Return Dispersion Research Articles

Did the Covid-19 Pandemic Trigger Herding Behavior?

This study aims to examine the influence of the COVID-19 pandemic on mutual fund herding in Indonesia. The research population comprises open-end mutual funds in Indonesia. By adopting the purposive sampling method, we selected the stock mutual funds registered from 2017 to 2022 as our research sample. We define the 2017-2019 period as before COVID-19 and 2020-2022 as during COVID-19. This study employs CSAD and CSSD as proxies for return dispersion by considering two types of indices as benchmarks, namely LQ45 and IDX Composite. Our study found that COVID-19 did trigger mutual fund herding. However, we failed to find herding behavior when there is a large market movement. Instead, we found reverse herding.

Risk and Return Analysis on Selected 10 Companies: a Five-year Study (2018-2022)

The "Risk and Return Analysis on Selected 10 Companies: A Five-Year Study (2018-2022)" delves into the intricate dynamics of risk and return within the equity market landscape over a significant period. Through meticulous examination spanning five years, this study aims to unravel the performance metrics of ten carefully chosen companies, providing investors and financial analysts with invaluable insights into optimizing portfolio strategies and maximizing returns. Employing contemporary methodologies and empirical data, the study contributes to the ongoing discourse surrounding risk-return dynamics, addressing the escalating need for evidence-based decision-making in financial markets amidst increasing complexity and volatility. Methodologically, the study employs a multifaceted approach, including the meticulous selection of ten companies based on predefined criteria, comprehensive data collection, and the application of various quantitative techniques to evaluate risk and return metrics. The analysis focuses on equity stocks over the specified five-year period, utilizing variance and standard deviation as fundamental statistical measures to quantify dispersion and volatility of stock returns. The study aims to furnish stakeholders with actionable insights to optimize investment decisions and mitigate potential risks effectively, ultimately aiding stakeholders in making well-informed investment decisions aligned with their financial objectives and risk tolerance levels.

Volatility and Herding Bias on ESG Leaders’ Portfolios Performance

We here analyze the factor loadings given by the CAPM, the Fama–French three (FF3), and the five-factor model (FF5), and test the performance and the validity of adding two more factors (volatility and dispersion of returns) to the FF5 factor model of European index-based ESG leaders’ portfolios. Our ESG leaders’ portfolios generated significant negative alphas during 2012–2022, corroborating the literature’s negative argument. The negative abnormal returns of ESG leaders’ portfolios are homogeneous across the three ESG pillars. We conclude that European ESG leaders’ portfolios are biased toward large cap and value stocks with robust operating profitability and against aggressive investments. As robustness tests, we examine Global ESG leaders’ index-based portfolios, producing the same results but with reduced importance in some loading factors like profitability and investment strategy. Furthermore, we deduced that European and Global ESG leaders’ portfolios tilt towards volatility and herding bias.

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.

Herd behavior in cryptocurrency market: evidence of network effect

PurposeThis study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect.Design/methodology/approachThe authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect.FindingsThe empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect.Practical implicationsThese findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty.Originality/valueMost previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.

Entrepreneurial Wealth and Employment: Tracing Out the Effects of a Stock Market Crash

ABSTRACTUsing the dispersion in stock returns during the financial crisis as a source of exogenous variation in the wealth of Norwegian entrepreneurs who held listed stocks, I show that adverse shocks to the wealth of business owners had large effects on their firms' financing, employment, and investment. The effects on investment and employment are driven by young firms, that obtain differentially less bank financing following an owner wealth shock. The effects on employment operate primarily through reduced hiring. My findings highlight that equity‐financing frictions and the procyclicality of entrepreneurial wealth are important channels that can amplify economic shocks.

ANALISIS PERILAKU HERDING PADA SAHAM SEKTOR BANK DIGITAL YANG TERDAFTAR DI BEI PERIODE 2021

This research was conducted to detect and identify herding behavior that might occur in digital bank sector stocks in the 2021 period. The 2021 period is used for the market when market conditions are stressful and high returns. The approach method of Chang et al. (2000), namely CSAD-Cross Sectional Absolute Deviation to calculate return dispersion values ​​and use quantile regression analysis to detect herding behavior. The data used is secondary data, in the form of monthly stock return data and market returns for digital bank stocks. The results of this study indicate that digital bank stocks in 2021 have no indication of herding behavior. This means that Indonesian investors acted rationally during the COVID-19 pandemic.

