Cross-sectional Absolute Deviation Research Articles

Herding in Open-end Funds: Evidence from China

This paper aims to detect the herding of Chinese open-end fund managers and examine the role that structural features of their industry play in their herding. The herding behaviour is investigated by employing the cross-sectional absolute deviation (CSAD) model, drawing on the Chinese open-end fund market for the 2007–2020 period.Our findings suggest that the presence of herding has been confirmed in all other types of funds except for income funds, with their herding being mainly driven by non-fundamental factors. Most types of funds show more pronounced herding when the market is rising and the fund flow is positive. Up markets foster positive fund flows, which promote herding to a certain extent. The structural characteristics of fund networks of common asset holdings produce an impact on the fund herding. Herding is observed in the network with larger centrality index and the disassortative network. Moreover, we also find that herding in most types of funds is more pronounced under conditions of high volatility and high economic policy uncertainty, and the fund herding tends to grow inversely with fund-size.

What Drives Herding Behavior in the Cryptocurrency Market?

This paper uses the cross-sectional absolute deviation (CSAD) in static and time-varying versions to examine herding in the cryptocurrency market from April 2013 to November 2019. Results from the static model confirm the evidence of an anti-herding behavior over the considered period. However, the time-varying analysis suggests the presence of herding behavior around the end of 2013 and persists until the end of the sample period. Furthermore, by examining the factors relating to market microstructure and general economic conditions that can drive herding, we find that the level of herding in the cryptocurrency market rises as volatility, the S&P500, and the dollar index increase. However, the rise in the trading volume, gold price, and the economic policy uncertainty index (EPU) reduce the herding in the cryptocurrency market.

Comparative Analysis of Herding Behavior in Indonesia, Malaysia, and Singapore.

Herding Behavior is an investor bias that affects stock market price. Stock market is one of the factors that can influence economic condition in a country. This research examine the phenomenon of herding behavior in Indonesia, Malaysia, and Singapore from 2016 to 2019. This research used secondary data, stocks return and market return, and transformed it into Cross Sectional Absolute Deviation (CSAD) to test the dispersion level of stock return to find herding behavior indication using quantile regression. This research also comparing herding behavior between Indonesia and other two countries using independent sample t test. Result showed that in all countries there were no indication of herding behavior in all kind of market condition. This research also found the difference of herding behavior between Indonesia and other countries.

Investor Herding Behaviour in the Indian Equity Market: Evidence From Quantile Regression

The study empirically investigates the herd behaviour of investors in the The the Indian equity market. Quantile regression approach is adopted over OLS (ordinary least square). The method proposed by CCK (Chang, Cheng and Khorana, 2000), based on cross sectional absolute deviation is used. An empirical examination of asymmetric effect on returns, high and low trading volume and volatility over daily data period from 2007-2017, shows reverse herding behaviour in the The the Indian equity market. The robustness analysis of large, mid and small cap firms also revealed evidence against herding. However, sub-period analysis of data revealed evidence of herding in many years (2007, 2010 and 2014 etc.). In general herding behaviour is not pervasive in the The the Indian equity market.

Do Oil Prices and Financial Indicators Drive the Herding Behavior in Commodity Markets?

This study tests the presence of herding behavior in commodity markets (energy, industrial metals, precious metals, grains food, and livestock) from a constant and time-varying perspective. Additionally, we investigate whether oil prices and major financial indicators drive herding in these markets. Using daily data over the period 2003–2017, the constant model based on the cross-sectional absolute deviation model suggests no herding in any of the commodity sectors. However, using a time-varying approach, results reveal the existence of herding in almost all sectors, mainly during and after the global financial crisis. Furthermore, the oil price contributes significantly to herding among investors in the energy sector. We also find that the major exchange rate contributes to herding only for industrial metals sectors during a very limited period. Moreover, the US stock market also has a limited contribution to herding in the energy and the industrial metals sectors.

Impact of herding on the returns in the Indian stock market: an empirical study

PurposeHerd behavior has been studied herein and tested based on primary respondents from Indian markets.Design/methodology/approachThe paper expounds the empirical evidence by applying the cross-sectional absolute deviation method and reporting on herd behavior among decision-makers who are engaged in trading in the Indian stock market. Further, the study attempts to analyze the market-wide herding in the Indian stock market using 2230 daily, 470 weekly and 108 monthly observations of Nifty 50 stock returns for a period of nine years from April 1, 2009 to March 31, 2018 during the normal market conditions, extreme market conditions and in both increasing and decreasing market conditions.FindingsIn a span of a decade witnessing different market cycles, the authors’ results exhibit that there is no evidence of herding in any market condition in Indian stock market primarily due to the dominance of institutional investors and secondly because of low market participation by individual investors.Originality/valueThe results reveal that there is no impact of herd behavior on the stock returns in the Indian equity market during the normal market conditions. It highlights that the participation of individuals who are more prone to herding is more evident for short-run investments, contrary to long-term holdings.

