Herding Behavior Research Articles

Digital transformation and corporate ESG information disclosure herd effect

This study investigates how digital transformation influences the tendency of companies to engage in herd behavior regarding the disclosure of ESG information. The findings indicate that digital transformation notably increases herd behavior in ESG information disclosure among these companies. Additional analysis considering different types of companies shows that this effect is stronger in private sector firms and those operating in highly competitive environments. The research offers practical insights and serves as an empirical and theoretical resource to support the advancement of digital transformation in businesses, contributing to the broader goal of achieving high-quality economic growth.

Satisfaction with Behavior Trajectory Security Protection in Social Media Intelligent Recommendations

Abstract Intelligent recommender systems provide personalized recommendation for users based on their behavior trajectories. Intelligent recommendation is a double-edged sword with increasing impacts. This study investigates the influencing mechanism of social media users’ satisfaction with behavior trajectory security protection in the context of intelligent recommendation, with the aim of promoting healthy development of mobile social media. This study applied the grounded theory method to identify relations among concepts and categories in terms of three-level coding. During open coding, 271 initial concepts and 26 subcategories were elicited; during axial coding, 10 categories were elicited; and during selective coding, relations among categories were identified and a theoretical model was developed. The results indicate that satisfaction with behavior trajectory security protection is directly influenced by security protection information quality, security protection system quality, security protection service quality, net benefits, external support, privacy concerns, and privacy cognitive biases. Privacy literacy has direct impacts on privacy concerns and privacy cognitive biases. Meanwhile, herd behavior directly impacts privacy concerns. Implications for theory and practice are discussed.

Beyond hype: Unveiling the herd effect in ESG and non-ESG cryptocurrency portfolios

This paper investigates herding behavior in the cryptocurrency market, specifically comparing environmental, social, and governance (ESG)-designated assets and non-ESG assets. It addresses a research gap concerning investor behavior towards ESG and non-ESG cryptocurrencies. Our study reveals that investor herding behavior manifests in specific years and in response to significant events, such as El Salvador’s adoption of Bitcoin. Notably, the findings suggest the presence of intentional herding behavior in 2023 data for ESG and non-ESG cryptocurrencies. These insights offer valuable guidance for investors and policymakers seeking to mitigate risks and optimize portfolio allocation strategies.

Attention of women's liberation and investor herding behavior

In recent decades, the women's liberation movement is garnering attention on a global scale. To investigate the impact of women's liberation attention on herding behavior among male and female investors in the stock market, we obtain a unique account-level data set of retail investor transactions in the Chinese stock market. Our results document that investors in Chinese cities with higher women's liberation attention demonstrate less herding behavior than other investors. Moreover, by facilitating investors' confidence and risk tolerance, women's liberation attention indirectly reduces herding behaviors. Our results remain solid in robustness tests, and additional tests confirm that women's liberation attention helps narrow the gender gap in herding behavior. Moreover, women's liberation attention is particularly significant in reducing investor herding behavior under volatile market conditions. Our paper contributes to the studies of gender equality in the stock market and provides empirical evidence by using account-level data.

Selfish herd effects in aggregated caterpillars and their interaction with warning signals.

Larval Lepidoptera gain survival advantages by aggregating, especially when combined with aposematic warning signals, yet reductions in predation risk may not be experienced equally across all group members. Hamilton's selfish herd theory predicts that larvae that surround themselves with their group mates should be at lower risk of predation, and those on the periphery of aggregations experience the greatest risk, yet this has rarely been tested. Here, we expose aggregations of artificial 'caterpillar' targets to predation from free-flying, wild birds to test for marginal predation when all prey are equally accessible and for an interaction between warning coloration and marginal predation. We find that targets nearer the centre of the aggregation survived better than peripheral targets and nearby targets isolated from the group. However, there was no difference in survival between peripheral and isolated targets. We also find that grouped targets survived better than isolated targets when both are aposematic, but not when they are non-signalling. To our knowledge, our data provide the first evidence to suggest that avian predators preferentially target peripheral larvae from aggregations and that prey warning signals enhance predator avoidance of groups.