Unrealized return dispersion and the equity risk premium

This paper discusses how unrealized return dispersion across individuals affects the equity risk premium. We specify an intertemporal capital asset pricing model with heterogeneous preferences depending on investors’ unrealized returns and uncover that unrealized return dispersion negatively predicts the equity risk premium. An empirical study is performed on the Chinese stock market, and the results are consistent with the theoretical claims.

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.

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.

Dispersion in news sentiment and corporate bond returns

We construct a news sentiment index at the firm level by using textual analysis of news articles and find that dispersion in news sentiment is a significant predictor of corporate bond returns. Bonds of firms with high dispersion in news sentiment have a highly significant average return of 7.38 percent. A portfolio that longs bonds with high dispersion in news sentiments and shorts bonds with low dispersion earns an average biweekly return of 8.53 percent. This finding is in line with an argument that dispersion in news sentiment is a proxy for future cash flow uncertainty.

The Use and Misuse of Tracking Error

Equity portfolio tracking error to a benchmark is a most ubiquitous restriction for active portfolios as prescribed by fiduciaries. The restriction is typically a tight range with minimum and maximum ex-ante extremes. The typical capitalization-weighted benchmark (i.e., the S&P 500 Index) has significant changes in diversification character overtime. This brings into question the sensibility of holding tracking error (TE) constant for a skilled active manager. The authors demonstrate that as the concentration of names in the S&P 500 increases, its diversification fades. When this happens, constant tracking error is the enemy of the skilled diversified manager, other things equal. Using multivariate classification and regression trees (CART), they show that high tracking dominates low tracking when index concentration is low, trending lower, and return dispersion is high. Low tracking dominates when the opposite index conditions exist. The authors conclude that the power of a <italic>flexible</italic> TE process in wealth creation is dominant over <italic>inflexibility</italic> in the benchmark.

Is there Herding Behavior in the Indonesia Stock Market during the COVID-19 Pandemic?

This study investigates the signs of herding behavior during the COVID-19 pandemic in the Indonesian Stock Exchange. Various studies found no herding in Indonesian stock markets during the COVID-19 pandemic, but we believe those studies have a limited methodology to capture the herding behavior. We believe that herding appears in a short time during the pandemic period, so we have to reexamine the existence of herding behavior using sectoral stock indexes rather than the stock market-wide index (IHSG) and using the rolling regression technique to capture the possibilities of herding that might be existing during short window period in COVID-19 pandemic time. This study uses a model Chang et al. suggested (2000). Variables such as return dispersion (CSAD), absolute market return, and market squared return are employed in the analysis. We use the closing price of 715 stocks, nine sectoral stock indexes in IDX, and the closing price of IHSG from January 2, 2020, until April 30, 2021. The results show that herding cannot be found in the full sample of the market-wide stock index (IHSG) and sectoral indexes. The rolling regression indicates that herding was found for several days from January 2020 to December 2021.

Herding behavior in integrated financial markets: the case of MILA

PurposeIt has been argued in the literature that structural changes in the financial markets, such as integration, have the potential to cause herding behavior or correlated behavioral patterns in traders. The purpose of this study is to investigate whether there is any financial herding behavior in the Latin American Integrated Market (MILA), a transnational stock market composed of Chile, Peru, Colombia and Mexico stock exchanges and whether there is any ARCH or GARCH effect in the herding behavior models.Design/methodology/approachThis study uses the modified return dispersion approach on daily index return data. The sample period is from January 03, 2002 to May 07, 2019. The data are obtained from the MILA database. To count time-varying volatilities in herding models, the authors run ARCH family regression with GARCH (1,1) settings. Hwang and Salmon (2004) model is used as a robustness test.FindingsThe authors found strong herding behavior under the general market conditions and moderate and partial herding behavior under some specified markets circumstances, such as bull and bear markets and high-low volatility states. Moreover, the pre-MILA period exhibits more herding behavior than the post-MILA period. The empirical results show that most of the ARCH and GARCH effects are statistically significant, implying that the past information of stock returns and market volatility significantly affect the volatility of following periods, which can also explain the formation of herding tendency among investors. Finally, the results of the robustness tests (Hwang and Salmon, 2004) confirm herding in all periods, except full sample period for Mexico and post-MILA period for Mexico and Colombia.Research limitations/implicationsThis study investigates the herding behavior in the MILA market in terms of market return, volatility and timing. A limitation of the paper is that the authors have not included other factors on the formation of herding behavior, such as macroeconomic factors, effects of regional or international markets and policy influences. The authors will explore the issue in the extension of the paper.Practical implicationsAs MILA is the first virtual integration of stock exchanges without merging, the study provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are useful for academics, investors and policymakers in their investment and decision makings.Social implicationsThe paper provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are not only useful in practical implications, but also in social implications.Originality/valueThis study contributes to the herding literature by examining four different hypotheses in respect of the unique case of transnational stock exchange without fusions or corporate mergers, where each market maintains its independence and regulatory autonomy. The authors also contribute to the literature by including both ARCH and GARCH effects in the herding behavioral models along the Hwang and Salmon (2004) approach.

