Framing the Market: How Brand Visibility and Sentiment Shape Stock Reactions in the US,China,and India?

Abstract

This study explores the dynamic interplay between brand visibility, investor sentiment, and stock market reactions across the United States, China, and India. Leveraging signalling and media framing theories, we develop a novel Brand Visibility Score (BVS) that incorporates sentiment polarity, topic salience, and engagement metrics derived from financial social media platforms. Using panel regression models and marginal analysis over 5,490 firms from 2017 to 2023, we find that brand visibility significantly enhances stock returns and contributes to volatility, with investor sentiment acting as a critical moderator. The effects are more pronounced in emerging markets, reflecting greater sensitivity to media signals and investor mood. Moreover, the impact varies across different levels of media visibility, where firms in higher visibility tiers experience stronger market reactions to sentiment shifts. The findings underscore the importance of media visibility as a behavioural driver in financial markets and offer new insights into how digital brand exposure and sentiment collectively shape asset pricing outcomes in varying institutional contexts.

JEL Classification: G12, G14, M31, G15, C58.

Plain Language Summary

How Brand Popularity and Investor Feelings Affect Stock Prices in the US, China, and India

This study looks at how a company’s brand popularity and the emotions of investors impact stock prices in three major countries: the United States, China, and India. We created a new way to measure how visible a brand is online by combining how positive or negative people’s comments are, how important the topics are, and how much people interact with the content on social media. Analysing data from 5,490 companies between 2017 and 2023, we found that companies with more online attention tend to have higher stock returns but also experience more price swings. Investor feelings, such as excitement or fear, make these effects even stronger. Interestingly, the impact of brand visibility is greater in China and India than in the US, showing that investors in emerging markets are more influenced by news and online discussions. Our findings highlight how important media visibility and public sentiment are in shaping stock market movements today.

Keywords

brand visibility investor sentiment stock returns stock volatility

Introduction

Brands are intangible assets that significantly impact firm performance (Haji et al., 2018). A firm’s brand embodies its core value by transmitting positive signals to investors and enhancing its value through increased investor attention and corporate income (Huang et al., 2021). According to the signalling theory, companies use branding as a strategic tool to communicate quality and potential to the market. In media economics, media acts as a powerful intermediary in this process, amplifying these signals through various channels, from traditional news outlets to social media platforms. This amplification is crucial because it ensures that the brand’s message reaches a broad audience, including investors, whose perceptions and actions are influenced by media-driven signals. Firms with stronger brand images, consistently highlighted by the media, tend to exhibit superior financial and stock market performances (Haji et al., 2018).

Brands are widely recognised as strategic intangible assets that significantly influence firm valuation and long-term performance (Haji et al., 2018). A brand represents a firm’s symbolic capital, transmitting credible signals about quality, reliability, and innovation to the market. Consistent with signalling theory, firms utilise branding as a communicative mechanism to reduce information asymmetry and enhance investors’ perceptions of firm quality (Huang et al., 2021). The media acts as an intermediary in this process, amplifying brand-related signals through news coverage and digital dissemination. This amplification is critical, as it extends the reach of brand information to a broader audience of investors whose perceptions and trading decisions are increasingly shaped by media-driven narratives. Firms with stronger and more visible brands tend to attract greater investor attention and, consequently, demonstrate superior financial and stock-market performance (Haji et al., 2018).

However, the media’s influence extends beyond the mere transmission of information. According to framing theory, the media selectively structure and present information in ways that guide interpretation and behavioural response (Entman, 1993; Roslyng & Dindler, 2023). Media framing can accentuate certain aspects of a brand, such as innovation, ethical standing, or financial risk, while suppressing others, thereby shaping investor sentiment and decision-making (Aljanabi, 2023). Positive frames that highlight corporate credibility and market leadership tend to strengthen investor confidence, whereas negative frames emphasising controversy or uncertainty may trigger scepticism and increase sell-offs. The framing process thus mediates how investors perceive firm-level signals and how these perceptions translate into market reactions.

This framing effect is particularly salient in financial markets characterised by information asymmetry and high uncertainty. Investors often rely on media interpretation to evaluate complex information, making them vulnerable to framing biases. As a result, investor sentiment, which reflects the collective optimism or pessimism of the market, becomes a behavioural channel through which media framing influences price movements (Baker & Wurgler, 2016). Positive sentiment reinforced by favourable framing can amplify the positive association between brand visibility and stock performance, whereas negative sentiment can magnify downside reactions, increasing price volatility (Smales, 2021).

Despite the established relevance of visibility and sentiment in market behaviour, several gaps persist in the literature. Prior research has predominantly examined the relationship between brand visibility and stock returns (Fang & Peress, 2009), yet limited attention has been devoted to how media framing and investor sentiment jointly shape stock volatility. Volatility captures the uncertainty embedded in investors’ expectations and is especially sensitive to emotionally charged or inconsistent media coverage (Audrino et al., 2020; Huang et al., 2021). Moreover, existing studies have been mainly conducted in single-country contexts, primarily focusing on developed economies, without accounting for cross-country heterogeneity in information environments and regulatory institutions. Such limitations constrain understanding of how cultural, institutional, and regulatory differences influence the relationship between brand visibility, sentiment, and market outcomes.

To address these limitations, the present study develops a novel Brand Visibility Score (BVS) that integrates sentiment polarity, topic salience, and online engagement intensity derived from financial social-media data. This composite indicator extends traditional attention measures by incorporating both quantitative visibility and qualitative sentiment dimensions, capturing the behavioural nature of digital information flows. Using a panel dataset of 5,490 publicly listed firms across the United States, China, and India from 2017 to 2023, this study examines the association between brand visibility and stock performance, with investor sentiment as a moderating factor. The inclusion of both developed and emerging markets enables comparative insights into how differing institutional structures, media transparency, and investor sophistication shape visibility–performance dynamics.

Theoretically, the study integrates signalling theory, media-framing theory, and behavioural asset-pricing theory to establish a multidimensional framework linking media-driven brand exposure to market behaviour. By combining these perspectives, the study contributes to behavioural finance literature by elucidating the mechanism through which digital visibility and sentiment jointly influence stock returns and volatility. Furthermore, the findings provide managerial and regulatory implications, demonstrating how brand management and communication strategies can enhance information transparency while mitigating sentiment-driven instability in financial markets.

The remainder of this paper is organised as follows. Literature Review Section reviews relevant literature and theoretical foundations. Methdology Section outlines the data and methodology. Result Section presents empirical results and robustness analyses. Conclusion Section concludes with key findings, implications, and directions for future research.

Literature Review

Signalling Theory posits that firms convey information to the market through strategic actions such as branding, marketing communication, and disclosure practices, which investors interpret as indicators of firm quality and future performance (Spence, 1973). Brand prominence and visibility act as informational signals that attract investor attention, shaping perceptions of credibility and expected returns. Empirical evidence supports this view: Bank et al. (2020) find that portfolios comprising firms with high brand equity deliver superior returns and lower risk exposures, consistent with brands signalling firm strength. Likewise, Audrino et al. (2020) reveal that sentiment and attention metrics strongly influence market volatility, reinforcing the informational content embedded in visibility cues. Within this framework, the BVS conceptualised in this study operationalises brand-related signals by quantifying the intensity and reach of media exposure as an observable market signal.

Framing Theory, within the domain of media economics, emphasises that the interpretation of information depends not only on its content but also on how it is presented (Entman, 1993). Media outlets select and frame specific aspects of corporate narratives, influencing investor cognition and emotional response (Roslyng & Dindler, 2023). Frames highlighting innovation and leadership tend to foster investor optimism, whereas those focusing on financial risks or ethical controversies can trigger negative sentiment and risk aversion (Cheema et al., 2020; Tumasjan et al., 2021). In this sense, framing transforms media coverage from a neutral information channel into a behavioural driver of market reactions.

The interaction between signalling and framing mechanisms creates a multidimensional process in which visibility and sentiment jointly affect asset valuation. Studies show that greater brand visibility amplifies market responsiveness to information shocks (Bank et al., 2020; Chen et al., 2016), but the direction and magnitude of this response depend on the framing tone and prevailing investor sentiment (Smales, 2021; Sreenu & Naik, 2022). Li et al. (2023) observed significant herding behaviour in social media–driven markets, suggesting that sentiment-mediated framing can intensify collective reactions to brand signals. Similarly, Dasgupta et al. (2015) and Aljanabi (2023) highlight that reputational concerns and news tone can interact to magnify volatility through feedback loops. Cheema et al. (2020) further demonstrate that sentiment moderates the visibility–volatility relationship, implying that the quality and framing of information alter how signals are priced.

