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Institutional Herding
Abstract
Institutional investors' demand for a security this quarter is positively correlated with their demand for the security last
quarter. We attribute this to institutional investors following each other into and out of the same securities (“herding”)
and institutional investors following their own lag trades. Although institutional investors are “momentum” traders, little
of their herding results from momentum trading. Moreover, institutional demand is more strongly related to lag institutional
demand than lag returns. Results are most consistent with the hypothesis that institutions herd as a result of inferring information
from each other's trades.
... First, the paper examines whether loan herding exists by testing the intertemporal dynamic dependence of lending demand by banks in China. According to the argument in Sias (2004), an investigation into whether other lending following (i.e., loan herding) occurs for Chinese banks as a whole and by different types and whether the intertemporal correlation is mainly from loan herding. In contrast to Fang et al. (2019), this study simultaneously focuses on a comparison of the degree of loan herding by different types of banks, which includes the possibility of loan herding by state-owned commercial banks (SOCBs). ...
... Thus, CCBs can exhibit greater following behavior in other banks' industrial lending than can JSCBs. Sias (2004) uses the intertemporal herding model to examine the cross-sectional correlation of institutional investors' dynamic trading and decomposes this into the two correlation portions of institutions following these institutions' own trades (own cascades) and other institutional trades (other cascades, namely, herding). Extending the approach in Sias (2004) to the loan market, the paper decomposes the intertemporal correlation into "own lending following" in which a bank follows the previous lending and "other lending following" in which a bank follows other banks' previous lending. ...
... Sias (2004) uses the intertemporal herding model to examine the cross-sectional correlation of institutional investors' dynamic trading and decomposes this into the two correlation portions of institutions following these institutions' own trades (own cascades) and other institutional trades (other cascades, namely, herding). Extending the approach in Sias (2004) to the loan market, the paper decomposes the intertemporal correlation into "own lending following" in which a bank follows the previous lending and "other lending following" in which a bank follows other banks' previous lending. According to the argument in Sias (2004), other lending following indicates loan herding. ...
This study first examines whether Chinese banks exhibit the inter-temporal herding behavior in industrial lending. We decompose the inter-temporal correlation in lending into own lending following and other lending following (loan herding). Our results find that the correlations in lending for banks primarily result from loan herding. Next, we clarify the two motivations for loan herding include reputational herding and characteristic herding. Finally, we find that loan herding may cause bank inefficiently lending to risky industrial firms, leading to significant harmful impacts on real economic activities such as the industrial GDP growth, foreign direct investment, and price-earnings ratio.
... Asset managers also stay close to the pack because underperformance may adversely impact their remuneration or their career prospects (Scharfstein and Stein, 1990;Rajan, 2006). Informational herding occurs when investors infer information from other investors' trades (Banerjee, 1992;Bikhchandani et al., 1992;Sias, 2004). In this case, investors mimic the trading behavior of others, which they deem to be informed. ...
... Furthermore, we weigh the herding measure by transaction size, which considers all transactions. Third, the LSV herding measure is unable to capture the inter-temporal trading pattern (implementation of trading strategies over multiple months), as suggested by Sias (2004). Therefore, we identify position continuing trades, which are observations where the direction of trade has not changed in the subsequent month. ...
... Position continuing trades could play a role in the observed herd behavior (Table 2). Pension funds can potentially follow their own investments (e.g., Sias, 2004). Implementation of an investment decision can take several months, especially if the decision is taken at the end of the month. ...
This study investigates herd behavior exhibited by pension funds in the sovereign bond market before, during and after the European debt crisis. It uses unique monthly data on sovereign bond holdings of pension funds and transactions between December 2008 and December 2014. The dataset covers 67 large Dutch pension funds that invest in bonds from 109 countries. We find evidence of intensive herd behavior of Dutch pension funds in sovereign bonds. We also distinguish between European countries which suffer from the European debt crisis, such as Cyprus, Greece, Ireland, Italy, Portugal and Spain, and those that have not. We find high sell herding and low buy herding for the crisis countries during the European debt crisis, whereas in the non-crisis period their herd behavior does not differ substantially from that in non-crisis countries. When we control for institutional, macroeconomic, financial market and pension fund factors, sell herding in crisis countries is still significantly higher. However, we find no evidence of destabilizing behavior with respect to bonds of crisis countries during the European debt crisis.
... In particular, two anomalies of great practical relevance arise when decision makers interact in social groups, influencing each other. The first anomaly is the herd behaviour leading to irrational outcomes, observed for example during financial bubbles, where decision makers can follow the opinion of others, apparently disregarding their own interests [10][11][12][13]. The second anomaly has been empirically observed when decision makers are organized in groups and decide collectively their strategy [2-4, 14] (a precious source of insights and experimental data is offered by the studies on trial juries conducted in the Seventies [15][16][17][18][19]). ...
... However, over the years a series of empirical evidences in contrast with the theoretical predictions have clearly pointed out the descriptive limitations of these theories and have undermined their very fundamental assumptions. The alternative explanations and models that have been proposed are able to justify these discrepancies by incorporating ad hoc behavioural mechanisms in the decision making process [1,[3][4][5][7][8][9][10][11][12][13]. In particular, two anomalies of great practical relevance arise when decision makers interact in social groups, influencing each other. ...
... In particular, two anomalies of great practical relevance arise when decision makers interact in social groups, influencing each other. The first anomaly is the herd behaviour leading to irrational outcomes, observed for example during financial bubbles, where decision makers can follow the opinion of others, apparently disregarding their own interests [10][11][12][13]. The second anomaly has been empirically observed when decision makers are organized in groups and decide collectively their strategy [2][3][4]14] (a precious source of insights and experimental data is offered by the studies on trial juries conducted in the Seventies [15][16][17][18][19]). ...
We introduce an evolutionary game on hypergraphs in which decisions between a risky alternative and a safe one are taken in social groups of different sizes. The model naturally reproduces choice shifts, namely the differences between the preference of individual decision makers and the consensual choice of a group, that have been empirically observed in choice dilemmas. In particular, a deviation from the Nash equilibrium towards the risky strategy occurs when the dynamics takes place on heterogeneous hypergraphs. These results can explain the emergence of irrational herding and radical behaviours in social groups.
... Consequently, we investigate the secular trend in the tendency of mutual funds to trade in herds as a possible driver for their deteriorating trading performance. We measure time-series trends in herding using average LSV as well as the intertemporal herding measure of Sias (2004). The LSV herding measure captures a temporal dimension of fund herding, that is, the tendency of funds to trade in the same direction as Table IX. ...
... other funds during the same time period. On the other hand, the Sias (2004) captures the intertemporal dimension of fund herding, defined as the tendency of funds to trade in the same direction as other funds in the previous time interval. More specifically, the intertemporal herding measure is calculated as the estimated coefficient from a cross-sectional regression (using all stocks i in quarter t) of the relative number of traders on its lagged value: ...
... In Model 1 of Panel B, we present the average slope coefficients of Equation (13). Similarly to Sias (2004), we find that funds exhibit positive intertemporal herding-there is a positive correlation of the fraction of funds buying stock i in quarter t with the fraction of funds buying stock i in quarter t-1. This positive association is significantly stronger after 2000-the estimated slope coefficient in Equation (2) increases from 0.108 to 0.231 across the two periods. ...
We study active investment skills in relation to returns to scale in the active mutual fund industry. Using a sample of 13,807 funds from sixteen domicile countries investing in forty-two equity markets from 2001 to 2014, we find that they achieve negative trading performance on average, driven mainly by particularly low returns to their trades in US equities. Exploring their investment environment, we find convincing evidence of decreasing returns to scale around the world, especially for the US market. Based on theory of optimal fund size, we estimate the optimal size of the active mutual fund industry. We find that the active mutual fund industry in USA has exceeded the optimal level, whereas in the international markets, there may still be room for further expansion. Consistent with this view, we find that mutual fund managers have been gradually reallocating their assets away from the USA and more into international equity markets.