Cross‐sectional return dispersion and stock market volatility: Evidence from high‐frequency data

AbstractThis paper investigates whether the cross‐sectional variance (CSV) of stock returns and its asymmetric components contain incremental information to predict stock market volatility under a high‐frequency, heterogeneous autoregressive (HAR) model framework. We present novel evidence that CSV is a powerful predictor of future realized volatility, both in‐ and out‐of‐sample, even after controlling for the well‐established predictors obtained from intraday data. Further analysis suggests that distinguishing between positive and negative CSV components in the forecasting model enhances the predictive capability of volatility models at all out‐of‐sample forecasting horizons, with the asymmetric HAR‐type‐ACSV model consistently outperforming all alternative HAR‐type variations. We argue that the asymmetries in the predictive relation between CSV and volatility are largely driven by the disagreement among market participants that spikes during bad times. Finally, economic analysis shows that incorporating CSV in the forecasting model can generate sizeable economic gains for a mean–variance investor, suggesting that out‐of‐sample predictive ability of CSV can be exploited in forward looking investment strategies to enhance investment returns.

Herding to Market Consensus: An Empirical Investigation of its Presence in Nepal Stock Exchange

The study examines herd formation in Nepalese equity market. The study uses linear regression models including quadratic variant to model the relationship between return dispersion of individual scrips and aggregate market return for finding the herding behavior. The study does not find presence of herding. On the contrary, when presence of herding is tested for bearish and bullish trend, herd formation is detected in bullish trend. The study supports the notion that Nepalese equity market is prone to behavioral biases leading to pricing inefficiencies. The findings of the study can be helpful to make policies to protect retail investors from downside risk arising from these behavioral biases.

Investor trading behavior and asset prices: Evidence from quantile regression analysis

AbstractConsidering the behavior anomaly under both rising and falling market conditions, this paper aims to address whether the investor trading behavior is sensitive to a different quantile of stock return dispersions by using quantile regression model. Results show that investor trading behavior has significant impacts on different quantiles of stock return dispersions, and reveal the smile slope of investor trading behavior effect which is stronger at the extreme quantile distributions than the median distribution. Moreover, results evidence that the investor trading behavior effect with optimistic investor sentiment should be stronger than the investor trading behavior effect with pessimistic investor sentiment. Finally, this paper sheds light on the anchoring effect of investor trading behavior, and demonstrates that the anchor of investor trading behavior has a positive and significant impact on stock returns. These patterns hold when accounting for stock specific characteristics, various factors and market conditions.

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.

Financial Contagion in European Equity Markets: Evidence from the US Subprime and the Eurozone Crises

The study explored the financial contagion between the five European stock markets (Germany, Belgium, France, Netherlands, and Spain) and the impact of the US subprime crisis and Eurozone crisis on it by employing multivariate DCC-GARCH methodology. The dynamic conditional correlation coefficient between Germany and other European markets was statistically significant for the period under review. The rise in the value of the conditional correlation coefficient was more evident during the US subprime crisis period, which signified the impact of this crisis on the financial interdependence of the European stock markets. But not much influence of the Eurozone crisis was observed on the dispersion of returns for all the markets, which was an important finding. The mechanism of rolling regression also corroborated these results. The study also validated the presence of financial contagion between the stock markets (except the Netherlands) during the US subprime crisis period. In contrast, the effect of the Eurozone crisis on financial contagion was significant only for Spain and Belgium. Further, the strength of financial contagion was highest for the stock market of Spain, followed by Belgium, and the least for the Netherlands. The timid reaction of the stock market of the Netherlands reflected that it could be a good choice for portfolio diversification during periods of crisis for investors. Further, governments and other regulators can learn from these findings and formulate appropriate policies to absorb the financial shocks coming from foreign markets, especially during the crisis period.