Cross-sectional Absolute Deviation Research Articles

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.

Herding Behavior in Stock Market of Bangladesh: A Case of Behavioural Finance

The study investigates herding behavior in the stock market of Bangladesh using a measure called Cross-Sectional Absolute Deviation (CSAD). The findings suggest that herding behavior does exist in the stock market of Bangladesh, based on the analysis of daily data from 169 stocks listed on the Dhaka Stock Exchange (DSE). Furthermore, the study indicates the presence of potential asymmetries in herd behavior concerning market returns and trading volume. The herding behavior, as observed in the Bangladeshi stocks, exhibits asymmetric tendencies, implying that the strength of herding varies depending on different market conditions. Specifically, the results show that herding behavior is more pronounced when the market trading volume is high. In other words, when there is an increased trading activity in the market, the tendency for investors to engage in herding behavior becomes stronger.

Investor Herd Behaviour during the COVID-19 Pandemic: Evidence from the Johannesburg Stock Exchange

The COVID-19 pandemic has caused extreme volatility in financial markets globally. During periods of extreme volatility, investors tend to exhibit herd behaviour. However, herd behaviour results in market anomalies, which causes incorrect valuations and pricing. Hence, the objective of this study is to test whether COVID-19 has induced herd behaviour in South Africa’s stock market. To achieve this objective, the FTSE/JSE Top 40 Index is used as a proxy for stocks trading on the Johannesburg Stock Exchange. The cross-sectional absolute deviation (CSAD) methodology is employed, and the sample period ranges from July 3, 2017 to June 30, 2021 with March 5, 2020 dividing the pre- and post-COVID-19 periods. The results suggest an absence of herd behaviour in the FTSE/JSE Top 40 Index during the full, pre- and post- COVID-19 sample periods. Therefore, this study concludes that the COVID-19 pandemic has not induced herd behaviour in the South African stock market. This could indicate that stock investors on the JSE follow their own thought and decision-making processes in periods of extreme volatility. Moreover, this may suggest that the South African stock market is efficient and that investors execute rational and well-thought out trades.

Animal Behavior in Capital markets: Herding formation dynamics, trading volume, and the role of COVID-19 pandemic

This paper provides new evidence on herding behavior. Using daily frequency data for 336 US listed firms over a five-year period, we investigate three important elements of financial herding behavior. First, trading volume, representing market interest, as a significant variable in capital markets apart from stock prices. Second, herding dynamics since herding formation is a dynamic process. Third, the reaction of possible financial herding to exogenous events-threats, as we use the pandemic event in order to investigate a market under stress. Even though the benchmark herding model used does not provide evidence of herding behavior, our results verify the significance of the above herding elements. We also find that trading volume and positive changes in trading volume result in increased cross-sectional absolute deviation (CSAD). Most importantly, we find that herding behavior is evident during the COVID-19 pandemic confirming that investors tend to herd during major crisis periods.

The influence of Indonesia’s macroeconomic factors: Inflation and interest rate on large-cap cryptocurrency herding behavior

Research aims: This study aims to investigate herding behavior in the large-capitalization cryptocurrency market and analyze the role/influence of Indonesia's macroeconomic factors, namely inflation and interest rates, on herding behavior in the large-cap cryptocurrency market.Design/Methodology/Approach: This study used secondary data from the daily closing prices of five large-cap cryptocurrencies and Indonesia's macroeconomic data (inflation and interest rates) from April 2019 to December 2022 by using the Cross-Sectional Absolute Deviation (CSAD) model and Newey-West estimator regression approach to detect herding behavior with a modified independent variable model involving factors influencing herding behavior.Research findings: Based on the results using the Newey-West estimator, three main results were obtained. First, large-cap cryptocurrency investors tend to be irrational in their decisions and follow the decisions of others without reference to their beliefs or herding during the sample period. Second of the two macroeconomic factors studied, i.e., inflation and interest rates, only changes in inflation rates influence investor herding behavior. Third, the market is inefficient with the proven tendency of herding behavior in large-cap cryptocurrencies.Practical and Theoretical contribution/Originality: This study narrows down the research of previous studies by using cryptocurrency research objects with a large market capitalization (large cap). In addition, this research extends the research of previous studies by considering external factors related to macroeconomic conditions in Indonesia in general, such as the inflation rate and the interest rate. This study can provide information about financial behavior in the cryptocurrency market, especially herding behavior, so that investors and policymakers can be assisted in formulating investment strategies and regulating cryptocurrencies.Research limitation: This research was limited to using only cryptocurrency assets by not using crypto-tokens, non-fungible tokens (NFT), and other crypto-assets.

