The Role of Human Capital in Explaining Asset Return Dynamics in the Indian Stock Market During the COVID Era

Abstract

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model’s performance on thirty-two portfolios of non-financial firms sorted by size, value, profitability, investment, and labor income growth in the Indian market over the period July 2010 to June 2023. Moreover, the current study extends the Fama and French five-factor model by incorporating a human capital proxy by labor income growth as an additional factor thereby proposing an augmented six-factor asset pricing model (HC6FM). The Fama and MacBeth two-step estimation methodology is employed for the empirical analysis. The results reveal that small-cap portfolios yield significantly higher returns than large-cap portfolios. Moreover, all six factors significantly explain the time-series variation in excess portfolio returns. Our findings reveal that the Indian stock market experienced heightened volatility during the COVID-19 pandemic, leading to a decline in the six-factor model’s efficiency in explaining returns. Furthermore, Gibbons, Ross, and Shanken (GRS) test results reveal mispricing of portfolio returns during COVID-19, with a stronger rejection of portfolio efficiency across models. However, the HC6FM consistently shows lower pricing errors and better performance, specifically during and after the pandemic era. Overall, the results offer important insights for policymakers, investors, and portfolio managers in optimizing portfolio selection, particularly during periods of heightened market uncertainty.

1. Introduction

The capital asset pricing model (CAPM, hereafter) of Sharpe (1964) and Lintner (1965) has been a foundational approach in the asset pricing literature for understanding the variability in asset returns (Khan and Afeef 2024). Conceptualized as a single-factor model that incorporates only market risk, CAPM has faced substantial criticism regarding its assumptions and empirical limitations (Bhandari 1988; Friend et al. 1978; Ross 1978; Levy 1983; Roll 1977). In response to this, Ross (1976) laid the foundation of arbitrage pricing theory (APT), a multifactor model that sought to address CAPM’s shortcomings. Subsequently, Cox et al. (1985) extended the CAPM framework by incorporating savings and capital formation to enhance optimal portfolio selection. Compared to CAPM, recently developed multifactor models have demonstrated improved performance in explaining the variability in portfolio returns (Thalassinos et al. 2023). While multifactor models have improved the explanation of asset returns, they remain insufficient in fully accounting for the complexities of return behavior, highlighting the need to consider additional anomalies (Kan et al. 2024). The persistent limitations of CAPM have led scholars to identify numerous return anomalies, further highlighting the necessity for more comprehensive models in asset pricing (Harvey et al. 2016). In the financial literature, numerous researchers have identified a wide range of financial anomalies that exert a persistent influence on asset returns (Linnainmaa and Roberts 2018). For instance, Hou et al. (2020) highlighted 452 financial anomalies and documented that approximately 35% of the anomalies exhibited a statistically significant relationship with asset returns.

Over the last several decades, numerous asset pricing models have been proposed in response to these anomalies. For instance, Fama and French (1993) extended the CAPM by introducing two additional factors, namely size and value premiums, resulting in the well-known three-factor model (FF3FM, hereafter). Building upon this, Carhart (1997) incorporated a momentum factor into the FF3FM, giving rise to the four-factor asset pricing model. Later, drawing on the dividend discount model (DDM) of Miller and Modigliani (1961), Fama and French (2015) proposed a five-factor model (FF5FM) by adding investment and profitability factors to the FF3FM. Furthermore, advancing the model, Fama and French (2018) introduced a momentum-based six-factor model (FF6FM) by integrating the momentum factor into the FF5FM.

A growing body of academic literature has recognized human capital as a critical component of total wealth and a significant determinant of expected stock returns which is not fully captured by the traditional market beta (Qin 2002; Campbell 1996). Studies, such as Jagannathan and Wang (1996) and Jagannathan et al. (1998), demonstrate that incorporating human capital betas improves the explanatory power of the CAPM in both United States and Japanese markets. In emerging economies, human capital plays a vital role in addressing persistent issues, like poverty, inequality, and political instability (Amar and Pratama 2020; Wang et al. 2020; Vianna and Mollick 2018) and is considered a central driver of long-term economic growth and structural transformation (Romer 1990; Diebolt and Hippe 2019; Tridico 2007). Human capital is estimated to account for up to 90% of total wealth (Lustig et al. 2013). Recent studies, including Barras (2019), Park et al. (2021), and Roy and Shijin (2018, 2019), highlight the growing recognition of intellectual capital, namely human capital, as a key factor in the asset pricing model. Campbell (1996) highlighted that human capital represents the true wealth of the economy; therefore, this factor should be integrated into the asset pricing models. In recent years, several anomalies have been incorporated into asset pricing frameworks to capture variability in asset or portfolio returns. One critical risk-mimicking factor that remains overlooked is human capital (Prasad et al. 2024). Subsequently, several studies have demonstrated the importance of human capital in the multifactor model in the international market (Belo et al. 2017; Kim et al. 2011; Kuehn et al. 2017; Lettau et al. 2019; Khan et al. 2022; Tambosi Filho et al. 2022; Khan et al. 2023; Khan et al. 2025). Therefore, the above studies in various international markets support the human capital (HC)-based asset pricing model and confirm its superior performance in global markets. Some of the notable contributions to the six-factor multifactor model are from Shijin et al. (2012), who state that the HC-based multifactor model offers more predictive returns than CAPM.

Over the past few decades, the world has experienced unprecedented disruptions, causing severe impacts on both human life and the global economy. Among various global crises, the COVID-19 pandemic stands out as one of the most destructive and widespread, causing long-lasting and, in some cases, irreversible economic recessions across the world (Reuters 2020). Since January 2020, COVID-19 has spread worldwide to varying degrees, producing serious issues and crises for the world’s financial markets and economy. In the recent past, wars, natural disasters, financial crises, and the observance of recent pandemics have enhanced the level of uncertainty in the market, which exponentially increases the risk aversion level among investors (Baker et al. 2020). On 11 March 2020, the World Health Organization (WHO) officially declared COVID-19 a global pandemic. The emergence of COVID-19 crashed the market, and the spillover transmitted to other markets, which caused instability around the stock markets (Haroon and Rizvi 2020; Zaremba et al. 2020; Onali 2020; Mzoughi et al. 2020; Contessi and De Pace 2021; He et al. 2020; Liu et al. 2020a). In March 2020, the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE) collectively suffered a 23% contraction in their total market capitalization (Singh and Neog 2020). Simultaneously, a growing body of research highlights the detrimental effects of the COVID-19 pandemic on global stock markets (Setiawan et al. 2021; Yilmazkuday 2021; Setiawan et al. 2022).

Recently, several studies tested the performance of the asset pricing model in the Indian equity market. For instance, Harshita and Yadav (2015) tested the efficiency of competing asset pricing models in the Indian equity market. The authors documented that in all cases, FF3FM performs better than CAPM and FF5FM. Mishra and Barai (2024) found that entropy, market, size, and value factors substantially account for the fluctuations in excess portfolio returns. Mohanasundaram and Kasilingam (2024) found that firms with low ESG premiums yield higher excess portfolio returns than firms with high ESG premiums. Shegal et al. (2024) found that the behavioral asset pricing model outperforms FF5FM.

The aforementioned studies have predominantly contributed to the empirical literature on asset pricing, but these studies failed to consider human capital’s role in asset pricing. Furthermore, these studies also overlooked the performance of the asset pricing model during the COVID-19 pandemic. Additionally, most of the existing studies take monthly data to construct a set of portfolios, while studies taking daily data to construct portfolios remain scarce. To the best of our knowledge, this study is the first to explore the relevance of the HC6FM (human capital six-factor model) in India’s equity market during the COVID-19 era. Concurrently, Prasad et al. (2024) established a future avenue for researchers to explore the significance of the HC6FM during the COVID-19 pandemic, which is considered to be the prime motivation of this study. Furthermore, among Asian emerging markets, the Indian economy has experienced remarkable growth across all sectors over the past decade. Similarly, India currently ranks fifth1 in the global gross domestic product (GDP) ranking and is expected to emerge as the third-strongest economy by 20302. As the fifth-largest equity market in the world by market capitalization (World Bank 2023), India has become increasingly integrated with global capital flows while maintaining unique domestic characteristics. Its rapid expansion, driven by economic reforms and technological adoption, has made it a focal point for testing asset pricing models in emerging economies (Bekaert and Harvey 2003; Chittedi 2015). More specifically, the market’s sensitivity to macroeconomic shocks, policy interventions, and global volatility renders it an ideal setting for studying risk factor behavior during periods of heightened uncertainty (National Stock Exchange of India (NSE) 2022; Securities and Exchange Board of India (SEBI) 2023). Additionally, it is pertinent to note that the human factor, as the sixth factor in the asset pricing model, has received significant attention in the global market, but that it has not received significant attention in the Indian market in recent times after 2017 (Prasad et al. 2024). More specifically, India, with its vibrant and rapidly evolving stock market and diverse offerings of stocks from various sectors and industries, requires an investigation of the effectiveness and superiority of the six-factor model.

Therefore, to bridge this gap, the current study contributes to the existing literature in many ways. First, our study is the first which examine the role of human capital in the asset pricing model in the Indian stock market during the COVID-19 pandemic. Second, daily data are employed to form a set of thirty-two portfolios. Third, earlier studies predominantly used monthly data to construct portfolios, while studies employing daily data for portfolio construction, particularly those constructing 2 × 2 × 2 × 2 × 2 portfolios, remain scarce. Fourth, the two-stage estimation method of Fama and MacBeth (1973) is employed to examine the risk–return relationship. Fifth, this study employs the Gibbons et al. (1989) test to evaluate the degree of mispricing and compare the performance of the CAPM, FF3FM, FF5FM, and the HC6FM in the Indian equity market during the COVID-19 era. Employing Fama and MacBeth (1973) regression has many advantages over other methodologies of asset pricing. First, this approach is widely used in the empirical literature to capture the effect of different risks beyond market risk (Jagannathan and Wang 1996; Zada et al. 2018). Second, this approach provides a robust framework for testing whether anomalies persist after controlling for other risk factors (Jegadeesh and Titman 1993). Third, this methodology highlights the relevance of identifying whether certain risk factors influence excess portfolio returns, which has significant insights for investors and portfolio managers (Cochrane 1996).