... Academicians and researchers specializing in behavioral finance may find this study equally advantageous in comprehending and contributing to forthcoming theoretical and empirical advancements, considering the current research gaps and directions outlined in the study. Sias (2004) Measures points out that the magnitude and the decomposition of the correlation in mutual fund demand could be influenced by the variation in the number of mutual funds. It examines the average "following their own trades," as well as the average "following others' trades" contributions to the correlation for each security-quarter Sias (2004) Measure Average following their own trades contribution k,t ¼ PN * k;t n¼1 ðD n;k;t − RawΔt ÞðD n;k;t−1 − RawΔ t−1 Þ N * k;t Where N * k;t is the number of managers trading security k in both quarter Average following others' trades contribution k,t (2017) Overconfidence Turnover Ratio Measure gauges the buying and selling of assets in a fund, i.e. total trading activity which may be influenced by past performance and fund flows, potentially masking the influence of overconfidence Turnover Ratio Measure TR ¼ min ðaggregated sales or aggregated purchases of securitiesÞ average 12 − months Total Net Assets of the fund Source: Puetz and Ruenzi (2011) Familiarity Bias "Geographic distance" measures the distance between two locations using the great distance circle method Geographic distance Measures TR ¼ min ðaggregated sales or aggregated purchases of securitiesÞ average 12 − months Total Net Assets of the fund Source: Fong et al. (2008) "Home state Managers" ratio provides insights into the regional composition of fund management teams "Home state Managers" ratio is the number of managers of fund from state to the total number of managers of fund during a period Source: Pool et al. (2012) (continued ) For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com ...
... Sias (2004) Measures points out that the magnitude and the decomposition of the correlation in mutual fund demand could be influenced by the variation in the number of mutual funds. It examines the average "following their own trades," as well as the average "following others' trades" contributions to the correlation for each security-quarter Sias (2004) Measure Average following their own trades contribution k,t ¼ PN * k;t n¼1 ðD n;k;t − RawΔt ÞðD n;k;t−1 − RawΔ t−1 Þ N * k;t Where N * k;t is the number of managers trading security k in both quarter Average following others' trades contribution k,t (2017) Overconfidence Turnover Ratio Measure gauges the buying and selling of assets in a fund, i.e. total trading activity which may be influenced by past performance and fund flows, potentially masking the influence of overconfidence Turnover Ratio Measure TR ¼ min ðaggregated sales or aggregated purchases of securitiesÞ average 12 − months Total Net Assets of the fund Source: Puetz and Ruenzi (2011) Familiarity Bias "Geographic distance" measures the distance between two locations using the great distance circle method Geographic distance Measures TR ¼ min ðaggregated sales or aggregated purchases of securitiesÞ average 12 − months Total Net Assets of the fund Source: Fong et al. (2008) "Home state Managers" ratio provides insights into the regional composition of fund management teams "Home state Managers" ratio is the number of managers of fund from state to the total number of managers of fund during a period Source: Pool et al. (2012) (continued ) For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com ...
Purpose
The purpose of the study is to investigate, synthesize and critically evaluate empirical research findings on the behavioral traits of fund managers from 1994 to 2024. The ultimate goal is to provide a unified body of literature on three broad topics: first, fund managers' demographic and professional characteristics, such as age, gender, level of education and years of industry experience; second, fund managers' social and political connections; and third, fund managers' behavioral biases that lead to irrational investment decisions.
Design/methodology/approach
The relevant papers from selected journals were discovered and manually validated using the Scopus database. From 317 retrieved documents, 57 relevant articles were chosen and analyzed after the forward and backward search of the existing articles.
Findings
This paper presents a categorized summary of behavioral factors that have gained a foothold in influencing the behavior of fund managers in fund management research, with several studies demonstrating their significance leading to improved prediction and model precision, as this review indicates. In addition, the study summarized the contributions of prior empirical studies within the aforementioned three major categories and illustrated their consequences.
Originality/value
The present study contributes to the understanding of the effects of behavioral finance theories on fund managers by providing meaningful explanations of their behavioral traits based on empirical evidence and existing trends and knowledge gaps, both of which can influence the future direction of research.
... Hence, by taking up monitoring roles and threatening to exit, institutional investors lessen the conflicts that may occur between managers and shareholders (Admati& Pfleiderer (2009), and Levit (2012)). More so, by engaging in trading activities based on the information collected, institutions investors reduce information asymmetry problems usually connected with equity (Sias (2004)). In addition, institutional investors as equity holders, have relative tax advantage over individual investors as such ease the burdens associated with tax. ...
... As a result of these processes, institutional investors are armed with more information than other types of investors. More so, adverse selection costs of equity is affected because it reduces the gap in the information outside and inside shareholders have, since at least a portion of the information they collect is reflected in their trading patterns (Sias, 2004). ...
... US mutual funds were also explored by Wermers (1999), who indicated high levels of herding in small stock and growth-oriented fund trading. Sias (2004) found that institutional investors tend to follow each other in buying and selling the same securities and their own lag trades. Sias (2004) also suggested that institutions herd as a result of inferring information from each other's trades. ...
... Sias (2004) found that institutional investors tend to follow each other in buying and selling the same securities and their own lag trades. Sias (2004) also suggested that institutions herd as a result of inferring information from each other's trades. Furthermore, no support was found for the hypothesis that institutional herding drives prices away from fundamental values. ...
... These implications have been quantified and investigated using various measures proposed by academicians which are further categorized as volume based and return based. The former considers the quantum of assets traded (or transaction) as a surrogate for coordinated financial behavior (Lakonishok et al., 1992;Sias, 2004;Wermers, 1999, among others) whereas the capital asset returns have been the essence of the latter technique (Chang et al., 2000;Christie and Huang, 1995, among others). Both the aforesaid methods have been used in international markets. ...
... Using similar model, Holmes et al. (2013) discovered that institutions moved in tandem with each other by virtue of sharing similar information in a less fragmented market like Portugal. Blake et al. (2017) applied Sias (2004) and Choi and Sias (2009) empirical method on monthly data of 189 UK based pension funds and revealed strong convergent purchase and sale of financial assets. ...
The current study empirically investigates sector-wide flock activity for the S&P BSE 500 stocks over 8 years spanning from October 2010 till September 2018. Drawing on absolute deviation model by Chang et al. (2000), the present analysis tends to unravel the curvilinear relationship between consensus return and dispersion via Ordinary Least Squares and Quantile Regression approaches. Using conventional regression, a nonexistent herd hunch is inferred under both normal and asymmetric scenarios. However, the examination of distribution tails discovers herding in auto and engineering sector during bull markets and healthcare sector during bearish conditions. However, the two crises namely the oil crisis of 2014 and the Chinese crash of 2015 subject the Indian bourse to mimicking behavior. This may be a matter of concern for the policy makers as the evidences reflect on the unstable nature of the S&P BSE 500 index and the Indian stock market as a whole. Therefore, the regulatory bodies have to make consistent efforts to bridge the informational distance between various classes of investors and corporate houses to ensure more transparent and honest practices so that investors can make informed and better decisions. Finally, the investors may resort to active trading rules during turbulence to earn more than what market warrants.
... In order to investigate whether this behavior can be observed on the financial market, this study uses a method similar to Sias (2004) to measure the herding behavior of institutional investors. It is first shown that these investors in general demonstrate herding behavior and to be more specific, follow the trades of other investors rather their own. ...
... First, using traditional herding measures (following Sias (2004)), it is shown that there are groups of investors who, through their trades, trigger a large number of follow-up trades in the next quarter. Subsequently, this finding is combined with a classification of the firms with respect to their ESG score. ...
