Herding Behavior Research Articles

Social trust, audit quality and institutional investor herd behaviour

Trust is essential for a well-functioning market economy and is influenced by social institutions and technical advancement, not just cultural traditions. This paper examines how social trust affects institutional investor herd behaviour. Based on data from 2011 to 2021, this paper adopts a fixed effect model to explore the effects and underlying mechanisms. The findings indicate that increasing social trust can inhibit institutional investor herd behaviour, especially investor sell-side herd behaviour. The impact is achieved by enhancing audit quality, especially in the international Big Four. The findings advise the government on reducing institutional investors' herd behaviour from a macro perspective.

The effect of financial literacy and demographic variable on behavioral biases

The current research investigates the association between demographic factors, including financial knowledge and demographic characteristics like gender, occupation, age, education, and income, and investor behavior biases, including bias against overconfidence, disposition effect, and herding bias. Further enhancement of behavioural finance research is required in Indonesia. This study applied a quantitative analysis technique called structural equation modeling. Stock investors aged 17 and 40 were given questionnaires for this study. Respondent data collection was carried out by distributing questionnaires through several social media sites, such as Line, WhatsApp, and Instagram, to members of the investor’s community. The total number of respondents obtained from distributing this questionnaire was 170. According to the study, overconfidence bias significantly affects income and financial literacy. However, overconfidence bias does not affect gender, occupation, investment experience, education, or age. Furthermore, for behavioral biases, the disposition effect does not affect financial literacy or all demographic factors. Finally, for herding behavior bias, bias significantly affects financial literacy, investment experience, and income, but herding bias does not affect gender, occupation, education, age, or income. These results have implications for investors with a high level of financial literacy, which will help them determine rational investment decisions. This research also has implications for high-income investors who tend not to follow herding behavior.

HERDING BEHAVIOR IN FINANCIAL MARKET – SYSTEMATIC LITERATURE REVIEW

The aim of this research is to present a comprehensive theoretical analysis and provide concrete empirical support pertaining to the phenomenon of herding behavior within financial markets. The current paper can be classified as a literature review, which employed data collection techniques through an extensive survey of existing literature. The body of knowledge accumulated over a span of more than twenty years, consisting of both empirical investigations and theoretical inquiries, has yielded significant insights into the intricate nature of investor herding behavior. However, it is important to acknowledge that the author possesses relatively limited knowledge regarding markets other than capital markets, thereby presenting a constraint in terms of exploring the complexities associated with shifting behavior patterns. Furthermore, the available evidence concerning the existence of following behavior is not particularly compelling. Consequently, this paper advocates for the development and application of an empirical methodology that can effectively address these aforementioned limitations and offer a comprehensive evaluation of herding behavior. Additionally, this study critically examines recent empirical findings in order to identify areas that remain unexplored and require further investigation in future research endeavors.

ESDC-LSH: Ensemble Support-Vector Deep Convolutional Based Levy Selfish Herd Optimization for Prediction and Classification of Parkinson’s Disease

ESDC-LSH: Ensemble Support-Vector Deep Convolutional Based Levy Selfish Herd Optimization for Prediction and Classification of Parkinson’s Disease

Sectoral Herding During Global Rare Events: Evidence from the Indonesian Capital Market

