Purpose The negative distress–return relationship has been documented not only in the U.S. but also in China, posing a puzzling challenge for financial economics research. This paper aims to provide a behavioral explanation for the distress puzzle in China. Design/methodology/approach The data sample includes more than 4,700 A-shares listed on the Shenzhen and Shanghai stock exchanges from 2005 to 2025. Distress risk is measured by the distance-to-default of Merton (1974) or the Z″-score of Altman et al. (2017). Stock mispricing is measured based on the decomposition framework proposed by Rhodes-Kropf et al. (2005). To examine the effect of mispricing on the distress risk anomaly, we perform double-sort analyses, asset pricing models and Fama–MacBeth cross-sectional regressions. Findings We document a distress risk anomaly in China, whereby average stock returns systematically increase as distress risk declines, regardless of the risk proxy. In the double-sort analysis, the distress risk premium disappears in the undervalued group, whereas it becomes more robust in the overvalued group. The anomalous distress–return relationship is also insignificant after controlling for mispricing in asset pricing models and Fama–MacBeth regressions. Therefore, stock mispricing provides a compelling explanation for the distress risk puzzle in China. Originality/value The paper is the first to examine the effect of mispricing on the anomalous distress–return relationship in China, the second-largest market with unique characteristics. We also contribute to the asset pricing literature by constructing a mispricing factor for the Chinese stock market. Our findings have several essential implications for both investors and policymakers. Investors seeking abnormal returns from default-risk strategies should focus on undervalued distressed stocks and incorporate mispricing factors into asset pricing models. Chinese policymakers should develop effective policies to strengthen institutional investor participation and reduce market frictions.
Purpose This study examines the daily closing level of the Borsa Istanbul 100 (BIST100) Index using a unified framework that combines machine-learning (ML) and deep-learning (DL) methods with macro-financial, behavioral and market-based indicators. Design/methodology/approach The empirical model analyzes a dataset consisting of 3,222 trading days spanning January 2010 to October 2022. The explanatory variable set includes foreign ownership, the policy rates of the Central Bank of the Republic of Türkiye and the US Federal Reserve, the consumer price index, the USD/TRY exchange rate, the Amihud illiquidity measure, and an investor-sentiment index derived from securities investment-trust discounts. Forecasting performance is evaluated within a common model-comparison framework. Findings The results show that both ML and DL models provide strong forecasting performance for the BIST100 Index. Across specifications, macro-financial variables contain the strongest predictive information, while liquidity and sentiment contribute complementary explanatory content. Overall, the findings suggest that BIST100 dynamics are shaped by the joint influence of economic fundamentals, market sentiment and liquidity conditions. The study contributes to the BIST100 and emerging-market forecasting literature by evaluating these determinants within an integrated data-driven framework. Originality/value This study pioneers a unified forecasting framework that simultaneously evaluates macroeconomic fundamentals, market illiquidity and behavioral sentiment, moving beyond the isolated approaches common in the literature. Furthermore, it bridges the gap between high predictive accuracy and economic interpretability, employing rigorous lagged-return validations to capture genuine predictive alpha without look-ahead bias.
Purpose This paper investigates the dynamic connectedness between US sectoral equity markets and key green financial segments – namely green bonds, clean energy and ESG equities. It aims to assess how investor sentiment shapes intermarket linkages and systemic risk transmission during periods of economic and geopolitical stress. Design/methodology/approach The study employs the generalized R2 connectedness framework and the quantile-on-quantile regression approach to capture asymmetric and nonlinear interactions across markets. The analysis covers the period from February 2015 to November 2024, encompassing major global events such as the USA–China trade war, the COVID-19 pandemic and the Russia–Ukraine conflict. Findings Results indicate strong systemic interdependence among US sectors and green financial assets, with contemporaneous connectedness dominating lagged spillovers, suggesting rapid shock transmission. Clear sectoral asymmetries emerge: Materials, Industrials and the S&P ESG index act as major shock transmitters, whereas Healthcare, Utilities, Communication Services, green bonds and clean energy serve as net volatility absorbers. Furthermore, investor sentiment exerts nonlinear effects – neutral and bullish moods amplify connectedness, while bearish sentiment dampens it. Practical implications These findings reinforce financial contagion and risk aversion theories, highlighting sustainable finance's evolving role in systemic stability. They provide valuable guidance for portfolio diversification, risk management and policy design aimed at fostering financial resilience during the ecological and energy transition. Originality/value This study is the first to jointly apply the generalized R2 connectedness and quantile-on-quantile frameworks to explore sentiment-driven linkages between US sectors and green financial assets. By integrating sustainability dynamics with behavioral factors, it offers a novel perspective on how ecological finance interacts with traditional markets under varying sentiment and crisis conditions.