An institutional investor is an organization that invests money on behalf of others or for itself in a variety of financial instruments and asset classes, controlling a significant proportion of all financial assets worldwide and having significant influence in all markets. Due to the large number of investments, the portfolios of these investors are exposed to a variety of different types of risk which must be assessed and considered by the portfolio managers. Krueger et al. (2020) highlight the fact that institutional investors have to consider at least six different sources of risk, namely financial, operational, governance, social, climate and other environmental risks. Their study shows that institutional investors consider financial risks, i.e. risks related to earnings or leverage of their investments, as most threatening, followed by operational and governance risks. These financial risks are determined, inter alia, by the composition of the portfolio and the characteristics of the assets under management. As shown by Benz et al. (2019) for mutual funds and Aragon and Martin (2012) for hedge funds, institutional investors not only invest in trivial assets such as equities and bonds, which expose the investor mainly to linear risks, but also in more complex securities such as options, swaps, and futures, exposing a portfolio to more complex sources of non-linear risk. After all, about 10% of all investors consider climate risk to be the most threatening one, which underlines the importance of this risk factor. The general climate risk includes, among others, the so-called carbon risk, which arises from the growing awareness of the impact of climate change on companies and includes all uncertainties arising from the transition process from a brown to a green economy (Görgen et al., 2020). In recent years, there have been a vast number of articles, both theoretical (e.g., Busch and Hoffmann (2011)) and empirical (e.g., Cunha et al. (2019)), which have shown that investors should consider this risk factor in their investment process. In summary, the portfolios of institutional investors are constantly exposed to a variety of different types of risk. Therefore, one of the key tasks of portfolio managers is to be able to correctly assess and measure the risks taken and to act and react accordingly to portfolio changes or to external influences. This dissertation discusses several types of risk arising in the investment and portfolio allocation processes of institutional investors.
... The direction of the future stock returns is positively driven by the institutional herding (Nofsinger & Sias, 1999). Studies like those of Bennett, Sias, and Starks (2003) and Sias (2004) have explained positive correlations between the institutional ownership changes and stock returns over a period of the same quarter. Undoubtedly, stock returns are affected by the institutional trading but some studies have evidenced that stock returns are positively impacted by the institutional purchases and not by their sales (Cai & Zheng, 2004). ...
... [61], investigated momentum and herd behavior in the US mutual fund industries, employing the herding LSV, and discovering evidence consistent with momentum but not herding behavior. [62], studied institutional investors in NYSE, AMEX, and NASDAQ stocks, start March 1983 to December 1997 and discovered that institutional investors herded and followed momentum strategies, but there was no strong proof that herding by institutional caused prices to move far from basic values. Other research has found that herd behavior between institutional investors in the US capital market [63], [64]. ...
Herding behavior is one of the most prominent forms of behavioral bias in the face of high uncertainty affecting investors' investment decisions. After the 1990s, herding behavior became more widely recognized in the capital market. The goal of this study is to explain herding behavior using a literature review approach, in order to obtain a comprehensive description and classification of existing research on herding behavior that occurs in various capital markets around the world. This study employs a number of credible international journals to explain the evolution of herding behavior research in various capital markets around the world. The findings of the literature review show a variety of empirical evidence from various studies on herding behavior, both for specific stocks using micro data or ownership data and for the entire market using aggregate market activity data through stock price movements. Most of the outputs of literature review point that herd behavior is more frequently detected in emerging and frontier capital markets. Finally this literature review study will provide an opportunity related the prospects for research on herding behavior in the future.
... Banerjee (1992) and Bikhchandani et al. (1992) were the first to investigate the role of social influence and information cascades in investor herding. Successive research by Devenow and Welch (1996) and Sias (2004) established herding as a real phenomenon in the stock market, demonstrating how it exacerbates market volatility and magnifies price fluctuations. ...
n this study, we look at how herd mentality affects the efficiency of mutual funds. It is well-documented that investors often engage in herding behaviour in the financial markets, when they blindly follow the actions of others rather than evaluating information independently. Yet, there has been a dearth of empirical research into its particular consequences for mutual funds. To analyse the impact of herding behaviour on fund performance, this research uses a thorough dataset of mutual fund returns and investor flows. The first step of the research is to use literature-established metrics to determine whether mutual fund investors herd. After that, we use risk-adjusted returns, volatility, and alpha to evaluate the herding behaviour vs. fund performance connection. The research further delves into the ways in which herding affects the performance of funds by looking at things like trading fees, liquidity restrictions, and market circumstances. Herding behaviour has a substantial impact on mutual fund performance, according to preliminary data. However, the exact nature of this impact varies across fund kinds and market conditions. Although herding may provide short-term gains in price momentum and liquidity-driven returns, it eventually causes mispricing and reversals if it continues. In addition, less liquid asset classes may have their long-term performance eroded by herding-induced trading costs and portfolio churn. These results have consequences for fund managers and investors alike. By learning the ins and outs of herd mentality, investors may make better investing choices that stay true to their long-term value drivers and avoid the traps of herd mentality. To lessen the impact of market emotion on fund performance, fund managers should be aware of investors' herding tendencies so they can create portfolios and use risk management methods. Keywords – Herding Behavior, Mutual Funds, Investor Behavior, Market Sentiment, Performance Measurement
... [7] Moreover, institutional herding influences stock price more considerably than individual herding does. [44,45] ...
In line with the development of behavioural finance in developed markets, research on investor sentiment has increased in recent years. The primary purpose of this study is to investigate the development of research on investor sentiment in emerging and frontier markets. This study will help researchers understand the interest of authors and journals in finding appropriate coordinators and future research topics in this research field. Using bibliometric analysis, we assessed 508 documents between 1999 and 2020 located in the Scopus database. The results show that publications on investor sentiment in emerging and frontier markets grew steadily in the 21 st century. "Herding behaviour" is the most prominent research theme in this area. In the following years, return predictability, principal component analysis, investor attention, and economic policy uncertainty with asymmetric effects are the dominant topics that have reshaped research on investor sentiment in emerging and frontier markets.
... Friesen and Weller (2006) use Bayesian methods to assess consensus forecast precision by exploiting the ordering of analyst valuations. Nofsinger and Sias (1999), Sias (2004), Choi and Sias (2009), and Choi and Skiba (2015) exploit dynamic institutional holdings data to document herding in institutional investment decisions with regard to the US stock market, individual industries, and international financial markets. Guo et al. (2020) integrate individual analyst recommendations and institutional holdings to challenge prior findings and demonstrate that herding is most likely spurious. ...
This study proposes two novel tests for security analyst herding based on binomial correlation and forecast error volatility scaling and applies it to investigate herding patterns in analyst target prices in 2008-2020 in the UK. Analysts robustly herd in their valuations, with results consistent across years, sectors, in panel fixed effect, quantile, instrumental variable regressions, and when controlled for optimism and conservatism. Herding becomes prominent for stocks followed by at least five analysts and towards the long sides of Fama-French sorts, reinforcing its non-spurious and behavioral nature. Analyst herd more strongly subject to low volatility and uncertainty.
... Herding is investors' tendency to follow one another into and out of the same stocks. Herding is an individual's inclination to mimic a large group's actions (Sias, 2002). Herd instincts are often motivated by observing financial market booms and busts and tend to be highly aggregative, typically summarized by a single-agent model representing the herd (Burton and Shah, 2013). ...
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.
... Empirical literature classifi es herding estimation in two major categories based on investor type (Spyrou, 2013), the former grounded on the information cascade model and deals with the herding of specifi c investor class namely, institutional investors. Lakonishok, Shleifer and Vishny (1992) fi rst proposed this measure and then Sias (2004) further enhanced it. ...
This paper investigates the presence of the time-varying herding behavior and its existence under both normal and crisis situation at the Pakistan stock market. For this purpose, this study utilizes the return dispersion models based on the aggregate market returns. Unlike previous research, this study not only confi rms the existence of herding but also observe time variation in this behavior. This time variation in herding behavior was examined by applying Kalman fi lter estimation to the return dispersion model. Furthermore, all three domestic crisis amplifi ed herd intensity. However, of the two major international crisis, only global fi nancial crisis signifi cantly affect the behavior of Pakistani investor. Evidence suggests that tests for herding behavior should consider its dynamic nature. Crisis in domestic and global markets play a signifi cant role in modeling the structure of the dynamic behavior of Pakistani investors.
... The idea behind this metric is that whenever a market actor tends to buy (sell) an individual stock massively, herding at the individual level of stocks is verified. Similarly, Sias (2004) insists that herding can be detected and measured if a cross-sectional correlation between demand for an asset by institutional investors in quarter T(q t ) and demand for the asset by the same investors in the past quarter (q t-1 ) is verified. ...