The estimation yields several notable findings based on monthly data spanning from July 2010 to June 2023. First, the results indicate that the COVID-19 pandemic induced significant volatility in the Indian stock market, leading to inefficient returns accompanied by heightened risk across most portfolios. Notably, among the thirty-two constructed portfolios, small-cap portfolios consistently generate higher returns compared to large-cap portfolios. Furthermore, the market premium substantially captures variability in portfolio returns. Moreover, other factors also exhibit a statistically significant relationship with excess portfolio returns, reinforcing their relevance in asset pricing within the Indian context. Our findings indicate that while the six-factor model captures portfolio return variability well across the full sample, its performance diminishes significantly during the COVID-19 and post-pandemic periods.

The organization of this paper is as follows: Section 2 presents a literature review focusing on risk factors and asset returns. Section 3 covers the data, portfolio development, and methodological approach. Section 4 focuses on the empirical analysis and Section 5 provides the conclusions.

2. Literature Review

2.1. Theoretical Framework and Model Development in Asset Pricing

The modern portfolio theory (MPT) of Markowitz (1952, 1959) is widely regarded as the origin of asset pricing. This theory emphasizes the role of utility and risk in identifying optimal investment portfolios by adjusting portfolio weights. Tobin (1958) contributed further to asset allocation theory by introducing the “separation theorem”, which holds that risk-averse investors balance their portfolios between a risk-free asset and a selection of risky assets. Using the Tobin–Markowitz mean-variance framework, Sharpe (1964) advanced the development of the CAPM, which defines a theoretical relationship between expected returns and risk. The CAPM has been expanded in various ways to incorporate multi-period portfolio selection (Mossin 1966; Samuelson 1969). Later, Fama (1970) introduced the concept of the efficient market hypothesis (EMH), grounded in the principles of CAPM, which asserts that asset prices fully reflect all available information. The author argued that if predicted returns on stocks are calculated using Sharpe, Lintner, and Mossin’s model, then the prices of securities accurately reflect all information. This is attributed to the possibility that the stock market may be in equilibrium, with all available information being incorporated into prices, allowing additional rewards for taking additional risks.

An increasing number of studies have highlighted several financial anomalies that help to explain variations in asset returns. These include the price-to-earnings ratio of Basu (1977), the size effect highlighted by Banz (1981), the earnings-to-price ratio by Basu (1983), the debt-to-equity ratio proposed by Bhandari (1988), and the book-to-market equity ratio introduced by Rosenberg et al. (1985). In a related development, Connor and Korajczyk (1989) proposed an equilibrium version of the APT, showing that both the traditional and equilibrium APT models offer similar explanatory power for portfolio return variability. According to Fama and French (1992), market beta, firm size, and book-to-market equity emerge as key variables in explaining the cross-sectional variation in expected returns. Subsequently, Jegadeesh and Titman (1993) investigated stock market efficiency and reported the momentum effect, where stocks that showed strong performance (recommended for purchase) and weak performance (recommended for sale in the past) tend to earn significant positive returns in subsequent periods. In a similar vein, several studies document the extent to which mutual fund returns remain persistent across strategies over differing temporal horizons (Hendricks et al. 1993; Goetzmann and Ibbotson 1994; Brown et al. 1992; Wermers 1996; Elton et al. 1993, 1996b).

Subsequently, Papp (2022) examined the role of risk premium in excess portfolio returns in BRICS economies. Using a linear regression model, their findings indicate that risk-related factors in BRICS countries incorporate more pricing information regarding anomalies, like the mispricing factor. Son and Lee (2022) proposed a novel latent asset pricing model estimating risk exposures through firm-specific data. Using a graph convolutional Newton (GCN) framework, their approach consistently outperforms conventional asset pricing techniques. In another comparative study, Kolari et al. (2022) examined the zero-beta CAPM, relative to competing asset pricing models. Using a global sample, they found that ZCAPM demonstrates superior explanatory power in terms of return dispersion. Hu (2022) investigated the effect of the COVID-19 pandemic on the U.S. stock market using the FF5FM. Their study found that the model’s explanatory power increased during the pandemic, as more industries were influenced by known factors. Moreover, the author remarked that certain industry characteristics remained stable, while new factors emerged, suggesting potential refinements to the model for better crisis adaptation.

Anuno et al. (2023) considered the applicability of the FF5FM in Timor-Leste and emphasized that the SMB and HML factors contribute negatively to the excess returns, while the profitability factor contributes positively to explaining the returns. Eun et al. (2023) examined the dual role of country factors in asset pricing. Their findings indicate that the country factor significantly explains the variability in asset returns. Furthermore, they documented that, in asset pricing, the country factor performs well, while the local factor performs worse. Wei et al. (2023) examined the performance of CAPM, FF3FM, and FF5FM across regions and industries during COVID-19. The authors employed various statistical methods, namely regression and correlation analysis, to assess model effectiveness. The findings provide insights into market behavior and the impact of the pandemic on asset pricing. Gao (2023) analyzed the impact of COVID-19 on the U.S. stock market using the FF5FM. Applying multiple linear regression, their findings indicate increased market volatility and returns, with a stronger market value effect and enhanced influence of profitability and investment factors. In the context of risk, Kausar et al. (2024) analyzed the factors of idiosyncratic risk (IR) within BRICS nations, revealing that firms with greater IR are associated with diminished returns. Additionally, Mohanasundaram and Kasilingam (2024) used the Fama and MacBeth (1973) two-step estimation procedure to examine the role of ESG in the asset pricing framework; their findings indicate that ESG considerations enhance portfolio performance.

2.2. Human Capital: A Key Factor in Asset Pricing Models

The intertemporal consumption-based asset pricing model (ICAPM), initially proposed by Lucas (1978) and further extended by Breeden (1979), has been widely applied in the financial literature to bridge asset valuation with intertemporal consumption and investment decisions. The conceptual underpinnings of this model can be traced back to Fisher’s (1907) consumption-based theory of interest rates, which posits that the equilibrium interest rate reflects the trade-off between the marginal utility of consumption today and in the future. This paradigm integrates macroeconomic preferences with financial market dynamics, offering a theoretically elegant approach to asset pricing. As Mayers (1972) documented, individuals may hold a significant portion of their wealth, which cannot be easily traded in financial markets. Consequently, the existence of these assets affects individuals’ portfolio selection and investment strategies. Similarly, Kim et al. (2011) suggested that HC has predictive value in explaining asset returns. Another study documented that the CAPM’s performance in explaining asset returns increases when human capital replaces market returns (Jagannathan and Wang 1996).

Concurrently, several studies have demonstrated the importance of human capital in the multifactor model (Belo et al. 2017; Kim et al. 2011; Kuehn et al. 2017; Lettau et al. 2019; Khan et al. 2022; Roy and Shijin 2018; Maitai and Balakrishnan 2018; Maiti and Vukovic 2020; Khan and Afeef 2024). Similarly, Maharani and Narsa (2023) found that intellectual capital (IC) plays a significant role in explaining asset returns in the Indonesian stock market. More specifically, Khan et al. (2023) highlighted the importance of human capital in investment decisions and document the size, value, and human capital value of the firms. Later, Shijin et al. (2012) highlighted that the human capital-augmented six-factor model surpasses the CAPM in forecasting asset returns. Maitai and Balakrishnan (2018) documented evidence that the six-factor model provides a more robust explanation for asset return variability over time compared to the FF3FM and FF5FM. Recently, Prasad et al. (2024) examined the role of HC in the asset pricing model, employing the generalized method of movement (GMM) framework. Their findings indicate that human capital successfully prices time-series variability in excess portfolio returns.

3. Data Collection and Research Methodology

In this study, we examine the performance of the HC6FM in the Indian equity market over the full sample period as well as during the COVID-19 pandemic, using data from non-financial companies listed on the National Stock Exchange (NSE) of India. Daily stock price data were collected from July 2010 to June 2023, while annual firm-level financial statement data spanning from 2010 to 2022 were used for portfolio construction. For the calculation of market risk premium, we took daily data from the Nifty-500 index and the daily yield on Indian Treasury bills. All data were sourced from Thomson Reuters DataStream. Moreover, to ensure the robustness of our results, we applied several data screening filters. For instance, companies with inadequate or missing values for market capitalization, profitability, investment, and human capital were excluded from the sample. Additionally, firms with irregularities in their daily closing prices, those involved in mergers, incorporations, or amalgamations, and those with a negative book value for equity were also removed from the sample.

Additionally, we acknowledge the potential impact of survivorship bias, which is particularly prevalent in emerging markets due to data inconsistencies and firm delisting. In our study, survivorship bias may arise from the exclusion of delisted firms, a limitation commonly observed in empirical asset pricing research in such markets (Bekaert and Harvey 2000). However, to partially mitigate this concern, we followed the recommendation of Elton et al. (1996a), who suggested that careful adjustment of the sample size can help reduce the effects of survivorship bias. Accordingly, we applied rigorous data screening criteria, resulting in a final sample of 178 non-financial firms used for portfolio construction and empirical analysis. To further evaluate the robustness and empirical validity of the HC6FM, we conducted the analysis across multiple time horizons. First, we examined the entire sample period from July 2010 to June 2023. Second, to explore the model’s performance under conditions of heightened uncertainty, we divided the data into two sub-periods, namely the COVID-19 period and the post-COVID-19 period.