Purpose This paper compares and discusses the leading economic roles of China and the USA from a different perspective, that is, in terms of herd spillover to 32 international stock markets.Design/methodology/approach We expand the herding spillover models of Gebka and Wohar (2013) and Lai and Zhang (2020) to encompass all types of herd spillovers, including severe, moderate and anti-herd effects.Findings Our research provides a more precise basis for understanding herd spillover effects in global equity markets. During tranquil periods, the trading patterns of Chinese stocks predominate over those of other regions, whereas the influence of the USA appears less significant. Nonetheless, more substantial evidence of the US market is observed during economic downturns. Throughout the financial crisis, investors in other countries tend to follow their respective trading behaviors in both nations as market uncertainty escalates. In addition, prior findings on herd spillover are underestimated because the model lacks an absolute foreign market return. A total of 76 cases of herd spillover is reported in this study. If severe herd spillover is neglected, we will underestimate it by 35.53%.Research limitations/implications Although beyond the scope of the present study, examining herd spillovers across different investor types as well as distinguishing between intentional and unintentional forms of herding spillover would be of considerable interest. Such analyses could further enhance our understanding of common trading behaviors across global financial markets. We leave these issues for future research.Practical implications These findings suggest that the advantages of international diversification through investments in the USA and China may sometimes be overstated. Additionally, regulators should maintain vigilance in supervising cross-border behavioral channels to ensure market stability.Originality/value We compare and discuss the foremost economic influences of the USA and China on trading behaviors across 32 countries worldwide. Our study is extensive in comparison to other research that concentrates on specific countries or regions. Individual stock data in this paper surpass the industrial index in capturing return dispersion, thereby enabling a more precise inference of herd behavior. In addition to moderate herd spillover, which has been the primary focus of previous research, we also examine severe and anti-herd spillovers to provide a comprehensive understanding of the overall effect.
PurposeThis study aims to profile Portuguese investors in cryptoassets and examine the sociodemographic and behavioral determinants of ownership. Data were collected from a 2023 survey conducted by the CMVM (Portuguese Securities Market Commission) in collaboration with a consortium of Portuguese universities.Design/methodology/approachLogistic regression models with sequential variable elimination were applied to the full sample. The sample was also divided by gender and financial confidence to examine differences in the determinants of cryptocurrency ownership across these subgroups.FindingsResults show that younger, male, employed individuals who trade more frequently and have higher subjective digital literacy are more likely to invest. Objective financial literacy is positively correlated with ownership, while subjective financial literacy is negatively correlated. Partitioning by gender indicates broadly similar determinants, but with some differences. For men, securities ownership and regular social media use increase the likelihood of investing, whereas for women, higher education and objective literacy are most relevant. Confidence also matters. Overconfident male employees and underconfident individuals with strong digital literacy and social media use are more likely to hold cryptoassets.Originality/valueThis study is among the first to analyze the determinants of cryptoasset ownership in Portugal by integrating demographic, behavioral and literacy factors with financial confidence. In particular, it offers a unique examination of how overconfidence and underconfidence affect the influence of information sources, digital engagement and trading behaviors on investment decisions.