While these studies confirm the behavioural relevance of media influence, most have concentrated on single-market contexts, limiting understanding of how institutional diversity shapes framing effects. Post-COVID studies (e.g., Li et al., 2023) reveal that social media discourse and digital engagement have intensified cross-market sentiment spillovers, underscoring the importance of comparative frameworks. This study extends the literature by integrating behavioural asset-pricing and media richness perspectives with signalling and framing theories to capture both quantitative (visibility) and qualitative (sentiment tone) dimensions of information flow. The BVS thus provides a theoretical bridge linking these perspectives, enabling a more comprehensive examination of how digital visibility and framing jointly shape stock returns and volatility across heterogeneous markets.

Hypothesis Development

Brand visibility, which reflects a firm’s public prominence and recognition, plays a vital role in shaping stock returns and volatility. Signalling theory posits that branding communicates credible information about a firm’s quality and stability to the market, whereas framing theory emphasises that the media’s presentation of these signals influences investor perception and behaviour (Roslyng & Dindler, 2023; Spence, 1973). Empirical evidence demonstrates that visibility attracts investor attention, increasing trading activity and influencing valuation. For instance, Drake et al. (2017) and Padungsaksawasdi et al. (2019) report that heightened investor attention is associated with higher stock returns, reflecting the value investors attribute to visible and reputable firms. Similarly, Ben El Hadj Said and Slim (2022) find that investor attention significantly affects volatility across global markets, reinforcing the behavioural sensitivity to visibility. Moreover, Andrei and Hasler (2015) highlight that attention and uncertainty jointly determine asset prices, where higher visibility intensifies market reactions to new information.

However, despite extensive research on attention and performance, the combined influence of brand visibility on returns and volatility across institutional settings remains underexplored, particularly in emerging Asian markets. Existing studies have primarily focused on developed markets, leaving open the question of whether the same visibility–return dynamics hold under different regulatory, cultural, and informational conditions. This study, therefore, extends the literature by testing visibility effects across the United States, China, and India, where media transparency and investor sophistication vary considerably.

Hypothesis 1 (H1): Brand visibility has a significant positive impact on stock returns and volatility in both the US and emerging Asian markets.

Signalling theory suggests that strong brands signal enduring quality and stability, while framing theory posits that favourable media representation amplifies these signals, enhancing investor confidence (Liu et al., 2024). Empirical findings indicate that brand value moderates the relationship between investor sentiment and stock performance. Huang et al. (2021) demonstrate that firms with high brand equity exhibit more stable performance and lower volatility. In contrast, Liu et al. (2024) demonstrate that strong brand values mitigate the adverse effects of sentiment and enhance returns under optimistic market conditions. Conversely, Gan et al. (2020) and Alsomaidaee et al. (2023) report that weaker brands are more susceptible to volatility driven by fluctuations in sentiment. Ahmed (2020) finds that brand heterogeneity produces asymmetric volatility responses, with strong brands remaining resilient against negative sentiment shocks. These insights highlight the importance of examining how brand value differentially moderates the impact of visibility and sentiment across institutional contexts. Given the heterogeneity in investor bases and media landscapes between developed and emerging markets, variations in brand strength are likely to yield distinct market reactions.

Hypothesis 2 (H2): The impact of brand visibility and investor sentiment on stock returns and stock volatility differs based on brand value in both the US and emerging Asian markets.

Signalling theory explains that positive investor sentiment strengthens brand signals, while framing theory highlights the media’s influence in amplifying or attenuating these signals (Baker et al., 2020; Cheema et al., 2020). Investor sentiment has long been recognised as a critical behavioural determinant of market outcomes. Abdelmalek (2022) found that the relationship between sentiment, volatility, and returns is asymmetric. Optimistic sentiment increases returns and reduces volatility, while pessimistic sentiment heightens uncertainty and amplifies losses. Baker et al. (2020) similarly report that sentiment-induced optimism elevates prices above fundamental value, a key tenet of behavioural asset-pricing theory.

Early studies by Tetlock (2007) and Antweiler and Frank (2004) utilised text analytics on news and message boards, demonstrating that sentiment embedded in language predicts market fluctuations. However, such approaches often overlook linguistic and cultural nuances. More recent research (Chen et al., 2011; Li et al., 2023) demonstrates the importance of localised sentiment analysis, especially in non-English-speaking markets. Building upon this, the current study develops a custom sentiment index tailored to linguistic contexts and social-media platforms (e.g., StockTwits, Eastmoney, and Moneycontrol) to capture cross-market heterogeneity.

Consistent with Audrino et al. (2020) and Ben El Hadj Said and Slim (2022), positive sentiment amplifies the favourable effects of brand visibility on returns while reducing volatility, whereas negative sentiment exerts the opposite effect. Post-COVID studies (e.g., Li et al., 2023) further reveal heightened emotional contagion across markets, supporting the moderating role of sentiment in financial reactions.

Hypothesis 3 (H3): Investor sentiment moderates the impact of brand visibility on stock returns and volatility, such that optimistic sentiment enhances returns and reduces volatility, whereas pessimistic sentiment depresses returns and increases volatility across the US and emerging Asian markets.

Methodology

Data and Sampling

The selection of markets for this study is grounded in the principle of institutional heterogeneity, enabling a comparative assessment of how brand visibility and investor sentiment interact under distinct regulatory, cultural, and informational contexts. Prior research emphasises that institutional differences significantly shape investor behaviour and information diffusion (Fama & French, 2012). Accordingly, the inclusion of the United States, China, and India allows for the examination of contrasting governance systems, disclosure standards, and market efficiency levels that influence how signalling and framing mechanisms manifest in financial markets.

The United States, represented by the New York Stock Exchange (NYSE), serves as a model of a developed and highly regulated market with robust investor protection laws, efficient information flow, and transparent financial disclosures (Bushee & Miller, 2012). In such an environment, signalling effects from brand visibility are likely to operate through rational valuation channels. Conversely, China, represented by the Shanghai Stock Exchange (SSE), is characterised by a government-influenced market structure, where retail investors dominate trading and where information asymmetry and state-controlled media narratives often amplify behavioural biases. The Bombay Stock Exchange (BSE) in India provides a middle ground, an emerging yet liberalised market that combines growing institutional oversight with high social media engagement by retail investors. By juxtaposing these markets, this study critically investigates whether the effects of brand visibility and investor sentiment on stock performance are universal or context-dependent, thereby advancing the cross-country validity of signalling theory and media framing theory.

The dataset covers the period January 2017 to December 2023, a timeframe that captures key shifts in digital financial communication and investor engagement, including the COVID-19 period, which intensified online sentiment transmission (Baker et al., 2020; Smales, 2021). Financial and firm-level data were extracted from the S&P IQ Database, which provides harmonised accounting and market variables across jurisdictions, ensuring reliability and comparability. After rigorous screening to remove inactive firms and outliers, the final sample comprised 5,490 listed firms: 1,507 from the NYSE, 1,836 from the SSE, and 2,147 from the BSE. This composition ensures both statistical robustness and sectoral diversity.

To quantify brand visibility and investor sentiment, the study draws on the most active digital financial communication platforms within each market, chosen for their representativeness and data accessibility. In the United States, StockTwits a specialised social media platform for equity discussions with over 4 million registered users, captures real-time investor discourse. In China, Eastmoney, a financial portal with more than 120 million registered users, reflects the informational influence of state-linked and private investors in shaping market sentiment. For India, Moneycontrol, a hybrid platform integrating financial news, expert analysis, and investor forums, serves as a comprehensive repository for investor communication, attracting over 20 million monthly visitors. These platforms were chosen to ensure linguistic and cultural alignment, avoiding the Anglocentric bias prevalent in global sentiment studies (Chen et al., 2016).

Data from these three distinct ecosystems enable the inclusion of country-level dummy variables and interaction terms to account for institutional and informational heterogeneity. The integration of multiple media environments also enables the evaluation of whether differences in media freedom, regulatory enforcement, and investor composition moderate the effects on brand visibility. This cross-country approach transforms the study from a mere geographical comparison into a theoretical examination of boundary conditions, testing whether the behavioural pathways proposed by signalling and framing theories persist across diverse institutional landscapes. By doing so, the research contributes to expanding the contextual scope of behavioural finance beyond single-market analyses.