The Prevalence of Herding in Indian Auto Sector: Implications for Market Efficiency

The worldwide catastrophes due to the spread of Wuhan virus, India had witnessed an explosive change in the economic activity, mainly in the investment pattern. This made the investors to do panic trading by following what others were doing in the market. We offer an empirical analysis to examine the presence of herding in Indian Automobile Sector during pre-pandemic and pandemic period (COVID-19). To detect the presence of herding, we applied Cross Sectional Absolute Deviation (CSAD) and One-way ANOVA was employed to examine whether there is any significant difference in the auto index during Pre pandemic and pandemic period. The result shows that herding behaviour is driven by panic and shocks during the pandemic period. However, herding is not prevalent during the pre-pandemic and whole period. This paper concludes that the Indian market is efficient unless there is any panic or fear among the investors.

Chinese stock market volatility and herding behavior asymmetry during the COVID-19 pandemic

The primary purpose of this paper is to explore the herding behavior in the Chinese stock market during COVID-19 and the asymmetry of that behavior using the daily returns of A- and B-shares from 2 January 2019, to 15 October 2021. The study uses the cross-sectional absolute deviation model to analyze stock market herding behavior by non-linear polynomial regression. We show that the herding behavior in the Chinese stock market is more prominent during the COVID-19 pandemic. Herding behavior has a negative effect on stock market volatility. Moreover, such a suppressing effect weakened during the COVID-19 pandemic. There is an asymmetry in herding behavior during the bull and bear markets, which is helpful in our investigation of the market’s volatility during the COVID-19 pandemic. The pronounced asymmetry in the herding behavior of the Chinese stock market during COVID-19 is assessed using the E-GARCH (p, q) model. The empirical results of the present study contribute to the literature about herding asymmetry by showing the herding behavior during the health crisis and bull and bear markets. It also helps reconcile the debate about the impact of herding on market stability and provides insightful guidance for investors wishing to invest in the Chinese stock market.

Do Firm Value and Institutional Investors Affect on Herding Behavior?

This research aims to determine the effect of firm value and institutional investors on herding behavior. The newnes of this research to add an indicator that have never been used for herding measurement to firm value. The existence of previous research result on herding that have not been conclusive raise empirical gaps and population gaps that are worthy of re-study. The measurement of firm value is carried out using the Book to Market Ratio and Firm Size. The size of institutional investors uses the company's institutional ownership, and the measurement of herding behavior uses the Cross-Sectional Absolute Deviation (CSAD) method. The sample in this study are LQ45. The data analysis method used in this study was carried out with multiple regression analysis. The study results show that there is indication of the occurrence of Herding Behavior in LQ45 companies in sample. Firm Value and institutional investors do not influence Herding Behavior. Keywords: Herding Behavior, Firm Value, Institutional Investors, LQ45 Companies

Detecting the Herding Behaviour in the South African Stock Market and its Implications

Herd mentality is associated with financial market bubbles and crises and is not a new concept. Despite the dotcom bubble in the late 90’s and the recent real estate bubble, investors still have the tendency to see irrational decision as rational. Thus the aim of this study was to investigate the presence of herding in the Johannesburg stock exchange during the Covid-19 pandemic. Studies conducted in other markets have detected the presence of herding which sends strong signal of issues that may arise in financial markets. Using a sample period from 02 January 2020 to 31 December 2021 and a cross sectional absolute deviation as an analysis tool, the findings reveals a significant presence of herding in the Johannesburg stock exchange. The main implications to this finding is that momentum investing strategies is more suitable for the Johannesburg Stock exchange and exploring past price movements may provide valuable insights on expected future price movements