3.1. Portfolio Construction

Following the methodology of Fama and French (2015), we constructed portfolios by first categorizing companies into small (S) and big (B) based on market capitalization. Then, within each size classification, firms were further grouped by book-to-market ratio into high (H) and low (L) value portfolios. Next, these portfolios were divided based on profitability into robust (R) and weak (W) categories. The profitability-sorted portfolios were then further classified based on investment strategy into conservative (C) and aggressive (A) groups. Finally, the resulting portfolios were sorted into low (Lhhr) and high (Hhr) labor income growth categories. Moreover, using this five-dimensional sorting approach (2 × 2 × 2 × 2 × 2), we constructed thirty-two portfolios and derived six risk factors, namely SMB (small-minus-big), HML (high-minus-low), RMW (robust-minus-weak), CMA (conservative-minus-aggressive), and LBR (low-minus-high labor income growth rate). Furthermore, Appendix A show the computation of the variables and factor construction.

3.2. Fama and MacBeth (1973) Regression

The recent empirical literature on asset pricing framework reflects a strong interest in the Fama and MacBeth (1973) two-step regression approach, particularly for its application in asset pricing models (Zada et al. 2018; Khan and Afeef 2024; Khan et al. 2025). To assess risk premia, Fama and MacBeth (1973) proposed a two-step regression process. The first stage regresses portfolio returns on common risk factors over time to estimate betas. In the second stage, these betas serve as explanatory variables in a cross-sectional regression to identify the risk premiums. However, Black et al. (1972) and Fama and MacBeth (1973) acknowledged that this approach faces an errors-in-variables (EIV) problem due to the estimation error in betas being estimated in the first step rather than observed directly. This issue is typically mitigated by employing diversified portfolios rather than using individual stock returns. Furthermore, Fama and MacBeth (1973) addressed the issue of residual cross-correlation by implementing monthly cross-sectional regressions, as opposed to averaging returns across the full sample. This technique accommodates time-varying betas and facilitates dynamic estimation. The initial time-series regression step generates beta coefficients, which are subsequently used in the second-step regressions to estimate expected returns. Furthermore, the standard Fama and MacBeth (1973) regression is summarized as follows:

$$R_{it}={\alpha }_i+{\beta }_{i1}{f}_{1t}+\dots +{\beta }_{ik}{f}_{Kt}+{ϵ}_{it}, i=1,., N, t=1,\dots ,T,$$

(1)

where $R_{it}$ denotes the return on asset i during period t, $f_{1t}$ is the realization of the jth factor in period t, ${ϵ}_{it}$ demonstrates the distribution of error terms, and N and T respond to the number of assets and periods, respectively.

Furthermore, the basic hypothesis underpinning asset pricing is specified as follows using the two-pass method:

$$H_0: E\left[R_t\right]={{\gamma }_{0}1}_N+{\gamma }_1{\widehat{\beta }}_1+\dots +{\gamma }_K{\widehat{\beta }}_K,$$

(2)

where $E\left[R_t\right]$ denotes the N-dimensional vector of expected returns, while ${\gamma }_o,\dots , {\gamma }_k$ represent the risk premia associated with each factor. More specifically, Fama and MacBeth (1973) procedure consists of two steps: First, each asset return is regressed by analyzing the return series against one or more systematic risk drivers, yielding the asset exposure to those factors, denoted as ($\widehat{\beta }$). Let $\widehat{\beta }=(\widehat{\beta ,}\dots , \widehat{{\beta }_K)}$ represent the resulting $N\times K$ matrix of the ordinary least squares (OLS) coefficient estimates. In the second step, a rolling window regression is applied in each period, regressing asset returns on the estimated betas from step one, as shown in Equation (2).

Therefore, this study follows the Fama and MacBeth (1973) approach to evaluate the performance of the HC6FM in the Indian equity market. In the first step (time-series regression), excess returns of the portfolios are regressed on the six risk factors to obtain factor loadings (betas), as follows:

$$R_{it}-R_{ft}={\alpha }_0+{\beta }_{1}MK{T}_t+{\beta }_{2}SM{B}_t+{\beta }_{3}HM{L}_t+{\beta }_{4}RM{W}_t+{\beta }_{5}CM{A}_t+{\beta }_{6}LB{R}_t+{ϵ}_{it}$$

(3)

where $R_{it}$ is the portfolio return i at time t and $R_{ft}$ is the risk-free rate. $MK{T}_t$ $SM{B}_t$ $HM{L}_t$ $RM{W}_t$ $CM{A}_t$, and $LB{R}_t$ denote the market size value, profitability, investment, and human capital factor, respectively, and ${\epsilon }_{it}$ is the error term.

Furthermore, we perform rolling window two-pass regression, and in each rolling window (e.g., 36 months), the excess returns of N portfolios are regressed on the six factors to obtain the estimated betas, as follows:

$$R_{it}-R_{ft}={\alpha }_t+{{\rm Y}}_{mkt,t}{\widehat{\beta }}_{mkt}+{{\rm Y}}_{SMB,t}{\widehat{\beta }}_{SMB}+{{\rm Y}}_{HML,t}{\widehat{\beta }}_{HML}+{{\rm Y}}_{RMW,t}{\widehat{\beta }}_{RMW}+{{\rm Y}}_{CMA,t}{\widehat{\beta }}_{CMA}+{{\rm Y}}_{LBR,t}{\widehat{\beta }}_{LBR}+{\epsilon }_{it}$$

(4)

where $R_{it}-R_{ft}$ represents the excess portfolio return, calculated as the difference between portfolio returns $R_{it}$ and the risk-free rate $R_{ft}$, and $\widehat{\beta }$ shows the estimated factor loading of market, size, value, profitability, investment, and human capital factor derived from the first-stage time-series regression for each of the six factors. The regression coefficients ${{\rm Y}}_{mkt,t}$, ${{\rm Y}}_{SMB,t}$, ${{\rm Y}}_{HML,t}$, ${{\rm Y}}_{RMW,t}$, ${{\rm Y}}_{CMA,t}$, and ${{\rm Y}}_{LBR,t}$ notate the risk premium on the estimated factor loadings.

4. Results and Analysis

4.1. Descriptive Analysis of Portfolios

Figure 1 shows the time-series plots of portfolio returns for small firms for the period spanning from 2010 to 2022. A noticeable pattern across most plots is the presence of return volatility clustering around the COVID-19 pandemic in 2020. For instance, SLRAL exhibits heightened fluctuations and deep troughs, reflecting the firms’ exposure to downturns when pursuing aggressive investment strategies despite having robust profitability. SLWAL and SLWAH also show sharp spikes during COVID-19, indicating greater vulnerability. Comparatively, firms with conservative investment tend to show more stable series. Overall, the figure demonstrates that among small firms, aggressive investment and weak profitability intensify downside risk during uncertainty periods, while firms with robust fundamentals show relatively smoother behavior. Similarly, Figure 2 shows time-series plots of portfolio returns for big firms for the period spanning from 2010–2022. Compared to small firms, big firms show relatively more stability in return patterns, but certain combinations, like BLRAL and BLRCL, still demonstrate high volatility, especially in late 2012 and 2020. This suggests that even larger firms with aggressive investment strategies are sensitive to economic shocks.

Table 1. Descriptive statistics of portfolios.

Portfolio	Mean	SD	Min	Max	Obs
SLWCL	0.0008	0.0181	−0.1214	0.1198	3422
SLWCH	0.0019	0.0179	−0.1342	0.4174	3422
SLWAL	0.0011	0.0206	−0.1337	0.6172	3422
SLWAH	0.0014	0.0227	−0.1374	0.7654	3422
SLRCL	0.0011	0.0220	−0.1532	0.5984	3422
SLRCH	0.0012	0.0216	−0.1273	0.4618	3422
SLRAL	0.0008	0.0156	−0.1536	0.0846	3422
SLRAH	0.0012	0.0197	−0.1628	0.4039	3422
SHWCL	0.0010	0.0185	−0.1596	0.1520	3422
SHWCH	0.0010	0.0190	−0.1795	0.3690	3422
SHWAL	0.0022	0.0268	−0.1317	1.1645	3422
SHWAH	0.0017	0.0195	−0.1487	0.3847	3422
SHRCL	0.0006	0.0152	−0.1496	0.2207	3422
SHRCH	0.0009	0.0162	−0.1317	0.1136	3422
SHRAL	0.0011	0.0223	−0.1591	0.8158	3422
SHRAH	0.0028	0.0224	−0.1342	0.7183	3422
BLWCL	0.0006	0.0186	−0.1308	0.5306	3422
BLWCH	0.0007	0.0156	−0.1619	0.1220	3422
BLWAL	0.0009	0.0167	−0.1231	0.6705	3422
BLWAH	0.0008	0.0162	−0.1557	0.4972	3422
BLRCL	0.0002	0.0186	−0.1754	0.1465	3422
BLRCH	0.0004	0.0172	−0.1861	0.2136	3422
BLRAL	0.0008	0.0228	−0.2034	0.3051	3422
BLRAH	0.0004	0.0164	−0.1424	0.1936	3422
BHWCL	0.0007	0.0169	−0.1149	0.4022	3422
BHWCH	0.0005	0.0123	−0.1119	0.0597	3422
BHWAL	0.0007	0.0144	−0.1477	0.3500	3422
BHWAH	0.0007	0.0151	−0.1231	0.5395	3422
BHRCL	0.0006	0.0149	−0.1324	0.2951	3422
BHRCH	0.0004	0.0164	−0.1406	0.4568	3422
BHRAL	0.0004	0.0148	−0.1344	0.2057	3422
BHRAH	0.0009	0.0185	−0.1315	0.5651	3422

Note: In this table, SD stands for standard deviation; Min and Max represent the lowest and highest observed values, respectively, and obs shows the number of observations. The abbreviations of portfolio (e.g., SLWCL, SHRAH, etc.) are explained in Appendix A.

summarizes the key statistics of thirty-two portfolios. Or findings reveal that among small portfolios, SHRAH exhibits the highest average return (0.0028) along with the highest standard deviation (0.0224). Furthermore, SHWAL reports the second-highest value among these groups following the highest value of standard deviation. Conversely, among these portfolios, SHRCL reports the lowest mean value of 0.0006 along with the lowest standard deviation value of 0.0152. Furthermore, among big portfolios, BHRAH reports the highest mean value along with the highest standard deviation value, while BLRCL reports the lowest mean and the highest value of standard deviation. These findings confirm that small stocks report a considerably higher mean value along with the standard deviation value as compared to big portfolios. Such findings are in line with the findings of Fama and French (1992, 1993, 2015), who documented that small stocks considerably earn higher returns than big stocks, along with the highest value of standard deviation.