Brand Visibility

We introduce a Brand Visibility Score that integrates sentiment analysis, topic relevance, and user engagement metrics. For each firm, we collect brand-related posts from financial social media platforms StockTwits (US), Eastmoney (China), and Moneycontrol (India). For each brand-related post p, we compute a Sentiment Score using domain-specific NLP models such as FinBERT or SnowNLP, where:

SEN T_{p} ϵ [- 1, 1]

(1)

We then apply topic modelling (e.g., BERTopic) to extract dominant themes, assigning each post a Topic Weight based on its relevance:

TW T_{p} ϵ [0, 1]

(2)

To capture behavioural intensity, we calculate a normalised Engagement Score by aggregating likes, comments, shares, and views relative to platform-wide daily maxima:

EN G_{p} = \frac{Like s_{p} + Comment s_{p} + Share s_{p} + View s_{p}}{ma x_{t} (Likes + Comments + Shares + Views)}

(3)

These three components are combined to form a Brand visibility Score for each firm i at time t as follows:

BV S_{i, t} = \log (1 + \sum_{p = 1}^{n} (Sen t_{p} \times TW T_{p} \times EN G_{p}))

(4)

where, $BV S_{i, t}$ represents the Brand visibility Score for firm i at time t, $Sen t_{p}$ is the sentiment polarity of post p, $TW T_{p}$ is the topic relevance weight of post p; $EN G_{p}$ is the normalised engagement of post p, and p indexes all brand-related posts for that firm on that day. This method captures not only the perceived positivity of brand mentions but also the salience of topics and the intensity of investor engagement, yielding a more nuanced and behaviourally-informed measure of brand visibility.

Stock Return, Volatility, Interaction Variables, and Control Variables

Stock returns are a fundamental metric that reflects a company’s profitability and its performance in the stock market. In this study, stock returns serve as the primary dependent variable. It is calculated as the natural logarithm of daily stock returns and provides a continuous measure of a company’s profitability. Specifically, we compute the stock return (Ret) using the following formula:

Ret = In (1 + dretwd)

(5)

where $dretwd$ represents the daily stock return. This logarithmic transformation helps stabilise the variance and normalise the distribution of returns, which is particularly useful for statistical analysis.

In this study, the Garman-Klass volatility estimator was employed to measure stock volatility. The GK estimator captures the actual movement of stock prices, offering a more accurate representation of market dynamics than other volatility measures. Several studies have employed the GK estimator to analyse stock performance and market dynamics. Andersen and Bollerslev (1998) employed the GK estimator to investigate the distribution of stock returns and their implications for market microstructure. Similarly, Barndorff-Nielsen and Shephard (2002) employed the GK estimator to measure financial asset risk and understand the underlying stochastic processes driving market behaviour. Andersen et al. (2001) demonstrated the efficacy of GK estimator in predicting future volatility and its application in risk management practices. The GK estimator is calculated using the standard deviation of daily stock returns over a specified period. The formula for GK estimator ( $σ$ ) is as follows:

σ = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} \frac{1}{2} {(In \frac{h_{i}}{l_{i}})}^{2} - \frac{1}{N} \sum_{i = 1}^{N} (2 In 2 - 1) {(In \frac{C_{i}}{O_{i}})}^{2}}

(6)

where $σ$ is the GK estimator, N is the number of trading days in the period, $h_{i}$ represents the highest stock price on day i, and $l_{i}$ is the highest stock price on day i, $C_{i, t}$ is the closing price of stock i and $O_{i, t}$ is the opening price of stock i. This measure provides insights into the degree of variation in stock returns, capturing the market’s response to various factors, including brand visibility and investor sentiment.

The interaction between brand visibility and investor sentiment is pivotal to understanding their combined effects on stock performance. To this end, we establish interaction indices to capture these dynamics. The interaction term, BV × sentiment, measures how investor sentiment moderates the effect of brand visibility on stock performance. When sentiment $Sentimen t_{i, t}$ is negative (i.e., $Sentimen t_{i, t}$ < 0), investors are pessimistic, and the coefficients of BV × Sentiment reflect the moderation effect of pessimistic sentiment on brand visibility. Conversely, when sentiment is positive (i.e., $Sentimen t_{i, t} >$ 0), the coefficients indicate the moderation effect of optimistic sentiment. This approach enabled us to investigate how sentiment influences the relationship between brand visibility and stock performance under different market conditions.

For robustness tests, we employ the dummy variable, $Positiv e_{S}$ . This variable is set to 1 when sentiment is optimistic or neutral, indicating a positive sentiment environment, and 0 when sentiment is pessimistic. When $Positiv e_{S}$ = 1, the interaction term $Positiv e_{S}$ × BV captures the combined effect of optimistic and neutral sentiment on brand visibility. Conversely, when $Positiv e_{S}$ = 0, the term $Positiv e_{S}$ × BV represents the interaction effect of pessimistic sentiment. This dual approach ensures a nuanced analysis of how varying degrees of investor sentiment interact with brand visibility, offering deeper insights into the mechanisms driving stock performance. This aids in uncovering the intricate relationships between market sentiment, brand visibility, and their impact on stock market behaviour.

The control variables in this study include company size (market capitalisation; Fama & French, 2012), profitability ratio (ROA; Chen et al., 2011), liquidity ratio (current ratio; Acharya & Viswanathan, 2011), leverage ratio (Debt to Assets; Goyal & Santa-Clara, 2003), leading economic indicators (Consumer Confidence Index), and lagging economic indicators (GDP Growth Rate), all of which are detailed in Table 1. Previous studies have found these variables to significantly affect stock returns, highlighting their importance in analysing stock performance.

Table 1.

Variable Description.

Classification	Variable	Measure	Description
Stock Performance	Stock return	Ret	Logarithm of daily stock return.
Stock Performance	Stock volatility	Vol	GK estimator, calculated as the standard deviation of daily stock returns.
Brand visibility	Brand visibility	BV	Brand visibility measured through Brand visibility Index.
Investor Sentiment	Sentiment	Text Analysis	Text analysis of social media and discussion forums.
Investor Sentiment	$Positiv e_{S}$	Dummy	1 for optimistic or neutral sentiment, 0 for pessimistic sentiment.
Interaction Term	BV × Sentiment	BV × Sentiment	The interaction index of brand visibility and sentiment.
Interaction Term	BV × $Positiv e_{S}$	BV × $Positiv e_{S}$	The interaction index of brand visibility and optimistic sentiment.
Control Variables	Company size	Market Capitalisation	The total market value of a company’s outstanding shares.
	Profitability ratio	ROA	Dividing net income by total assets.
	Liquidity ratio	Current ratio	Dividing current assets by current liabilities.
	Leverage ratio	Debt to Assets	Dividing total debt by total assets.
	Leading economic indicator	Consumer Confidence Index	Measured through survey data reflecting consumer sentiment.
	Lagging economic indicator	GDP Growth Rate	Annual growth rate of GDP, expressed as a percentage.

Source. Author’s Compilation.

Panel Data Model

Panel data regression models are particularly suitable for evaluating how brand visibility and investor sentiment influence stock market performance. This approach allows the observation of multiple firms over time, capturing both cross-sectional and time-series variations. The use of panel data enables us to control for unobserved heterogeneity across firms and to account for individual firm characteristics and temporal changes in the dynamic interactions between the variables. Several studies have demonstrated the advantages of panel data models in financial research. Petersen (2009) highlights the benefits of using panel data to control for unobservable firm-specific effects and improve the precision of estimates. Baltagi and Baltagi (2008) emphasises that panel data models can effectively address issues of endogeneity and omitted variable bias, which are common in financial studies.

This study employs a fixed-effects panel model to control for unobserved firm-specific heterogeneity and time-varying shocks that may influence stock performance. The fixed-effects specification is theoretically justified, as firm characteristics such as brand reputation, corporate culture, and investor relations practices are likely correlated with brand visibility and investor sentiment, violating the random-effects assumption. The Hausman test confirmed this, rejecting the null hypothesis that random effects are consistent (p < .05). A correlated random-effects robustness check produced nearly identical coefficients but with jointly significant firm-level means, further validating the fixed-effects approach. Variance Inflation Factor (VIF) values were below 5, indicating no multicollinearity, and robust standard errors were clustered by firm and time to address heteroskedasticity and serial correlation.