... Several studies propose a variety of measures and indicators to attest the existence of the of the herding behaviour, including Wermers (1999), Chang et al. (2000), Hwang and Salmon (2004), Sias (2004), Patterson and Sharma (2010). Despite the convincing arguments about the herding behaviour are numerous and pointed, and even market watchers observe their occurrence, the empirical evidence is scarce and relative few cases confirm its existence (Blasco et al., 2009). ...
The present work seeks to analyze the herding behavior phenomenon as a destabilizing factor of the capital market, while studying the relation between the herding behavior phenomenon and market profitability and volatility. The results allow us to verify the existence of a significant intensity of herding, especially when price variation occurs. Conversely, asymmetrical and elevated volatility levels ensue, with a higher probability of profit than losses of the same magnitude. However, results are less visible when one looks at the causality relation between herding and market volatility. This paper contributes to a deeper understanding of herding behavior and its relation with market efficiency. .
... 3 Alternatively, inaction could be a strategic investment decision to maximize performance by minimizing the cost of 1 Whenever possible, we control for the turnover ratio to assess the impact of inaction in our empirical models. 2 See, for example, Dasgupta et al. (2011aDasgupta et al. ( , 2011b, Froot et al. (1992), Lakonishok et al. (1994), Sias (2004), and Wermers (1999). 3 Investor inattention can influence a wide range of phenomena in the financial markets. ...
We document that institutional investors do not trade a single share, on average, in one of five stocks in their portfolio for an extended period. Investors with high inaction are likely to underperform in the future. Our results show a similar underperformance for stocks with a high non‐trading level of institutional investors. We investigate several behavioral biases as potential drivers of the non‐trades and find no evidence of distraction, overconfidence, and disposition effects. Institutional investors’ tendency to sell stocks with salient price movements and recency bias best explains their inactions. Overall, the non‐trading behavior of institutional investors serves as a unique predictor for their future performance and potential behavioral biases are driving this predictability.
... It can also happen when institutions trade on the same information and in the same direction. For example, Sias (2004) documents herding behaviour among institutions in the US that results in correlated trading. ...
We study the liquidity commonality impact of local and foreign institutional investment in the Australian equity market in the cross-section and over time. We find that commonality in liquidity is higher for large stocks compared to small stocks in the cross-section of stocks, and the spread between the two has increased over the past two decades. We show that this divergence can be explained by foreign institutional ownership. This finding suggests that foreign institutional investment contributes to an increase in the exposure of large stocks to unexpected liquidity events in the local market. We find a positive association between foreign institutional ownership and commonality in liquidity across all stocks, particularly in large and mid-cap stocks. Correlated trading by foreign institutions explains this association. However, local institutional ownership is positively related to the commonality in liquidity for large-cap stocks only.
... Last but not least, we discuss the herding behavior of mutual funds and its potential influence on our estimated effects. An extensive empirical literature shows that mutual funds tend to follow the crowd in their buying and selling decisions (Wermers 1999;Sias 2004;Dasgupta et al. 2011;Jiang and Verardo 2018). Managers herd for a variety of reasons, for instance, to appear as talented as others and to learn from others. ...
This study shows that the use of private information obtained during company visits is related to managerial skills. We construct a novel measure of a mutual fund’s capability of using such private information by considering the overlap between its stockholdings and its site visits. We find that the allocation of stocks of visited companies in a fund portfolio significantly improves its performance. The impact is more pronounced for mutual funds that hold relatively neglected stocks or stocks with inadequate information disclosure. Our findings suggest that communications with company managers provide significant information advantages for fund managers.
... A recent study by Gavriilidis et al. (2021) explored whether institutional investors herded in response to political uncertainty, thereby affecting stock prices. Herding behaviour was detected by employing empirical testing following Sias (2004), applied to professionals and institutional investors. Thus, the findings indicated that herding behaviour may have acted as a channel for political uncertainty to influence financial markets towards efficiency. ...
This study examined the impact of economic and political risks on herding behaviour in 7 Gulf Cooperation Council (GCC) stock markets between 2004 and 2020. The results indicated that herding behaviour was apparent in the lower quantiles while anti-herding behaviour manifested in the upper quantiles. Economic and political risk either increased or decreased herding behaviour. In the lower quantiles, economic risk either strengthened or weakened herding behaviour, while only in the upper quantiles was anti-herding behaviour strengthened. However, political risk either weakened or strengthened herding in the lower quantiles, while in the upper quantiles, anti-herding behaviour was either strengthened or weakened. Policymakers and supervisory authorities concerned with potential stock market integration should not simply focus on unifying regulations in their stock markets. Rather, they should invest in raising awareness and establishing transparency.
... In this way, it diminishes the data asymmetry issue between the firm and these financial specialists. In short, both data asymmetry and organization models anticipate a negative connection between block-holders and financial leverage (Lerner et al 2003;Sias 2004). ...
This research examines the intervention of capital structure with different characteristics of corporate governance and different measures of financial performance. Data was collected from 113 PSX listed firms ranging from 2013-2018. The study uses multiple regression approach for testing the required set of hypotheses. The results reveal that insider shareholding, and board size significantly but negatively affect financial performance whereas, audit committee’s size positively, and significantly affects financial performance. Furthermore, about 20% of CEOs hold dual positions in listed firms, which also has a positive impact on financial performance. The results also reveal that capital structure positively influences financial performance. This research adds to the literature on corporate governance and firm performance in emerging countries, particularly Pakistan.
... One could raise the question of whether our study can be compared to those of financial bubbles, which constitute the inspiration for the social bubble hypothesis, that involve investors who are prone to herding [17][18][19][20][21][22]. Can herding develop among scientists? ...
We present an analysis of a large emerging scientific project in the light provided by the social bubbles hypothesis (SBH) that we have introduced in earlier papers. The SBH claims that, during an innovation boom or technological revolution, strong social interactions between enthusiastic supporters weave a network of reinforcing feedbacks that leads to widespread endorsement and extraordinary commitment, beyond what would be rationalized by a standard cost–benefit analysis. By probing the (Future and Emerging Technologies) FET Flagship candidate FuturICT project, as it developed in 2010–2013, we aimed at better understanding how a favorable climate was engineered, allowing the dynamics and risk-taking behaviors to evolve. We document that significant risk-taking was indeed clearly found—especially during workshops and meetings, for instance, in the form of the time allocation of participants, who seemed not to mind their precious time being given to the project and who exhibited many signs of enthusiasm. In this sense, the FuturICT project qualifies as a social bubble in the making when considered at the group level. In contrast, risk-perception at the individual level remained high and not everyone involved shared the exuberance cultivated by the promoters of FuturICT. As a consequence, those not unified under the umbrella of the core vision built niches for themselves that were stimulating enough to stay with the project, but not on a basis of blind over-optimism. Our detailed field study shows that, when considering individuals in isolation, the characteristics associated with a social bubble can vary significantly in the presence of other factors besides exaggerated risk-taking.
... Smith and Sørensen, 2000). Herding may also be the result of indirect influence (Bikhchandani et al., 1992), including common knowledge (Grinblatt et al., 1995), fads (Sias, 2004), or common investment styles (Wermers, 2000). And now, it is a challenge for empirical studies to identify determinants of herding since the bases for investors' decision-making are rarely disclosed in actual stock markets. ...
Purpose
In recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause of the speculative bubble and leads to stock market deviations from their basic values it is necessary to examine the motivators which led to herding behavior among investors. The paper aims to discuss this issue.
Design/methodology/approach
In this study, the authors performed a two-phase analysis to address the research questions of the study. In the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors. The analysis of a text was performed using word frequency analysis. While in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.
Findings
Results of the study depicted Investor Cognitive Psychology (ICP), Market Information (MI), Stock Characteristics (SC) as the top-ranked factors driving herding behavior, while Socio-Economic Factors (SEF) emerged as the least important factor driving herding behavior.
Research limitations/implications
The current study was undertaken among stock investors from North India only. Moreover, numerous factors are not part of the study but might significantly influence the investors' herding behaviors.