PurposeIn the digital era, social media influencers act as informal advisors, shaping investor behaviour. Their opinions often drive market movements, especially among retail investors and younger demographics. This study aims to identify and prioritize the types of social media influencers based on their impact on investor decisions, while examining the interrelationships among key determinants to assess their influence.Design/methodology/approachThis study, conducted through an extensive literature review, examines the key determinants of the influence of social media influencers and their various types. The study first investigates the interrelationship between key determinants to assess the influence of social media influencers, using the Fuzzy DEMATEL method. It prioritizes social media influencers based on their influence on investor decisions using Fuzzy TOPSIS.FindingsAccording to the Fuzzy DEMATEL results, Relevance and Market Fit, as well as Risk Awareness and Decision Impact, are the key determinants for assessing the influence of social media influencers, as these determinants originate from the cause group. Based on the closeness index values calculated through Fuzzy TOPSIS, Key Opinion Leaders are identified as the most influential type of social media influencers, followed by YouTubers.Research limitations/implicationsThis study advances the existing literature on behavioural finance and social media influencers by presenting a structured framework for evaluating the effect of social media on investor decision-making.Originality/valueIt uniquely examines the interrelationships among key determinants and prioritizes the social media influencer type that influences investor decisions, using a hybrid Fuzzy DEMATEL and Fuzzy TOPSIS approach.
Purpose-Our study examines the association between pessimistic tones in earnings announcements and firm value, as well as the role of chief executive officers' (CEOs) financial experience during the COVID-19 pandemic compared to prior COVID-19 pandemic. Design/methodology/approach-Chow Test was employed to analyze 2,127 firm-year observations from Indonesia Stock Exchange-listed non-financial enterprises during the pandemic and before the pandemic. Findings-Employing a Chow test to examine structural changes, we find that the negative relationship between pessimistic disclosure tone and firm value strengthens significantly during the COVID-19 period, indicating heightened investor sensitivity to negative linguistic cues under conditions of elevated uncertainty. Furthermore, our results show that CEO financial expertise mitigates the adverse valuation effect of pessimistic disclosure tone, and this mitigating role becomes significantly stronger during the COVID-19 period. These findings suggest that while investors penalize pessimistic disclosures more severely during crises, they simultaneously place greater weight on credibility-related cues, such as CEOs' financial expertise, when evaluating firm value. Originality/value-Our study contributes to the disclosure and capital market literature by providing formal evidence of a structural change in investors' sensitivity to pessimistic disclosure tone using a Chow test framework. Unlike prior studies that rely on subsample comparisons, we formally examine whether the valuation effect of pessimistic tone differs structurally between the pre-pandemic and pandemic periods. Moreover, we show that CEO financial expertise mitigates the negative valuation impact of pessimistic disclosure tone and that this credibility-enhancing role becomes more pronounced during periods of heightened uncertainty, such as the COVID-19 crisis.
Purpose-The paper contributes to the famous "stock-market participation puzzle" by highlighting the interplay of the readability of disclosure documents and emotions in driving investment intention in a mutual fund. Design/methodology/approach-The study uses partial least squares structural equation modeling to link the theory of planned behavior along with the emotion of affect to the intention to invest in a mutual fund. We study the relationship using an experimental approach while incorporating the readability of the mutual fund's disclosure document. Findings-Affect, attitude and subjective norms significantly influence investment intentions. Affect emerges as the strongest predictor, irrespective of the readability of financial disclosures. However, attitude plays a larger role under low-readability conditions, while subjective norms significantly influence intentions when disclosures are highly readable. Originality/value-This is the first-of-its-kind experimental study that links the readability of disclosure documents and emotions to investment behavior. Further, the findings highlight investment decisions' emotional and cognitive dimensions, offering policy and practical insights.