Empirical Analysis of Global Markets Herding on COVID-19 Effect

This article investigates the presence of herding on global stock markets during COVID-19. Cross-Sectional Absolute Deviation developed by Chiang and Zheng (2010) and Chang et al. (2000) examine market-wide herding. Results show that herding is prevalent in Brazil, Germany, India, Italy, Korea, China-Shanghai, China-Shenzhen and Turkey. During the epidemic, no evidence of herding was detected in France, Hong Kong, Indonesia, Japan, Malaysia, Poland, the United Kingdom and the United States. It has been shown that the United States and Chinese cross-market herding substantially influences all markets. The impact of analyst information (target price, EPS forecasts and revenue predictions) on herding is shown before the pandemic and weakening during the pandemic. It indicates that investors disregard expert recommendations and liquidate equities during the pandemic. The findings shed light on the occurrence and determinants of herding that causes investors’ to behave irrationally during COVID-19. Policymakers, government and regulators should formulate a more robust policy to monitor, control and oversee the naïve herding that drives fundamentals away from stock prices.

COVID-19 pandemic and herd behavior: Evidence from a frontier market

This paper examines the presence of herd behavior in the Vietnamese stock market using the cross-sectional absolute deviation (CSAD) method and by applying quantile regression (QR). We detect herd behavior in the Vietnamese stock market from January 2016 to May 2022. Herd behavior is less pronounced for bullish markets, yet more prominent under other market conditions. Importantly, the paper provides insight into the herd phenomenon during COVID-19’s fourth wave outbreak in Vietnam. We discover that during the fourth wave outbreak, investors on the Hanoi Stock Exchange (HNX) do not engage in herding. However, herd behavior does manifest on the Ho Chi Minh Stock Exchange (HOSE) with falling stock prices engendering pessimistic herd selling. Knowledge of this empirical evidence of herd behavior in the Vietnamese stock market should prove useful to investors in determining the intrinsic value of stocks, and to policymakers wishing to enhance the efficiency of the equity market.

An Empirical Investigation of Herd Behaviour in Surplus Economy: Evidences from Muscat Stock Market

This study investigates herd behaviour in the surplus economy of the Sultanate of Oman under Bear and Bull market conditions. The first aim of the study is to determine if the investors at Muscat Stock Market (MSM) exhibit herd behavior or not. The second aim of the study is to compare the different stochastic time-series models commonly used to test and forecast herd behavior. Finally, the study aims to determine whether herd behavior in the MSM is predominantly risk-driven as measured by Cross-Sectional Standard Deviation (CSSD) or return-driven as measured by Cross-Sectional Absolute Deviation (CSAD) of stock returns from the overall market return. Our analyses are based on daily Muscat Stock Market’s (MSM) index returns for the period starting 1st of January 2010 and ending 31^st of December 2019, the pre Covide-19 pandemic. Our findings disclose: (1) that MSM exhibits herd behavior, (2) that there are statistically significant among the different stochastic time-series models used to test and forecast herd behavior in MSM, where ARIMA (1,0,0) model exhibits the highest predictive power of herd behavior in MSM (3), that MSM’s herd behavior is driven more by CSSD than CSAD in the bearish and bullish market conditions.

INVESTORS’ HERDING BEHAVIOUR IN INDIAN STOCK MARKETS DURING COVID 19: A CSSD AND CSAD APPROACH

The World has witnessed one of the most severe crises, a resulting effect of COVID 19 virus. This virus was never seen before in the human history and was declared as Pandemic by World Health Organization (WHO) on 11th March, 2020. Any crises like situation creates an imbalance in the economy which also gets penetrated in the stock markets of the country. The investors sentiments get weakened due to the negative impact of such incidences on various aspects of economy. Investors tend to exit from the market in such situation and look forward for better investment options or cash liquidity. Nevertheless, investors depict such behaviour in groups and herding tend to be me prevalent during such period of market stress (Bouri et al. 2021). Therefore, the present research tries to explore the herding behaviour of investors in Indian stock market. The study examines the presence of herding possibilities in Nifty 50 stocks of Indian stock market during the phase of Covid 19. The Cross-Sectional Standard Deviation (CSSD) methodology proposed by Christie and Huang (1995) and Cross-Sectional Absolute Deviation (CSAD) suggested by Chang et al. (2000) has been applied to detect the herding effect on stock prices of 50 composite stock of Nifty 50 index of Indian Stock Market.