Practical implications
Comprehending the influences of the different factors discussed in the study would enable stock investors to be more aware of their investment choices and not resort to herd behavior. This research enables decision-makers to understand the reasons for herd activity and helps them act accordingly to improve the stock market's performance.
Originality/value
The current study will provide an inclusive overview of herding behavior motivators among Indian stock investors. This study's results can be extremely useful for both academics and policymakers to gain some insight into the functioning of the Indian stock market.
... Herding in financial markets was measured using many different measures including cross-sectional standard deviation (CSSD) developed by Christie and Huang (1995), the cross-sectional absolute deviation (CSAD) originated by Chang, Cheng, and Khorana (2000), the measure of cross-sectional dispersion of betas by Hwang and Salmon (2004), the standardized herd measure introduced by Hwang and Salmon (2009), the measures of Lakonishok, Shleifer, and Vishny (1992), and the measure developed by Sias (2004). In this study, I utilized the measure of CSAD to detect herding in the Jordanian stock market before and during the pandemic of COVID-19. ...
The main purpose of this study was to test the effect of COVID-19 pandemic on the presence of herding behavior in Amman stock exchange at market-level and at industry-level. This quantitative study covered five years and three months before the COVID-19 pandemic (from January 2015 to March 2020) and eight months during the pandemic (from May to December 2020). The cross-sectional absolute deviation (CSAD) was used to test the presence of herding in the market and in its sectors. Study results suggested that the COVID-19 pandemic has no effect on herding behavior in the Jordanian market at both market-level and industry-level.
... One branch focuses on examining the cross-sectional dispersions of stock returns in response to extreme changes in market conditions (e.g. Christie & Huang, 1995; Advances in Pacific Basin Business, Economics and Finance, Volume 9, 173-191 Copyright © 2021 by Emerald Publishing Limited All rights of reproduction in any form reserved ISSN: 2514-4650/doi:10.1108/S2514-465020210000009009 a Da-Yeh University, Taiwan b Drexel University, USA c National Chung Hsing University, Taiwan Chang, Cheng, & Khorana, 2000;Demirer & Kutan, 2006;Tan, Chiang, Mason, & Nelling, 2008;Chiang & Zheng, 2010;Economou, Kostakis, & Philippas, 2011;Chang & Lin, 2015;Chen, Lin, & Yang, 2016), while the other branch explores investors' behavior by following their trading decisions on buying or selling specific securities (Lakonishok, Shleifer, & Vishny, 1992;Wermers, 1999;Sias, 2004;Chen, Wang, & Lin, 2008;Choi & Skiba, 2015) or industries (Choi & Sias, 2009;Chen, Yang, & Lin, 2012). Although the aforementioned studies have provided some insights into herd behavior in various markets, very few attempts have been devoted to investigating the common dynamic properties of national herd behavior across countries. ...
... It can also happen when institutions trade on the same information and in the same direction. For example, Sias (2004) documents herding behaviour among institutions in the US that results in correlated trading. ...
We study the liquidity commonality impact of local and foreign institutional investment in the Australian equity market in the cross‐section and over time. We find that commonality in liquidity is higher for large stocks compared to small stocks in the cross‐section of stocks, and the spread between the two has increased over the past two decades. We show that this divergence can be explained by foreign institutional ownership. This finding suggests that foreign institutional investment contributes to an increase in the exposure of large stocks to unexpected liquidity events in the local market. We find a positive association between foreign institutional ownership and commonality in liquidity across all stocks, particularly in large and mid‐cap stocks. Correlated trading by foreign institutions explains this association. However, local institutional ownership is positively related to the commonality in liquidity for large‐cap stocks only.
... The first is done by analyzing herding, especially on institutional investors. This method was introduced by Lakonishok et al. (1992) and developed by Sias (2004). The second is done by checking herding through market consensus based on existing data in the capital market. ...
... Managers tend to trade on sentiment depending on the fee structure. Managers suffer from herding (Sias, 2004), home bias (Strong and Xu, 2003) or loss aversion (Coval and Shumway, 2005) and managers' biased judgment may have an impact on economic volatility (World Bank Report, 2015). Monika Gehde-Trappa Linda Klingler (2019) finds that male managers show significant reactions concerning two of the measures-turnover and fund risk, but female managers react to only fund risk and decrease the unsystematic risk compared to male managers. ...
Purpose
A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.
Design/methodology/approach
Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.
Findings
Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.
Originality/value
The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.
... Parallel to these studies, Nofsinger and Sias (1999) have documented evidence of a positive relation between annual changes in institutional ownership and returns in the US market. Sias (2004) argued that intuitional investor often follows each other in buying and selling the same stock as they tend to follow momentum strategies. Gleason et al. (2004) worked on intraday data of nine sector ETFs traded on the American stock exchange and could report the absence of herd behaviour during periods of extreme market conditions. ...
We try in this article to detect and measure the presence
of the herding behavior in the Moroccan exchange market using a
quantile regression method. The authors seek not only to detect
herding on overall market conditions but also to analyze its
presence on different states of the market. The data used in this
study consists of daily closing prices of MASI as well as trading
data of a sample of most actively traded companies in the
Moroccan stock exchange market. The results of the study suggest
the existence of a strong herding bias which gets more pronounced
in times of financial stress. The results and conclusions drafted in
this research paper could help understand the dynamics and
mechanisms of herding in the local market of Morocco using a
newly constructed model, hence enabling a more thorough
analysis of herding under all market conditions.
... Thus, it is possible that this return continuation is not related to informed trading but captures a market frenzy fueled by sentiment-driven behaviors such as herding. This concern is reinforced by previous studies who document that herding is more prevalent in high information asymmetry stocks, and particularly in small (Zhou and Lai, 2009) and illiquid stocks (Sias, 2004). For example, when a high information asymmetry stock experiences a sudden decrease in its price, herding will exacerbate the movement as traders mimic the behavior of others rather than relying on their own information (Zhou and Lai, 2009). ...
This study investigates the relationship between the culture of secrecy and stock price comovement using a large sample of firms in 49 countries over the period 1990 to 2019. We find that stock prices in secretive societies comove more than stock prices in less secretive societies. This higher comovement occurs primarily because idiosyncratic volatility is lower. We attribute this finding to cultural biases in secretive societies which deter investors’ information-seeking behavior. To support these conjectures, we provide evidence of stronger mean reversals (less informed trading) in these societies. Our results persist when we account for cross-country differences in firms’ liquidity and information asymmetry, and when we control for cash flow uncertainty. Finally, the enforcement of insider trading laws in secretive countries is associated with less privately informed trading and lower idiosyncratic volatility.
... For balanced and money market funds, we obtain lower figures less than 20%. These results are coherent with the academic research, since redemption runs and contagions in bond and equity funds have been extensively studied and illustrated (Lakonishok et al., 1992;Wermers, 1999;Sias, 2004;Wylie, 2005;Coval and Stafford, 2007;Shleifer and Vishny, 2011;Cai et al., 2019). Remark 13 Another way to illustrate the intra-class correlation is to report the dependogram (or empirical copula) of redemption frequencies. ...
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers asset-liability liquidity risk management (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal to develop mathematical and statistical approaches, and provide appropriate answers.
In this first part that focuses on liability liquidity risk modeling, we propose several statistical models for estimating redemption shocks. The historical approach must be complemented by an analytical approach based on zero-inflated models if we want to understand the true parameters that influence the redemption shocks. Moreover, we must also distinguish aggregate population models and individual-based models if we want to develop behavioral approaches. Once these different statistical models are calibrated, the second big issue is the risk measure to assess normal and stressed redemption shocks. Finally, the last issue is to develop a factor model that can translate stress scenarios on market risk factors into stress scenarios on fund liabilities.
... For balanced and money market funds, we obtain lower figures less than 20%. These results are coherent with the academic research, since redemption runs and contagions in bond and equity funds have been extensively studied and illustrated (Lakonishok et al., 1992;Wermers, 1999;Sias, 2004;Wylie, 2005;Coval and Stafford, 2007;Shleifer and Vishny, 2011;Cai et al., 2019). Remark 13 Another way to illustrate the intra-class correlation is to report the dependogram (or empirical copula) of redemption frequencies. ...
