Cross-sectional Absolute Deviation Research Articles

Price delay and herding: evidence from the cryptocurrency market

PurposeThe purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.Design/methodology/approachThe paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).FindingsOur findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.Practical implicationsThe study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.Originality/valueThis paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.

Artificial intelligence and Big Data Tokens: Where Cognition Unites, Herding Patterns Take Flight

Artificial intelligence (AI) and big data tokens have emerged as unique investment options, garnering interest due to their connectedness with other assets and financial markets. Utilizing Chang et al. (2000)'s cross-sectional absolute deviation (CSAD) model, we investigate static and time-varying herding in the AI and big data token markets. This research contributes to the growing discourse on AI and big data token investment through the lens of behavioral finance, with a particular focus on examining investor herding. The study's findings confirm market-wide herding of AI and big data tokens. The results suggest that investors exhibit herding in up markets, low volatility, and low volume days. Conversely, anti-herding is more prevalent in down markets, high volatility, and high volume days. Our analysis shows that herding is time-varying and emerges during a crisis period. The finding carries robust regulatory and policy implications to mitigate systemic risk and safeguard investor interests, ensuring market stability and resilience. The provided insights offer a valuable understanding of investors’ behavior across various market scenarios.

Herding towards pygmalion: Examining the cultural dimension of market and bank based systems

The study aims to not only detect the presence of herd behavior in the countries studied, but also to examine the effect of cultural dimensions and market/bank-based systems on the herding behavior of financial market investors. The study employs the Cross-Sectional Standard Deviation and Cross-Sectional Absolute Deviation methods to analyze daily data from public companies traded in the capital markets in Emerging Seven and Group of Seven economies. The results suggest that being a member of E7-G7, a Future Oriented (FO), and a Performance Oriented (PO) cultures are the most important factors in explaining herd behavior. Additionally, the study found that the Ridge Classifier and CatBoost Classifier algorithms arethe most superior model for estimating herd behavior periods determined by the CSSD and CASD models, respectively. The feature selection results show that the Assertiveness (A) in-group collectivism (GC) are the three most important explanatory factors of the herd behavior.

Supply chain driven herding behavior during COVID-19: evidence of interdependence from India

PurposeThis study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.Design/methodology/approachUsing the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.FindingsThe event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.Originality/valueThe authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

Herding behaviour in the Indian stock market: An empirical study

The rapid growth of the Indian stock market in recent decades has prompted much study on investing, focusing on understanding and analysing investors' tactics for investment and trading. Over the years, researchers have cited and conducted in-depth investigations into various psychological biases. Herding bias is extensively studied to understand how investors participate in investment and trading in the Indian stock market. The current study on herding provides an investigation into the existence of herd activity in the share market of India. The study attempts to identify herd activity during positive, negative, extreme positive, and extreme negative market conditions, as well as during structural breakpoints and trade volumes that are low as well as high. The researchers have used the modified CSAD (Cross Sectional Absolute Deviation) model to enhance its validity. The findings reveal the rationality of investors during the different market states. However, herding behaviour was evidenced during certain periods at the structural breakpoint. This implies that during positive as well as negative market conditions, herding behaviour is absent, except during certain structural breakpoints and high trading volumes.

Time-varying window-based herding detection in the non-fungible token (NFT) marketplace

PurposeThe non-fungible token (NFT) market had undergone dramatic growth and a sudden decline during 2021–2022. The market experienced a surge in prices in late 2021 and early 2022, with NFTs being sold at inflated prices. Despite this, by April 2022, the market underwent a correction, and the prices of NFTs returned to more reasonable levels. This can be a result of imitating the actions or judgments of a larger group, which is not systematically proven yet. Therefore, this study systematically investigates the applicability of herding behavior in the NFT market.Design/methodology/approachThis research employs cross-sectional absolute deviation (CSAD) of returns and ordinary least squares (OLS) to test herding behavior with moving time windows of 10, 20 and 30 days based on the sales data collected from public interface of OpenSea between July 1, 2021 and June 30, 2022. Additionally, NFT-related keyword usage analysis is done for the detected herding periods.FindingsAs per the results of the data analyzed, herding behavior was evidenced using 10-, 20- and 30-day time windows from July 1, 2021 to June 30, 2022because of media movement. The findings revealed that this behavior was present and aligned with the overall behavior of the market.Originality/valueThis study introduces CSAD to examine herding behavior patterns within the NFT market. Complementing this method, keyword count-based analysis is employed to identify the underlying causes of herding behavior. Through this comprehensive approach, this study not only uncovers the roots of herding behavior but also offers an assessment of the time windows during which it occurs, considering the plausible socioeconomic contexts that influence these trends.