Asymmetric effect of oil prices on herding in commodity markets

PurposeThis study aims to test the presence of herding behavior in commodity markets, including energy, metals and agriculture. Additionally, the authors investigate the possible asymmetric effect of oil price changes on the herding behavior in these markets.Design/methodology/approachThe authors examine herding based on the cross-sectional absolute deviation (CSAD) model in a static and time-varying perspective.FindingsBy using daily data over the period 2003–2017, the authors’ findings firstly support the dynamic nature of investor behavior in commodity markets, which oscillates between antiherding during the normal period and herding during and after the global financial crisis of 2008. Furthermore, results highlight that the asymmetric impact of oil shocks on herding differs across commodity sectors and periods. Additionally, herding seems to be more pronounced when the oil market declines, which may be due to the pessimistic investors' sentiments.Practical implicationsThis study provides insight into what factors influence herd behavior in commodity markets. The understanding of factors driving herding aids investors to avoid the impact of this behavior and its consequencesOriginality/valueTo the authors’ knowledge, this study is the first to examine whether the level of herding depends on the oil price fluctuations, as well as the asymmetric effect of the oil price on herding behavior in commodity markets.

Herding in Fast Moving Consumer Group Sector: Equity Market Asymmetry and Crisis

This study empirically examines herd behavior for fast moving consumer goods (FMCG) sector stocks under varied market return conditions and the period during the global financial crisis and its aftermath. We examine the sample of stocks trading on the Nifty FMCG Index of the Indian equity market from January 2008 up to December 2018 using the dispersion measure of cross sectional absolute deviation and examine its relationship with the market return to explore herd phenomenon. Quantile regression estimate is used and the results of the study validate rational asset pricing models as the sector does not display herding. In contrast, anti-herd behavior at lower and median quantile values is observed. A possible reason can be the non-cyclical nature of the industry where investors rely more on the fundamentals rather than crowd chasing. We also findthe absence of herd phenomenon during the market asymmetries of bull and bear phases, extreme movements, the period of the global financial crisis, and afterward. We further examine herding under the impact of the information technology (IT) industry and conclude that significant return movements in IT sector impact dispersions in the FMCG industry. Also, there is a co-varying risk between the two sectors confirming the spillover in an integrated market.

Endangering China's environmental health security goals through negative environmental investor behaviours.

China sees the need to maximise its environmental health security as a major priority in its sustainable development agenda. This is at the heart of China's "ecological civilisation" and "beautiful China" dream. One of the objectives of this dream is to sensitize investors to invest in health and environmental stocks to support environmental health goals. However, both the Shanghai and the Shenzhen stock markets continue to witness contemporaneous movement (herding behaviour) by investors from environmental stock to perceived safer stocks and this is stifling the growth of the environmental health sector due to capital deprivation. Our paper evaluates the significance and potential effect of this herding trend among environmental stocks using a collection of sophisticated econometric models namely, the state-space model, enhanced state-space model, the cross-sectional SD (CSSD) and the cross-sectional absolute deviation (CSAD). The models are used to evaluate firm-level data collected from the 80 environmental stocks indexed by the KGRM MSCI China IMI Environment 10/40 Index. Three of the models confirm the presence of endemic negative (herding) investor behaviour among environmental stocks in China and this threatens the sustainability of environmental stock capital to promote China's environmental health goals. We have proposed measures to ameliorate the risks posed by such negative contemporaneous investor behaviours.

Remeasuring Sectoral Herding in the Financial Markets

Herding behavior was concluded to exist in some sectors and under some market conditions in the Jordanian stock market when measured using the cross-sectional absolute deviation. The purpose of this study was to retest the existence of the sectoral herding using the cross-sectional dispersion of betas and compare the results with those reached using the measure of the cross-sectional absolute deviation. Behavioral finance theory represents the main base on which this study was built. In this study, the researcher tried to answer questions related to whether herding behavior exists in the Jordanian market and its sectors if measured using cross-sectional dispersion of betas and whether results will be different from those reached using other measures. In this quantitative study, data from Amman stock exchange were used and the period covered was from 2000 to 2018. These data were used to calculate the cross-sectional dispersion of betas which was tested using t-test, Kruskal–Wallis test, Mann-Whitney U test, and Wilcoxon Signed-Rank test. Results indicated that herding behavior existed in market and in each sector at the same level which was not affected by the financial crisis. Furthermore, the study revealed that herding level was the same when the market (sector) was rising and when it was falling and this similarity has not been changed by the occurrence of the global financial crisis. Finally, results indicated that herding was at its lowest level in the entire market and in the industrial sector during the time of financial crisis. These results are different from those of the study conducted in Jordan using cross-sectional absolute deviation which implies that using different herding measures yields different results.