Table 2. Descriptive statistics of risk factors.

	Mean	SD	Min	Max	Obs
RM-RF	−0.0655	0.0205	−0.1761	0.0352	3422
SMB	0.0007	0.00681	−0.0964	0.1800	3422
HML	−0.0003	0.0042	−0.1060	0.053	3422
RMW	−0.0001	0.0023	−0.0220	0.027	3422
CMA	0.0005	0.0016	−0.0221	0.0114	3422
LBR	0.007	0.0014	−0.0406	0.0127	3422

Note: Refer to Table 1.

shows the summary statistics of the risk factors. The market risk factor reports the highest mean value among other risk factors. Such findings indicate that market risk premiums yield higher returns along with the highest value of standard deviation. Furthermore, the labor income growth premium reports the second-highest mean value following the market risk premium. Additionally, this factor reports the lowest standard deviation value as compared to other risk factors. Moreover, size and investment premium report the third- and fourth-highest mean values among the group, along with standard deviation values. Conversely, value and profitability report a negative mean value as compared to other risk factors.

Table 3. Correlation matrix.

	RM-RF	SMB	HML	RMW	CMA	LBR
RM-RF	1
SMB	−0.0023	1
HML	−0.0047	−0.8523	1
RMW	−0.0331	−0.5907	0.6532	1
CMA	−0.0006	0.2826	−0.3573	−0.5657	1
LBR	0.0139	−0.0028	−0.0337	−0.2671	0.5655	1

illustrates the correlations among the study variables, indicating that size, value, profitability, and investment premiums are negatively correlated with the market risk premium, whereas the human capital premium shows a positive correlation with the market risk premium. Additionally, all correlation coefficients are below 0.80, confirming the absence of multicollinearity concerns (Gujarati 2009).

4.2. Fama and MacBeth (1973) Regression for Full Sample and COVID-19 Period

The Fama and MacBeth (1973) regression results presented in

Table 4. Human capital six-factor model: full sample performance insights.

	Intercept	MKT	SMB	HML	RMW	CMA	LBR	Adj-R2	F-Stat
SLWCL	−0.016	0.747	0.472	0.830	−2.072	0.041	4.922	0.5151	601.4478
	(−16.309) ***	(50.484) ***	(5.455) ***	(5.607) ***	(−10.536) ***	(0.156)	(18.695) ***
SLWCH	−0.017	0.721	1.164	1.168	−2.207	2.776	−5.064	0.5508	694.2576
	(−18.757) ***	(52.413) ***	(14.443) ***	(8.478) ***	(−12.064) ***	(11.313) ***	(−20.680) ***
SLWAL	−0.016	0.749	0.828	0.188	−4.689	−4.843	−1.854	0.5574	712.8815
	(−15.883) ***	(49.522) ***	(9.338) ***	(1.243)	(−23.295) ***	(−17.949) ***	(−6.883) ***
SLWAH	−0.017	0.728	0.491	−0.176	−4.577	−4.738	−3.588	0.5006	567.6558
	(−15.108) ***	(43.318) ***	(4.987) ***	(−1.047)	(−20.471) ***	(−15.800) ***	(−11.996) ***
SLRCL	−0.016	0.749	1.135	−1.131	0.576	−2.164	−1.030	0.4594	481.3671
	(−14.088) ***	(43.560) ***	(11.271) ***	(−6.569) ***	(2.519) **	(−7.053) ***	(−3.367) ***
SLRCH	−0.016	0.749	0.1211	−1.448	−0.162	−2.150	−1.402	0.3795	346.7658
	(−12.808) ***	(41.024) ***	(1.131)	(−7.914) ***	(−0.670)	(−6.598) ***	(−4.313) ***
SLRAL	−0.015	0.766	0.797	0.169	−0.167	−1.002	0.868	0.4762	514.9120
	(−15.563) ***	(53.099) ***	(9.428) ***	(1.168)	(−0.871)	(−3.895) ***	(3.379) ***
SLRAH	−0.016	0.748	0.915	−0.921	−0.284	−2.605	−0.569	0.4832	529.5348
	(−15.199) ***	(47.470) ***	(9.914) ***	(−5.833) ***	(−1.357)	(−9.265) ***	(−2.031) **
SHWCL	−0.016	0.748	1.339	2.257	−1.639	−0.979	1.432	0.4189	408.5613
	(−14.565) ***	(45.896) ***	(14.029) ***	(13.827) ***	(−7.560) ***	(−3.370) ***	(4.938) ***
SHWCH	−0.015	0.752	1.310	2.473	−1.331	−0.471	1.240	0.4034	383.2050
	(−13.784) ***	(44.780) ***	(13.315) ***	(14.698) ***	(−5.960) ***	(−1.572)	(4.147) ***
SHWAL	−0.017	0.732	2.448	0.918	−2.229	−4.536	−3.887	0.5281	633.7345
	(−13.704) ***	(39.978) ***	(22.827) ***	(5.006) ***	(−9.156) ***	(−13.886) ***	(−11.907) ***
SHWAH	−0.015	0.761	1.978	1.944	−1.630	−2.033	−1.130	0.4787	520.1856
	(−13.786) ***	(47.587) ***	(21.095) ***	(12.128) ***	(−7.661) ***	(−7.124) ***	(−3.966) ***
SHRCL	−0.016	0.750	1.432	1.466	−1.215	−1.503	−0.072	0.5165	604.9256
	(−18.065) ***	(55.204) ***	(17.992) ***	(10.775) ***	(−6.729) ***	(−6.205) ***	(−0.301)
SHRCH	−0.015	0.757	1.374	1.717	−0.853	−0.524	−0.471	0.4698	501.8840
	(−15.774) ***	(51.637) ***	(15.993) ***	(11.687) ***	(−4.377) ***	(−2.006) **	(−1.803) *
SHRAL	−0.016	0.754	1.753	1.125	−1.679	−3.118	−2.937	0.4660	494.3445
	(−13.667) ***	(43.497) ***	(17.254) ***	(6.476) ***	(−7.281) ***	(−10.077) ***	(−9.515) ***
SHRAH	−0.019	0.687	2.726	2.291	−0.813	−0.081	−3.889	0.5048	577.3112
	(−17.599) ***	(42.747) ***	(28.962) ***	(14.237) ***	(−3.808) ***	(−0.283)	(−13.595) ***
BLWCL	−0.017	0.740	0.780	0.726	−1.584	−3.140	−4.310	0.5032	573.5707
	(−16.651) ***	(49.204) ***	(8.862) ***	(4.823) ***	(−7.922) ***	(−11.710) ***	(−16.106) ***
BLWCH	−0.017	0.734	0.639	0.751	−0.501	−5.1485	2.417	0.5016	570.0349
	(−17.886) ***	(52.960) ***	(7.864) ***	(5.410) ***	(−2.717) **	(−20.809) ***	(9.791) ***
BLWAL	−0.018	0.714	0.513	0.969	0.642	−0.173	−1.048	0.4033	383.1169
	(−17.330) ***	(46.691) ***	(5.726) ***	(6.325) ***	(3.155) ***	(−0.636)	(−3.851) ***
BLWAH	−0.017	0.739	0.783	0.351	−0.130	−1.304	−2.215	0.4705	503.2758
	(−17.189) ***	(51.236) ***	(9.258) ***	(2.433) ***	(−0.677)	(−5.067) ***	(−8.621) ***
BLRCL	−0.016	0.745	0.353	1.955	−2.706	−1.161	−0.352	0.4038	383.9604
	(−14.738) ***	(44.862) ***	(3.633) ***	(11.758) ***	(−12.258) ***	(−3.919) ***	(−1.190)
BLRCH	−0.0159	0.757	0.506	2.010	−2.363	−0.929	−0.573	0.4410	447.0834
	(−14.924) ***	(48.894) ***	(5.588) ***	(12.962) ***	(−11.471) ***	(−3.364) ***	(−2.081) **
BLRAL	−0.014	0.770	0.761	4.038	−3.727	−0.455	−2.117	0.4204	411.1000
	(−11.346) ***	(41.437) ***	(6.996) ***	(21.698) ***	(−15.089) ***	(−1.373)	(−6.402) ***
BLRAH	−0.015	0.757	0.197	1.356	−2.210	−1.427	−0.442	0.4454	455.0700
	(−15.198) ***	(49.959) ***	(2.218) **	(8.929) ***	(−10.963) ***	(−5.280) ***	(−1.640)
BHWCL	−0.016	0.742	−0.103	0.181	−1.746	−1.931	−1.329	0.4205	411.1779
	(−15.463) ***	(47.656) ***	(−1.138)	(1.162)	(−8.433) ***	(−6.951) ***	(−4.794) ***
BHWCH	−0.016	0.751	−0.216	0.174	−1.101	−0.617	0.546	0.5265	629.7362
	(−18.949) ***	(60.457) ***	(−2.976) **	(1.401)	(−6.663) ***	(−2.787) ***	(2.468) **
BHWAL	−0.016	0.752	0.238	0.156	−1.478	−1.866	−1.466	0.5053	578.5120
	(−17.543) ***	(56.163) ***	(3.036) **	(1.169)	(−8.295) ***	(−7.808) ***	(−6.147) ***
BHWAH	−0.017	0.736	0.024	−0.590	−1.318	−2.292	−2.352	0.5126	595.5022
	(−18.579) ***	(54.788) ***	(0.311)	(−4.388) ***	(−7.372) ***	(−9.558) ***	(−9.833) ***
BHRCL	−0.017	0.733	−0.306	0.184	−1.646	−1.710	−0.275	0.4554	473.6692
	(−17.571) ***	(51.666) ***	(−3.682) ***	(1.293)	(−8.718) ***	(−6.753) ***	(−1.089)
BHRCH	−0.016	0.743	−0.394	0.259	−1.427	−1.317	−0.425	0.4274	422.9418
	(−15.787) ***	(48.802) ***	(−4.414) ***	(1.697) *	(−7.046) ***	(−4.848) ***	(−1.569)
BHRAL	−0.017	0.738	0.119	0.274	−1.663	−1.949	−0.540	0.4671	496.5039
	(−17.963) ***	(52.702) ***	(1.461)	(1.956) *	(−8.927) ***	(−7.803) ***	(−2.167) **
BHRAH	−0.016	0.746	0.393	0.074	−2.017	−2.510	−2.942	0.4529	468.9103
	(−15.187) ***	(47.240) ***	(4.247) ***	(0.468)	(−9.597) ***	(−8.906) ***	(−10.465) ***