Asymmetry risk and herding behavior: a quantile regression study of the Egyptian mutual funds

PurposeThis paper deeply investigates the herd behavior of the Egyptian mutual funds under changing and different conditions of the market pre- and post-events and compares the impact of asymmetric risk conditions on the herding behavior of the Egyptian mutual funds in both up and down markets.Design/methodology/approachWe test for the existence of herding for the whole period from 2003 to 2022, as well as for the pre-and post-different Egyptian uprising periods. We employ two well-known models, namely the cross-sectional standard deviation (CSSD) and cross-sectional absolute deviation (CSAD) models. Additionally, we use the quantile regression approach.FindingsWe find that the behavior of mutual funds does not change following the different political and social events. For the whole period, we find evidence of herding behavior using only the model of CSAD in down-market conditions. We generalize our finding to be evidence of the existence herding behavior in different quantiles, under only the down market in specific points’ pre, post or both given events throughout the whole series. Conversely, during the upper market, we show a full absence of herding behavior considering all different quantiles. When the market is down, managers are afraid of the condition of uncertainty, neglecting their own private information, avoid acting independently and consequently, following other mutual funds. When the market is up, managers become rational and act fully independent.Research limitations/implicationsFuture research should delve deeper into the drivers of herding behavior, assess its longer-term effects, develop risk management strategies and consider regulatory measures to mitigate the potential negative impact on mutual fund performance and investor outcomes.Practical implicationsThe study reveals that the behavior of mutual funds remains consistent despite various political and social events, suggesting a degree of resilience in their investment strategies. The research uncovers evidence of herding behavior in both high and low quantiles, but exclusively in down markets. In such conditions of market decline, fund managers appear to forsake their private information, exhibiting a tendency to follow the crowd rather than acting independently.Social implicationsThe study reveals that the behavior of mutual funds remains consistent despite various political and social events, suggesting a degree of resilience in their investment strategies. The research uncovers evidence of herding behavior in both high and low quantiles, but exclusively in down markets. In such conditions of market decline, fund managers appear to forsake their private information, exhibiting a tendency to follow the crowd rather than acting independently. Future research should delve deeper into the drivers of herding behavior, assess its longer-term effects, develop risk management strategies and consider regulatory measures to mitigate the potential negative impact on mutual fund performance and investor outcomes.Originality/valueThe paper investigates the herd behavior of the Egyptian mutual funds under asymmetric risk conditions, the study follows the spectrum of the herding behavior analysis and Egyptian mutual funds, extending the research with imperial analysis of market conditions pre- and post-events including currency floating, COVID-19 and political elections. The study gives substantial recommendations for policymakers and investors in emerging markets mutual funds.

Herding Behavior in The Asean Stock Market During The Covid-19 Pandemic

This study aims to detect indications of herding behavior that occurred in the ASEAN capital market throughout the COVID-19 pandemic. The research spanned from December 31, 2019, to June 30, 2021, encompassing the duration of the COVID-19 pandemic. This research used panel regression and rolling window regression methods on the dependent variable of Cross-Sectional Absolute Deviation (CSAD) to detect indications of herding behavior. This research also employed daily stock return data for all companies on several stock exchanges in five ASEAN countries. The panel regression model was then utilized in the analysis. The panel regression results demonstrated the absence of herding behavior on the stock exchanges in ASEAN. This was evident as the Rm2 coefficient consistently revealed a significant positive sign throughout the entire observation period. However, upon rechecking through rolling window regression, it was uncovered that, at some times, herding did occur on the ASEAN stock exchange during the COVID-19 pandemic. Furthermore, the government’s policy response by limiting short selling could reduce herding on the stock market. Simultaneously, the government’s policy response to the COVID-19 epidemic, proxied by the Stringency Index, apparently heightened investor anxiety and promoted herding behavior. Lastly, the heightened levels of anxiety and volatility experienced during the pandemic (variable IVI) led to a rise in herd behavior among the stock markets of ASEAN countries.