HERD BEHAVIOR IN TERMS OF SOCIAL PSYCHOLOGY: THE EXAMPLE OF CRYPTO ASSET MARKETS

The four main causes of sudden price fluctuations in crypto asset markets are speculative news within the scope of global developments, positive market perceptions that increase the price, rising stock market values, and herd behavioral tendencies on crypto assets. The purpose of this study is to examine herd behavior trends in terms of social psychology, exemplified by the crypto asset market. In this framework, firstly, the daily yield pricing of twenty-two crypto assets with the highest transaction volume was examined in the period of August 2013 - December 2019, and then the trend of herd behavior of these assets on the market was investigated. Cross Sectional Absolute Deviation (CSAD) method was used to measure the herd behavior trend. The analysis revealed the presence of herd behavior trend in regime 1 and regime 3 models.

ANALISIS DESKRIPTIF HERDING PADA JAKARTA ISLAMIC INDEX

Herding is irrational investor behavior, because investors do not make investment decisions based on economic fundamentals, but based on other investors in the same condition, or following market consensus. Herding is measured by looking at the relationship between return market portfolio and Cross-Sectional Absolute Deviation (CSAD). This study used quantile regression to measure herding behavior. The result shows that there is no indication of herding behavior in JII, which means that investors tend to behave rationally in making investment decisions. Keywords: CSAD, Herding Behavior, Quantile Regression

Uncertainty and herding behavior: evidence from cryptocurrencies

The aim of this study is to examine the existence of herding behavior in the cryptocurrency market under uncertainty by employing cross-sectional absolute deviation (CSAD) of returns, ordinary least squares (OLS), generalized autoregressive conditional heteroscedasticity (GARCH) methods and Time-Varying Markov-Switching (TV-MS) model for both overall sample and sub-periods which was determined based on the results of Quandt-Andrews and Bai-Perron breakpoint tests. We utilized the daily data of the 14 leading cryptocurrencies in terms of closing price, market cap and transaction volume. We also used dummy variables to analyze whether or not an asymmetric behavior occurred during the "up and down" market periods. Our results for the overall sample refer to an anti-herding behavior in each model. However, the results of the TV-MS model for the 3rd sub-period (2/28/2017−1/16/2018) imply the existence of a herding behavior in the low volatility regime, an anti-herding behavior occurred during the high volatility regime and the effect of uncertainty was significant on the anti-herding behavior. Finally, our results suggest that there was no significant asymmetric behavior during the "up and down" market periods.

Uncertainty, Fear and Herding Behavior: Evidence from Size-Ranked Portfolios

This study is aimed at testing the relationship between investors’ uncertainty reflected by market sentiment and herding behavior phenomenon. Using data for 1990–2019 for size-ranked portfolios, the evidence documented here indicates that herding is strongly related to market sentiment as captured by the CBOE’s Volatility Index, the VIX. The results indicate that the VIX, which is also recognized as the fear index, has a substantial impact on herding across all groups and subgroups of size-ranked portfolios. Overall, the effect of VIX exists in most of the quantiles of the cross-sectional absolute deviation distribution. In this context, the scale and magnitude of the fear index impact rises toward the highest parts of the herding distribution. We also show that herding behavior is more pronounced when the market is overwhelmed by sentiment. The findings have several practical implications for investment professionals such as portfolio managers, investment officers, analysts, and other market participants. They also provide academic insights for researchers dealing with market efficiency and investors’ behavior.

Investor Sentiment and Herding Behavior in the Korean Stock Market

This paper investigates herding behavior and the connection between herding behavior and investor sentiment. We apply a Cross-Sectional Absolute Deviation (CSAD) approach and the quantile regression method to capture herding behavior in the KOSPI and KOSDAQ stock markets. The analysis results are outlined as follows. First, we find that herding behavior is exhibited during down-market periods in the KOSPI and KOSDAQ stock markets. However, we show that adverse herding behavior occurs in low-trading volume and low-volatility periods. Second, according to the results of the quantile regression, herding behavior is found in the low and high quantiles of the KOSPI and KOSDAQ stock markets. However, adverse herding behavior is also found, which means that investors herd in extreme market conditions. Third, the relationship between investor sentiment and herding behavior is analyzed through regression and quantile regression, and investor sentiment is confirmed to be one of the important factors that can cause herding behavior in the Korean stock market.