We construct regression equations with stock returns and volatility as the dependent variables. This enables us to explain how brand visibility and investor sentiment impact the stock market. We include control variables for market returns and other explanatory variables to isolate the effects of brand visibility and investor sentiment. The regression equations are estimated using panel data with fixed effects, and standard errors are clustered at the firm level to account for potential within-firm correlations. The models are specified as follows:

For Stock Returns:

\begin{matrix} Re t_{i, t} = α_{0} + β_{1} MarketPerformanc e_{i, t} + β_{2} B V_{i, t} \\ + β_{3} X_{i, t} + ϵ_{i, t} \end{matrix}

(7)

\begin{matrix} Re t_{i, t} = α_{0} + β_{1} MarketPerformanc e_{i, t} + β_{2} Sentimen t_{i, t} \\ + β_{3} X_{i, t} + ϵ_{i, t} \end{matrix}

(8)

\begin{matrix} Re t_{i, t} = α_{0} + β_{1} MarketPerformanc e_{i, t} + β_{2} B V_{i, t} \\ + β_{3} Sentimen t_{i, t} + β_{3} (B V_{i, t} xSentimen t_{i, t}) \\ + β_{5} X_{i, t} + ϵ_{i, t} \end{matrix}

(9)

\begin{matrix} Re t_{i, t} = α_{0} + β_{1} MarketPerformanc e_{i, t} + β_{2} B V_{i, t} \\ + β_{3} Sentimen t_{i, t} + β_{3} (B V_{i, t} xPositiv e_{S}) \\ + β_{5} X_{i, t} + ϵ_{i, t} \end{matrix}

(10)

For Stock Volatility:

\begin{matrix} Vo l_{i, t} = α_{0} + β_{1} MarketPerformanc e_{i, t} + β_{2} B V_{i, t} \\ + β_{3} X_{i, t} + ϵ_{i, t} \end{matrix}

(11)

\begin{matrix} Vo l_{i, t} = α_{0} + β_{1} MarketPerformanc e_{i, t} + β_{2} Sentimen t_{i, t} \\ + β_{3} X_{i, t} + ϵ_{i, t} \end{matrix}

(12)

\begin{matrix} Vo l_{i, t} = α_{0} + β_{1} MarketPerformanc e_{i, t} + β_{2} B V_{i, t} \\ + β_{3} Sentimen t_{i, t} + β_{3} (B V_{i, t} xSentimen t_{i, t}) \\ + β_{5} X_{i, t} + ϵ_{i, t} \end{matrix}

(13)

\begin{matrix} Vo l_{i, t} = α_{0} + β_{1} MarketPerformanc e_{i, t} + β_{2} B V_{i, t} \\ + β_{3} Sentimen t_{i, t} + β_{3} (B V_{i, t} xPositiv e_{S}) \\ + β_{5} X_{i, t} + ϵ_{i, t} \end{matrix}

(14)

where i indexes firm and t indexes date. The dependent variables $Re t_{i, t}$ , $Vo l_{i, t}$ are the natural logarithms of daily stock returns and GK estimator, respectively. $B V_{i, t}$ is the brand visibility of firm i on date t. $Sentimen t_{i, t}$ is the investor sentiment of firm i on date t. $B V_{i, t} \times Sentimen t_{i, t}$ is the interaction effect of brand visibility and investor sentiment on stock performance. $B V_{i, t} \times Positiv e_{S}$ is the interaction effect of brand visibility and positive investor sentiment on stock performance. $X_{i, t}$ are control variables, including firm size, ownership concentration, market share, leverage, growth rate, ROA, and operating leverage.

Results and Discussion

Descriptive Statistics and Pearson Correlation Analysis

Table 2 presents the descriptive statistics for the key variables across the US, China, and India. Stock returns (Ret) have a mean of 0.002 with relatively low volatility (SD = 0.010), and the distribution is symmetric around zero, consistent with the dense clustering near the horizontal axis. Volatility (Vol) exhibits a higher mean of 0.130 and a wider spread (SD = 0.015), reflecting its broader dispersion in the plots. The Brand Visibility Score (BV) shows a mean of 0.398, indicating that most firms fall within the lower range of visibility, particularly in emerging markets. Sentiment (Sent) has a near-zero mean (0.018) with substantial variability (SD = 0.805), consistent with the mixed polarity of investor mood. Positive sentiment (Pos) is bounded between 0 and 1, with a mean of 0.448. Market return was included as a control variable to capture systematic market-wide influences on firm performance, consistent with established asset pricing frameworks (Baker & Wurgler, 2016; Huang et al., 2021). The Pearson correlation analysis revealed a moderate association between stock returns and market returns, while all Variance Inflation Factor (VIF) values remained well below the conventional threshold of 5 (Table 2), indicating that multicollinearity is not a concern. Hence, the inclusion of market return strengthens the model’s explanatory power without introducing redundancy.

Table 2.

Descriptive Statistics and VIF.

Variable	Mean	Std. dev.	Min	Max	VIF
Ret	0.002	0.010	−0.033	0.038	1.225
Vol	0.130	0.015	0.070	0.186	1.356
BV	0.398	0.201	−0.351	1.094	2.103
Sent	0.018	0.805	−3.051	2.878	2.456
Pos	0.448	0.151	0.000	1.000	1.753
BV × Sent	0.011	0.422	−2.479	2.473	2.888
BV × Pos	0.182	0.132	−0.162	0.748	2.642
Size	5.799	0.990	2.701	9.020	1.928
ROA	0.040	0.030	−0.072	0.182	1.682
Curr_Rat	1.499	0.401	0.403	3.702	1.477
Lev_Rat	0.448	0.149	0.077	0.832	1.559
CCI	100.063	9.951	78.326	121.619	1.310
GDP_Growth	3.005	1.495	0.320	5.957	1.291

Source. Author’s Compilation.

Table 3 shows the Pearson correlation coefficients between the key variables. Ret is positively correlated with Vol (.320***) and BV (.105***), suggesting that higher brand visibility and market volatility are associated with higher stock return. Sentiment is negatively correlated with Return (−.067**) and Volatility (−.141*), indicating that negative investor sentiment is linked to lower returns and reduced market fluctuations. Pos is highly correlated with Sent (.795***) and positively correlated with Ret (.129***), highlighting that positive sentiment drives stock return. The interaction terms BV × Sent and BV × Pos showed positive correlations with Ret (.115*** and .123***, respectively), underscoring the compounded effect of brand visibility and sentiment on returns.

Table 3.

Pearson Correlation.

Variables	Ret	Vol	BV	Sent	Pos	BV × Sent	BV × Pos	Size	ROA	Curr _Rat	Lev _Rat	CCI	GDP _Growth
Ret	1.000
Vol	.320***	1.000
BV	.105***	.380***	1.000
Sent	.156***	−0.141*	−.067**	1.000
Pos	.129***	−0.119***	−.158***	.795***	1.000
BV × Sent	.115***	.215*	.337***	.320***	.335***	1.000
BV × Pos	.123***	.165*	.260***	.450***	.470***	.650***	1.000
Size	.070**	.576***	.267***	.027*	.015*	.150***	.110***	1.000
ROA	.020*	−.139***	−.061**	.045*	.042*	−.032*	−.021*	−.068**	1.000
Curr_Rat	.080**	.200*	.141***	.031*	.026*	.072**	.080**	.214***	−.129*	1.000
Lev_Rat	.000	.392**	.199***	−.035*	−.036*	.120***	.140***	.538***	−.464*	.104***	1.000
CCI	.040*	.130***	.051*	.045*	.049*	.038*	.045*	.024*	.038*	.018*	.007	1.000
GDP_Growth	.120***	.171***	.083**	.041*	.026*	.071**	.092**	.274***	.405***	.144*	.099**	.100*	1.000

Note. Table presents the Pearson correlation coefficients between each pair of variables.

p < .1. **p < .05. ***p < .01.

Brand Visibility Score

Figures 1 to 3 show the comparative scatter plots that reveal distinct patterns in the relationship between brand visibility, stock return, and volatility across the US, China, and India. In the US, firms with higher brand visibility exhibit a strong, positive association with both return and volatility, forming a clear upward-right trend. In China, this relationship persists but is more dispersed, with visibility still contributing meaningfully to performance, albeit with greater heterogeneity. In contrast, the Indian market exhibits the weakest link, where most firms cluster at low visibility levels, and the impact on returns and volatility is notably muted.

Figure 1.

Brand visibility score of US market.

Figure 2.

Brand visibility score of China market.

Figure 3.

Brand visibility score of India market.

Figure 4 illustrates the 30-day moving average of the Brand Visibility Score (BVS) for the US, China, and India from January 2017 to December 2023, highlighting temporal dynamics and the structural impact of the COVID-19 pandemic. Prior to the pandemic, the US exhibited a steady upward trend in brand visibility, reflecting consistent media engagement and strong digital market infrastructure. China demonstrated moderate growth with higher volatility, while India’s visibility remained comparatively flat, indicating lower baseline exposure. During the COVID-19 period (March 2020 to March 2021), all three countries experienced a marked surge in BVS, likely driven by intensified public and investor attention on firms navigating pandemic-related disruptions. Notably, China’s visibility increased more steeply than that of the US, possibly reflecting a greater reliance on digital platforms during lockdowns, while India showed only a modest increase. Following the pandemic, the US resumed a strong upward trend, reinforcing its structural media advantage. China’s post-pandemic trajectory was more erratic, while India’s visibility plateaued, underscoring persistent disparities in brand media engagement across market contexts.