Note: The value in parentheses shows the t-statistic, and *, **, *** show the level of significance at the 1, 5, and 10% levels, respectively.

indicate a significant positive effect of market premium (MKT) on the portfolio excess returns of small and big portfolios. The findings of the size premium (SMB) indicate that a small portfolio (SLWCL, SLWCH, SLWAL, SLWAH, and SLRCL) exhibits a significant and positive relationship with excess returns of small-stock portfolios. Contrastingly, some small portfolio reports (SLRCH) report positive and insignificant associations. Furthermore, for big portfolios, we find that the size premium exhibits a positive and statistically significant relationship with excess returns of large portfolios, except for BHWAH and BHRAL, where we find a positive and insignificant association. Conversely, we report that SMB has a significant and insignificant association with excess portfolio returns of big stocks (BHWCL, BHWCH, BHRCL, and BHRCH). For value premium, we report that HML demonstrates a significant positive association with excess portfolio returns of small stocks, except SLWAL and SLRAL, where we report a positive and insignificant association. Moreover, our findings report that the value premium demonstrates a significant positive association with excess portfolio returns of big stocks, except for BHWCL, BHWCH, and BHWAL, where we report positive and insignificant associations. Furthermore, we observe that the profitability premium exhibits a substantial positive and negative association with excess portfolio returns of small and big stocks. Additionally, we find that investment premium exhibits notable positive and negative influences on excess portfolio returns of small and big stocks, except for SHRAH, BLWAL, and BLRAL, where we report negative and insignificant associations. Lastly, for the human capital premium, we report that LBR is positively and significantly related to excess portfolio returns of small and big stocks, except for SHRCL, BLRCL, BHRCL, and BHRCH, where we report that LBR has a negative and insignificant impact on excess portfolio returns. Moreover, to validate the robustness of the main findings, we used a subsample analysis by dividing our data into during- and post-COVID-19 periods. This approach is methodologically justified and empirically supported in the recent literature that highlights the structural breaks and shifts in market dynamics caused by global crises (Hu 2022; Wei et al. 2023). Therefore, splitting the sample allows us to examine the temporal consistency of factor pricing and assess whether the model maintains its explanatory power under varying market conditions. Prior studies have shown that the COVID-19 pandemic introduced significant uncertainty and anomalies in financial markets, making such period-based analysis particularly useful for robustness checks (Maharani and Narsa 2023; Prasad et al. 2024).

Table 5. Human capital six-factor model: insights from the COVID-19 crisis period.

	Intercept	MKT	SMB	HML	RMW	CMA	LBR	Adj-R2	F-Stat
SLWCL	−0.027	0.269	0.896	1.115	−2.300	−3.378	12.029	0.4738	95.8393
	(−14.854) ***	(5.681) ***	(4.462) ***	(3.798) ***	(−4.409) ***	(−4.350) ***	(15.374) ***
SLWCH	−0.027	0.240	1.035	1.177	−1.829	3.626	−5.667	0.3149	49.4072
	(−17.337) ***	(5.696) ***	(5.811) ***	(4.519) ***	(−3.951) ***	(5.262) ***	(−8.164) ***
SLWAL	−0.027	0.245	1.015	0.922	−7.069	−8.383	0.517	0.4381	83.1231
	(−16.500) ***	(5.566) ***	(5.445) ***	(3.378) ***	(−14.584) ***	(−11.612) ***	(0.712)
SLWAH	−0.026	0.278	0.548	0.785	−6.167	−7.621	0.788	0.3547	58.9071
	(−16.984) ***	(6.816) ***	(3.172) ***	(3.104) ***	(−13.708) ***	(−11.394) ***	(1.170)
SLRCL	−0.026	0.291	1.345	0.629	−1.318	−3.050	0.613	0.1890	25.5431
	(−12.686) ***	(5.404) ***	(5.893) ***	(1.883) *	(−2.222) ***	(−3.454) ***	(0.689)
SLRCH	−0.024	0.324	1.203	0.318	−0.929	−2.289	0.007	0.2037	27.9477
	(−12.434) ***	(6.255) ***	(5.481) ***	(0.991)	(−1.629)	(−2.696) ***	(0.008)
SLRAL	−0.024	0.325	1.668	0.948	−1.614	−3.903	1.070	0.2423	34.6785
	(−12.000) ***	(6.093) ***	(7.381) ***	(2.866) ***	(−2.747) ***	(−4.463) ***	(1.214)
SLRAH	−0.024	0.342	1.384	0.483	−2.174	−3.919	−0.257	0.2657	39.1085
	(−12.250) ***	(6.540) ***	(6.259) ***	(1.494)	(−3.782) ***	(−4.580) ***	(−0.301)
SHWCL	−0.025	0.312	2.346	2.276	−1.745	−2.432	−0.463	0.2942	44.9149
	(−13.608) ***	(6.290) ***	(11.184) ***	(7.416) ***	(−3.201) ***	(−2.997) ***	(−0.567)
SHWCH	−0.025	0.329	2.544	2.771	−2.558	−3.764	0.701	0.3116	48.6676
	(−12.759) ***	(6.422) ***	(11.724) ***	(8.734) ***	(−4.537) ***	(−4.487) ***	(0.829)
SHWAL	−0.025	0.276	2.371	2.330	−1.146	−2.129	−0.043	0.2937	44.8044
	(−14.580) ***	(5.961) ***	(12.092) ***	(8.125) ***	(−2.250) **	(−2.808) **	(−0.054)
SHWAH	−0.022	0.342	2.323	2.538	−0.708	−1.366	0.643	0.2453	35.2409
	(−11.891) ***	(6.788) ***	(10.901) ***	(8.140) ***	(−1.279)	(−1.657) *	(0.774)
SHRCL	−0.029	0.212	2.011	2.220	−2.149	−3.309	1.201	0.2756	41.0832
	(−17.739) ***	(4.881) ***	(10.939) ***	(8.253) ***	(−4.498) ***	(−4.652) ***	(1.676) *
SHRCH	−0.028	0.206	2.394	2.865	−2.579	−3.554	−0.122	0.2216	30.9870
	(−13.420) ***	(3.686) ***	(10.124) ***	(8.281) ***	(−4.196) ***	(−3.886) ***	(−0.131)
SHRAL	−0.024	0.342	2.114	2.428	−1.813	−2.299	−0.664	0.2148	29.8234
	(−11.725) ***	(6.310) ***	(9.216) ***	(7.234) ***	(−3.040) ***	(−2.590) **	(−0.742)
SHRAH	−0.028	0.211	1.645	2.001	−2.165	−3.159	0.307	0.1780	23.8136
	(−15.569) ***	(4.425) ***	(8.135) ***	(6.763) ***	(−4.119) ***	(−4.038) ***	(0.390)
BLWCL	−0.027	0.269	0.896	1.115	−2.300	−3.378	−3.973	0.2003	27.3782
	(−14.854) ***	(5.681) ***	(4.462) ***	(3.798) ***	(−4.409) ***	(−4.350) ***	(−5.074) ***
BLWCH	−0.027	0.240	1.035	1.177	−1.829	−12.378	10.332	0.4364	82.5690
	(−17.337) ***	(5.696) ***	(5.811) ***	(4.519) ***	(−3.951) ***	(−17.958) ***	(14.883) ***
BLWAL	−0.027	0.270	0.781	0.763	1.403	−0.181	0.510	0.1284	16.5125
	(−19.475) ***	(7.371) ***	(5.042) ***	(3.369) ***	(3.485) ***	(−0.303)	(0.846)
BLWAH	−0.027	0.254	0.783	0.943	1.366	0.178	0.795	0.0855	10.8531
	(−16.458) ***	(5.901) ***	(4.300) ***	(3.541) ***	(2.886) **	(0.253)	(1.120)
BLRCL	−0.024	0.352	1.228	2.513	−3.491	−2.946	−0.163	0.1365	17.6478
	(−10.339) ***	(5.749) ***	(4.742) ***	(6.631) ***	(−5.186) ***	(−2.940) ***	(−0.162)
BLRCH	−0.023	0.365	1.235	2.433	−3.265	−3.072	0.340	0.1540	20.1678
	(−11.033) ***	(6.455) ***	(5.166) ***	(6.955) ***	(−5.254) ***	(−3.322) ***	(0.365)
BLRAL	−0.026	0.247	2.319	11.770	−13.393	−3.827	−0.120	0.5668	138.8337
	(−7.853) ***	(2.758) ***	(6.120) ***	(21.243) ***	(−13.598) ***	(−2.611) **	(−0.086)
BLRAH	−0.024	0.320	0.819	1.655	−1.887	−3.316	1.382	0.1263	16.2255
	(−13.531) ***	(6.644) ***	(4.022) ***	(5.554) ***	(−3.565) ***	(−4.209) ***	(1.741) *
BHWCL	−0.025	0.281	−0.091	0.056	−1.426	−2.165	−0.353	0.0673	8.6001
	(−14.415) ***	(6.159) ***	(−0.465)	(0.201)	(−2.837) ***	(−2.894) ***	(−0.475)
BHWCH	−0.027	0.253	0.391	0.643	−1.656	−2.237	0.025	0.0830	10.5288
	(−17.135) ***	(5.998) ***	(2.189) **	(2.461) **	(−3.566) ***	(−3.237) ***	(0.036)
BHWAL	−0.027	0.240	0.110	0.270	−1.450	−1.853	0.289	0.0618	7.9418
	(−17.032) ***	(5.752) ***	(0.625)	(1.047)	(−3.159) ***	(−2.712) **	(0.420)
BHWAH	−0.027	0.234	0.104	0.091	−1.319	−2.119	0.458	0.0788	10.0093
	(−19.548) ***	(6.286) ***	(0.666)	(0.395)	(−3.224) ***	(−3.479) ***	(0.746)
BHRCL	−0.027	0.268	0.461	0.686	−1.888	−2.815	−0.689	0.0851	10.7959
	(−14.705) ***	(5.570) ***	(2.258) **	(2.297) **	(−3.560) ***	(−3.566) ***	(−0.855)
BHRCH	−0.024	0.334	0.155	0.402	−2.506	−3.403	0.024	0.1103	14.0643
	(−13.707) ***	(7.087) ***	(0.778)	(1.377)	(−4.827) ***	(−4.404) ***	(0.031)
BHRAL	−0.025	0.308	0.352	0.704	−1.800	−3.132	1.098	0.1013	12.8765
	(−15.234) ***	(6.952) ***	(1.883) *	(2.571) **	(−3.698) ***	(−4.322) ***	(1.504)
BHRAH	−0.024	0.311	0.265	0.576	−2.817	−4.289	0.698	0.1080	13.7541
	(−13.218) ***	(6.306) ***	(1.271)	(1.888) *	(−5.195) ***	(−5.313) ***	(0.858)