Analisis Herding Behavior di Bursa Efek Indonesia pada Tahun 2020

The aim of this study is to analyze herding behavior in the Indonesian capital market as an emerging market which has experienced high JCI volatility throughout 2020. The analysis of herding behavior takes place in three market periods: (a) throughout the 2020 period with 242 observation dates (233 after the outliers were removed), (b) ) bearish (down) conditions with 59 observation dates (51 after the outliers were removed), and (c) bullish (rising) conditions with 183 observation dates. The sample consisted of 36 companies taken from members of the LQ-45 index for the periods August 2019-January 2020, February 2020-July 2020, and August 2020-January 2021. Herding behavior was identified through regression analysis and the Cross-Sectional Absolute Deviation (CSAD) method. The results of the study did not show any herding behavior during the observation period, which means that investors make investment decisions by considering the available information.
 Keywords: Herding; Cross Sectional Absolute Deviation.

Does macroeconomic environment stimulate herding behaviour among investors in Malaysia stock market? an empirical evidence

Herding behaviour could cause stock prices to deviate from the fundamentals, resulting in market instability. One of the influencing factors that stimulate herding behaviour amongst investors is macroeconomic environment. Among the major macroeconomic variables that play important role in influencing herding behaviour are economic growth, exchange rate, interest rates and inflation rate. However, empirical evidence on this is still lacking especially in the case of emerging markets such as Malaysia. Therefore, the main objective of this paper is to empirically examine the influence of macroeconomic variables on the existence of herding behaviour in the Malaysian stock market.  In this study, two measures of herding behaviour have been used: Cross-Sectional Standard Deviation (CSSD) and Cross-Sectional Absolute Deviation (CSAD).  The estimation used daily data of 346 companies listed on the Malaysian stock market from the year 2000-2019. The regression estimation results using CSSD with individual macroeconomic variable found that herding behaviour is detected in the case of interest rate and inflation. Meanwhile, for CSAD, the results demonstrate that herding behaviour is present in the case of interest rate, inflation rate and growth. Meanwhile, for the regression that consists of all macroeconomic variables, both measures are negatively and significantly related to interest rates and inflation rates. The findings offer additional evidence in supporting the existence of herding behaviour in Malaysia under the influence of selected macroeconomic variables.  Therefore, establishing better routes for economic information dissemination could assist investors to make a better investment decision. With a better understanding about herding behaviour could assist regulators to detect price efficiency issues hence to maintain market stability and efficiency.

Modeling Herding Behavior in the Indonesian Capital Market

Herding behavior generally occurs in developing markets and emerging markets. The condition of a capital market in a bullish or bearish state coupled with information asymmetry between investors will cause an increase in herding behavior phenomena. The phenomenon of herding behavior occurs when investors follow a crowd's behavior at a certain period and ignore their beliefs. Herding behavior explains the tendency of investor behavior to follow the behavior of other investors in the capital market. The study aims to analyze the herding behavior in the general, bearish, and bullish market conditions in the Indonesian capital market for the 2021- 2022 period. This research is quantitative research based on the formulation of the problem. This research is classified as associative research. The data used is secondary data in the form of monthly data from LQ45 for 2021-2022. The analysis was carried out in two stages: 1) crosssectional absolute deviation (CSAD) was used to determine to herd. 2) Multiple linear regression analysis with three market conditions: general, bearish, and bullish. The results showed that the general market and bearish market conditions explained the herding behavior in the Indonesian capital market for the 2021-2022 period, while for bullish market conditions, there was no herding behavior in the Indonesian capital market for the 2021-2022 period. Investors should still conduct fundamental and technical analysis before making investment decisions during bullish and bearish market conditions in the Indonesian capital market.