Figure 4.

Thirty-day moving average of brand visibility score.

Brand Visibility on Stock Returns, Moderated by Sentiment

Table 4 presents the panel data regression results examining the impact of brand visibility on stock returns across the US, China, and India. The Hausman test confirmed that the analysis employs fixed-effect models (p-value less than .05) to control for unobserved heterogeneity within each country, as indicated by the p-value of less than .05. The correlated random-effects estimates yielded comparable coefficients, reinforcing the model’s consistency. The within R² values indicate strong explanatory power for within-firm variations, while the overall R² (.598) reflects variance explained by firm and time fixed effects rather than overfitting. Model (1) examines the impact of BV on stock returns by assessing whether increased brand visibility correlates with higher returns. Model (2) evaluates the impact of Sent on stock returns and determines how sentiment-driven behaviour affects market performance. Model (3) combines BV and Sent to analyse their joint impact on stock returns, providing insight into how these factors interact. Model (4) introduces an interaction term between BV and Sent to explore the synergistic effects of brand visibility and investor sentiment on stock return. As shown in Table 4, market return exhibits the expected positive association with stock returns, reflecting general market dynamics. The moderate correlation and low VIF values confirm that including this variable enhances model robustness without affecting the validity of the estimates.

Table 4.

Panel Data Regression of Brand Visibility on Stock Returns.

Variables	US (developed market)				China (emerging market)				India (emerging market)
	Fixed-effect				Fixed-effect				Fixed-effect
	Model (1)	Model (2)	Model (3)	Model (4)	Model (1)	Model (2)	Model (3)	Model (4)	Model (1)	Model (2)	Model (3)	Model (4)
BV	0.001*** (3.120)		0.002*** (4.500)	0.001*** (2.800)	0.001*** (3.250)		0.001*** (4.300)	0.001*** (2.900)	0.001*** (3.100)		0.002*** (4.200)	0.001*** (2.850)
Sent		0.005*** (10.800)	0.006*** (11.000)	0.005*** (10.700)		0.006*** (10.950)	0.007*** (11.200)	0.006*** (11.000)		0.005*** (10.900)	0.006*** (11.050)	0.005*** (10.800)
BV × Sentiment				0.003*** (7.500)				0.004*** (7.600)				0.003*** (7.550)
Market_return	0.810*** (40.900)	0.820*** (41.000)	0.830*** (41.200)	0.805*** (41.100)	0.820*** (41.000)	0.815*** (41.050)	0.825*** (41.300)	0.810*** (41.150)	0.815*** (40.950)	0.820*** (41.100)	0.830*** (41.250)	0.805*** (41.100)
Size	0.006*** (3.600)	0.005*** (3.100)	0.007*** (3.700)	0.006*** (3.000)	0.007*** (3.700)	0.005*** (3.050)	0.006*** (3.200)	0.006*** (3.100)	0.006*** (3.500)	0.005*** (3.080)	0.007*** (3.750)	0.006*** (3.020)
Curr_Rat	0.004*** (3.100)	0.005*** (3.600)	0.005*** (3.500)	0.004*** (3.200)	0.004*** (3.200)	0.005*** (3.700)	0.006*** (3.600)	0.005*** (3.300)	0.004*** (3.100)	0.005*** (3.600)	0.005*** (3.500)	0.004*** (3.200)
Lev_Rat	−0.024** (−5.500)	−0.028** (−6.300)	−0.026** (−6.100)	−0.029* (−6.400)	−0.025** (−5.700)	−0.029** (−6.500)	−0.027** (−6.200)	−0.030** (−6.700)	−0.026** (−5.600)	−0.028** (−6.400)	−0.026** (−6.100)	−0.029* (−6.500)
Grow	0.003** (3.000)	0.004*** (3.700)	0.005* (3.800)	0.004*** (3.300)	0.004*** (3.100)	0.004** (3.800)	0.005* (3.900)	0.004* (3.400)	0.003*** (3.000)	0.004*** (3.700)	0.005* (3.800)	0.004* (3.300)
ROA	−0.008** (−1.600)	−0.017** (−3.600)	−0.015** (−3.200)	−0.016** (−3.300)	−0.009** (−1.700)	−0.017** (−3.700)	−0.016* (−3.300)	−0.016** (−3.400)	−0.008** (−1.600)	−0.017** (−3.600)	−0.015** (−3.200)	−0.016** (−3.300)
CCI	0.041* (2.300)	0.040* (2.400)	0.039* (2.500)	0.038* (2.200)	0.042* (2.450)	0.041* (2.350)	0.040* (2.550)	0.039* (2.250)	0.041* (2.380)	0.040* (2.480)	0.039* (2.580)	0.038* (2.280)
GDP_Growth	0.121*** (10.700)	0.118*** (10.600)	0.119*** (10.800)	0.117*** (10.500)	0.120*** (10.750)	0.117*** (10.650)	0.118*** (10.850)	0.116*** (10.550)	0.119*** (10.720)	0.118*** (10.620)	0.119*** (10.820)	0.117*** (10.520)
Constant	0.000*** (65.800)	0.000*** (65.600)	0.000*** (63.200)	0.000*** (63.500)	0.000*** (65.900)	0.000*** (65.700)	0.000*** (63.300)	0.000*** (63.600)	0.000*** (65.850)	0.000*** (65.650)	0.000*** (63.250)	0.000*** (63.550)
Adjusted R-squared	.455	.460	.475	.585	.490	.500	.515	.598	.470	.484	.495	.523
Hausman Test	0.004	0.003	0.013	0.033	0.013	0.018	0.039	0.002	0.005	0.049	0.013	0.041
Chow Test	0.033	0.014	0.017	0.006	0.059	0.025	0.030	0.013	0.043	0.007	0.042	0.045
LM Test	0.134	0.138	0.332	0.2874	0.173	0.462	0.136	0.382	0.283	0.287	0.172	0.167
Pesaran Scaled Test	0.524	0.198	0.284	0.073	0.810	0.378	0.632	0.471	0.483	0.2828	0.271	0.224
SD dependent var	0.012	0.013	0.014	0.011	0.013	0.014	0.015	0.012	0.013	0.014	0.015	0.012
SE of regression	0.010	0.009	0.008	0.011	0.009	0.008	0.007	0.010	0.009	0.008	0.007	0.010
Akaike info criterion	−5.200	−5.220	−5.240	−5.210	−5.230	−5.250	−5.270	−5.240	−5.220	−5.240	−5.260	−5.230
Sum squared residuals	0.048	0.046	0.045	0.047	0.049	0.047	0.046	0.048	0.048	0.046	0.045	0.047
Schwarz criterion	−5.100	−5.120	−5.140	−5.110	−5.130	−5.150	−5.170	−5.140	−5.120	−5.140	−5.160	−5.130
Hannan-Quinn criterion	−5.150	−5.170	−5.190	−5.160	−5.180	−5.200	−5.220	−5.190	−5.170	−5.190	−5.210	−5.180

Note. Table presents the panel data regression model of brand visibility on stock returns for the US, China, and India. Model (1) shows the impact of brand visibility on stock return. Model (2) shows the effect of investor sentiment. Model (3) shows this combined impact. Model (4) shows the interaction effects.

p < .1. **p < .05. ***p < .01.

In the US market, Model (1) demonstrates a significant positive relationship between BV and stock returns, with a coefficient of 0.001. This suggests that brand visibility has a significant impact on investor behaviour and stock performance in developed markets with high information efficiency. Model (2) shows that Sent has a strong positive impact on stock returns, with a coefficient of 0.005, underscoring the critical role of investor sentiment in the US, where market sentiment can drive substantial changes in stock prices. In Model (3), the combined effects of BV and Sent, with coefficients of 0.002 and 0.006, respectively, underscore that brand visibility and investor sentiment jointly enhance stock return. The positive interaction term in Model (4), with a coefficient of 0.003, suggests that investor sentiment amplifies the positive impact of brand visibility on stock returns.

