lower return potential. Additionally, such firms are associated with lower financial distress risk (Boubaker et al., 2020) and superior overall performance (Liu et al., 2021). Jin (2018) identified a distinct risk differential between responsible and conventional investment strategies within a risk–return paradigm. By augmenting the FF model with an ESG component, the study analyzed 1,425 US open-end equity funds spanning from April 2009 to December 2016. The findings reveal that ESG-related risk is effectively incorporated into market pricing and portfolios oriented toward responsible investment provide notable downside risk mitigation on ESG-related exposures, which is even difficult to eliminate through diversification. Similarly, Bermejo Climent et al. (2021) investigate the association between corporate financial performance indicators and ESG disclosure practices and found progressive increase in ESG disclosure in European context. Further, the empirical findings indicate that both Gov and Soc factor exerts significant and positive impact on portfolio returns. However, the influence of Gov factor found to be stronger compared to Soc factor. Interestingly, Env factors do not appear to yield abnormal returns. In line with the above findings, Lee et al. (2021) suggest that incorporating ESG criteria into investment decision-making does not negatively impact risk-adjusted performance in Australian context. Additionally, portfolios with superior ESG ratings demonstrate consistent outperformance, improved diversification, and reduced exposure to risk compared to those with lower ESG ratings.

Off late research examining the effect of ESG disclosure on portfolio performance has also gained considerable attention in emerging market alongside the extensive evidence from developed market. Cho (2023) studied time-varying preferences for ESG investing in the Korean stock market and discovered that investor demand for high ESG stocks is influenced by prevailing economic conditions. The findings revealed that high ESG stocks displays negative abnormal returns amid adverse economic occasions but deliver positive excess returns during favorable macroeconomic conditions, reflecting stronger investor preference for ESG consideration. In line with above findings, Yadav et al. (2025) explored the impact of the ESG disclosure on stock returns for Nifty-50 firms using panel regression analysis. The findings of the study indicate that higher ESG disclosure companies generate higher stock returns, and this positive association remained robust despite controlling for firm-specific variables (such as profitability, leverage) and macroeconomic factors, (including GDP, inflation and the Covid-19 crisis). Therefore, the study further suggests that ESG disclosure enhances stock returns even in times of economic uncertainty, highlighting ESG as a crucial determinant of stock returns in the Indian equity market. In contrast to the aforementioned results, Bell and van Vuuren (2022) employ portfolio optimization approach to examine the impact of ESG disclosure on portfolio performance in emerging markets and provide statistically significant results showing that higher ESG disclosures result in lower risk and higher returns. Nonetheless, they also conclude that portfolios with higher ESG disclosure firms do not yield better risk-adjusted performance. In fact, the authors attribute that firms with exceptionally high ESG disclosure allocate a substantial proportion of their revenues toward Env initiatives, thereby constraining financial returns. Consequently, such Env responsible firms do not consistently outperform less sustainable-oriented counterparts in terms of returns. This evidence aligns with the findings of Zhang et al. (2022) who revealed an insignificant linkage between ESG and portfolio excess returns, indicating that ESG disclosures do not significantly differentiate portfolio performance in China, where both high and low ESG score portfolios exhibit strong excess returns.

ESG considerations have assumed growing significance in the Indian context, largely driven by the imperative to address critical Env concerns, including resource scarcity, pollution and climate-related risks, alongside the need to mitigate socio-economic inequalities. As a result, ESG focused investment strategies have garnered increasing attention, given their potential to enhance the long-term performance of equity portfolios through improved risk management and strong stakeholder engagement. In this regard, it is essential for both market participants and regulatory bodies to extensively evaluate the influence of ESG disclosure on portfolio performance. An extensive body of research is carried out in Indian context considering the impact of ESG disclosure on the financial performance of the firms (Hasan et al., 2021; Veeravel et al., 2024). Surprisingly, few researchers sought to check the effect of ESG factor on the portfolio performance that includes Indian firms, constructed following asset pricing approach. For instance, Mohanasundaram and Kasilingam (2024) reported a positive impact of ESG risk premium on the portfolio performance by using Fama-Macbeth and FF methodology. Further, they also found that portfolio consisting lower ESG firms generate comparatively higher returns than portfolios with higher ESG firms. Aggarwal et al. (2024) demonstrated that ESG based investment strategies produces negative alphas and predict lower future returns. Besides, the study also documented that the high ESG score firms fail to generate long term risk-adjusted returns for institutional investors. Similarly, Hasan et al. (2025) found that ESG sorted portfolios reflect lower market risk compared to traditional portfolios, exhibiting greater resilience during periods of market stress, highlighting their ability to withstand adverse market conditions.