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers asset-liability liquidity risk management (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal to develop mathematical and statistical approaches, and provide appropriate answers.
In this first part that focuses on liability liquidity risk modeling, we propose several statistical models for estimating redemption shocks. The historical approach must be complemented by an analytical approach based on zero-inflated models if we want to understand the true parameters that influence the redemption shocks. Moreover, we must also distinguish aggregate population models and individual-based models if we want to develop behavioral approaches. Once these different statistical models are calibrated, the second big issue is the risk measure to assess normal and stressed redemption shocks. Finally, the last issue is to develop a factor model that can translate stress scenarios on market risk factors into stress scenarios on fund liabilities.
... Despite this possibility, there is strong evidence to support the persistence in institutional trading. For example, prior research shows that institutional investors' holdings are stable over time(Gompers and Metrick, 2001) and institutions perform the same trading strategy over several quarters(Froot et al., 1992;Sias, 2004;Puckett and Yan, 2008;Dasgupta et al., 2011aDasgupta et al., , 2011b Kaniel et al., 2012. 6 SeeCella et al. (2013) for applications of the portfolio churn ratio ofGaspar et al. (2005) andYan and Zhang (2009). ...
... Studies commonly describe herding as a behavioral tendency in which investors suppress their own beliefs and mimic collective actions in the market that leads to a convergence or a correlated pattern of actions (see Nofsinger and Sias, 1999;Welch, 2000;Hwang and Salmon, 2004). In a single market set-up herding has been thoroughly discussed for investors' trades at the security level (Lakonishok et al., 1992;Sias, 2004;Barber et al., 2009). More recent studies observed that herding also emerges at the industry level. ...
In this study, we test the herding towards a market consensus in the main financial industries of the United States and the Eurozone equity markets. We find that herding is more likely to be present in high quantiles that reflects turbulent market conditions. This herding appears to be more pronounced during financial crisis periods and in cases of asymmetric conditions of volatility, credit deterioration, and illiquid funding. Furthermore, we provide evidence that the cross-sectional dispersion of returns throughout the domestic equity market can be partly explained by the corresponding dispersions of the financial industries. In our analysis we cover the last two main global financial crises and identify new evidence of “spurious” and “intentional” herding by corporates. Further, our results are robust when considering short-selling bans.
... Although a more (less) mood-congruent regulatory policy can lead a new industry to appear less (more) risky and, hence can promote or inhibit herding (as discussed previously), this may not be uniform across the universe of that industry's stocks, which are expected to vary in riskiness-terms. Herding, for example, has been found ( Lakonishok et al., 1992 ;Wermers, 1999 ;Sias, 2004 ;Dang and Lin, 2016 ;Cui et al., 2019 ) to maintain an inverse relationship with size (appearing the strongest for smaller capitalization stocks) due to the high information risk of small stocks prompting investors to monitor their peers' actions when trading them. Additionally, sectors with greater uncertainty (in terms, e.g., of their cash flows) may be more prone to exhibiting herding, as evidence from Technology stocks has suggested in several studies ( Brunnermeier and Nagel, 2004 ;Griffin et al., 2011 ;Singh, 2013 ;Gavriilidis et al., 2013 ;Andrikopoulos et al., 2017 ;Uwilingiye et al., 2019 ). ...
Although social mood can motivate herding towards new industries, the extent to which regulators cater to social mood may affect that herding. We explore this issue in the context of the nascent cannabis industry by examining herding among the cannabis stocks listed in the US and Canada, where the regulatory treatment of cannabis varies in its congruence with the prevailing social mood on cannabis’ legalization. Canadian-listed cannabis stocks entail strong herding across all market states and sectors, alongside most capitalization-segments; conversely, herding among their US-listed counterparts is relatively limited, appearing on up-market/high-volume days, for the smallest capitalization segment, as well as for several cannabis-sectors. Herding is present (almost always absent) around cannabis’ legalization announcement-days in Canada (the US), while cross market herding between US- and Canadian-listed cannabis stocks is very weak. We attribute Canadian (US) cannabis stocks’ strong (weak) herding to cannabis’ more (less) mood-congruent regulatory treatment, which promotes (reduces) certainty and encourages investors to herd more (less) on them.
Carrasco-Villanueva, M. (2022). Herd Behavior in Emerging Stock Markets: China, Taiwan, and Japan. In: Contemporary Issues on Central Bank Digital Currency. New Delhi, India: Kunal Books, pp. 23-55. ISBN 978-93-91908-09-6.
This paper investigates asset allocation decisions made by three types of traders depending upon incomplete information in market equilibrium. Limited participation phenomenon is observed in the equilibrium. Moreover, we show that traders with more information might not hold more risky assets than others who have less information. Less‐informed traders trade‐off between a diversification effect induced by risk‐averse attitude and a “flight‐to‐quality” effect by their aversion towards correlation ambiguity. In equilibrium, the magnitudes between equilibrium positions of different traders are affected by the true correlation coefficient, the upper‐bound correlation conceived by naive traders and the quality of risky assets simultaneously. In some scenarios, we observe that naive and uninformed traders “escape” from low‐quality assets to high‐quality ones.
This paper investigates institutional herding behaviors in the U.S. Treasury market. We find that the level of herding is higher for bonds with a longer time to maturity, and this pattern is significant only for buy herding, not sell herding. This term structure of herding is stronger for funds with shorter investment horizon. These patterns remain strong for Treasury Inflation‐Protected Securities (TIPS) and for Treasuries with high coupon rates. Overall, our findings support investors’ short‐termism as a channel for the term structure of herding and are inconsistent with other herding explanations, such as spurious herding, reputational concerns, and information cascades. This article is protected by copyright. All rights reserved.
The purpose of this study is to investigate the presence of herding effects at the Dar-es-Salaam Stock Exchange. It employs a dataset of daily closing prices and market capitalizations of companies composing the industrial and allied sector, and those covering banks, finance, and investment sector. The study used cross-sectional dispersion of stock return tests to examine the presence of herding for the two sectors. The findings provide evidence of herding in the banks, finance, and investment sector throughout the full-sample period, with the herding being driven mainly by large-capitalization stocks. Furthermore, the results indicate clear presence of herding asymmetries conditional on the performance of the market and on the market’s volatility. On the case of the industrial and allied sector, herding is found to be stronger on days with low volatility only. The economic implication of this evidence is that the observed correlated trading patterns for the banks, finance, and investment sector may undermine financial stability.
This paper examines liquidity commonality is caused by correlation in institutional herding and shareholder disputes due to irrational investors over the period from 2007 to 2017 in China. Consistent with the fund liquidity hypothesis, we find that shareholders dispute is negatively associated with liquidity commonality and that this negative relationship is more pronounced in firms with more excess control rights and desirability of liquidity for governance. We conclude that it is important to consider not only control-ownership divergence, which has been shown to be a supply-side factors as institutional herding from foreign investors, but also liquidity commonality in information environment. The block holders can cause the liquidity when shareholder disputes are governed by block holders to intervene and sell their stake to exit by threaten, or disagreement trade against the mispricing. This work contributes to the growing literature by analyzing the impacts of controlled ownership divergency on commonality in liquidity as well as the impact of investor sentiment related to the information environment.
We examine the Kondor theoretical explanation of an enduring puzzle: trading volumes and stock return volatility peak after the release of public information. Using a comprehensive data set of institutional holdings and earnings announcements, we find supporting evidence that the proportion of short‐term investors is positively associated with post‐announcement spikes in trading volume and return volatility. This finding survives in the identification test based on the annual reconstitutions of the Russell 1000 and 2000 indices. We show our results largely withstand several alternative explanations related to the constitution of institutional investors, informed trading, and heterogeneous beliefs.
We investigate asset returns using the concept of beta herding, which measures cross-sectional variations in betas due to changes in investors’ confidence about their market outlook. Overconfidence causes beta herding (compression of betas towards the market beta), while under-confidence leads to adverse beta herding (dispersion of betas from the market beta). We show that the low-beta anomaly can be explained by a return reversal following adverse beta herding, as high beta stocks underperform low beta stocks exclusively following periods of adverse beta herding. This result is robust to investors’ preferences for lottery-like assets, sentiment, and return reversals, and beta herding leads time variation in betas.