PurposeThis study aims to investigate the impact of gender equality (GE) on home bias (HB) in investment decisions, using data from 67 countries spanning the period 2012 to 2019.Design/methodology/approachData are extracted from multiple sources, including the Coordinated Portfolio Investment Survey (CPIS), the World Bank, and the World Economic Forum. The study employs a fixed effects model and further incorporates dynamic empirical methods as well as quantile regression to ensure robustness across different levels of home bias.FindingsThe results reveal a significant negative relationship between gender equality and home bias, suggesting that more gender-equal societies exhibit less home bias in their investment portfolios. Country-level Governance, capital controls, inflation, and economic indicators also play important roles.Practical implicationsThis study demonstrates that more pronounced gender equality is associated with less pronounced home bias in portfolio investment, implying that policymakers and financial institutions might promote international diversification by increasing gender equality.Originality/valueThis paper examines the relationship between gender equality and home bias, addressing a critical gap in the literature on portfolio allocation decisions. Unlike prior research, which has primarily focused on factors such as governance, capital controls, and macroeconomic indicators, this study introduces a sociocultural dimension by exploring the role of gender equality, an aspect largely overlooked in existing studies.
PurposeThis paper introduces a novel stochastic dominance (SD) theory tailored for investors with combined risk-averse and risk-seeking utilities. It aims to address the Friedman-Savage paradox, which questions why individuals simultaneously purchase insurance and lottery tickets.Design/methodology/approachDrawing on concepts from Fishburn and Kochenberger (1979), Thon and Thorlund-Petersen (1988) and Chew and Tan (2005), we define a j-order utility framework - called AD utility - that incorporates both risk-averse and risk-seeking components. We develop an ascending-and-descending stochastic dominance (ADSD) theory to analyze the investment behavior of AD investors.FindingsThe ADSD theory possesses key properties such as expected utility maximization, hierarchy, transitivity and diversification, even under equal means conditions. It offers new solutions to the Friedman-Savage paradox, demonstrating that AD investors may invest in both diversified portfolios and individual assets, as well as pairs of less-risky and more-risky assets.Research limitations/implicationsFuture research could extend the ADSD framework by exploring empirical applications and testing the theory in different market conditions. Further work is needed to refine the model's assumptions and evaluate its predictive power across various investor profiles.Practical implicationsADSD theory provides a valuable framework for understanding investor behavior in real-world financial markets. It explains why investors might simultaneously choose low-risk and high-risk investments, informing portfolio management strategies and investment decision-making.Social implicationsThis study enhances understanding of diverse investment behaviors, offering insights into financial decision-making processes. It may inform policies promoting balanced investment strategies and responsible financial planning.Originality/valueThis paper introduces a unique approach to SD theory by integrating risk-averse and risk-seeking utilities. The ADSD framework advances existing literature and provides a novel solution to long-standing economic paradoxes.
PurposeInvestors saving for retirement have an unfortunate tendency to switch investments (more so in volatile periods), which leads to worse ex-post investment return outcomes - the "behaviour tax". This paper uses machine learning clustering algorithms to better understand this behaviour.Design/methodology/approachIt improves the clustering efficacy of a previous study by Nixon and Gilbert (2022) in several ways: by using a different clustering algorithm (k-means), adding a new variable (portfolio drawdowns) to the feature set, using a large new dataset of investor switching behaviour which includes a period of extreme investment volatility (2020) and clustering on six separate calendar years (instead of only a pooled sample).FindingsThe seven behavioural clusters identified are more clearly defined with different levels of behaviour tax or value eroded by switching between mutual funds. The algorithm organically differentiates the population into different groupings of investors, each incurring different levels of behaviour tax.Research limitations/implications(1) We don't know whether the switch was initiated by the adviser or the client. (2) We don't know in all cases whether the switch is behavioural in nature (i.e. not as a result of a changing goal). There are no causal relationships between the behaviour and feature set, only correlational.Practical implicationsFinancial services firms using this methodology can more effectively segment their customer bases, allowing for more personalised engagement.Originality/valueThis research deals with a large customer database, which lends itself to the application of machine learning techniques to better understand customer behaviour. In doing so, more effective and personalised engagement is possible and manage/reduce/eliminate the behaviour tax.