From the above discussion, it is clearly evident that there exists a lively debate about the effect of ESG disclosure on the portfolio performance in emerging economies including India. Further, it is also seen that the results are inconclusive in nature as some studies found a positive relation between ESG factors and portfolio returns while some researchers contradict these findings. Moreover, plethora of research concentrated in examining the effect of ESG on firm performance. Furthermore, there exist a lot of ambiguity with regards to the integration of ESG as a firm characteristic alongside other prominent factors such as “size, value, profitability, and investment.” Then, handful of studies in India have considered ESG as an additional factor to explain the average excess portfolio returns constructed by combining the mentioned firm characteristics. There is also no clarity about the explanatory power of ESG if used as an additional factor. The novelty of our study lies in applying “Zero-Centered scores (ZCS)” to measure the peer-relative ESG scores, individually examining the Env, Soc, and Gov dimensions, and using a comprehensive sample of Indian listed firms. It further compares the traditional, augmented and modified FF models to examine whether ESG provides incremental explanatory power for portfolio returns. These research gap motivated us to examine the underlying impact of ESG on the portfolio performance and to check if ESG factor plays any role in explaining the average excess returns in Indian context. This study attempts to incorporate ESG as an additional factor within an asset pricing framework to explain the cross-section of excess portfolio returns. Accordingly, the study first investigates whether portfolios constructed using ESG characteristics yield significant average excess returns. In the presence of abnormal returns, the second objective is to assess the explanatory power of ESG in accounting for such return patterns. The third objective involves a comprehensive assessment of each ESG component with respect to stock performance and their relative significance. Furthermore, the study investigates whether incorporating ESG into a four-factor framework enhances the explanatory power of the model compared to the traditional three-factor framework, thereby contributing towards the development of a more robust and sustainable model for the Indian equity market.

Data

The study employs the Nifty 500 index as the sample, as it encompasses large, mid and small capitalization firms representing the broader Indian equity market and constitute around 90 percent of total Market Capitalization (MC). The sample comprises firms that are current constituents of the Nifty 500 index, for which historical data was retrieved over the entire sample period. The study utilizes month-end adjusted “closing share prices, MC as a proxy for firm size, and Price-to-book (P/B) ratio as an indicator of the value factor.” In addition, the study incorporates ESG scores along with their individual dimensions—Env, Soc, and Gov to capture firm’s sustainability performance. The time period for this study is from April 2015 to March 2023. The rationale behind selecting this time period is attributable to the fact that SEBI made it compulsory for top 500 firms in India to report ESG score from 2015 onwards. The data are collected till 2023 as BLOOMBERG database from which all the data are sourced, has not reported ESG data for India since 2023. To facilitate meaningful comparisons across firms, the analysis employs ZCS which measures the difference between a firm’s ESG score and the median ESG score of its peer group from the prior fiscal year. Unlike conventional ESG ratings, ZCS captures not only a firm’s relative position but also the magnitude of its sustainability performance relative to its industry peers. Khambatta et al. (2025) systematically proves that the ZCS scores work better for portfolio construction optimization. The formula to calculate ZCS provided in methodology section. The risk-free rate of return (Rf) is approximated using the yield on 91-day T- bills, with data obtained from the “Reserve Bank of India (RBI).” The market returns (Rm) are proxied using the Nifty 200 index returns which also is considered as broad-based market index that covers 80 percent MC on the NSE.