Influence of the Russia–Ukraine War and COVID-19 Pandemic on the Efficiency and Herding Behavior of Stock Markets: Evidence from G20 Nations

Efficiency in stock markets is essential for economic stability and growth. This study investigates the efficiency and herding behavior of the stock markets from the top economies of the world (known as G20 countries). We classify stock market indices using MSCI classification for the developed and emerging markets to provide a comparative examination using the latest data and by employing the robust multifractal detrended fluctuation (MFDFA) method. In addition to the full sample, the analysis uses sub-sample periods to reveal the hidden features and efficiencies of the G20 markets during the Russia–Ukraine War and COVID-19 for the first time. The findings show the availability of varied multifractality among all G20 stock markets during the overall and crisis periods, exhibit long-range correlations, and may support the fractal market hypothesis. In addition, Italy remains the least efficient, while Germany remains the most efficient stock market. The sub-sample results further reveal unevenness in the local fluctuations and resultant higher inefficiency considering the sheer magnitude and impact of crises on the G20 stock markets. However, the efficiency of developed stock markets performed better as compared to emerging markets. The study of G20 stock markets is useful and provides several implications for a wider audience.

Herding during different types of crises: The COVID-19 health crisis and Russia–Ukraine political crisis

We analysed herding behaviour in the recent pandemic and conflict. We employed the cross-sectional dispersion of daily stock returns to estimate herding's intensity in the Saudi stock market. We conducted all analyses for the entire sample and four sub-samples. Additionally, we investigate the existence of the asymmetry in the investors' responses; whether there are differences between up and down markets and between high-volatility and low-volatility days. We found that herding did not occur in the pre-COVID-19, occurred in the during-COVID-19, disappeared in the post-COVID-19 and did not occur during the Russia–Ukraine conflict. Robustness checks confirm our finding that herding manifested in the during-COVID-19 period. Additionally, no difference exists between bearish and bullish or high-and low-volatility days, pushing aside the asymmetry in the investors' responses. This study may raise investors' awareness of their cognitive bias's influence on their decisions, improving market efficiency by increasing the rationality of investors' decisions.

Do Intra-industry Peer Effects Influence the Debt Financing Policy of Companies Listed in Thailand?

Rational imitation of financial policies, namely peer effects, was documented to prevail among firms within an industry in both developed and top emerging economies. Lacking evidence from medium-sized emerging Asian countries, this study examined intra-industry peer effects, i.e., imitation, on debt financing at Thai listed companies. The sample consisting of the firms listed on the Stock Exchange of Thailand (SET) during the years 2001–2018 was incorporated to discover those effects. Numerous databases associated with the SET (e.g., SETSMART) and the firms’ websites were used to collect financial numbers. With 6,058 firm-year observations, unbalanced panel data was then analyzed by using the random-effects and fixed-effects ordinary least squares models as well as the two-stage least squares regression. The main findings show that a focal firm imitates the long-term debt financing of its industry peers. Using the instrument approach to address endogeneity, the key results are confirmed. Peers’ equity shock as the instrumental variable decreases their financing with debt, subsequently lowering debt levels at a focal firm. This mimicking was operated through peer action, not peer character, i.e., performance. In additional tests, severe market competition was found to strengthen the uniformity of decision-making on debt finance among industry peers. Meanwhile, the high cost of information embedded in innovation-driven businesses has not been found to both promote and reduce peer effects on debt finance. Including the extra control variables at firm-specific levels, the key results remain robust. This study highlights a vein of social learning theory and competition-based theory in discussing herding behaviors toward all member firms in a certain industry. The conclusions enrich the literature on peer effects on debt finance policy.

An Empirical Analysis of Investor Behavior: A Post-COVID-19 Examination of Herd Behavior in the National Stock Exchange of India (NSE)

This research paper investigates investor behavior, specifically focusing on herd behavior, in the National Stock Exchange of India (NSE) during the post-COVID-19 period, from 2020 to the present. Utilizing daily closing prices of companies listed on the Nifty50, the study employs data sourced from the NSE website. In contrast to earlier research that demonstrated herding in developing economies, this paper adopts the methodology outlined by Christie & Huang (1995) to analyze the data. The study finds no significant evidence of herding during the broader period of market stress in the NSE, suggesting a divergence from previous trends. The results suggest that Indian investors, despite operating in a volatile market environment post-COVID-19, exhibit rational behavior. Unlike some developing economies where herding behavior has been identified, Indian investors seem to rely on private information rather than imitating the actions of their peers.