PurposeThis study aims to examine the effect of investor sentiment and institutional trading behavior on stock market returns and volatility in India, emphasizing asymmetric, causal and time-varying dynamics.Design/methodology/approachThe study uses daily data from January 2010 to December 2024 and follows a stepwise empirical strategy. Ordinary least squares (OLS) models are employed to assess the impact of investor sentiment on returns and volatility, including asymmetries captured through positive and negative sentiment components. Pairwise Granger causality tests are used to identify directional linkages, while a term structure framework evaluates effects across different horizons. Robustness is ensured using an alternative equity index and exponential generalized autoregressive conditional heteroscedastic (EGARCH)-based volatility measures.FindingsThe results indicate that investor sentiment significantly affects returns and volatility, with negative sentiment exhibiting greater persistence than positive sentiment. Trading imbalances of both domestic and foreign institutional investors reduce volatility, highlighting their stabilizing roles. Granger causality tests indicate mutual feedback between sentiment and institutional activity, except for a one-way influence from domestic trading to sentiment. Term-structure analysis reveals that these effects weaken across horizons, though the influence of foreign trading remains comparatively more persistent.Practical implicationsThe findings guide regulators in designing risk-sensitive policies and implementing timely interventions, while providing investors with evidence-based strategies for managing sentiment-driven risks.Originality/valueThis study provides evidence on the asymmetric and temporal effects of investor sentiment and institutional trading behavior in India's equity market, advancing behavioral finance research in emerging economies.
PurposeThis paper addresses the conflicting views on whether traders become more risk-averse or more risk-seeking after experiencing trading shocks. It aims to uncover how the magnitude of prior gains or losses influences subsequent risk-taking behavior in retail trading.Design/methodology/approachUsing over 349,000 daily retail trading records, the study classifies trading shocks into 18 levels and examines their lagged effects on traders' risk-taking behavior, measured by leverage.FindingsTraders' responses to prior shocks exhibit clear asymmetry. After small shocks, whether gains or losses, traders become more risk-averse. In contrast, after large shocks, especially gains, they become increasingly risk-seeking. These effects scale with shock levels and fade over time. The switching point between risk aversion and risk seeking lies deeper in the loss domain.Practical implicationsBy revealing nonlinear post-shock risk preferences, this study suggests design opportunities for trading platforms, such as nudging tools to prevent excessive risk-taking after large shocks. It also informs risk management systems that aim to reduce over-leverage during volatile periods.Originality/valueThis paper introduces a detailed framework for analyzing aftershock risk preferences, offering a behavioral explanation for previous mixed findings. It contributes to the literature by revealing a nonlinear, asymmetric pattern in how risk preferences respond to shocks.
PurposeOne personality trait of managers that may influence organizational risk-taking and financial decisions is narcissism. This research examines the impact of CEO narcissism on the cost of capital and explores how corporate governance mechanisms moderate this relationship.Design/methodology/approachThis study utilized a descriptive-correlational research design. Data were collected from 126 corporations listed on the Tehran Stock Exchange (TSE) between 2013 and 2023, resulting in a final unbalanced panel dataset of 1,096 corporation-years of data. CEO narcissism was assessed using the "CEO signature size index" (Ham et al., 2018), while the cost of capital was calculated using the capital asset pricing model (CAPM) and Gordon models. Multivariate regression and panel data methods were employed for analysis.FindingsThe results indicate that CEO narcissism has a statistically significant positive impact on the cost of capital. Additionally, board independence, audit committee independence, and big auditors weaken this relationship. In contrast, institutional ownership strengthens the connection between CEO narcissism and the cost of capital. In summary, our findings suggest that managerial personality traits and corporate governance mechanisms play a crucial role in mitigating the cost of capital.Originality/valueThe effect of managerial behavioral characteristics on the cost of capital has been shown in recent studies. Building upon prior studies, this paper investigates the relationship between CEO narcissism and the cost of capital, an area of research that has received inadequate coverage so far. Finally, this study is also more general in terms of contributing to a better understanding of decision-making behavior in emerging markets.