Methodology

Calculation of ESG ZCS scores for portfolio optimization

Following (Khambatta et al., 2025), we construct ZCS to capture the magnitude of firm’s sustainability outperformance or underperformance, relative to its peer group, like Z-score thereby going beyond the ordinal information conveyed by simple ESG scores. In our study, the peer group for each year is defined as the full cross-section of NSE 500 firms with available ESG data in that year, rather than industry or sector- specific sub groups (Strike out - sign); this choice reflects the relatively limited number of firms with consistent ESG disclosure within individual sectors in the Indian market. For each firm i in year t, the ZCS is calculated as follows:

ZC S i , t = ES G i , t − max ( Median ESG scor e t − 1 , 1.5 )

Where the median is calculated using the prior fiscal year ESG scores of all NSE 500 firms. The median is floored at 1.5 to prevent ZCS from being disproportionately inflated in years when the overall market median ESG score is very low. Without this floor, firms could appear to be strong sustainable outperformers not due to genuinely superior ESG practices, but simply because the benchmark against which they are measured was itself low. This procedure is applied separately to the individual ESG components (Env, Soc, and Gov). Firms with missing ESG scores in a given year are excluded from that year median calculation and from portfolio formation for that year. ZCS can range from (−10 to 8.5) with higher values indicating stronger relative sustainable performance. A firm at the market median ESG score has a ZCS close to zero, firms outperforming the median record positive ZCS values, and firms underperforming record negative values, allowing sustainable performance to be interpreted on a consistent scale across the sample rather than merely ranked ordinally. This structure made ZCS more comparable over time rather than raw ESG scores, providing a more reliable basis for analysis of corporate sustainability performance.

Construction of single-sorted portfolios

Following (Bender and Wang, 2015) this study employs rule-based portfolio approach in which stock selection and portfolio allocation are governed by predefined set of objective and transparent screening, ranking, and rebalancing criteria, thereby reducing the influence of subjective judgment and short-term market sentiment. Consistent with this approach, the portfolios in the present study are designed following the methodology proposed by Fama and French (2015) and Fama and French (1993) employing both single and double-sorting techniques to classify stocks into distinct portfolio groups. Under the single-sort approach, all stocks are ranked annually in June 2015 of (year t) according to their MC and subsequently classified into five equally-weighted portfolios from (P₁ to P₅). Portfolio P₁ comprises the bottom 20% of stocks ranked by MC, representing small-sized stocks, whereas P₅ consists of the top 20% of stocks, representing large-sized stocks. The remaining stocks are distributed equally across P₂, P₃, and P₄, each containing 20% of the sample firms positioned between the two size groups.

Following portfolio formation, the average monthly excess returns of the five equally-weighted portfolios are calculated over the subsequent period, extending from July 2015 of (year t) to June 2016 of year (t + 1). The portfolios are rebalanced and reconstituted annually in June of each year, and this procedure is repeated throughout the entire sample period until 2023. Applying the same methodology as used in the case of size factor, five additional sets of equally-weighted portfolios are formed based on P/B ratio, ESG, Env, Soc, and Gov factors. The timing of portfolio formation follows the convention that is in line with FF, ensuring that portfolios are constructed using only information genuinely available to market participants at the time. MC which is used as a proxy for size, is measured at the end of June (year t), and stocks are ranked and sorted into portfolios accordingly. The accounting-based variables and ESG scores are taken in fiscal year ending March of (year t), are incorporated only from July of (year t) onwards, allowing sufficient time for the underlying disclosures to become publicly available and absorbed by the market. For all variables, the holding period runs from July of (year t) to June of year (t + 1), with portfolios rebalanced annually in June of each year throughout the sample period (2015–2023), thereby preventing look-ahead bias (Balakrishnan, 2014). Since returns (Ri-Rf) are computed as percentage changes rather than as absolute price levels, they are inherently stationary and do not display unit-root behavior, and formal stationary tests are therefore not conducted, consistent with standard practice in the asset pricing literature (Fama and French, 1993).

Construction of mimicking portfolios

The mimicking portfolios are also designed using (Fama and French, 2015; Fama and French, 1993) method. The methodological procedure to construct these portfolios are explained below.