DYNAMICS OF HERDING BEHAVIOUR DURING EXTREME MARKET MOVEMENTS IN CHINA AND PAKISTAN

This paper investigates herding behaviour in China and Pakistan by using dynamic herding approaches and herding during extreme market movements. We use daily returns of the Shanghai stock exchange (SSE) and Pakistan stock exchange (PSX) from 2006 to 2021. Several models are used to study herding, such as least squares model, generalized linear model, and Bai and Perron's structural change model. Overall, results through the linear model show that China has significant herding, but no herding is found in Pakistan. According to Bai and Perron's model, herding is evident in two different regimes of both countries. The first regime shows herding during USA global financial crisis of 2008. The second regime was during the Hong Kong protest, which drove herding in China. In Pakistan, herding is found during political crisis and terrorism activities. From the perspective of extreme market movements, China and Pakistan both showed herding during 10% values. Contrary, no herding was found during 1% values, but Pakistan showed herding during 5% values at both extremes, and China only showed herding during the upper extreme. Hence, herding should be measured at 10% rather than 5% and 1%. Keywords: Behavioural finance, herding behaviour, extreme market movements, cross-sectional absolute deviation, structural change model. Jel classification: G01, G11, G12.

Sentimental Herding in Stock Market: Evidence from India

The present study examines herd behaviour in the Indian equity market using daily prices of 11 broad market indices, 12 sectoral indices, 16 strategic indices and 11 thematic indices from 1 January 2016 to 31 March 2022. For examining herd behaviour, daily returns, overnight returns, intraday spread, absolute market return and squared market return are calculated. Nifty 500 is used as a proxy index for the whole market. The regression models suggested by Christie and Huang (1995) and Chang et al. (2000) are used to calculate Cross-Sectional Standard Deviation (CSSD) and Cross-Sectional Absolute Deviation (CSAD) respectively, which measure the equity return dispersions. It is observed that herding exists in the Indian equity market when we calculate return dispersions using intraday spread. However, no herd presence is found when return dispersions are calculated through daily return, overnight return, absolute market return and squared market return. These results may be useful for investors, policy makers and regulatory bodies. Investors might design their investment strategy while making investment decision. Regulators might be able to formulate their policies to ensure market efficiency in the Indian equity market.

Anatomy of a Stablecoin’s failure: The Terra-Luna case

We quantitatively describe the main events that led to the Terra project’s failure in May 2022. We first review, in a systematic way, news from heterogeneous social media sources; we discuss the fragility of the Terra project and its vicious dependence on the Anchor protocol. We hence identify the crash’s trigger events, analysing hourly and transaction data for Bitcoin, Luna, and TerraUSD. Finally, using state-of-the-art techniques from network science, we study the evolution of dependency structures for 61 highly capitalised cryptocurrencies during the down-market and we also highlight the absence of herding behaviour analysing cross-sectional absolute deviation of returns.

Effect of presidential elections on investor herding behaviour in African stock markets

PurposeThe purpose of this paper is to investigate investor herding behaviour and the effect of presidential elections on investor herding behaviour in African stock markets at the sector level.Design/methodology/approachThe study segregates listed firms into financial, consumer goods, consumer services and basic materials sectors and uses the cross-sectional absolute deviation approach as a metric of detecting herding in each of the sectors. The authors extend the model to tease out the effect of presidential elections on investor herding behaviour.FindingsThe study reveals that sectoral differences are fundamental to the evolution of herding. Herding is prominent in a financial services sector dominated by banks. The phenomenon also prevails in markets with smaller consumer goods and services sectors. A post-presidential election effect on investor herding is found for the consumer goods and services sectors of Ghana and a pre-presidential election effect is documented in Nigeria's consumer services sector. The authors conclude that post-presidential election effect is as a result of political connections whilst a pre-presidential election effect is attributable to political business cycles.Research limitations/implicationsThe study is based on four African countries due to data constraints. Nonetheless, the study is the first in Africa to the best of the authors' knowledge, and the results are very solid and have a lot of practical and policy implications.Practical implicationsThe study has implications for investors as it guides investment behaviour in pre- and post-presidential election periods.Originality/valuePast studies on investor herding behaviour in African stock markets have largely concentrated on the aggregate market. Knowledge on sectoral differences in investor herding is almost non-existent for African stock markets. Furthermore, premised on the fact that stock markets react to presidential elections, there is no known study that have attempted to examine the effect of presidential elections on investor herding behaviour. This paper contributes to the literature by providing evidence on sectoral differences in investor herding behaviour and the effect of presidential elections on sectoral herding behaviour.