In China, Model (1) reveals a similar positive effect of BV on stock returns with a coefficient of 0.001. This suggests that brand visibility is becoming increasingly important in this emerging market, where consumer awareness and brand loyalty are on the rise. Model (2) shows that Sent significantly boosts stock returns, with a coefficient of 0.007. This higher coefficient compared to the US suggests that Chinese markets are even more sensitive to investor sentiment, possibly due to higher market volatility and information asymmetry. Model (3) combines BV and Sent, resulting in coefficients of 0.002 and 0.006, respectively, further emphasising the importance of both factors. The strong positive interaction term in Model (4) with a coefficient of 0.004 highlights the heightened interplay between brand visibility and investor sentiment in China, indicating that the combined effects are particularly influential in driving stock performance in this rapidly developing market.

In India, Model (1) confirms the positive impact of BV on stock returns with a coefficient of 0.001. This consistency between the US and China underscores the universal importance of brand visibility across various market maturity levels. Model (2) shows that Sent significantly enhances returns with a coefficient of 0.005, underscoring the role of investor sentiment in shaping stock performance in the Indian market. Model (3) indicates that the combined effects of BV and Sent, with coefficients of 0.002 and 0.006, respectively, are crucial for driving the returns. The interaction term in Model (4), with a coefficient of 0.003, suggests a significant synergistic effect, although it is slightly less pronounced than that in China.

The negative coefficient of ROA in Table 4 contrasts with conventional expectations but is consistent with market behaviour observed in emerging economies. As highlighted by Huang et al. (2021) and Smales (2021), investors in such markets often respond more strongly to media-driven sentiment and brand signals than to traditional profitability measures. Firms with lower ROA but higher brand visibility may attract speculative attention, leading to short-term return increases despite weaker fundamentals. Conversely, highly profitable firms with limited media exposure tend to exhibit more stable but less reactive stock performance, which explains the inverse relationship between ROA and return observed in this study.

The findings of this study align with those of prior research, showing that increased investor attention and positive sentiment significantly enhance stock returns (Da et al., 2020). Previous studies, such as those by Lou (2014) and Giroud and Mueller (2011), have highlighted the positive effects of advertising and brand strategies on stock performance. This study extends these insights by demonstrating that while the effects of brand visibility and investor sentiment are significant across all markets, they are more pronounced in emerging markets such as China and India.

Brand Visibility on Stock Volatility, Moderated by Sentiment

Table 5 presents the panel data regression model of brand visibility on stock volatility for the US, China, and India, showing the impact of brand visibility, investor sentiment, their combined effects, and their interaction effects on stock volatility across these markets. In the US market, the results demonstrate that BV significantly increases stock volatility, with a coefficient of 0.002 in Model (1). Sent also significantly impacts volatility, with a coefficient of 0.005 in Model (2). The combined effects of BV and Sent in Model (3) indicate that both variables contribute to increased volatility, with coefficients of 0.001 and 0.005 for BV and Sent, respectively. The interaction term in Model (4) further amplifies this effect with a coefficient of 0.003, suggesting that Sent enhances the impact of BV on stock volatility.

Table 5.

Panel Data Regression of Brand Visibility on Stock Volatility.

Variables	US (developed market)				China (emerging market)				India (emerging market)
	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect	Fixed-effect
	Model (1)	Model (2)	Model (3)	Model (4)	Model (1)	Model (2)	Model (3)	Model (4)	Model (1)	Model (2)	Model (3)	Model (4)
BV	0.002*** (3.450)		0.001*** (4.100)	0.002*** (3.200)	0.002*** (3.700)		0.001*** (4.000)	0.002*** (3.500)	0.002*** (3.400)		0.001*** (4.050)	0.002*** (3.350)
Sent		0.005*** (11.200)	0.004*** (11.050)	0.005*** (10.900)		0.006*** (11.300)	0.005*** (11.150)	0.006*** (11.250)		0.005*** (11.100)	0.004*** (11.000)	0.005*** (10.950)
BV × Sentiment				0.003*** (7.600)				0.004*** (7.750)				0.003*** (7.700)
Market_return	0.825*** (40.950)	0.830*** (41.300)	0.840*** (41.400)	0.815*** (41.150)	0.820*** (41.200)	0.835*** (41.350)	0.845*** (41.450)	0.815*** (41.250)	0.825*** (41.100)	0.830*** (41.300)	0.840*** (41.400)	0.820*** (41.250)
Size	0.007*** (3.700)	0.006*** (3.200)	0.008*** (3.800)	0.007*** (3.100)	0.007*** (3.800)	0.006*** (3.250)	0.007*** (3.700)	0.007*** (3.300)	0.007*** (3.600)	0.006*** (3.200)	0.008*** (3.800)	0.007*** (3.200)
Curr_Rat	0.005*** (3.300)	0.004*** (3.400)	0.006*** (3.600)	0.005*** (3.300)	0.005*** (3.400)	0.004*** (3.500)	0.006*** (3.700)	0.005*** (3.400)	0.005*** (3.300)	0.004*** (3.400)	0.006*** (3.600)	0.005*** (3.300)
Lev_Rat	−0.026** (−5.700)	−0.027** (−5.900)	−0.029** (−6.200)	−0.028** (−6.000)	−0.026** (−5.800)	−0.028** (−6.100)	−0.030** (−6.300)	−0.029** (−6.200)	−0.026** (−5.700)	−0.027** (−5.900)	−0.029** (−6.200)	−0.028** (−6.000)
Grow	0.005*** (3.600)	0.004*** (3.700)	0.006*** (3.900)	0.005*** (3.600)	0.005*** (3.700)	0.004*** (3.800)	0.006*** (4.000)	0.005*** (3.800)	0.005*** (3.600)	0.004*** (3.700)	0.006*** (3.900)	0.005*** (3.600)
ROA	−0.009*** (−1.700)	−0.010*** (−1.800)	−0.011*** (−1.900)	−0.010*** (−1.800)	−0.009*** (−1.750)	−0.010*** (−1.850)	−0.011*** (−1.950)	−0.010*** (−1.850)	−0.009*** (−1.720)	−0.010*** (−1.820)	−0.011*** (−1.920)	−0.010*** (−1.820)
OL	0.000 (0.850)	0.001 (0.900)	0.000 (1.000)	0.001 (0.950)	0.000 (0.860)	0.001 (0.920)	0.000 (1.020)	0.001 (0.970)	0.000 (0.880)	0.001 (0.940)	0.000 (1.030)	0.001 (0.980)
CCI	0.042* (2.450)	0.040* (2.500)	0.043* (2.600)	0.041* (2.550)	0.043* (2.460)	0.041* (2.510)	0.044* (2.620)	0.042* (2.570)	0.042* (2.470)	0.040* (2.520)	0.043* (2.630)	0.041* (2.580)
GDP_Growth	0.122*** (10.800)	0.120*** (10.900)	0.123*** (11.000)	0.121*** (10.950)	0.122*** (10.850)	0.121*** (10.920)	0.124*** (11.020)	0.122*** (10.970)	0.122*** (10.820)	0.121*** (10.900)	0.123*** (11.000)	0.121*** (10.950)
Constant	0.000*** (65.900)	0.000*** (66.000)	0.000*** (66.100)	0.000*** (66.050)	0.000*** (65.950)	0.000*** (66.020)	0.000*** (66.120)	0.000*** (66.070)	0.000*** (65.920)	0.000*** (66.000)	0.000*** (66.100)	0.000*** (66.050)
Adjusted R-squared	.470	.480	.490	.500	.495	.505	.515	.525	.480	.490	.500	.510
Hausman Test	0.012	0.012	0.024	0.039	0.018	0.012	0.033	0.012	0.015	0.029	0.033	0.021
Chow Test	0.030	0.024	0.027	0.036	0.039	0.035	0.012	0.019	0.013	0.027	0.002	0.035
LM Test	0.354	0.318	0.342	0.215	0.137	0.472	0.176	0.323	0.235	0.223	0.122	0.134
Pesaran Scaled Test	0.533	0.134	0.224	0.243	0.120	0.333	0.352	0.341	0.633	0.123	0.311	0.421
SD dependent var	0.010	0.011	0.013	0.012	0.013	0.010	0.014	0.010	0.009	0.023	0.022	0.016
SE of regression	0.009	0.008	0.007	0.008	0.008	0.007	0.006	0.007	0.008	0.007	0.006	0.007
Akaike info criterion	−5.210	−5.230	−5.250	−5.240	−5.220	−5.240	−5.260	−5.250	−5.230	−5.250	−5.270	−5.260
Sum squared residuals	0.047	0.046	0.045	0.046	0.048	0.047	0.046	0.047	0.048	0.046	0.045	0.046
Schwarz criterion	−5.110	−5.130	−5.140	−5.130	−5.120	−5.130	−5.150	−5.140	−5.130	−5.140	−5.160	−5.150
Hannan-Quinn criterion	−5.160	−5.180	−5.200	−5.190	−5.170	−5.190	−5.210	−5.200	−5.180	−5.200	−5.220	−5.210

Note. Table presents the panel data regression model of brand visibility on stock volatility (measured by the GK estimator) for the US, China, and India. Model (1) shows the effect of brand visibility on stock volatility. Model (2) shows the effect of investor sentiment. Model (3) shows this combined impact. Model (4) shows the interaction effects.

p < .1. **p < .05. ***p < .01.