Construction of SMB/LMH

In the first stage, stocks are ranked by MC and classified into two equally-weighted portfolios. Following the ranking procedure, securities falling within the bottom 50% of the distribution are classified as “small portfolio (S),” while those in the top 50% are classified as “big portfolio (B).” In the second stage securities are ranked by their P/B ratio and allocated into three groups. The bottom tercile comprises the “low (L)” portfolio, the middle tercile constitutes the “medium (M)” portfolio, and the top tercile forms the “high (H)” portfolio. Combining the two size groups with three P/B groups results in six portfolios, referred as 2 × 3 size-value sort mimicking portfolios. These portfolios are denoted as “small/low (S/L), small/medium (S/M), small/high (S/H), big/low (B/L), big/medium (B/M), and big/high (B/H).” Portfolio S/L comprises small-cap firms with low value stocks, whereas B/H portfolio consists of large-cap firms with high value stocks. Small minus Big (SMB) is constructed as the difference between the average monthly returns of the three small stock portfolios (S/L, S/M, and S/H) and the average monthly returns of the three big stock portfolios (B/L, B/M, and B/H). Similarly Low Minus High (LMH) is calculated as the difference between the average monthly returns of the value portfolios (S/L and B/L) and those of the growth portfolios (S/H and B/H). After portfolio formation, equally-weighted returns on these six portfolios are calculated from July 2015 of (year t) to June 2016 of year (t + 1). The ranking and portfolio assignment process is repeated each June throughout the study period. Finally, the monthly excess returns of the portfolios are analyzed for further analysis.

Construction of LesgMHesg

Similarly, at the end of March 2015 (year t), stocks are sorted according to their ESG scores and subsequently categorized into three groups using the same breakpoints applied in the case of P/B. The bottom tercile are classified as the “low (L) ESG” portfolio, those in the middle constitute the “medium (M) ESG” portfolio, and those in the highest tercile are assigned to “high (H) ESG” portfolio. The two size portfolios (small and big) are then combined with the three ESG portfolios to construct six equally-weighted, (2 × 3) size-ESG sorted mimicking portfolios. Specifically, the portfolios comprise “small/low_esg(S/L_esg), small/medium_esg(S/M_esg), small/high_esg(S/H_esg), big/low_esg(B/L_esg), big/medium_esg(B/M_esg), big/high_esg (B/H_esg).” Portfolio S/L_esg consists of small-cap firms with low ESG scores, whereas B/H_esg portfolio comprises large-cap firms with high ESG scores. Subsequently, the monthly equal-weighted returns for the six portfolios are estimated over July 2015 (t) to June 2016 (t + 1) and repeated over the entire study period.

Low ESG minus High ESG (L_esgMH_esg) is constructed to capture the return differentials associated with firm’s sustainability characteristics. It is calculated as the difference between the average monthly returns of the low ESG portfolios (S/L_esg and B/L_esg) and the average monthly returns of the high ESG portfolios (S/H_esg and B/H_esg). Following the portfolio formation methodology, firms are jointly classified according to size and each of the individual ESG dimensions. Based on these classifications, three sets of six equally-weighted portfolios are formed, namely “size-Env, size-Soc, and size-Gov.” Further, the study conducted the residual diagnostics test and found that there is no evidence of serial correlation or heteroskedasticity in the residuals.

The mathematical formula used to calculate these mimicking portfolios are as follows:

SMB = ( S / L + S / M + S / H ) / 3 – ( B / L + B / M + B / H ) / 3

LMH = ( S / L + B / L ) / 2 – ( S / H + B / H ) / 2

L esg MH esg = ( S / L esg + B / L esg ) / 2 – ( S / H esg + B / H esg ) / 2

L env MH env = ( S / L env + B / L env ) / 2 – ( S / H env + B / H env ) / 2

L soc MH soc = ( S / L soc + B / L soc ) / 2 – ( S / H soc + B / H soc ) / 2

L gov MH gov = ( S / L gov + B / L gov ) / 2 – ( S / H gov + B / H gov ) / 2

This study has used six baseline regression models to examine the explanatory power of various factors, including the prominent FF three factor model, augmented FF three factor model, by replacing value with ESG factor, modified FF three factor model, by replacing value factor with individual ESG dimensions -Env, Soc, and Gov respectively, and one four factor model by including the ESG factor alongside FF three factors. The model specifications are presented from Equation 1 to Equation 6 and are as follows:

R Pt − R Ft = α + b ( R Mt − R Ft ) + sSMB t + lLMH t + e t (1)

R Pt − R Ft = α + b ( R Mt − R Ft ) + sSMB t + esgL esg MH esg + e t (2)

R Pt − R Ft = α + b ( R Mt − R Ft ) + sSMB t + envL env MH env + e t (3)

R Pt − R Ft = α + b ( R Mt − R Ft ) + sSMB t + socL soc MH soc + e t (4)

R Pt − R Ft = α + b ( R Mt − R Ft ) + sSMB t + govL gov MH gov + e t (5)

R Pt − R Ft = α + b ( R Mt − R Ft ) + sSMB t + lLMH t + esgL esg MH esg + e t (6)

Where R_Pt- R_Ft denotes the excess returns on the portfolio (where portfolio returns are adjusted by the risk-free rate); R_Mt- R_Ft indicates the excess returns on the market portfolio, with market returns similarly adjusted. SMB, LMH, L_esgMH_esg, L_envMH_env, L_socMH_soc, and L_govMH_gov mimic the factors in returns relating to size, value, ESG, Env, Soc, and Gov, respectively. α signifies the abnormal returns while b, s, l, esg, env, soc, and gov represent the portfolio’s responsiveness to the corresponding mimicking portfolio.

Empirical results

Mean excess returns of double sorted portfolios and explanatory variables

The mean excess returns of double sorted portfolios constructed based on size-value, size-ESG, size-Env, size-Soc, and size-Gov factors are presented in Table 1. It depicts that all S/L portfolios outperform B/H portfolios in terms of returns. Further, the annualized mean excess return differentials between S/L and B/H portfolio are 20.4, 28.8, 21.6, 18, and 27.6% for size-value, size-ESG, size-Env, size-Soc, and size-Gov, respectively. These returns differentials suggest the presence of size, value, and ESG effect. Interestingly, it is also worth noting that the return differentials of the size-ESG based portfolio are higher compared to other portfolios, signifying the importance of ESG factor in constructing the portfolios by the investors. The large difference between S/L and B/H indicates that these excess returns could be influenced by the traditional as well as sustainability related factors. Hence, it is important to examine if these excess returns provided by the double sorted portfolio can be explained by additional ESG factor. These results with regards to size and value effect are in line with findings of Sehgal and Balakrishnan (2013) and Balakrishnan (2016). The findings related to ESG effect are similar to the recent findings of Mohanasundaram and Kasilingam (2024) where they reported that firm with low ESG score earns higher returns and vice versa.

Table 1. Mean excess returns of double-sorted portfolios.

Source: Author’s own analysis.

The mean excess returns of explanatory variables are reported in Table 2. The results further substantiate the above findings of Table 1. The mean returns of all variables are found to be positive indicating the presence of strong market, size, value, and ESG premium in Indian stock market.

Table 2. Mean excess returns of explanatory variables.

Source: Author’s own analysis.

The correlation matrix among the factors indicates that all pairwise correlation coefficients remain below 0.50 (see Table 3). These relatively low correlations suggest that multicollinearity is not a significant concern among the explanatory variables employed in this study.

Table 3. Correlation matrix.

Source: Author’s own analysis.

Regression results of FF three factor, augmented FF three factor (AFFTF) and four factor model

Next, we run both FFTF model, AFFTF model by replacing the value factor with ESG factor and a four-factor model that incorporate both value and ESG factor along with market β, and size. There are couple of reason why the value factor is replaced in AFFTF model. First, historically the redundancy of value factor is observed across the world including India. Second, we want to see if ESG can give better explanation if combined with other two factor such as market premium and size. After that we also investigate how the model works if all four factors are considered to check the effect on the excess average portfolio returns. The results of FFTF, AFFTF, and Four factor model are presented in Tables 4–6 respectively.

Table 4. FFTF model.

Regression results of R Pt − R Ft = a + b ( R Mt − R Ft ) + sSM B t + lLM H t + e t ..

Table 5. AFFTF model.