I need some space: solitary nesting Adélie penguins demonstrate an alternative breeding strategy at Cape Crozier

According to the ‘selfish herd’ hypothesis, most seabird species breed colonially so that individuals can decrease their risk of predation by forming compact groups. However, costs and benefits associated with colonial breeding may not be evenly distributed among individuals within a colony. At Adélie penguin colonies, individuals nesting on the periphery of subcolonies (distinct groups of nests) may experience higher rates of nest predation by south polar skuas, and thus the optimal aggregation pattern for Adélie penguins may be within groups that minimize the proportion of edge nests. Nevertheless, some penguins choose to nest solitarily, at significant distances from conspecifics. We tracked 50 of these “solitary-nesting” Adélie penguins at Cape Crozier, a large colony on Ross Island, during the 2021 nesting season and compared their breeding success to individuals nesting within subcolony boundaries. We found that both solitary and subcolony nests successfully raised chicks large enough to join crèches and left unattended by adults. However, chicks from solitary nests exhibited a rate of mortality more than six times higher during the transition from nest brooding/guarding to crèche stage. In the 2022 nesting season, we found that solitary nests which had previously hosted actively breeding penguins were more likely to be re-occupied. Solitary nesting therefore appears to be a less-successful alternative to breeding within subcolonies, but enough individuals could be successful with this approach to maintain the apparently disadvantageous behavior and effectively pioneer previously unused locations, possibly including eventual new colony locations.

The Influence Between the Decay Speed of Information and Future Asset Price—A New Herd Behavior Model with the Decay Factor

The Influence Between the Decay Speed of Information and Future Asset Price—A New Herd Behavior Model with the Decay Factor

PSYCHOLOGICAL FACTORS AFFECTING INVESTORS DECISION MAKING

The paper investigates the psychological factors influencing investors' decision-making processes, with a particular focus on how biases affect risk tolerance and investment decisions. Utilizing a comprehensive methodology that includes ANOVA, regression, and correlational analysis, this study examines the effects of biases such as overconfidence, anchoring, herd behaviour, regret aversion and optimism biases on investment decisions. The research findings reveal significant correlations between these psychological biases and investor behaviour, highlighting their impact on risk assessment and decision-making strategies. To ensure the reliability of the research tools, a Cronbach’s Alpha test was conducted, confirming the consistency of the instruments used. The study's results challenge the traditional notion that states that investment decisions are driven solely by logical reasoning and underscoring the substantial influence of emotional and cognitive biases. These insights contribute to the field of behavioural finance by enhancing understanding of the psychological underpinnings in investment decision-making and suggesting practical applications for financial education and advisory services. The paper advocates for the inclusion of psychological bias recognition in investor education, proposing that such an approach can significantly improve decision-making and lead to better investment outcomes. The implications of this research extend beyond individual investors to financial advisors and policymakers, offering them a foundation to develop strategies and regulations that help mitigate bias-driven investment mistakes, ultimately fostering greater financial well-being and market efficiency. Keywords: Investor Decision Making, Risk Tolerance, ANOVA, Regression Analysis, Correlational Analysis, Overconfidence Bias, Anchoring Bias, Herd Behaviour Bias, Regret Aversion Bias, Optimism Bias, Behavioural Finance.

Complex dynamics of a discrete-time Leslie-Gower predator-prey system with herd behavior and slow-fast effect on predator population

Complex dynamics of a discrete-time Leslie-Gower predator-prey system with herd behavior and slow-fast effect on predator population

Impact of Herding Behavior on Mutual Fund Performance

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

HERDING BEHAVIOR IN THE SHARIA CAPITAL MARKET ON INVESTMENT DECISIONS

Herding behaviour is an irrational decision-making because it is the following of other investors. Using information asymmetry as a moderating variable, this research aims at identifying the factors that influenced investment decisions. The research object was the Jakarta Islamic Index, and the data used were quarterly data from 2019 to 2021. The methods of sampling and analysis used were the panel data regression method and the purposive sampling method. The results showed that volatility, exchange rate, market sentiment and firm size positively and significantly affected herding behaviour. Information asymmetry strengthened the influence of herding behaviour on investment decisions. Furthermore, investors and potential investors could use the results to make future investment decisions.

A Comparative Study on Factors Influencing Investment Decisions of Investors Based on Income Level in Coimbatore

This research project is a comparative analysis between the levels of income of individuals making investment decisions based on different factors from the Traditional Finance Theory and Behavioural Finance Theory. The study involves data that was collected using quota sampling method from the students and housing community in Coimbatore. There are about 154 responses collected for the purpose of the study. The non-parametric test Mann-Whiteney U Test was done to compare the impact of various variables in the different income groups- Low and High by analysing the statistically significant differences. The findings reveal that the factors of traditional finance and behavioural finance differ amongst the income groups. The perception of financial literacy has no significant difference between the categories. Risk & Return are almost perceived similarly by both the groups. Frame dependence and Herd behaviour have significant difference in its impact on the income groups. Although the findings reveal insightful information regarding the factors of investment decision which can be utilised by various stakeholders like investors and financial advisors, the study has its own limitations of using non-probability sampling method.