Research Aims: This research aims to examine the effects of increased levels of herding on abnormal returns during rare events.Design/Methodology/Approach: Time series regression including all stocks across 9 sectors in the Indonesia Stock Exchange from 1997 to 2020, totaling 5,615 observations is used. The primary model is predicated on three factors derived from Fama-French and prospect theory to incorporate herding as a primary risk factor in assessing the impact of on abnormal returns during rare events.Research Results: The results show that various events produce impacts on herding behavior across different sectors. During bearish market conditions, this behavior manifests significant negative effect leading to greater abnormal returns. Conversely, positive and significant anti-herding behavior is observed in bullish market conditions. Rare events do not necessarily induce herding behavior but may lead to anti-herding behavior.Theoretical Contribution/Originality: In this research, the variables are developed from the Efficient Market Hypothesis, Capital Asset Pricing Model, prospect theory, and market integration theory. The estimation model is grounded in prospect theory and the contribution addresses the research gaps.Practitioners/Policy Implications: The provision of insights to stakeholders in the capital market regarding the impact of rare events on financial behaviors influences investors' decision-making processes in stock investments.Research Limitations/Implications: The measurement of herding refers to Chang et al. (2000) due to the availability of aggregate data from the Indonesian Stock Exchange. Comprehensive micro-level data is not unavailable and the accessibility of complete micro-level data can be conducted. The presence of these data in the capital markets of other countries should be investigated.

Behavioral Bias of Equity Investors in Different Market Conditions and Events in Pakistan Stock Exchange (PSX)

The herding behavior of equity investors in the Pakistan Stock Exchange (PSX) is examined in this study. There are two ways to look at herding behavior: first, in different market conditions, like bull and bear, extreme and less extreme, high and low trading volume, and high and low volatility; second, in different events, like the month of Ramadan, the financial crisis of 2007–2008, and the Covid-19 pandemic. Financial herding behaviour is when equity investors follow the actions of other equity investors without making their own decisions because they lack information or they think others have more trustworthy information. Regression analysis using Newey West Consistent Estimators is performed on Pakistan Stock Exchange (PSX) daily stock market data ranging from January 2001 to June 2021. The study found that the herding behavior among equity investors is absent during extreme market conditions, less extreme market conditions, high trading volume, high market volatility, and low market volatility; however, it is present during low trading volume, the month of Ramadan, financial crisis (2007-2008) and during Covid-19 pandemic as well.

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

AbstractThe negative herding effect tends to cause the spread of negative emotions among netizens, resulting in significant public opinion crises. Therefore, linking the negative herding effect and online public opinion, we aim to exploring the connection between netizens' emotions, opinions, and the negative herding effect. To better distinguish the emotions in texts, a new BiGADG (Bidirectional Gated recurrent unit based on Attention with Distance‐based dependency syntax Graph convolutional network) model is proposed in this article. This model includes primarily the proposed dependency syntax based on dependency distance, Bidirectional Gated Recurrent Unit (BiGRU), multi‐head self‐attention and Graph Convolutional Network (GCN). Particularly, the dependency syntax based on dependency distances incorporates word‐to‐word dependency distances into the traditional dependency syntax, which enriches the syntactic structural features of the text. After the experiments, the accuracy of BiGADG model is 86.53%, which is 3.11% higher compared to BERT model. It is evident that our model for sentiment classification has been improved. Based on the results of sentiment classification, we investigate the existence of negative herding behaviour and its underlying motivation in online public opinion. We have found that the negative herding effect, if not effectively managed, may trigger large‐scale and uncontrollable public opinion crises.

An investigation of corporate social responsibility conformity: The roles of network prominence and supply chain partners

AbstractNumerous studies on corporate social responsibility (CSR) indicate that firms adopt CSR practices for various reasons related to their supply chain. However, the necessity to conform to a firm's own industry CSR norm is overlooked. Conforming to one's industry CSR norm—a herding behavior known as CSR conformity—ensures firm in‐group legitimacy and preserves internal resources for core business activities. On the other hand, deviating from industry norms sets a firm apart from its peers, making the firm more appealing to supply chain partners. Motivated by this dilemma, this study draws on middle‐status conformity theory and explores how a firm's network prominence determines its CSR conformity. Panel data analyses of 1650 firm‐year observations reveal an inverse U‐shaped relationship between firm network prominence and its CSR conformity, indicating that firms with a mid‐level network prominence engage in higher CSR conformity. However, the inverse U is flattened when a firm's supply chain partners (and their respective industries) share similar CSR standards, suggesting that a firm can only prioritize its own industry CSR norms if its supply chain partners share a compatible CSR standard. These findings highlight the importance of understanding CSR from an organizational conformity perspective, especially in the context of supply chain network.