R Pt − R Ft = a + b ( R Mt − R Ft ) + sSM B t + esg L esg M H esg + e t .

Table 6. Four factor model.

R Pt − R Ft = a + b ( R Mt − R Ft ) + sSM B t + lLM H t + esg L esg M H esg + e t.Source: Author’s own analysis.

The findings indicate that all three models adequately capture the average portfolio returns as the estimated alpha coefficients are indistinguishable from zero and statistically insignificant across all portfolio groups with only a limited number of portfolios exhibiting significant t alphas. However, a comparison of the adjusted R² values, reveal differences in the explanatory power of the models. The Four-factor model exhibits superior performance relative to both the FFTF, and the AFFTF model, as evidenced by the higher adjusted R² values across the majority of portfolios. This improvement suggests that the model incorporating ESG factor along with the traditional risk factors enhance the model’s ability to explain variations in portfolio returns.

Across portfolios categories, the FFTF model demonstrates the strongest explanatory power for size-value sorted portfolios, particularly among small size and low-value stocks. The model explains approximately 96.6% of the variation in returns of the S/L portfolio which strongly loads on size factor compared to value factor (see Table 4). In contrast, AFFTF model provides stronger explanatory power for size-ESG sorted portfolios, with the results being most pronounced for small size—low ESG firms. Specifically, the model explains about 92% of the variation in the returns of S/L_esg portfolio (see Table 5). It also can be noted that these results are driven by positive factor loadings of size factor followed by ESG factor which are statistically significant. The Four-factor model offers the highest explanatory power, particularly for the size-value sorted portfolios, accounting for 96.7% of the variation in the returns of the S/L portfolio (see Table 6). This result can be attributable to the fact that, the inclusion of ESG factor along with size and value factor improves the explanation about the variation in portfolio returns which is well supported by the positive factor loadings and significant t-statistics.

An interesting observation can be made with respect to the individual dimensions of ESG. The FFTF model, AFFTF model, and the four-factor consistently exhibit higher explanatory power for size-Gov sorted portfolios, as reflected by comparatively larger average adjusted R² values. This result suggests that corporate Gov characteristics plays a relevant role in explaining stock return behavior. The results are in line with the evidence provided by Pedersen et al. (2021) in the case of US market, who documented a positive relationship between Gov and stock returns. They argue that firms with stronger Gov practices tend to exhibit better financial performance and profitability, thereby enhancing shareholder value and future stock returns.

Overall, the evidence suggests that co-existence of ESG factor along with size and value factor provides the best explanation about the variations in portfolio returns, which could have implications for investment decision-making if the investors want to include the sustainability factor in it. Hence, it can be concluded that ESG factor complements rather than replacing traditional risk factors in Indian set up.

Regression results of modified FF three factor model (MFFTF) with individual ESG component

In this section, we present the regression results of the individual ESG component such as Env, Soc, and Gov which is employed in place of the value factor to examine the robustness of our results in line with Maiti (2021). This will provide a clear explanation about the effect of individual components on the portfolios returns. Further, it will help to understand the contribution of each component toward ESG factor in explaining the variation in excess portfolio returns. Tables 7–9 reports the regression results for the MFFTF models in which the value factor (LMH) is replaced by Env, Soc, and Gov factors, respectively. The evidence shows that all three MFFTF models successfully account for the average returns of the portfolios, as none of the estimated alpha coefficients are significantly different from zero across the portfolio groups supported by insignificant t-statistics. The only exception is the B/L portfolio which exhibit significant t alphas under all the three models. Significant alphas are also observed for B/L_esg and B/L_env for two models that that replace the value factor with Env and Gov, respectively. In addition, B/L_gov portfolio reports significant t alphas under two model that replaces value with Soc and Env factor. The above findings are corroborated by the adjusted R² statistics which highlights variations in the explanatory power of the MFFTF models. Among the three MFFTF models, the specification that replaces the value with Gov factor demonstrates superior overall performance. The higher adjusted R² values observed across most portfolio indicate that the inclusion of Gov factor improves the model ability to explain variation in portfolio returns.

Table 7. MFFTF with Env.