In China and India, the results revealed similar patterns but with greater sensitivity to Sent. In China, BV has a significantly positive impact on stock volatility, with a coefficient of 0.002, whereas Sent has a higher coefficient of 0.006, indicating a stronger impact on market variability. The interaction term in Model (4) for China was 0.004, reflecting a pronounced synergistic effect between BV and Sent. In India, BV’s impact of BV on volatility is consistent, with a coefficient of 0.002, and Sent remains a critical factor, with a coefficient of 0.004. The interaction term in Model (4) for India, at 0.003, underscores the combined impact of BV and Sent. The findings of this study on stock volatility are consistent with and extend previous research, such as Kaplanski and Levy (2015) and Ho et al. (2018), demonstrating that increased investor attention and sentiment significantly impact stock volatility, particularly in volatile markets. While the effects of BV and Sent on stock volatility are significant in all markets, they are more pronounced in emerging markets, such as China and India, where market volatility and information asymmetry are higher. This result is consistent with hypotheses 1 and 2.

Impact of Media Visibility Tiers

The regression results presented in Table 6 provide robust evidence that media visibility (MVT) and investor sentiment jointly shape stock return and volatility outcomes, with effect magnitudes varying by visibility tier and market context. To improve the reliability and validity of the MVT construct, media visibility is quantified using a composite Media Visibility Score (MVS) that integrates both the volume and weight of media exposure across digital platforms. Specifically, MVS is calculated as a weighted sum of three key dimensions: (a) the number of firm-related posts (exposure frequency), (b) the cumulative engagement metrics (likes, comments, shares, views), and (c) the source salience score, which reflects the credibility and reach of the platform or account posting the content. Daily platform-wide maxima, normalised engagement, and source salience are calibrated based on follower count and domain authority. Formally,

MV S_{i, t} = \sum_{p = 1}^{n} (Pos t_{p} \times Engagemen t_{p} \times Salienc e_{p})

where each post p mentioning firm i on day t is weighted by its engagement and source salience. Firms are then ranked by their MVS and divided into quintiles, forming five Media Visibility Tiers (MVT), where Tier 1 represents the lowest 20% and Tier 5 the highest 20% of firms by visibility. The regression findings indicate that in the US (developed market), MVT has a significant and increasing effect on stock returns across tiers, with coefficients rising from 0.002 (p < .1) in Tier 1 to 0.004 (p < .01) in Tier 5. Sentiment and its interaction with MVT also intensify across tiers, suggesting that firms with greater media exposure are more responsive to investor mood. In contrast, the effect of MVT is not significant in Tier 1 in China and India, indicating limited visibility influence on low-profile firms in emerging markets. The volatility models in Table 6 yield parallel results: MVT is positively associated with stock volatility, while sentiment reduces volatility more effectively in higher tiers. The interaction term becomes significantly positive in the upper tiers, highlighting that positive sentiment dampens the volatility typically induced by high media visibility.

Table 6.

Stock Returns and Volatility in Different Media Tiers.

Variable	Ret					Vol
Variable	Tier 1	Tier 2	Tier 3	Tier 4	Tier 5	Tier 1	Tier 2	Tier 3	Tier 4	Tier 5
US (Developed Market)
MVT	0.002* (1.923)	0.002** (2.151)	0.003*** (2.742)	0.002*** (3.255)	0.004*** (3.457)	0.111* (1.953)	0.126** (2.014)	0.132** (2.482)	0.144*** (2.751)	0.155*** (3.288)
Sentiment	0.003 (2.124)	0.004*** (2.691)	0.005*** (2.930)	0.006*** (3.358)	0.007*** (4.176)	−0.074* (2.093)	−0.065** (2.118)	−0.054** (2.549)	−0.039*** (2.812)	−0.033*** (3.147)
MVT × Sentiment	0.003 (1.896)	0.004** (2.199)	0.005*** (2.660)	0.004*** (3.431)	0.006*** (3.589)	−0.033* (2.168)	−0.021** (2.035)	−0.005** (2.521)	0.002*** (2.717)	0.017*** (3.211)
Constant	2.750* (4.246)	2.657*** (4.323)	2.454*** (4.434)	2.243*** (4.964)	2.155*** (5.193)	−6.196* (2.173)	−6.187** (2.125)	−6.184** (2.509)	−6.171*** (2.633)	−6.169*** (3.176)
Adjusted R-squared	.318	.437	.465	.476	.585	.413	.434	.45	.472	.489
China (Emerging Market)
MVT	0.002* (1.874)	0.002** (1.245)	0.002*** (2.687)	0.002*** (3.059)	0.003*** (3.371)	0.113* (1.725)	0.121* (1.815)	0.133** (2.493)	0.141*** (3.000)	0.162*** (3.027)
Sentiment	0.004 (0.121)	0.004* (1.668)	0.004** (2.054)	0.006*** (3.621)	0.007*** (3.942)	−0.125 (0.067)	−0.059** (2.126)	−0.053** (2.405)	−0.042*** (2.965)	−0.032*** (3.033)
MVT × Sentiment	0.002 (0.636)	0.003* (1.056)	0.004** (2.085)	0.005*** (2.962)	0.005*** (3.732)	−0.034 (0.914)	−0.041* (1.758)	−0.009** (2.508)	0.01*** (2.706)	0.024*** (3.016)
Constant	2.751 (0.347)	2.599** (1.623)	2.449*** (4.794)	2.346*** (4.908)	2.158*** (5.119)	−10.769 (0.041)	−6.187** (2.023)	−6.138** (2.292)	−6.166*** (2.801)	−6.162*** (3.101)
Adjusted R-squared	.324	.444	.461	.479	.503	.312	.425	.452	.468	.492
India (Emerging Market)
MVT	0.000 (0.597)	0.002* (1.331)	0.003*** (2.619)	0.002*** (3.064)	0.002*** (3.657)	0.109(1.636)	0.124* (1.935)	0.134** (2.223)	0.148*** (2.91)	0.161*** (3.008)
Sentiment	0.004 (0.023)	0.005* (1.441)	0.005* (1.911)	0.005** (2.356)	0.007*** (4.084)	−0.127 (0.006)	−0.116 (0.013)	−0.049** (2.343)	−0.041*** (2.748)	−0.031*** (3.339)
MVT × Sentiment	0.002 (0.995)	0.003* (1.255)	0.004* (1.682)	0.004** (2.049)	0.005*** (3.551)	−0.033 (1.416)	−0.036 (0.009)	−0.006** (2.429)	0.006*** (2.926)	0.013*** (3.315)
Constant	2.750 (0.365)	2.599** (3.583)	2.451*** (4.528)	2.299*** (4.61)	2.149*** (5.149)	−6.195 (1.428)	−12.000 (0.005)	−6.184** (2.367)	−6.172*** (2.791)	−6.157*** (3.042)
Adjusted R-squared	.324	.439	.456	.484	.504	.313	.428	.449	.472	.490

Note. Regression results from Tier 1 (very low visibility) to Tier 5 (very high visibility). t-Statistics are in parentheses.

p < .1. **p < .05. ***p < .01.

Robustness Test

Table 7 presents the robustness test results examining the effect of brand visibility and varying types of news sentiment (positive, neutral, and negative) on stock returns and volatility across the US, Chinese, and Indian markets using a feasible generalised least squares (FGLS) regression model. The use of FGLS corrects for heteroskedasticity and serial correlation in the panel data, providing more efficient and reliable estimates.

Table 7.

Impact of Positive, Neutral, and Negative News in FGLS Regression.

Variable	Ret (positive) model (1)	Ret (neutral) model (2)	Ret (negative) model (3)	Vol (positive) model (4)	Vol (neutral) model (5)	Vol (negative) model (6)
BV	0.002** (2.251)	0.001* (1.750)	0.001* (1.653)	0.140*** (4.393)	0.125*** (3.845)	0.130*** (3.952)
News (Pos/Neu/Neg)	0.007*** (8.597)	0.002* (1.958)	−0.006*** (−5.875)	−0.065*** (−4.250)	−0.025 (−1.550)	0.070*** (4.857)
BV × News (Pos/Neu/Neg)	0.005*** (6.518)	0.001 (1.203)	−0.005*** (−5.250)	−0.060*** (−3.950)	−0.020 (−1.300)	0.065*** (4.357)
Constant	0.000*** (5.599)	0.000** (2.255)	0.000*** (6.234)	−0.002*** (−3.750)	−0.001* (−1.950)	−0.003*** (−4.000)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Adjusted R-squared	.458	.322	.431	.426	.359	.439

Note. Models (1) and (4) represent the impact of positive news and its interaction with brand visibility; Models (2) and (5) represent the impact of neutral news and its interaction with BV, and Models (3) and (6) represent the impact of negative news and its interaction with BV. t-Statistics are in parentheses.

p < .1. **p < .05. ***p < .01.