Neural stochastic agent‐based limit order book simulation with neural point process and diffusion probabilistic model

SummaryModern financial exchanges use an electronic limit order book (LOB) to store bid and ask orders for a specific financial asset. As the most fine‐grained information depicting the demand and supply of an asset, LOB data are essential in understanding market dynamics. Therefore, realistic LOB simulations offer a valuable methodology for explaining the empirical properties of markets. Mainstream simulation models include agent‐based models (ABMs) and stochastic models (SMs). However, ABMs tend not to be grounded on real historical data, whereas SMs tend not to enable dynamic agent‐interaction. More recently, deep generative approaches have been successfully implemented to tackle these issues, whereas its black‐box essence hampers the explainability and transparency of the system. To overcome these limitations, we propose a novel hybrid neural stochastic agent‐based model (NS‐ABM) for LOB simulation that incorporates a neural stochastic trader in agent‐based simulation, characterised by (1) representing the aggregation of market events' logic by a neural stochastic background trader that is pre‐trained on historical LOB data through a neural point process model; (2) learning the complex distribution for order‐related attributes conditioned on various market indicators through a non‐parametric diffusion probabilistic model; and (3) embedding the background trader in a multi‐agent simulation platform to enable interaction with other strategic trading agents. We instantiate this hybrid NS‐ABM model using the ABIDES platform. We first run the background trader in isolation and show that the simulated LOB can recreate a comprehensive list of stylised facts that demonstrate realistic market behaviour. We then introduce a population of ‘trend’ and ‘value’ trading agents, which interact with the background trader. We show that the stylised facts remain and we demonstrate order flow impact and financial herding behaviours that are in accordance with empirical observations of real markets.

Multi-information and epidemic coupling propagation considering indirect contact on two-layer networks

This research provides an in-depth examination of the interplay between diverse information propagation and epidemic spread within a two-layer network, placing particular emphasis on the implications of both direct and indirect modes of disease transmission. Individuals are categorized based on their susceptibility to and their capacity for disease propagation, while the influence of varying infection rates on epidemic expansion is investigated. Information is bifurcated into constructive narratives with beneficial outcomes, and detrimental narratives originating from rumors. The method of information dispersion incorporates considerations such as herd behavior, preventative costs, and individual risk perception. Utilizing the Microscopic Markov Chain Approach (MMCA), a dynamic equation is formulated to delineate epidemic propagation and the epidemic threshold for the proposed model is calculated. Results demonstrate that promoting swift dissemination of positive information, concurrently with curbing the spread of negative narratives, can appreciably mitigate epidemic proliferation. The findings also imply that facilitating access to beneficial information, minimizing the consequences of harmful narratives, decreasing prevention-related expenses, and enhancing personal risk perception can significantly reduce the ultimate magnitude of the epidemic. Notably, the study reveals that indirect contact can considerably augment epidemic spread. As the infection rate via indirect contact escalities, the extent of epidemic transmission broadens and the threshold for epidemic transmission diminishes.

Supply chain driven herding behavior during COVID-19: evidence of interdependence from India

PurposeThis study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.Design/methodology/approachUsing the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.FindingsThe event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.Originality/valueThe authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

Rational herding: evidence from equity crowdfunding

PurposeThis study explores whether herd behavior exists for equity crowdfunding investors in China and whether this herding is rational.Design/methodology/approachBased on signaling theory and social learning theory, two hypotheses were proposed. This study employed two approaches to collect data. First, this paper analyzed 3,041 investments on an equity crowdfunding platform in China using Python programming and built a panel data model. Second, based on a unique experiment design, this study conducted several relevant herd behavior simulation experiments.FindingsWe found that investors in the Chinese equity crowdfunding market exhibit herd behavior and that this herding is rational. Project attributes play a negative role in moderating the relationship between the current investment amount and cumulative investments. Experimental results further support our findings.Originality/valueThis study contributes to the emerging literature on herding in crowdfunding by focusing on equity crowdfunding in China. We are the first to explore whether Chinese equity crowdfunding investors exhibit rational herding behavior. The study is also original in applying social learning theory to equity crowdfunding and in using both actual crowdfunding campaigns and experimental approaches to collect data. This study has valuable implications to practice.