R Pt − R Ft = a + b ( R Mt − R Ft ) + sSM B t + env L env M H env + e t .

Table 8. MFFTF with Soc.

R Pt − R Ft = a + b ( R Mt − R Ft ) + sSMB t + socL soc MH soc + e t.

Table 9. MFFTF with Gov.

R Pt − R Ft = a + b ( R Mt − R Ft ) + sSMB t + govL gov MH gov + e t.Source: Author’s own analysis.

Across the different portfolio categories, the model incorporating the Env factor exhibits the strongest explanatory power for the size-Env sorted portfolios, particularly among small-size firms with low Env scores. Specifically, the model explains approximately 92% of the variation in the returns of the S/L_env portfolio (see Table 7). Similarly, the model that replaces the value with Soc factor, provides the best fit for the size-Soc sorted portfolios. The highest explanatory power is observed for the S/L_soc portfolio, where nearly 92.8% of the variation in portfolio returns is accounted by the model (see Table 8). The strongest evidence however, emerges from the Gov based specification. The model replacing the value with the Gov factor provides the highest explanatory power for the size-Gov sorted portfolios explaining approximately 94.5% of the variation in the returns of the B/H_gov portfolio. (see Table 9). These findings suggest that Gov related characteristics plays an important role in explaining variations in portfolio returns.

Overall, the findings provide supportive evidence for the relevance of ESG factors in asset pricing models. In particular, the Gov factor offers the comparatively strongest explanatory power among the individual ESG components, suggesting its importance as a relevant factor in explaining stock return variations in Indian Stock market. These results are consistent with the findings of Bermejo Climent et al. (2021) who reported that the Gov pillar generates relatively higher and statistically significant portfolio returns in European market. It is due to the fact that firms usually disclose Gov practices in a more transparent manner, thus making such information easier for investors to assess and incorporate into their investment decision. In contrast, Env and Soc disclosures are often more subjective in nature, and depends upon the different rating methodologies, which may reduce their reliability and comparability across firms.

GRS test (Gibbons, Ross and Shanken)

This study applies the standardized (Gibbons et al., 1989) test to examine whether the intercepts (α) across the asset pricing models are jointly equal to zero. Following Fama and French (2015) the objective of GRS test is not merely to accept or reject an asset pricing model, but to compare the models by jointly considering the GRS F-statistics and average R² values. The model with lower F-statistics together with higher R² values indicate that the model better describes the cross-section of excess portfolio returns. The results of GRS test are depicted in Table 10. For nineteen out of thirty models p-values are greater than 0.05; meaning the null hypothesis that the intercepts are jointly zero and cannot be rejected. The results clearly show that the four-factor model outperforms the traditional FFTF and AFFTF models as reflected by relatively lower GRS F-statistics and high average R² values across all the portfolio combinations, except for the MC_ESG sorted portfolios. However, for the portfolios constructed using firm size and ESG disclosure, the AFFTF model provides best description of excess portfolio returns, indicating its superiority over two other models. Hence, it can be confirmed from the GRS results that inclusion of ESG factor improves the explanatory power of the model justifying the growing importance of sustainable investment in the Indian Stock market. These GRS test results also substantiate our findings from the regression models.

Table 10. GRS test.

*****5% level of significance. Source: Author’s own analysis.

Conclusion

The study contributes to the expanding body of literature on sustainable finance and ESG investing within Indian equity market. Using monthly data from April 2015 to March 2023 for Nifty 500 index, the study evaluates if the portfolios constructed using ESG factor generate excess average returns and if so, the extent to which ESG factors explain average excess stock returns relative to traditional risk factor such as size and value. In addition, it also provides a detailed analysis of the MFFTF with individual ESG components to evaluate their respective influence on stock performance. The findings report that the average excess returns across portfolios are largely positive indicating the presence of strong size, value, ESG, Env, Soc, and Gov effects in the Indian equity market. Moreover, the results reveal that portfolios consisting of small-cap stocks generates higher average excess returns compared to those of large-cap stocks with the return differentials being more pronounced for size-ESG sorted portfolios relative to other portfolios combinations.