PurposeThis study examines whether increases in preannouncement attention can predict increases in stock returns or decreases in future volatility around monetary, inflation and employment announcements in the Turkish stock market.Design/methodology/approachWe regress announcement-day stock returns and postannouncement 30-day realized volatility on preannouncement attention separately. For monetary announcements, we also check the stock response on the day before announcements. Lastly, we regress postannouncement attention on positive and negative announcement day returns to determine which one attracts greater attention.FindingsThe results show that preannouncement attention positively predicts stock returns on monetary announcement days, but not on inflation and employment announcement days. Attention cannot predict any premonetary stock response. Positive returns on monetary and inflation announcement days attract higher postannouncement attention.Research limitations/implicationsWhile we can study price effects on the day before monetary announcements, the lack of intraday data prevents us from examining the hours immediately preceding them.Practical implicationsThis analysis can reveal the effect of public (individual) attention on announcement premium, which is rarely examined in the literature.Social implicationsBy monitoring Google search volume, individual investors can detect signals about upcoming announcements and capture the associated risk premium around announcement days.Originality/valueWe provide early evidence supporting Fisher et al.'s (2022) attention-based explanation of the announcement premium. By examining individual attention, we offer one of the earliest tests of this mechanism outside the institutional setting.
PurposeThe purpose of this paper is to analyze the impact of financial inclusion and digital financial inclusion on the gender gap in developing economies and, specifically, to examine the role that education can play in this process.Design/methodology/approachThe authors employ a linear probability model to estimate the difference between the likelihood of men and women receiving wages or income from sales, using a dataset comprising 82,114 observations from 110 developing countries included in the Global Findex 2021 report. The issues of robustness and endogeneity are addressed by estimating two instrumental variable models and one model based on propensity score matching.FindingsThe results suggest that financial inclusion exerts a positive impact on both women and men, though this effect is contingent on educational attainment. Specifically, the benefits of digital financial inclusion appear to be more pronounced for women with a higher level of education.Research limitations/implicationsLongitudinal studies would be necessary to analyze the impact of financial inclusion over time.Practical implicationsThe findings underscore the importance of promoting policies aimed at mitigating the specific barriers faced by women, particularly with regard to education, in order to ensure equitable access to and utilization of digital financial services.Originality/valueWhile previous studies have examined general trends in financial inclusion, there is limited research on how education specifically influences the gender gap in digital financial inclusion.
PurposeThe purpose of this paper is to investigate how ESG ratings ultimately affect funds' financial performance after considering the endogenous choices funds make to maintain social responsibility. We specifically analyze the financial performance of funds with varying ESG ratings using the Lipper fund ranking system.Design/methodology/approachWe analyze the effect of investment funds' ESG ratings on their financial performance, measured by Lipper ratings for expense, consistent return, capital preservation and total return. We use ordered logistic regression to examine the impact of ESG scores on Lipper rankings in a cross-sectional data as of 2022.FindingsOur findings suggest that an increase in a fund's ESG ratings is associated with lower operating expense in general, although funds that market themselves as "green" or "ethical" have significantly higher operating expenses. Furthermore, after controlling for endogeneity, the impact of ESG rating on mutual fund's risk-adjusted financial performance is significantly positive and high-ranking ESG funds are more likely to preserve capital.Practical implicationsOur results suggest that higher costs associated with "green" and/or "ethical" labeled funds are not justified since higher ESG ratings does not command a higher expense on average. The financial performance of high ESG funds outperforms similar funds in its benchmark group.Social implicationsInvesting in funds that have higher ESG records is not necessarily more costly, and the financial performance of such funds is not lacking compared to counterparts. Thus, greater management fees charged by funds that classify themselves as "ethical" or "socially responsible" might not be justified.Originality/valueThis study contributes to our understanding of the effect of ESG ratings on fund characteristics and performance through the perspective of Lipper ratings.