Examining Herding Behaviour and Its Impact on Stock Market Volatility: Insights from Asian Economies

Objective: This study empirically investigates herding bias in six key Asian countries—Indonesia, Singapore, Taiwan, China, Hong Kong, and India—across different periods (pre-, during, and post-COVID-19). It analyzes herding behaviour during COVID and non-COVID periods, exploring its impact on volatility and examining asymmetry during bearish and bullish market conditions. Design/Methods/Approach: The investigation employs the Cross-Sectional Absolute Deviation (CSAD) model with a polynomial regression to scrutinize herding behaviour. A GARCH (1,1) volatility model is also established to assess the relationship between herding and volatility. The sample includes daily stock returns from the mentioned countries from January 2, 2019, to September 30, 2023. Findings: The study reveals the presence of herding behaviour in China and Singapore. In Indonesia and China, herding is evident, specifically during and after the COVID period. The research confirms that herding influences volatility and exhibits asymmetry. Herding is more pronounced during bearish market conditions in China, Indonesia, and Taiwan. Originality/Value: This study contributes to the existing literature by providing empirical insights into herding behaviour comparing in Asian markets, while others research usually only focus on one country. This study further distinguishes itself by examining post-pandemic periods, a unique aspect as most studies typically focus only on pre- and during-COVID periods. Including volatility and asymmetry aspects enriches understanding the nuanced relationship between herding and market conditions. Practical/Policy implication: Investors should remain cautious of short-term herding-induced volatility, leveraging stability for consistent profits. Recognizing limited diversification during market losses is crucial. Additionally, governments and regulators should focus on enhancing market transparency and investor education, investing in robust market infrastructure to mitigate the impact of excessive herding.

Sectoral Comparison of Herding During Global Financial Crisis and Covid-19: Comparative Analysis of Shariah Compliant and Conventional Stocks

The worldwide financial environment has experienced substantial upheavals amid both the Global Financial Crisis (GFC) and the COVID-19 pandemic. Gaining insight into investor behavior, with a specific focus on herd behavior, during these periods is essential for a comprehensive understanding of market dynamics. The authors intend to compare and analyze investor herding behavior in the Pakistani Stock market, specifically focusing on shariah-compliant and conventional stocks during both the Global Financial Crisis (GFC) and the COVID-19 pandemic. The study explores how stock return dispersions behave in response to significant upward and downward movements in the market index. Additionally, the research distinguishes between the overall and sector-specific performance of Shariah-compliant and conventional stocks. To examine participant herding behavior, the authors applied the cross-sectional absolute deviation model (CSAD) to the daily data of the Karachi stock market. The results indicate that both Shariah-compliant and conventional stocks exhibited a weak form of herding during the GFC. Furthermore, different sectors displayed varying degrees of herding intensity during this crisis. Notably, a substantial increase in herding behavior was observed during the COVID-19 pandemic. These findings have crucial implications for portfolio diversification strategies during financial crises, emphasizing the identification of safe havens by constructing portfolios across diverse segments and sectors. This research contributes to the existing knowledge on herding behavior by examining two distinct hypotheses related to conventional and shariah-compliant stocks, and the empirical evidence supports these hypotheses.

Herding Behaviour in Investors: A Study of Tadawul Financial Market

Purpose: The study investigates herding behaviour in the Tadawul stock exchange based on daily data between the years 1999 to 2019 .
 
 Theoretical framework: Existence of uncertain situations affect the confidence level of the investor. This leads to investors changing their approach from rational to behavioural wherein they prefer to follow the judgements of others, thus engaging in herding behaviour. Several factors such as institutional investment, external factors and fundamental factors impact herding behaviour.
 
 Methodology: The study uses OLS regression approach and accounts for the various factors such as institutional investment (percentage ownership in a stock, institutional ownership type, nationality) , external factors (global financial crisis, political instability, oil market volatility) and fundamental factors (P/E ratio, Share Turnover, Market Capitalization, Market Liquidity).
 
 Findings: The empirical results indicate that herding is not present during the period of study. External factors and fundamental factors do not significantly impact the Cross-Sectional Absolute Deviation (CSAD) and there is no evidence of herding when accounting for these variables. However, CSAD is significantly impacted with the percentage of institutional investment, ownership and the nationality of institutional investors and there is evidence of herding when accounting for these variables.
 
 Research Implications: As further scope the study proposes that the length of the time period may affect the findings and more robust analysis can be conducted by structural break models to understand the relative significance of the factors that may lead to herding during different time periods.
 
 Originality: Institutional investment related factors have a significant impact on Herding behaviour of investors in the Tadawul stock exchange.