It is a small world: The effect of analyst-media school ties on analyst performance

We examine the effect of school ties between sell-side analysts and media executives on the improvement in analysts' performance during the release of informative negative news. Our baseline results and mechanism tests show that analysts obtain fragmented private information to improve their earnings forecast accuracy by taking advantage of the school ties with financial media executives. To establish causality, we perform several identification strategies, such as entropy balancing matching, cross-sectional analyses during university anniversary gatherings, and a placebo test. Further, we find that analysts with less herding behaviors benefit more from the school tie effect. Besides, we reveal that connected analysts are more likely to issue reports in a timely manner after the release of informative negative news, and these revisions in earnings forecast elicit significantly positive market reactions. At the same time, we notice that the media are more likely to interview alumni-affiliated analysts, providing potential evidence of an informational interaction between financial analysts and the media.

The impact of the Russo-Ukraine conflicts on price discovery on the financial markets

ABSTRACT This paper investigates the response of financial markets to two significant events within the Russo-Ukraine conflicts: the November 2013 Euromaidan protest and the February 2022 Russian invasion of Ukraine. Employing diverse methods, including event studies, price discovery models, and time-varying parameter regression, we analyse abnormal returns, price discovery, time-varying herding behaviour, and market dynamics across various financial instruments, such as stock indices, currencies, and commodities. Overall, the Russian invasion of Ukraine had a more pronounced impact on financial markets compared to the Euromaidan protest. Moreover, during both events, the US dollar and yen consistently functioned as safe-haven assets. The findings also reveal variations in the responses of different assets to the conflicts, indicating potential effects on market efficiency, investor perceptions, and expectations.

Findings of Herd Behavior in Borsa Istanbul During Covid-19

In cases where investors' emotional and behavioral responses are influenced, mimicking fellow investors leads to the manifestation of herd behavior. This study is dedicated to scrutinizing and contrasting instances of herd behavior within the Borsa Istanbul during the Covid-19 period against those in the pre-pandemic phase. The analysis specifically encompasses the banking, food, transportation, tourism, and technology sectors traded on the Borsa Istanbul, as well as the BIST 30 and BIST 100 indices. The CSAD (cross-section absolute deviation) methodology is employed as the analytical framework. The findings delineate a lack of herd behavior during the pre-pandemic period. However, contrasting this, the pandemic period witnesses the emergence of herd behavior, evident within the BIST 30, BIST 100 indices, and the banking, food, and technology sectors.

Health Care REIT Returns & Covid-19: A Note

This study tests for pricing efficiency in Healthcare Real Estate Investment Trusts (REITs) surrounding the Covid-19 pandemic. Fractal analysis is used to ascertain whether any persistence or anti-persistence in returns can be observed over the “pre-Covid” and “Covid/post-Covid” periods for the 15 healthcare equity REITs included in the FTSE NAREIT US Real Estate Index. The results point to weak form efficiency during the complete sample period with some exceptions. A relative stability in Hurst exponents exists across the sub-periods for several of the REITs. However, there is evidence that many other REITs series switch from being anti-persistent to persistent, a result consistent with possible herding behavior. Some very limited evidence is found of series switching from being persistent to anti-persistent, consistent with investor overreaction for these REITs. Overall, inefficient pricing is seen in a limited number of cases, but there is a strong suggestion of a possible change like the return dynamic in the latter part of the study period associated with the pandemic.