Does herding behavior exist in the Mongolian stock market?

This investigation is among the first to examine the presence of herd behavior in the Mongolian stock market. Herding behavior was analyzed for the full sample data (1999 to 2019) including during bull and bear market periods, as well as high and low volatility states of markets. Additionally, the impact of four important events which occurred during these period were also tested: the establishment of the Finance Regulatory Committee of Mongolia (FRCM), the Global Financial Crises, Mongolia's inclusion in the FTSE Russell Watch list and economic boom in 2011. Using a cross-sectional absolute deviation (CSAD) model evidence of herding behavior was found in all the situations.

Peut-On Expliquer La Volatilite Du Marche Boursier Marocain Par Un Comportement Mimetique Des Investisseurs?

The purpose of this paper is to test empirically, if during the period 2002-2017, the volatility of the Moroccan stock market could be linked to mimetic behavior of investors. On a sample made up of 22 firms listed on the Casablanca stock exchange, we adopted an estimate of this behavior according to the measure of cross sectional absolute deviation CSAD to show that there is no solid evidence on the presence of mimicry at least for the period considered in this study.

An Examination of Herding Behavior in Chinese A-Share Market by Cross-Sectional Absolute Deviation (CSAD)

The study aims to test the existence and the continuity of herding behaviour in Chinese A-share market by the method called cross-sectional absolute deviation (CSAD). Herding behavior, defined as individuals tend to follow others when making decision, has a large impact on stock markets. The latest data from July 2016 to July 2019 in Shanghai and Shenzhen A-share markets are used. The data are divided into both three-year period and annual subperiod. The results indicate that there is a lasting influence of herding behavior on these A-share markets during these three years. Besides, the value of CSAD of Shenzhen A-share market is higher than the one of Shanghai and both of them are shown in an increasing tendency over the years. Future researches can explore more on this tendency, including its verification and explanation.

Herding Behaviour in Sharia Stock: The Moderation Effect of Good Governance Business Sharia Disclosure

Research aims : One of the irrational behavior of investors is herding behavior. Disclosure of certain information is one of the triggering factors for herding behavior. However, “whether all the various types of information disclosure can encourage herding?” This is a big question related to the success of Good Governance Business Sharia (GGBS) information disclosure in stimulating herding behavior in Sharia stock trading. Hence, this study aims to examine the role of GGBS disclosure in herding behaviour. Design/Methodology/Approach : This study uses the object of companies listed on the ISSI (Indonesian Sharia Stock Index 2018. Data analysis in this research is using the Regression and moderation Test. Research findings : The results show that the GGBS disclosure was able to moderate herding behavior in Indonesian Sharia Stock. In this study it was found that the presence of GGBS Disclosure further strengthens the existence of herding behavior, meaning that investors conduct herding behavior reinforced by the information coming from within the company in the form of disclosure of GGBS Disclosure. Theoretical contribution/Originality : The theoretical impact is to expand the study of herding behavior and relate it to the disclosure of information. This study is also an initiator that examined the role of GGBS as moderating variable. Practitioner/Policy implication : The implication of this research is to be able to increase company concern in terms of GGBS disclosure so that the sharia stock market can be more transparent. Research limitation/Implication : This research only focuses on one information, namely GGBS Disclosure. This cannot generalize that information disclosure can strengthen or even weaken the existence of herding behavior. This research only uses one method of detection of herding, namely using Cross-Sectional Absolute Deviation (CSAD). This study uses all objects in the Indonesian Sharia Stock Index (ISSI) without considering sharia activities in each company.

The herding behaviour on Pakistan stock exchange – using firm-level data

This paper analyses herding behaviour in the Pakistan stock exchange (PSX, formerly known as Karachi stock exchange, KSE) for a sample of 663 firms over a period of 13 years, from 2004 to 2017. For detecting herding behaviour, two dependent variables are used, i.e., cross-sectional standard deviation (CSSD) of Christie and Huang (1995) and cross-sectional absolute deviation (CSAD) of Chang et al. (2000). The results show no herding behaviour on the basis of both methods at different levels of market movements. The absence of the herding behaviour may be because these firms belong to different sectors which may follow their respective industry portfolios but not the overall market; for example, Shah et al. (2017) documented that firms in several industries herd toward their industry portfolios for Pakistani data. Future research can be done using a primary data collection method from investors about their opinion on herding behaviour.