The access to fundamental information prompts traders to buy or sell a security collectively until its market price arrives at the equilibrium value, which this paper terms informed herding. Using a large sample covering the tick‐by‐tick data, we conduct a cross‐country study to examine whether informed herding pervades in the international markets. The empirical finding lends strong support to the global presence of informed herding. Moreover, the effect of informed herding remains significant despite using various robustness checks. Eventually, we discover that informed herding is likely to be driven by two macro‐level institutional characteristics: corporate transparency and foreign accessibility.
This paper examines how analyst recommendations and investors’ herding influence stock returns and trading performance. Different types of investors display distinct herding intensity. Institutional investors herd significantly more than individuals do and investment trusts exhibit the highest herding intensity. Foreign investors tend to herd in buying (selling) stocks when analysts issue better (worse) recommendations. Investment trusts are inclined to herd in the sell side when analysts issue “sell” recommendations. Institutional buy (sell) herding positively (negatively) correlates to current‐day returns. Individual herding shows the exact opposite pattern. Finally, institutional investors’ higher buy intensity has better trading performance following analysts’ buy‐oriented recommendations.
In this study, empirical evidence is presented to explain the momentum reversal phenomenon in the Chinese stock market in terms of the manipulation of institutional information. On the institutional “sell” side, we demonstrate that institutional traders send manipulated information to the market using a large volume of buy orders in order to boost the stock price and thus induce trading by retail investors. The reverse is also true on the institutional “buy” side. Thus instead of the traditional view of order flow information—“the more, the merrier”—the authors argue that “more is less” in the case of individual investors on the Chinese stock market. As a result, the empirical results presented in this study provide another feasible explanation for momentum reversal.
This paper examines the magnitude and determinants of transactions costs for a sample of institutional traders with different investment styles. Using order-level data for recent equity transactions totaling $83 billion, we find that trading costs are economically significant and increase with trade difficulty. In addition, costs vary with traderspecific factors such as investment style and order submission strategy, as well as stock-specific factors such as exchange listing. We find evidence that institutional trades in exchange-listed stocks have lower costs than in comparable Nasdaq stocks.
An informational cascade occurs when it is optimal for an individual, having observed the actions of those ahead of him, to follow the behavior of the preceding individual with regard to his own information. The authors argue that localized conformity of behavior and the fragility of mass behaviors can be explained by informational cascades. Copyright 1992 by University of Chicago Press.
This investigation of the cross-section of mutual fund equity holdings for the years 1991 and 1992 shows that mutual funds have a significant preference towards stocks with high visibility and low transaction costs, and are averse to stocks with low idiosyncratic volatility. These findings are relevant to theories concerning investor recognition, a potential agency problem in mutual funds, tests of trend-following and herd behavior by mutual funds, and corporate finance. Copyright 1996 by American Finance Association.
The net buying (selling) volume of the most net buyer (seller) brokers over a unit period is a widely followed piece of information in Istanbul Stock Market, which most market commentaries inaccurately refer to as “the net money in- or outflow”. It is, in fact, a proxy for big investors’ trading. In this note, we test whether this information has predictive value, whether market participants’ emphasis on this information is justified, or just an illusion. By doing so, we add to the literature on the relationship between big investors’ trading and stock returns, using a unique information set. Results suggest a significant contemporaneous association between the “net inflow” and current returns, but little predictive value
This study analyzes the extent to which mutual funds purchase stocks based on their past returns as well as their tendency to exhibit 'herding' behavior (i.e., buying and selling the same stocks at the same time). The authors find that 77 percent of the mutual funds were 'momentum investors,' buying stocks that were past winners; however, most did not systematically sell past losers. On average, funds that invested on momentum realized significantly better performance than other funds. The authors also find relatively weak evidence that funds tended to buy and sell the same stocks at the same time. Copyright 1995 by American Economic Association.
Changes in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates.
All trades executed by 37 large investment management firms from July 1986 to December 1988 are used to study the price impact and execution cost of the entire sequence (“package”) of trades that we interpret as an order. We find that market impact and trading cost are related to firm capitalization, relative package size, and, most importantly, to the identity of the management firm behind the trade. Money managers with high demands for immediacy tend to be associated with larger market impact.
Recent studies document a strong positive relation between quarterly and annual changes in institutional ownership and returns measured over the same period. The source of this positive correlation could arise from institutional investors' intra-period positive feedback trading, institutions forecasting intra-period price changes, or from price pressure caused by institutional trades. Price pressure can in turn arise for inventory/liquidity reasons, or because market participants infer information from institutional trades. Our results suggest that the price impact of institutional trading is primarily responsible for the documented positive covariance between quarterly changes in institutional ownership and quarterly returns. Moreover, our analyses suggest this price pressure results from information revealed through institutional trading.
This paper examines the relationship between ownership structure and informed trading. We attempt to reconcile some puzzling results in recent empirical literature about the impact of ownership on informed trading using a comprehensive set of proxies for informed trading and a recent sample of firms from three U.S equity exchanges. As proxies for informed trading, we use four measures: (1) The relative spread, (2) the adverse selection component of the spread, constructed as in Huang and Stoll (1997), (3) the price impact of a trade, following Foster and Viswanathan (1993) and Hasbrouck (1991), and (4) the probability of informed trading constructed as in Easley, Kiefer, O'Hara and Paperman (1996). We find strong evidence of a cross-sectional relationship between our measures of informed trading and ownership by institutions and insiders. Our results are robust to a variety of estimation techniques, control variables, and proxies for informed trading. Overall, our results suggest that individual investors are less informed relative to institutions and insiders. These findings are consistent with economies of scale in information acquisition and aggregation, and with recent research that indicates that market makers move prices in response to trades by institutions.
In this study, we explore the dynamics of the relation between institutional trading and stock returns. We find that stock returns Granger-cause institutional trading (especially purchases) on a quarterly basis. The robust and significant causality from equity returns to institutional trading can be largely explained by the time-series variation of market returns, that is, institutions buy more popular stocks after market rises. Stock returns appear to be negatively related to lagged institutional trading. A further analysis of the behavior of trading and the returns of the traded stocks reveals evidence that stocks with heavy institutional buying (selling) experience positive (negative) excess returns over the previous 12 months.
We investigate whether institutional investors “vote with their feet” when dissatisfied with a firm's management by examining changes in equity ownership around forced CEO turnover. We find that aggregate institutional ownership and the number of institutional investors decline in the year prior to forced CEO turnover. However, selling by institutions is far from universal. Overall, there is an increase in shareholdings of individual investors and a decrease in holdings of institutional investors who are more concerned with holding prudent securities, are better informed, or are engaged in momentum trading. Measures of institutional ownership changes are negatively related to the likelihoods of forced CEO turnover and that an executive from outside the firm is appointed CEO.
Using audit trail data for a sample of NYSE firms we show that medium-size trades are associated with a disproportionately large cumulative stock price change relative to their proportion of all trades and volume. This result is consistent with the predictions of Barclay and Warner's (1993) stealth-trading hypothesis. We find that the source of this disproportionately large cumulative price impact of medium-size trades is trades initiated by institutions. This result is robust to various sensitivity checks. Our findings appear to confirm street lore that institutions are informed traders.
This paper uses a disaggregated approach to study the volatility of common stocks at the market, industry, and firm levels. Over the period from 1962 to 1997 there has been a noticeable increase in firm-level volatility relative to market volatility. Accordingly, correlations among individual stocks and the explanatory power of the market model for a typical stock have declined, whereas the number of stocks needed to achieve a given level of diversification has increased. All the volatility measures move together countercyclically and help to predict GDP growth. Market volatility tends to lead the other volatility series. Factors that may be responsible for these findings are suggested. The definitive version is available at www.blackwell-synergy.com. Economics
This article introduces a new measure of portfolio performance and applies it to study the performance of a large sample of mutual funds. In contrast to previous studies of mutual fund performance, the measure used in this study employs portfolio holdings and does not require the use of a benchmark portfolio. It finds that the portfolio choices of mutual fund managers, particularly those that managed aggressive growth funds, earned significantly positive risk-adjusted returns in the 1976-85 period. Copyright 1993 by University of Chicago Press.