The study on mechanical model considering optimal self-adaption in the bottleneck area

It aims to solve the problem that the evacuation state of pedestrians depicted by the traditional social force model in a crowded multiexit scenario has a relatively large difference with the actual state, especially the 'optimal path' considered by the self-driving force is the problem of shortest path, and the multiexit evacuation mode depicted by the 'herd behavior' is the local optimum problem. Through in-depth analysis of actual evacuation data of pedestrians and causes of problem, a new crowd evacuation optimization model is established in order to effectively improve the simulation accuracy of crowd evacuation in a multi-exit environment. The model obtains the direction of motion of pedestrians using a field model, fully considers the factors such as exit distance, distribution of pedestrians and regional crowding degree, makes a global optimization for the self-driving force in the social force model using a centralized and distributed network model, and makes a local optimization for it using an elephant herding algorithm, so as to establish a new evacuation optimization method for optimal self-adaption in the bottleneck area. The performance status is compared between the improved social force model and the new model by experiments, and the key factors that affect the new model are analyzed in an in-depth manner. The results show that the new model can optimize the optimal path choice at the early stage of evacuation and improve the evacuation efficiency of pedestrians at the late stage, so as to ensure relatively even distribution of pedestrians at each exit, and also make the simulated evacuation process be more real; and the improvement in overall evacuation efficiency is greater when the number of pedestrians to be evacuated is larger. Therefore, the new model provides a method to solve the phenomenon of disorder in overall pedestrian evacuation due to excessive crowd density during the process of multi-exit evacuation.

THE IMPACT OF DIGITAL INFLUENCER TOWARD PURCHASE DECISION ON INDONESIAN STOCK MARKET (STUDY CASE OF GEN Z IN JABODETABEK)

<p>The purpose of this research is to analyze the impact of digital influencer toward purchase decision on Indonesian stock market. The population in this research is Gen z investor or trader in Jabodetabek area. The sample in this research was taken by non-probability using purposive sampling and had received 231 returned e-questionnaire. The research method used in this research is quantitative research and the analyze method was conducted by using Partial Least – Structural Equation Model (PLS-SEM) using SmartPLS. The result of this research indicates that Digital Influencer has not significant influence on purchase decision. In mediating variable, Only the psychology that can significantly mediate the digital influencer on purchase decision but the herding behavior does not have a significant influence.<strong></strong></p>

Herding behaviour and monetary policy: Evidence from the ZAR market

We investigated the presence of herding and its interactions with monetary policy in the ZAR market. We achieved this using both the standard herding tests and Sim and Zhou’s (2015) quantile-on-quantiles regressions. Similar to previous results in other markets, we found that extreme market events mainly drove herding behaviour in the ZAR market. This result was also significant in the presence of monetary policy announcements. However, herding in the ZAR markets was not related to market fads. It, therefore, was, in the main, a rational response to public information, indicating central bank credibility. This credibility gives scope to the central bank to improve communication in periods of market crisis to dampen potential volatility. Further studies on the herding of specific ZAR market participants can be invaluable.

Herding Behavioral Motivation conducted by Coronial Investors: Study on Indonesian Stock Exchange’s

The objective of this research is to determine whether periods of market down- turn are accompanied by herding behavior among investors. During the Covid-19 pandemic, not only the stock market but also the number of investors in the Indonesian capital market increased significantly. The increase in new investors is also accompanied by an increase in herding behavior, as these new investors have limited experience and literacy in investing in the capital market. The sampling technique used was simple random sampling, with a total of 196 investor samples collected. The analysis method used was Structural Equation Modeling Partial Least Square. The research findings indicate that investor cognitive psychology and market information influence herding behavior among investors. However, stock characteristics do not affect herding behavior. Socio-economic factors moderate the influence of investor cognitive psychology and market information.