Note: Refer to previous tables for significance level indicators.

presents the empirical findings of the model during the COVID-19 pandemic. The results indicate that the market premium (MKT) is positively and significantly related to excess portfolio returns of small and big portfolios, but the predictive power for explaining the association significantly decreased. More interestingly, we report that SMB exhibits a significant and positive association with the excess portfolio return of small stocks during COVID-19. Such findings indicate that size premiums are significantly priced in the Indian equity market during the COVID-19 pandemic. Conversely, in big stocks, we report positive, negative, and insignificant associations. Furthermore, we report that the performance of the value premium significantly improved during the COVID-19 pandemic compared to the full sample estimation. The results indicate that HML exhibits a significant, positive association with excess portfolio returns of small and big stocks. For profitability premium, we report positive, negative, and significant associations between RMW and excess portfolio returns of small and big stocks. Contrastingly, we observe that during COVID-19, investment premiums exhibited a significant negative influence on excess portfolio returns. Additionally, LBR shows a positive but statistically insignificant relationship with excess portfolio returns during the pandemic period. Lastly, in comparison to the predictive power of the augmented six-factor model, the performance of this model significantly decreased during the COVID-19 pandemic, as highlighted by the significant and insignificant association of risk factors with excess portfolio returns.

Table 6. Human capital six-factor model: insights from the post-pandemic era.

	Intercept	MKT	SMB	HML	RMW	CMA	LBR	Adj-R2	F-Stat
SLWCL	−0.049	0.234	0.100	−0.655	1.352	3.000	11.334	0.6170	57.3849
	(−8.538) ***	(2.644) **	(0.322)	(−1.072)	(1.557)	(2.313) **	(8.187) ***
SLWCH	−0.043	0.337	0.268	−0.349	1.641	14.028	−11.929	0.5003	36.0362
	(−8.471) ***	(4.337) ***	(0.982)	(−0.652)	(2.159) **	(12.352) ***	(−9.838) ***
SLWAL	−0.045	0.299	0.030	−0.615	−3.345	−2.660	−1.184	0.1777	8.5627
	(−6.894) ***	(2.975) ***	(0.085)	(−0.888)	(−3.395) ***	(−1.810) *	(−0.755)
SLWAH	−0.040	0.367	0.464	0.949	−3.415	−1.619	1.288	0.2177	10.7415
	(−7.197) ***	(4.261) ***	(1.533)	(1.598)	(−4.050) ***	(−1.285)	(0.958)
SLRCL	−0.038	0.400	0.224	−2.241	3.707	3.345	0.009	0.1680	8.0685
	(−5.453) ***	(3.710) ***	(0.590)	(−3.023) ***	(3.509) ***	(2.120) **	(0.005)
SLRCH	−0.041	0.363	0.220	−2.400	3.428	2.815	0.118	0.1981	9.6476
	(−6.389) ***	(3.688) ***	(0.636)	(−3.535) ***	(3.555) ***	(1.955) *	(0.077)
SLRAL	−0.048	0.256	−0.254	−3.151	4.150	2.218	−1.285	0.1642	7.8748
	(−8.146) ***	(2.829) ***	(−0.797)	(−5.046) ***	(4.679) ***	(1.674) *	(−0.908)
SLRAH	−0.047	0.287	0.135	−3.147	4.584	2.201	0.924	0.1931	9.3749
	(−6.942) ***	(2.780) ***	(0.373)	(−4.427) ***	(4.541) ***	(1.460)	(0.574)
SHWCL	−0.046	0.295	1.052	1.298	1.087	1.558	1.008	0.0750	3.8369
	(−7.086) ***	(2.992) ***	(3.034) ***	(1.912) *	(1.127)	(1.081)	(0.655)
SHWCH	−0.047	0.276	2.059	1.681	1.649	2.431	0.947	0.1551	7.4271
	(−6.306) ***	(2.419) **	(5.118) ***	(2.133) **	(1.473)	(1.454)	(0.531)
SHWAL	−0.046	0.269	0.829	−0.074	0.772	0.719	−0.591	0.1121	5.4194
	(−8.716) ***	(3.306) ***	(2.900) **	(−0.136)	(0.970)	(0.605)	(−0.466)
SHWAH	−0.042	0.351	0.820	0.366	0.298	1.095	−0.413	0.1211	5.8222
	(−7.653) ***	(4.136) ***	(2.742) **	(0.625)	(0.358)	(0.881)	(−0.312)
SHRCL	−0.039	0.396	0.998	0.330	1.339	2.546	−0.096	0.1521	7.2767
	(−6.416) ***	(4.226) ***	(3.028) ***	(0.511)	(1.461)	(1.859) *	(−0.065)
SHRCH	−0.053	0.200	1.830	0.546	1.625	3.106	−1.236	0.1757	8.4584
	(−7.348) ***	(1.833) ***	(4.749) ***	(0.723)	(1.516)	(1.940) *	(−0.723)
SHRAL	−0.046	0.275	1.235	1.626	−0.019	1.929	−1.444	0.1010	4.9317
	(−8.015) ***	(3.143) ***	(4.010) ***	(2.695) ***	(−0.022)	(1.507)	(−1.057)
SHRAH	−0.041	0.345	1.185	1.666	−0.197	0.980	−0.032	0.0943	4.6443
	(−6.581) ***	(3.595) ***	(3.509) ***	(2.519) **	(−0.210)	(0.698)	(−0.021)
BLWCL	−0.049	0.234	0.100	−0.657	1.352	3.000	−4.665	0.0747	3.8257
	(−8.538) ***	(2.644) **	(0.322)	(−1.072)	(1.557)	(2.313) **	(−3.370) ***
BLWCH	−0.043	0.337	0.268	−0.349	1.641	−1.971	4.070	0.1176	5.6636
	(−8.471) ***	(4.337) ***	(0.982)	(−0.652)	(2.159) **	(−1.736) *	(3.356) ***
BLWAL	−0.040	0.368	−0.083	−0.403	2.363	1.822	−0.024	0.1723	8.2872
	(−9.424) ***	(5.646) ***	(−0.363)	(−0.898)	(3.704) ***	(1.911) *	(−0.023)
BLWAH	−0.046	0.298	0.578	0.737	6.880	9.897	0.127	0.2957	15.6947
	(−6.049) ***	(2.582) **	(1.423)	(0.926)	(6.086) ***	(5.861) ***	(0.070)
BLRCL	−0.041	0.361	−0.020	1.471	−1.724	0.654	2.878	0.1303	6.2459
	(−6.259) ***	(3.580) ***	(−0.062)	(2.116) **	(−1.746) *	(0.443)	(1.828) *
BLRCH	−0.040	0.385	0.345	1.536	−0.771	1.919	0.110	0.1061	5.1532
	(−6.713) ***	(4.244) ***	(1.080)	(2.455) **	(−0.868)	(1.446)	(0.078)
BLRAL	−0.047	0.275	−0.519	0.289	−0.133	2.502	−2.302	0.1242	5.9641
	(−10.104) ***	(3.881) ***	(−2.080) **	(0.590)	(−0.192)	(2.411) **	(−2.078) **
BLRAH	−0.047	0.285	0.522	1.754	2.499	5.505	−0.920	0.1715	8.2466
	(−7.345) ***	(2.933) ***	(1.527)	(2.616) **	(2.626) **	(3.872) ***	(−0.606)
BHWCL	−0.050	0.225	−0.978	−1.174	0.800	1.915	−0.820	0.0651	3.4377
	(−8.611) ***	(2.547) **	(−3.147) ***	(−1.928) *	(0.925)	(1.482)	(−0.595)
BHWCH	−0.056	0.147	−0.562	−1.056	1.525	2.491	−0.360	0.0705	3.6536
	(−12.013) ***	(2.080) **	(−2.261) **	(−2.168) **	(2.205) **	(2.411) **	(−0.326)
BHWAL	−0.045	0.310	−0.499	−1.020	1.414	1.595	−0.526	0.0712	3.6812
	(−8.427) ***	(3.826) ***	(−1.752)	(−1.828)	(1.784) *	(1.347)	(−0.416)
BHWAH	−0.042	0.340	−0.603	−1.119	−0.036	0.865	0.302	0.0754	3.8539
	(−6.705) ***	(3.538) ***	(−1.785) *	(−1.689)	(−0.038)	(0.615)	(0.201)
BHRCL	−0.041	0.362	−1.140	−0.723	−0.100	1.792	0.757	0.1110	5.3716
	(−6.114) ***	(3.558) ***	(−3.184) ***	(−1.031)	(−0.100)	(1.204)	(0.476)
BHRCH	−0.039	0.405	−0.454	−1.246	1.526	1.786	0.314	0.0795	4.0240
	(−5.903) ***	(3.994) ***	(−1.273)	(−1.782)	(1.536)	(1.204)	(0.198)
BHRAL	−0.044	0.313	−1.263	−1.085	−0.136	1.587	0.203	0.1300	6.2313
	(−7.717) ***	(3.575) ***	(−4.100) ***	(−1.797) *	(−0.158)	(1.239)	(0.148)
BHRAH	−0.045	0.306	−0.485	−1.134	1.561	2.333	−1.728	0.0530	2.9571
	(−7.245) ***	(3.246) ***	(−1.464)	(−1.745) *	(1.692) *	(1.693) *	(−1.174)