Herding Behaviour in ESG Stock Index: Evidence from Emerging Markets

This article studies the herding behaviour of environmental, social and governance stock indices in the emerging market based on International Monetary Fund emerging market criteria. We use the daily data as a sample. Cross-sectional absolute deviation is employed to identify the herding behaviour of the investors. The findings report that all the indices do not exhibit herding behaviour, even under the upwards and downwards episodes of the market. When oil price and implied volatility are included in the model, the herding only existed in some samples, such as Egypt and India, during upwards oil price fluctuation and implied volatility, respectively. Meanwhile, Brazil, China and Taiwan also show evidence of herding behaviour during downwards oil price fluctuation. It is due to the large size of the companies with solid governance norm included in the indices, and the information on the companies are widely available. It gives the implication that the investors do not rely on others in the investment decision.

Laplacian-energy-like measure: Does it improve the Cross-Sectional Absolute Deviation herding model?

Laplacian-energy-like measure: Does it improve the Cross-Sectional Absolute Deviation herding model?

Herding Behaviour in Investors: A Study of Tadawul Financial Market

Purpose: The study investigates herding behaviour in the Tadawul stock exchange based on daily data between the years 1999 to 2019 .
 
 Theoretical framework: Existence of uncertain situations affect the confidence level of the investor. This leads to investors changing their approach from rational to behavioural wherein they prefer to follow the judgements of others, thus engaging in herding behaviour. Several factors such as institutional investment, external factors and fundamental factors impact herding behaviour.
 
 Methodology: The study uses OLS regression approach and accounts for the various factors such as institutional investment (percentage ownership in a stock, institutional ownership type, nationality) , external factors (global financial crisis, political instability, oil market volatility) and fundamental factors (P/E ratio, Share Turnover, Market Capitalization, Market Liquidity).
 
 Findings: The empirical results indicate that herding is not present during the period of study. External factors and fundamental factors do not significantly impact the Cross-Sectional Absolute Deviation (CSAD) and there is no evidence of herding when accounting for these variables. However, CSAD is significantly impacted with the percentage of institutional investment, ownership and the nationality of institutional investors and there is evidence of herding when accounting for these variables.
 
 Research Implications: As further scope the study proposes that the length of the time period may affect the findings and more robust analysis can be conducted by structural break models to understand the relative significance of the factors that may lead to herding during different time periods.
 
 Originality: Institutional investment related factors have a significant impact on Herding behaviour of investors in the Tadawul stock exchange.

Sectoral Herding Contagion on Eve of New Year

The purpose of this study is to investigate herding contagion on the eve of the new year (2015-2019) last three years data excluding due to Covid 19 effect on stock markets. This research work focuses on the cross-country behavioural linkages of investors between US and Chinese stock markets. This study employs a return dispersion model to identify the consensus among the participants of the market (Chang et al., 2000). The first aim of this study is to examine herding contagion by employing Cross Sectional Absolute Deviation (CSAD) between Chinese and USA Stock markets. The findings of this study depict that herding contagion appeared during the new year, however, the intensity of herding contagion negligible across the aggregate and sectoral data sets. This paper highlights sectors for investors to get the maximum advantage of portfolio diversification. Therefore, stockholders require to alter their portfolios according to the situation in the market to diversify the risk. The study suggests that investors and asset managers should analyze the sectoral performance of stock markets before making a portfolio during calendar events.

ANALYSIS OF THE EFFECTIVENESS OF THE BROKER CODE CLOSING POLICY IN RUNNING TRADE ON HERDING BEHAVIOR

The broker code closure policy in running trades carried out by the Indonesia Stock Exchange on December 6, 2021, aims to overcome herding behavior and make better price spreads in the Indonesian capital market. This study aims to determine the policy's success by examining the existence of herding behavior before and after the broker code closure policy is enforced using the Cross-Sectional Absolute Deviation method and by looking at price volatility during the research period. Based on the research that has been carried out, it is known that there is a herding behavior before the policy is implemented, and there is no herding behavior after the policy is enforced. This means that the broker code closure policy in running trades carried out by the Indonesia Stock Exchange has succeeded in overcoming the herding behavior contained in the Indonesian capital market but is still not effective in overcoming price spreads that occur in the Indonesian capital market because there is the market sentiment towards economic conditions that occur both nationally and globally.

Sectoral Herding Contagion on Eve of New Year

The purpose of this study is to investigate herding contagion on the eve of the new year (2015-2019) last three years data excluding due to Covid 19 effect on stock markets. This research work focuses on the cross-country behavioural linkages of investors between US and Chinese stock markets. This study employs a return dispersion model to identify the consensus among the participants of the market (Chang et al., 2000). The first aim of this study is to examine herding contagion by Cross Sectional Absolute Deviation (CSAD) between domestic and foreign countries. The findings of this study depict that herding contagion appeared during the new year, however, the intensity of herding contagion negligible during the new year within the aggregate and sectoral data sets. This paper highlights stock markets and sectors for investors to get the maximum advantage of portfolio diversification. Therefore, stockholders require to alter their portfolios according to the situation in the market to diversify risk. The study suggests that investors and asset managers should analyse the sectoral performance of stock markets during such situations before making a portfolio.