Heterogeneous beliefs with preference interdependence and asset pricing

We study the equilibrium implications of heterogeneous beliefs and preference interdependence on the dynamics of asset prices and the resulting market behaviors. For the purpose of capturing the preference interdependence, we allow each investor’s utility not only depends their own terminal wealth, but also depends on the other one’s. We show that the preference interdependence generates a form of “herding behavior”, that induces the investors’ beliefs to coverage with each other. Therefore, the degree of the influences of the heterogeneous beliefs is attenuated. We also present that the heterogeneous beliefs impose negative effects on the prices of stocks and call options, whereas generate positive effects on the prices of put options. Due to the attenuation effect, the preference interdependence can have a strong promoting effect on the prices of stocks and call options and a strong depressing effect on the prices of put options. Furthermore, the attenuation effect also imposes a strong depressing effect on the volatilities of stocks even there exists high degree of heterogeneous beliefs among the investors. Finally, we present that both the heterogeneous beliefs and the preference interdependence can result in co-movement between stock and option markets.

Digital transformation and corporate innovation with herd effects

This paper empirically examines the impact of digital transformation on the herd behavior in corporate innovation based on the A-share listed companies in the Chinese capital market. The empirical results show that digital transformation significantly enhances the herd behavior in corporate innovation. Further analysis of heterogeneity reveals that the promoting effect of digital transformation on corporate innovation herd behavior is more pronounced in non-state-owned enterprises and in companies with high levels of competition. This study provides empirical evidence and theoretical references for promoting corporate digital transformation and aiding in high-quality economic development.

Impact of stock market liquidity and external factors on herding behavior in the Amman Stock Exchange

Purpose — The research investigation was centered on the potential for herding behavior in the Amman Stock Exchange. The major goal of the current investigation is to identify any herding behavior that may occur, including behavior caused by liquidity and global factor drivers (such as oil prices and Fed fund rates).Method — The study employed the quantitative research method. The sample consists of 50 observations from 171 companies, and the data were collected from monthly records spanning January 2019 to March 2023. The Generalized Method of Moments (GMM) and a deductive approach were utilized in the quantitative methodology of this study.Result — At this stage, employing a CSSD regression analysis makes it possible to observe evidence of herding behavior in the left tail. The study also found no evidence suggesting that stock market liquidity affects herding behavior in the right tail during periods of both high and low liquidity. However, it did find evidence indicating that liquidity affects herding behavior in the left tail. The Amman capital market has demonstrated that herding behavior is not significantly influenced by global factors.Practical implications — This study suggests that the Amman Stock Exchange should make information accessible to all investors to encourage them to take an active role in making their own investment decisions.

Empirical Research on the Impact of Open Fund Sheep Behavior on Stock Price Volatility

In the past, economic research took the “rational man hypothesis” as the premise, but in reality, this premise is difficult to hold. In a large number of financial practices, a large number of special phenomena that are inconsistent with standard financial theory have emerged. Behavioral finance is premised on the assumption of irrational people, and herd behavior is a relatively special irrational behavior. It refers to the behavior of investors who are influenced by other investors when the information environment is uncertain, imitate other people’s decisions, or rely too much on public opinion without considering their own information. Sheep behavior is a related behavior involving multiple investment entities, which has a great impact on the stability and efficiency of the market, and is also closely related to the financial crisis. Therefore, herd behavior has attracted widespread attention from academia, investment and financial supervision.China’s capital market started late. In 1990, the Shanghai Stock Exchange was established, marking the birth of China’s stock market, the distribution of market investors have changed again and again, unchanged is that the behavior of investment subjects has a strong similarity, at the same time, China’s fund market has persistent fluctuations.Whether there is a causal connection, this paper will focus on the development of China’s fund market, use LSV model and VAR test method to test and analyze the fund and stock trading data of Shanghai and Shenzhen from the first quarter of 2017 to the first quarter of 2022, and empirically study whether there is significant herd behavior in China’s open-end funds. On this basis, the impact of stock investment herd behavior on stock price volatility is empirically studied.Finally, according to the research conclusion, from the three aspects of investor investment rationality, the effectiveness of capital market and the optimization of market structure, suggestions on how to reduce the impact of the herd effect and the healthy and sustainable development in the future.