Note: Refer to previous tables for significance level indicators.

illustrates the augmented six-factor model’s performance after the pandemic, showing that market premium (MKT) positively and significantly impacts excess returns in small and big portfolios. Such findings indicate that market risk premium is significantly priced in three sample conditions (full, during, and post-COVID-19). Conversely, the predictive power of SMB, HML, RMW, CMA, and LBR significantly decreased during this period.

Table 7. Model comparison based on Adj-R².

Portfolio	Full Sample	During COVID-19	Post-COVID-19
SLWCL	51.51%	47.38%	61.70%
SLWCH	55.08%	31.49%	50.03%
SLWAL	55.74%	43.81%	17.77%
SLWAH	50.06%	35.47%	21.77%
SLRCL	45.94%	18.90%	16.80%
SLRCH	37.95%	20.37%	19.81%
SLRAL	47.62%	24.23%	16.42%
SLRAH	48.32%	26.57%	19.31%
SHWCL	41.89%	29.42%	7.50%
SHWCH	40.34%	31.16%	15.51%
SHWAL	52.81%	29.37%	11.21%
SHWAH	47.87%	24.53%	12.11%
SHRCL	51.65%	27.56%	15.21%
SHRCH	46.98%	22.16%	17.57%
SHRAL	46.60%	21.48%	10.10%
SHRAH	50.48%	17.80%	9.43%
BLWCL	50.32%	20.03%	7.47%
BLWCH	50.16%	43.64%	11.76%
BLWAL	40.33%	12.84%	17.23%
BLWAH	47.05%	8.55%	29.57%
BLRCL	40.38%	13.65%	13.03%
BLRCH	44.10%	15.40%	10.61%
BLRAL	42.04%	56.68%	12.42%
BLRAH	44.54%	12.63%	17.15%
BHWCL	42.05%	6.73%	6.51%
BHWCH	52.65%	8.30%	7.05%
BHWAL	50.53%	6.18%	7.12%
BHWAH	51.26%	7.88%	7.54%
BHRCL	45.54%	8.51%	11.10%
BHRCH	42.74%	11.03%	7.95%
BHRAL	46.71%	10.13%	13.00%
BHRAH	45.29%	10.80%	5.30%

presents a model comparison based on adjusted R² (Adj-R²) values. Our findings demonstrate that risk factors substantially account for the variability in excess portfolio returns over the full sample. The augmented six-factor model exhibited a notable decline in predictive power during the COVID-19 pandemic relative to the entire sample period. More specifically, post-COVID-19 analysis showed a gradual improvement in the model’s efficiency in explaining time-series variability relative to the pandemic period.

Table 8. Comparison of competing asset pricing models (CAPM, FF3FM, FF5FM, and HC6FM) in a crisis period using the GRS test.

	GRS 2 Test (Mean Alpha)				GRS F Test
Period	CAPM	FF3FM	FF5FM	H6FM	CAPM	FF3FM	FF5FM	H6FM
Full sample result	0.0012	0.0005	0.0008	0.0005	1.6214	1.5311 *	1.3686	1.3530
During COVID-19	0.0051	0.0035	0.0033	0.0033	1.9090 **	1.5830 **	1.4774 **	1.9645 **
Post-COVID-19	0.0033	0.0033	0.0033	0.0024	1.4720	1.1574	1.0939	1.0549

Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.

presents the GRS and GRS-F test results, indicating partial rejection of the null hypothesis of portfolio efficiency in certain periods, suggesting that the models do not fully capture systematic risks. During the full sample period, the GRS statistics are relatively low, with the FF3FM being marginally significant. The mean absolute alpha values are also low, with the FF3FM and HC6FM models both showing the smallest value, indicating minimal mispricing and relatively efficient asset pricing throughout the sample period. However, during the COVID-19 period, the GRS statistics became more significant across all models, indicating a stronger rejection of portfolio efficiency during this crisis period. The mean absolute alpha values increase compared to the full sample, with the HC6FM and FF5FM both showing the smallest value. This suggests that although these models experienced increased mispricing during COVID-19, they were still relatively more efficient compared to the CAPM and FF3FM. In the post-COVID-19 period, the GRS statistics decreased slightly, indicating some improvement in model performance compared to the COVID-19 period. The HC6FM records the lowest GRS value, suggesting a relatively better performance among the models. Additionally, the mean absolute alpha value for HC6FM is the smallest, indicating that the human capital-based model has fewer pricing errors in the post-pandemic recovery period. Overall, the findings indicate that the HC6FM model generally performed better during both the COVID-19 and post-COVID-19 periods, as reflected by lower mean alpha values and GRS statistics. These results are consistent with Fama and French (2015), who noted that asset pricing models often face challenges in capturing systematic risks during turbulent periods, but the inclusion of human capital factors appears to improve pricing accuracy.

4.3. Rolling-Window Fama and MacBeth (1973) Regression

Table 9. Rolling-window estimation of the Fama–Macbeth two-pass regression.