Sectoral Herding Contagion on Eve of New Year

The purpose of this study is to investigate herding contagion on the eve of the new year (2015-2019) last three years data excluding due to Covid 19 effect on stock markets. This research work focuses on the cross-country behavioural linkages of investors between US and Chinese stock markets. This study employs a return dispersion model to identify the consensus among the participants of the market (Chang et al., 2000). The first aim of this study is to examine herding contagion by Cross Sectional Absolute Deviation (CSAD) between domestic and foreign countries. The findings of this study depict that herding contagion appeared during the new year, however, the intensity of herding contagion negligible during the new year within the aggregate and sectoral data sets. This paper highlights sectors for investors to get the maximum advantage of portfolio diversification. Therefore, stockholders require to alter their portfolios according to the situation in the market to diversify risk. The study suggests that investors and asset managers should analyse the sectoral performance of stock markets during such situations before making a portfolio.

Investment decisions determinants in the GCC cryptocurrency market: a behavioural finance perspective

PurposeThis study aims to investigate the impact of financial and behavioural factors on investment decisions in the cryptocurrency market within the Gulf Cooperation Council (GCC).Design/methodology/approachThe study uses the cross-sectional absolute deviation methodology developed by Chang et al. (2000) to determine the existence of herding behaviour during extreme conditions in the cryptocurrency market of four GCC countries: Bahrain, Saudi Arabia, Kuwait and UAE. In addition, a questionnaire survey was distributed to 322 investors from the GCC cryptocurrency markets to gather data on their investment decisions.FindingsThe study finds that the herding theory, prospect theory and heuristics theory account for 16.5% of the variance in investors' choices in the GCC cryptocurrency market. The regression analysis results show no multicollinearity problems, and a high F-statistic indicates the general model's acceptability in the results.Practical implicationsThe study's findings suggest that behavioural and financial factors play a significant role in investors' choices in the GCC cryptocurrency market. The study's results can be used by investors to better understand the impact of these factors on their investment decisions and to develop more effective investment strategies. In addition, the study's findings can be used by policymakers to develop regulations that consider the impact of behavioural and financial factors on the GCC cryptocurrency market.Originality/valueThis study adds to the body of literature in two different ways. Initially, motivated by earlier research examining the impact of behaviour finance factors on investment decisions, the authors look at how the behaviour finance factors affect investment decisions of the GCC cryptocurrency market. To extend most of these studies, this study uses a regime-switching model that accounts for two different market states. Second, by considering the recent crisis and more recent periods involving more cryptocurrencies, the authors have contributed to several studies examining the impact of behavioural financial factors on investment decisions in cryptocurrency markets. In fact, very few studies have examined the impact of behavioural finance on cryptocurrency markets. Therefore, to the best of the authors’ knowledge, this study is the first of its kind to investigate how behavioural finance factors influence investment decisions in the GCC cryptocurrency market. This allows to better illuminate the factors driving herd behaviour in the GCC cryptocurrency market.

Herding behavior and government policy responses: Evidence from COVID-19 effect

The purpose of this study is to investigate the impact of a sudden shock from the COVID-19 epidemic on the behavioral bias of investors in the stock market of Iran as a developing country. The study also examines whether the government response to the COVID-19 pandemic can reduce investor herding behavior. We have used the Cross-sectional absolute deviation (CSAD) to measure securities dispersion from market returns. The studied period includes the cross-sectional data of the top 50 companies listed on the stock exchange during 2381 working days of the market (from March 1, 2012, to March 1, 2022). Furthermore, we use the semi-parametric estimator of the quantile regression for the data on the Iranian government response during the COVID-19 epidemic taken from the Oxford COVID-19 Government Response Tracker (OxCGRT). The main findings are in order. First, results show that the COVID-19 pandemic caused the formation of herding behavior aggravated by market volatility. Second, we document that the government response stringency index is unsuccessful in reducing investor herding behavior in the Iranian stock market. Finally, given the evidence that herding behavior, as a form of behavioral distortion, can drive security prices away from equilibrium values supported by fundamentals and cause price bubbles, our findings have important implications for policymakers and investors to mitigate herding effects and mis valuations.