PurposeUnderstanding the forces that drive share price movements is central to asset pricing and market efficiency research. Among these, investor sentiment has emerged as a behavioural trajectory that can reflect the firm-specific information in stock prices. Therefore, the present study attempts to investigate whether, when and how the firm-specific investor sentiment index (FSISI) impacts the stock price synchronicity (SPS) of Indian listed firms.Design/methodology/approachThe present study used Bombay Stock Exchange (BSE) 500 companies from 2014 to 2024. The study employs firm fixed-effect panel regression to examine the nexus between FSISI and SPS. Moreover, to control for potential endogeneity concerns, two-stage least squares (2SLS) instrumental variable (IV) and system generalised method of moments (GMM) regression are utilised.FindingsThe findings indicate that FSISI, serving as a proxy for firm-specific information, is negatively associated with SPS, a relationship further supported by the robust assortment. Furthermore, the mechanism analysis reveals that FSISI leads to an increase in stock liquidity, thereby reducing SPS. Moreover, the heterogeneity analysis demonstrates that the association is accentuated in high institutional ownership (HIO) firms and during bullish (high) sentiments.Originality/valueThe study makes a novel contribution by examining the integral factors that reduce the SPS in the Indian context: the firm-specific investor sentiment index (FSISI), liquidity generated from FSISI, particularly in the case of HIO firms, and under bull or high sentiment conditions.
PurposeThis study investigates the dynamic and asymmetric spillover effects of cryptocurrency volatility on traditional financial markets (equities, FX, sovereign bonds) in emerging economies. It assesses the extent, evolution and country-level heterogeneity of these transmissions.Design/methodology/approachThe study employ a two-stage econometric framework. First, ARIMA-GARCH models filter returns and estimate conditional volatilities. Second, a Time-Varying Parameter Vector Auto-Regression (TVP-VAR) model with Generalized Forecast Error Variance Decomposition (GFEVD) captures the magnitude, direction and temporal evolution of volatility spillovers. The dataset comprises daily data (2015-2023) for Bitcoin, Ethereum and Binance Coin and financial indicators from India, Brazil, Turkey, Indonesia and South Africa.FindingsThe findings reveal significant, time-varying spillovers from cryptocurrencies to traditional markets, intensifying during bear markets and crises. Cryptocurrencies act as net transmitters of volatility under stress. Spillover intensity varies across countries, with Turkey and India exhibiting the highest exposure due to restrictive policies and regulatory ambiguity, while South Africa's neutral stance results in lower connectedness.Originality/valueThis study empirically assess asymmetric and country-specific volatility spillovers from cryptocurrencies to traditional markets using a TVP-VAR-GFEVD framework. The findings contribute to financial contagion theory and offer crucial insights for risk monitoring and regulatory design in emerging markets shaped by digital asset dynamics.
Purpose This study aims to investigate herding behaviour in US Clean Energy (CE) exchange-traded funds (ETFs) and examine the role of climate risks in influencing such behaviour over the period from May 1, 2016, to June 19, 2024. Design/methodology/approach We employ a baseline herding model and extend it to examine asymmetric effects across market conditions. The analysis incorporates time-varying herding measures and examines the impact of both transitional and physical climate risks on herding probability using regression techniques. Findings The baseline model reveals significant herding behaviour in CE ETFs. The extended model indicates that herding is present in both down and up markets, with a stronger effect in down markets, suggesting asymmetry. Herding is also found to be time-varying. Notably, high levels of transitional climate risk reduce the probability of herding in CE ETFs, whereas physical climate risk does not exert any significant impact on herding probability. Research limitations/implications The study focuses specifically on US CE ETFs over a defined period, which may limit generalizability to other markets or asset classes. The findings provide insights into the behavioural dynamics of sustainable investment markets during periods of varying climate risk. Practical implications The results suggest that high levels of transitional climate risk encourage market efficiency in CE ETFs and promote climate hedging behaviour by investors. This has important implications for portfolio managers and policymakers in understanding market dynamics in sustainable finance. Originality/value This study provides novel empirical evidence on the relationship between climate risks and herding behaviour in CE ETFs, contributing to the growing literature on behavioural finance in sustainable investment markets.