	Intercept	MKT	SMB	HML	RMW	CMA	LBR	Adj-R2
SLWCL	−0.0702	0.0024	0.0064	0.0020	0.0011	0.000916	0.0001	0.0489
T-stat	−72.2217	2.0110	1.7663	1.6441	1.4975	1.7701	1.1615
SLWCH	−0.0663	−0.0055	0.0067	0.0013	0.0004	−0.000	0.0000	0.0250
T-stat	−79.1715	−3.8846	1.4541	2.2648	1.7281	−0.1025	0.4129
SLWAL	−0.0661	−0.0075	0.0112	0.0042	0.0007	0.0007	−0.0003	0.0735
T-stat	−83.6256	−1.5418	1.6083	1.9448	3.4113	3.9300	−2.4380
SLWAH	−0.06445	0.0048	0.0058	0.0033	0.0004	0.0010	−0.0006	0.0351
T-stat	−90.6357	1.2346	1.3424	5.3093	1.8728	1.0163	−3.8694
SLRCL	−0.06688	0.0021	0.0054	0.0033	−0.0006	0.0006	−0.0001	0.0198
T-stat	−99.5328	1.7158	0.9093	1.0054	−3.0207	3.2063	−1.1147
SLRCH	−0.06598	0.0031	0.0130	0.0075	−0.0000	0.0019	−0.0011	0.0812
T-stat	−108.923	2.4986	1.4533	1.9839	−0.3911	1.9263	−1.4039
SLRAL	−0.06821	0.0021	0.0066	0.0024	−0.0013	−0.0018	0.0006	0.0496
T-stat	−115.357	1.5683	0.8843	0.4920	−3.5923	−6.3367	3.9575
SLRAH	−0.06569	0.0031	0.0117	0.0086	−0.0009	0.0016	−0.0012	0.0866
T-stat	−102.648	2.3803	1.5856	1.8306	−4.4226	2.2958	−1.0406
SHWCL	−0.06878	0.0021	0.0045	0.0010	0.0000	0.0014	−0.0010	0.0491
T-stat	−100.805	1.7825	1.7074	2.2476	0.1341	1.9226	−6.5919
SHWCH	−0.07025	−0.0001	0.0056	−0.0001	−0.0006	0.0002	−0.0007	0.0599
T-stat	−103.22	−0.1094	1.9891	−0.3210	−1.9550	1.3746	−1.9328
SHWAL	−0.0663	0.0000	0.0025	0.0021	−0.0005	0.0007	−0.0008	0.0090
T-stat	−77.3799	0.0397	1.1125	3.7453	−2.4147	1.6193	−1.5088
SHWAH	−0.06219	0.0002	−0.0009	−0.0013	0.0015	0.0010	0.0000	0.0175
T-stat	−80.7939	0.16333	−1.4755	−2.7981	6.2560	1.6104	0.0098
SHRCL	−0.0674	−0.0029	0.0062	0.0020	0.0016	0.0011	0.0000	0.0377
T-stat	−115.306	−1.9253	1.2492	3.9403	5.0360	5.0005	0.3667
SHRCH	−0.0687	−0.0005	0.0079	0.0007	0.0000	−0.001	0.0005	0.0652
T-stat	−122.519	−0.434	1.1147	1.2761	0.1098	−3.1259	2.5737
SHRAL	−0.0679	0.0034	0.0048	0.0006	0.0002	0.0013	−0.0011	0.0310
T-stat	−93.4774	2.0313	1.5209	0.9491	1.0368	5.4830	−5.9427
SHRAH	−0.06544	−0.0041	0.0028	−0.0013	0.0005	0.0015	−0.0009	0.0350
T-stat	−101.534	−2.9971	1.5302	−2.1888	2.5342	5.5717	−4.0773
BLWCL	−0.06368	−0.0033	0.0052	0.0017	0.0005	0.0014	−0.0003	0.0465
T-stat	−78.2327	−2.4137	0.5969	0.0763	2.6813	1.0016	−2.6296
BLWCH	−0.06534	0.0026	0.0049	−0.0011	0.0032	0.0003	0.0006	0.0754
T-stat	−106.335	2.1191	1.5301	−2.1043	12.0568	2.0090	4.5877
BLWAL	−0.0646	−0.0006	−0.0011	−0.0038	0.0023	−0.0007	0.0006	0.0213
T-stat	−129.276	−0.3872	−1.1048	−1.4324	7.1503	−2.1957	2.8409
BLWAH	−0.0657	−0.0045	0.0052	0.0022	0.0004	0.0011	−0.0009	0.0251
T-stat	−148.212	−2.7938	1.3308	1.4960	1.7822	1.0542	−4.4441
BLRCL	0.0647	0.0015	−0.0074	−0.0017	−0.0006	−0.0017	0.0007	0.1112
T-stat	171.5765	1.9102	−2.2557	−0.1849	−3.7956	−1.2916	0.5361
BLRCH	−0.06592	0.0035	0.0061	0.0021	0.0008	0.0020	−0.0011	0.0742
T-stat	−114.026	2.7645	1.6352	1.5818	3.0341	1.0968	−0.0673
BLRAL	−0.0693	0.0039	−0.0013	0.0023	0.0013	0.0022	−0.0009	0.0957
T-stat	−112.137	1.5975	−1.6827	1.4354	3.8294	1.0180	−1.1929
BLRAH	−0.0624	−0.0004	0.0055	0.0002	0.0024	0.0015	−0.0002	0.0867
T-stat	−119.741	−0.3256	1.1242	0.4291	10.2281	1.2697	−1.8392
BHWCL	−0.0618	−0.0059	0.0048	0.0003	0.0029	0.0012	0.0001	0.0752
T-stat	−122.718	−4.0883	1.6591	0.5589	11.5979	2.0974	0.6908
BHWCH	−0.0653	−0.0046	0.0128	0.0071	−0.0006	0.0014	−0.0014	0.0681
T-stat	−159.717	−3.1221	1.6462	1.9474	−2.2672	1.5043	−6.5962
BHWAL	−0.0623	−0.0128	0.0067	−0.0010	0.0024	0.0014	−0.0001	0.0999
T-stat	−142.911	−1.4526	1.2676	−1.4713	1.6671	1.5588	−0.9245
BHWAH	−0.0629	−0.0005	0.0159	0.0066	−0.0001	0.0025	−0.0012	0.1155
T-stat	−142.268	−0.3131	1.0563	2.6443	−0.3713	1.4085	−1.9908
BHRCL	−0.0622	−0.0022	0.0136	0.0067	0.0014	0.0028	−0.0014	0.1511
T-stat	−132.926	−1.6026	1.3609	1.1522	5.1163	1.8996	−1.3971
BHRCH	−0.0646	0.0049	0.0162	0.0092	−0.0003	0.0025	−0.0018	0.1476
T-stat	−142.829	1.7705	1.6240	15.4588	−1.0714	1.9393	−1.5075
BHRAL	−0.0622	−0.0064	0.0056	−0.0004	0.0034	0.0016	−0.0003	0.0878
T-stat	−132.686	−4.4835	2.5129	−0.7713	1.3850	1.5185	−1.8934
BHRAH	−0.0635	−0.0035	0.0055	−0.0007	0.0013	0.0008	−0.0005	0.0531
T-stat	−122.226	−2.5085	1.4195	−1.2746	1.2106	1.9202	−3.0780

reports the results of the rolling-window Fama and MacBeth (1973) two-pass regression. Factor loadings are estimated over a 36-month rolling-window that moves forward monthly, incorporating the subsequent month and dropping the earliest. The analysis reveals that the examined risk factors fail to adequately account for future portfolio return variations in the Indian equity market. More specifically, factor loadings show inconsistent statistical significance across portfolios in the two-pass regression. Therefore, associated risk premiums did not fully capture the variability in future returns. The model’s poor performance across portfolios implies that past betas are ineffective predictors. These outcomes concur with the findings of Khan and Afeef (2024) and Thalassinos et al. (2023) in emerging market contexts.

4.4. Discussion: Theoretical and Empirical Implications of the Study

The findings of this study align well with previous research. Consistent with the theoretical foundation laid by Markowitz (1952), a portfolio that maximizes returns while minimizing variance is considered efficient. In line with the CAPM framework, our findings reveal that the market premium significantly captures variability in portfolio returns. More specifically, our empirical findings reaffirm the central role of the market risk premium in explaining return variability across portfolios, consistent with foundational asset pricing theories, such as CAPM and its multifactor extensions. Moreover, the significant pricing of factors in the Indian market echoes the multifactor framework advocated by Fama and French (1992, 2015) and the emerging literature emphasizing the importance of human capital as a vital explanatory variable (Jagannathan and Wang 1996; Khan et al. 2023; Khan and Afeef 2024). Additionally, the Fama and French three-factor (FF3FM) and five-factor (FF5FM) models effectively capture risk exposures reflected in excess portfolio returns.

Similarly, Kolari et al. (2022) demonstrated that the ZCAPM outperforms traditional models, such as CAPM, FF3FM, and C4FM in explaining returns dispersion using global data. Liu (2023) documented that CAPM, FF3FM, and FF5FM significantly explain portfolio return variability during the COVID-19 pandemic. However, the observed decline in model performance during and after the COVID-19 pandemic aligns with recent findings that crisis periods introduce market anomalies and structural shifts that challenge traditional asset pricing frameworks (Hu 2022; Wei et al. 2023). Kausar et al. (2024), examining BRICS economies, found that firms with higher idiosyncratic risk (IR) experience lower returns compared to firms with lower IR. Our results corroborate the growing literature that identifies human capital, proxied by salaries and wages, as a significant factor in forecasting the time-series fluctuations of asset returns (Roy and Shijin 2018; Prasad et al. 2024). Furthermore, the superior explanatory power of the six-factor model during the full sample period supports prior studies suggesting that incorporating human capital enhances the model’s ability to capture asset return dynamics, especially in emerging economies with growing human capital potential (Maitai and Balakrishnan 2018; Maharani and Narsa 2023).

5. Conclusions

The MPT of Markowitz (1952, 1959) has attracted extensive scholarly attention in examining the risk–return relationship. Building on this foundation, the seminal works of Sharpe (1964) initiated the development of asset pricing models. Over the past decades, the CAPM has been widely used to explore the nuanced dynamics between risk and return. However, the restrictive assumptions of the CAPM have been challenged, leading researchers to propose multifactor models aimed at better explaining the variability in asset returns. Despite these advancements, such models often fall short of fully capturing asset return variations across different financial markets. Moreover, their performance and robustness during periods of economic and financial crises, geopolitical tensions, invasions, and pandemics have not been sufficiently explored. This study addresses existing research gaps by testing the HC6FM in the Indian equity market. Daily stock prices of Nifty-500 companies from July 2010 to June 2023, alongside yearly balance sheet data (2010–2022), were used to form 32 portfolios following the methodology of Fama and French (2015). The data were divided into full, COVID-19, and post-COVID periods to evaluate model robustness across different market phases. The Fama and MacBeth (1973) regression results highlight that the pandemic induced marked volatility, leading to many portfolios with inefficient returns and increased risk. Smaller portfolios generate superior returns but at the expense of higher risk relative to larger portfolios.

Our results confirm that, across all portfolios, the market premium plays a significant role in explaining return variability over time. In addition, size, value, profitability, investment, and human capital factors are significantly linked to excess portfolio returns, indicating these factors are meaningfully priced in the Indian market. The six-factor model demonstrates superior explanatory power during the full sample period compared to the pandemic and post-pandemic phases, with a marked reduction in model efficiency during and after the COVID-19 crisis. Importantly, our study extends the FF5FM by incorporating the human capital factor, operationalized as the labor income growth rate, which significantly prices the variability in asset returns. This finding suggests that investors should incorporate human capital considerations when conducting fundamental and technical analyses of Indian companies. Our results indicate that incorporating human capital, such as labor income growth, into asset pricing models greatly improves their explanatory power in emerging markets, like India. To enable this integration, labor data infrastructure and corporate reporting must be strengthened. During economic disruptions, models, like HC6FM, can offer deeper insights into market vulnerabilities.

Future research can extend the current study in several ways. First, future studies can test the performance of HC6FM in emerging and frontier markets to further enrich valuable insights into the model’s robustness and cross-market applicability. Second, the model could be enriched by incorporating additional risk factors that capture dimensions of investment behavior, such as Environmental, Social, and Governance (ESG) scores, geopolitical risk, and economic policy uncertainty. These additions may significantly enhance the model’s explanatory power, particularly during periods of heightened market volatility or systemic shocks. Third, future studies may benefit from employing more estimation techniques, like the generalized method of moments (GMM), instrumental variable (IV-GMM), and panel data models. Lastly, future studies could focus on firm-level analyses, investigating how human capital intensity interacts with firm characteristics to influence asset pricing. This would offer a more granular understanding of the economic mechanisms through which human capital contributes to expected returns.