This paper analyzes institutional investors' demand for stock characteristics and the implications of this demand for stock
prices and returns. We find that “large” institutional investors nearly doubled their share of the stock market from 1980
to 1996. Overall, this compositional shift tends to increase demand for the stock of large companies and decrease demand for
the stock of small companies. The compositional shift can, by itself, account for a nearly 50 percent increase in the price
oflarge-company stock relative to small-company stock and can explain part of the disappearance of the historical small-company
stock premium.
The author analyzes a sequential decision model in which each decisionmaker looks at the decisions made by previous decisionmakers in taking her own decision. This is rational for her because these other decisionmakers may have some information that is important for her. The author then shows that the decision rules that are chosen by optimizing individuals will be characterized by herd behavior; i.e., people will be doing what others are doing rather than using their information. The author then shows that the resulting equilibrium is inefficient. Copyright 1992, the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
The use of analyst forecasts as proxies for investors' earnings expectations is commonplace in empirical research. An implicit assumption behind their use is that they reflect analysts' private information in an unbiased manner. As demonstrated here, this assumption is not necessarily valid. There is shown to be a tendency for analysts to release forecasts closer to prior earnings expectations than is appropriate, given their information. Further, analysts exhibit herding behavior, whereby they release forecasts similar to those previously announced by other analysts, even when this is not justified by their information. These results are shown to have interesting empirical implications. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.
Although institutional investors have a preference for large capitalization stocks, over time they have shifted their preferences toward smaller, riskier securities. These changes in aggregate preferences have arisen primarily from changes in the preferences of each class of institution, rather than changes in the importance of different classes. Evidence also suggests that recent growth in institutional investment combined with this shift in preferences helps explain why markets in general, and smaller stocks in particular, have exhibited greater firm-specific risk and liquidity in recent years. Additional analyses suggest that institutional investors moved toward smaller securities because such securities offer "greener pastures." Copyright 2003, Oxford University Press.
This article presents a model that provides insights about various measures of portfolio performance. The model explores several
criticisms of these measures. These include the problem of identifying an appropriate benchmark portfolio, the possibility
of overestimating risk because of market-timing ability, and the failure of informed investors to earn positive risk-adjusted
returns because of increasing risk aversion. The article argues that these need not be serious impediments to performance
evaluation.
I examine the effect of prudent-man laws on the behavior of institutional investors. Variation in exposure to legal liability across types of investment managers allows me to disentangle the effect of the prudent-man laws from other potential influences on manager behavior. Bank managers significantly tilt the composition of their portfolios toward stocks that are viewed by the courts as prudent, while mutual fund managers choose not. I show that differences in the direction that bank and mutual fund managers choose to tilt may explain their portfolio performance differences over time.
The net buying (selling) volume of the most net buyer (seller) brokers over a unit period is a widely followed piece of information in Istanbul Stock Market, which most market commentaries inaccurately refer to as “the net money in- or outflow”. It is, in fact, a proxy for big investors’ trading. In this note, we test whether this information has predictive value, whether market participants’ emphasis on this information is justified, or just an illusion. By doing so, we add to the literature on the relationship between big investors’ trading and stock returns, using a unique information set. Results suggest a significant contemporaneous association between the “net inflow” and current returns, but little predictive value
We analyze the trading activity of the mutual fund industry from 1975 through 1994 to determine whether funds "herd" when they trade stocks and to investigate the impact of herding on stock prices. Although we find little herding by mutual funds in the average stock, we find much higher levels in trades of small stocks and in trading by growth-oriented funds. Stocks that herds buy outperform stocks that they sell by 4 percent during the following six months; this return difference is much more pronounced among small stocks. Our results are consistent with mutual fund herding speeding the price-adjustment process. Copyright The American Finance Association 1999.
In existing models of information acquisition, all informed investors receive their information at the same time. This article analyzes trading behavior and equilibrium information acquisition when some investors receive common private information before others. The model implies that, under some conditions, investors will focus only on a subset of securities ('herding'), while neglecting other securities with identical exogenous characteristics. In addition, the model is consistent with empirical correlations that are suggestive of oft-cited trading strategies such as profit taking (short-term position reversal) and following the leader (mimicking earlier trades). Copyright 1994 by American Finance Association.
A dynamic model of insider trading with sequential auctions, structured to resemble a sequential equilibrium, is used to examine the informational content of prices, the liquidity characteristics of a speculative market, and the value of private information to an insider. The model has three kinds of traders: a single risk neutral insider, random noise traders, and competitive risk neutral market makers. The insider makes positive profits by exploiting his monopoly power optimally in a dynamic context, where noise trading provides camouflage which conceals his trading from market makers. As the time interval between auctions goes to zero, a limiting model of continuous trading is obtained. In this equilibrium, prices follow Brownian motion, the depth of the market is constant over time, and all private information is incorporated into prices by the end of trading.
We use a new database to perform a comprehensive analysis of the mutual fund industry. We find that funds hold stocks that outperform the market by 1.3 percent per year, but their net returns underperform by one percent. Of the 2.3 percent difference between these results, 0.7 percent is due to the underperformance of nonstock holdings, whereas 1.6 percent is due to expenses and transactions costs. Thus, funds pick stocks well enough to cover their costs. Also, high-turnover funds beat the Vanguard Index 500 fund on a net return basis. Our evidence supports the value of active mutual fund management. Copyright The American Finance Association 2000.
A model is developed which implies that if an analyst has high reputation or low ability, or if there is strong public information that is inconsistent with the analyst's private information, she is likely to herd. Herding is also common when informative private signals are positively correlated across analysts. The model is tested using data from analysts who publish investment newsletters. Consistent with the model's implications, the empirical results indicate that a newsletter analyst is likely to herd on "Value Line's "recommendation if her reputation is high, if her ability is low, or if signal correlation is high. Copyright The American Finance Association 1999.
Standard models of informed speculation suggest that traders try to learn information that others do not have. This result implicitly relies on the assumption that speculators have long horizons, i.e., can hold the asset forever. By contrast, the authors show that if speculators have short horizons, they may herd on the same information, trying to learn what other informed traders also know. There can be multiple herding equilibria, and herding speculators may even choose to study information that is completely unrelated to fundamentals. Copyright 1992 by American Finance Association.
Market liquidity is modeled as being determined by the demand and supply of immediacy. Exogenous liquidity events coupled with the risk of delayed trade create a demand for immediacy. Market makers supply immediacy by their continuous presence. and willingness to bear risk during the time period between the arrival of final buyers and sellers. In the long run the number of market makers adjusts to equate the supply and demand for immediacy. This determine the equilibrium level of liquidity in the market. The lower is the autocorrelation in rates of return, the higher is the equilibrium level of liquidity.
Spreads, depths, and trading activity for U.S. equities are studied over an extended time sample. Daily changes in market averages of liquidity and trading activity are highly volatile, negatively serially dependent, and influenced by a variety of factors. Liquidity plummets significantly in down markets but increases weakly in up markets. Trading activity increases in either up or down markets. Recent market volatility induces less trading activity and reduces spreads. There are strong day-of-the-week effects; Fridays are relatively sluggish and illiquid while Tuesdays are the opposite. Long- and shortterm interest rates influence liquidity and trading activity. Depth and trading activity increase just prior to major macroeconomic announcements.
We document strong positive correlation between changes in institutional ownership and returns measured over the same period. The result suggests that either institutional investors positive feedback trade more than individual investors or institutional herding impacts prices more than herding by individual investors. We find evidence that both factors play a role in explaining the relation. We find no evidence, however, of return mean-reversion in the year following large changes in institutional ownership -- stocks institutional investors purchase subsequently outperform those they sell. Moreover, institutional herding is positively correlated with lag returns and appears to be related to stock return momentum. 1 "Herding" (a group of investors trading in the same direction over a period of time) and "feedback trading" (correlation between herding and lag returns) have the potential to explain a number of financial phenomena, e.g., excess volatility, momentum, and reversals in stoc...
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