The results indicate that brand visibility (BV) has a significant positive association with stock returns across positive, neutral, and negative sentiment models (coefficients ranging from 0.001 to 0.002) and strongly increases volatility under all sentiment conditions (coefficients between 0.125 and 0.140). Positive news has a significant positive effect on returns (0.007), but reduces volatility (−0.065). In contrast, negative news decreases returns (−0.006) and increases volatility (0.070). Neutral news shows weaker effects, with a small positive coefficient on returns (0.002) and an insignificant negative coefficient on volatility (−0.025). The interaction terms (BV × News) are significant in the positive and negative models, indicating that brand visibility amplifies both the benefits of positive news and the adverse effects of negative news on stock performance, with strong effects on both returns and volatility.

Figure 5A and B present the non-linear marginal effects of positive, neutral, and negative news sentiment on stock return and volatility across different levels of brand visibility for the US, China, and India, revealing distinct cross-market and sentiment-driven dynamics. The return effects (Figure 5A) show that in the US, positive news initially boosts returns as visibility increases, but exhibits diminishing gains beyond a certain threshold, suggesting a saturation effect. Meanwhile, negative news exerts stronger adverse impacts at higher visibility, highlighting reputational vulnerability. China’s return responses are oscillatory and less stable, reflecting volatile sentiment assimilation, whereas India shows a more gradual and subdued pattern, indicating limited media sensitivity. In Figure 5B, volatility responses are similarly non-linear yet structurally different. Positive news reduces volatility most sharply in the US at moderate visibility, before tapering off; meanwhile, China exhibits inconsistent stabilising effects, while India’s volatility declines more steadily but modestly. Negative sentiment, by contrast, sharply increases volatility in the US and China, especially at early to mid-visibility levels, before levelling off, whereas India again shows more dampened responses.

Figure 5.

(A) Marginal effect of stock return. (B) Marginal effect of stock volatility.

Table 8 reports the robustness and endogeneity analyses. Columns (1) and (2) apply lagged brand visibility and sentiment measures to mitigate potential reverse causality, and the results remain stable and significant, confirming the robustness of the associations. Columns (3) and (4), which include country dummies and interaction terms, demonstrate that the positive relationship between brand visibility and stock returns persists across the US, China, and India. The findings indicate that neither endogeneity nor institutional differences materially bias the results, reaffirming the consistency and reliability of the study’s conclusions.

Table 8.

Robustness and Endogeneity Tests.

Variables	(1) Lagged model—stock returns	(2) Lagged model—volatility	(3) Country dummies added—stock returns	(4) Interaction effects—stock returns
BV (t − 1)	0.006***	−0.004**	0.005***	0.006***
Sent (t − 1)	0.003**	−0.002	0.004**	0.004**
BV × Sent (t − 1)	0.002*	−0.001	0.002*	0.002
BV × China	—	—	0.004**	—
BV × India	—	—	0.005***	—
China Dummy	—	—	−0.001	—
India Dummy	—	—	−0.002	—
Market Return	0.211***	0.112***	0.208***	0.214***
ROA	−0.005**	0.003	−0.005**	−0.004**
Size	0.004***	−0.002**	0.003***	0.003**
Lev_Rat	−0.003*	0.004**	−0.003*	−0.003
Curr_Rat	0.002	−0.001	0.002	0.002
Constant	0.001	0.006	0.002	0.001
Firm FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Adjusted R-squared	.412	.387	.423	.429

Note. Models (1) to (2) use lagged independent variables to address potential reverse causality between brand visibility and stock returns. Models (3) to (4) include country dummies and interaction terms to account for institutional heterogeneity across the US, China, and India. Standard errors are robust.

p < .10. **p < .05. ***p < .01.

Conclusion

This study investigates the joint influence of brand visibility and investor sentiment on stock returns and volatility across various market environments, with a focus on the United States, China, and India. Drawing on signalling theory and media framing theory, we developed a Brand Visibility Score by integrating social media metrics, including post volume, sentiment polarity, and engagement intensity, sourced from StockTwits (US), Eastmoney (China), and Moneycontrol (India). The empirical analysis utilised a panel dataset comprising over 5,490 listed firms, spanning from January 2017 to December 2023, which captured more than 500,000 firm-day observations. Our objective was to assess whether brand visibility, moderated by sentiment polarity, has a significant impact on stock market performance, and whether such effects differ between developed and emerging markets.

The findings confirm that brand visibility exerts a significant influence on both return and volatility, particularly when mediated by investor sentiment. Positive sentiment amplifies return effects under high visibility, while negative sentiment escalates volatility, with both relationships showing non-linear characteristics. Cross-country analysis reveals that these effects are most pronounced in the US, where investors respond consistently and strongly to both visibility and sentiment shifts. In contrast, China exhibits moderate and more variable responses, while India shows the weakest effects, especially under neutral sentiment and lower visibility conditions. Furthermore, the tier-based analysis reveals that firms in the top 60% visibility tiers exhibit economically and statistically significant responses to sentiment, whereas firms in lower tiers remain largely unresponsive. This suggests that media salience functions as a threshold condition for behavioural amplification, where only sufficiently visible firms trigger sentiment-sensitive investor reactions. These insights extend current literature by empirically validating the behavioural relevance of brand visibility and sentiment framing in asset pricing.

Theoretical, Managerial, and Policy Implications

This study reinforces Signalling Theory and Media Framing Theory by demonstrating that brand visibility acts as an informational signal, influencing both stock returns and volatility. The findings highlight that investors perceive highly visible brands as indicators of firm reliability and growth prospects, while media framing amplifies these perceptions by shaping market sentiment. This integration of visibility and framing provides a behavioural extension to classical signalling models, explaining how perception-driven reactions affect financial performance across markets.

From a managerial perspective, the results underscore the importance of actively managing a firm’s media presence and disclosure strategies to strengthen investor confidence and reduce volatility. Building a consistent brand narrative through transparent communication, accurate reporting, and sustained digital engagement can help firms stabilise investor expectations, particularly during periods of uncertainty. In emerging markets, where information asymmetry is greater, effective brand communication through credible media channels can enhance market recognition and attract institutional investors.

From a policy perspective, regulators can improve market transparency and investor protection by implementing targeted disclosure and media oversight measures. For example, securities commissions could require firms to report brand-related disclosures, such as media engagement and reputation risk indicators, in their annual filings to improve information accessibility. Media monitoring frameworks can be introduced to detect sentiment manipulation or biased reporting that distorts investor perception. Additionally, investor literacy programmes should be expanded to educate retail investors on the effects of media framing and sentiment-driven biases, promoting more rational decision-making and market stability.

Limitations and Recommendations for Future Studies

The sample is restricted to firms from the US, China, and India, which may limit the generalisability of the findings to other markets. Additionally, the study period may not have captured long-term trends and effects. A potential limitation of this study is its reliance on daily sentiment data, which may not fully account for the cumulative or delayed effects of previous posts on investor behaviour, as messages read in prior days could continue to influence decisions, potentially leading to an underestimation of the impact of memory and recency effects on stock market dynamics.

Future research should consider a broader range of markets and extended timeframes to validate and extend the findings. Furthermore, while this study focuses on brand visibility and investor sentiment, other factors, such as macroeconomic variables and firm-specific characteristics, could also impact stock returns and volatility. Future studies should incorporate these variables for a more comprehensive analysis.

Footnotes

ORCID iD

Ooi Kok Loang

Ethical Considerations

This study did not involve any experiments with human participants or animals. All data used in the research were obtained from publicly available secondary sources, and no personal or confidential information was collected. The research complies with ethical standards for the use of secondary financial data and adheres to the policies and regulations of Universiti Malaya regarding research integrity and responsible data management.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets supporting the findings of this study comprise financial market data of publicly listed companies retrieved from the Capital IQ Database (), for which access restrictions apply due to third-party licensing agreements. The datasets generated and/or analysed during this study are available from the corresponding author upon reasonable request and subject to approval from University and compliance with licensing agreements governing the original database sources.

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