Overall, the results indicate that the four-factor model that includes ESG exhibits superior performance with the exception of the MC_ESG sorted portfolios, relative to both FFTF and AFFTF as evidence by alpha estimates which are indistinguishable from zero and higher adjusted R². This suggests that inclusion of ESG factor in risk models improves the explanation about variations in portfolio returns, which could have implications for investment-decision making. Among the individual ESG components, the Gov factor offers the strongest explanatory power, suggesting its relevance as a determinant of stock returns in the Indian market compared to other two individual components such as Env and Soc. The robustness of the proposed models is further validated through the GRS test results. The GRS test results confirm that four factor model outperforms the traditional FFTF and AFFTF models, as evidenced by its comparatively lower GRS F-statistics and higher average R² values across most portfolio combinations. Therefore, it can be concluded that both the value and ESG effect coexist in explaining cross-sectional variations in stock returns, indicating that ESG factors complement rather than replacing FFTF.

A key contribution of this study is the attempt to introduce a robust and more sustainable model that incorporates ESG as an additional explanatory factor. Unlike most of the existing studies, which fails to take into account ESG disclosures into their asset pricing model, this study incorporates ESG factor along with its individual components while analyzing stock return pattern in the Indian stock market. Overall, the evidence provides supportive grounds for considering ESG factors alongside traditional factors such as size and value in investment decision-making. As a further check on the robustness of our findings, we re-estimated the regression models using the raw ESG scores rather than the ZCS measure. The results are not significantly different, which shows that our findings are not entirely dependent on the specific ZCS construction.

Despite its contributions, this study has some limitations, including the specific sample period, selected market indices, and the particular model specification. The study relies exclusively on ESG data sourced from Bloomberg, and reliance on a single data provider, may result in measurement error, considering the well-documented divergence in ESG sorting methodologies across various rating agencies. Although, efforts were made to ensure data integrity, the possibility of survivorship bias cannot be entirely ruled out, as the sample is based on the current constituents of the Nifty 500 applied retrospectively across the study period, which may exclude firms that were delisted, merged, or removed from the index during this period potentially tilting the portfolio composition toward surviving firms. The sample period encompasses the COVID-19 pandemic, during which market volatility was high and this may have influenced the observed relationship; although this has been taken into account to the extent possible, some residual effects may persist. Finally, as the analysis is confined to the Indian market, the generalizability of the findings to other emerging or developed market contexts remains uncertain. Future researcher may extend this study by using alternative ESG measures obtained from various rating agencies in order to examine the robustness of the findings with regard to different methodologies, as well as by constructing value-weighted portfolios and carrying out sector-specific analyses to test the sensitivity of these findings to portfolio construction and to identify any heterogeneity among industries that have different risk profiles. Furthermore, extending the analysis to include cross-country comparisons between emerging and developed markets, would help to establish the external validity of the results beyond the Indian context.

Practical implications

• The Indian Stock Market is one of the most vibrant emerging market that attracts investors from across the globe due to its distinct dynamics and characteristics. Further, it is equipped with various positives such as high MC, diversified investor community participation, and ease of access. The investors across the globe are always on the lookout for good investment strategy in order to capitalize on this strength by earning higher returns from their equity investments.
• Against the backdrop, the findings of the study can be a motivational source of information for those investors who want to design the portfolios by incorporating the combinations of traditional factors and sustainability factor such as ESG.
• This study furnishes useful insights for institutional investors, mutual fund managers, and asset management firms who aim to incorporate sustainability into their investment approach. The findings will help them in developing an investment strategy that takes into account both profitability and sustainability objectives.
• As India attracts more foreign portfolio investments, ESG disclosures may serve as an additional criterion for evaluating firms characterized by strong governance practices, lower risk exposure, and long-term growth aspects.
• The findings can assist regulators and policymakers in promoting sustainable finance initiatives by improving ESG disclosure standards, transparency, and corporate accountability.

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Footnotes

Edited by: Maurício Leite, Regional Community University of Chapecó, Brazil

Reviewed by: Mochamad Rizal Yulianto, Universitas Muhammadiyah Sidoarjo, Indonesia

Matúš Panko, Technical University of Košice, Slovakia