Herding Behavior Research Articles

Does Uncertainty Drive Herding Behavior in Indonesia Stock Exchange

Herding behavior increases along with market risk, corporate uncertainty aggregate uncertainty. This research aims to investigate herding behavior in the Indonesian capital market by considering conditions of uncertainty. There are two uncertainty indicators used in this research, namely stock market volatility and COVID-19. By using daily data from companies listed on the LQ45 index, the research found that herding behavior was not detected when using the basic herding model, but when entering transaction volume variables and lagged dependent variables, we found that there was herding behavior during the 2017-2023 period. Furthermore, by including uncertainty variables, our research found an asymmetry of herding behavior: investors tend to herd when the market experiences high volatility and during the COVID-19 pandemic. Outside of these two periods, we found no empirical evidence of herding behavior in Indonesia. These results strengthen the theoretical framework that high uncertainty triggers herding behavior.

Heuristic Bias and Herding Behavior for Predicting Investor Decision in Cryptocurrency Trading

Heuristic Bias and Herding Behavior for Predicting Investor Decision in Cryptocurrency Trading

HERDING BEHAVIOUR MARKET INFORMATION AND INDIVIDUAL INVESTOR DECISION-MAKING AT THE NAIROBI SECURITIES EXCHANGE

This paper examined the relationship between herding behaviour and individual investor decision-making at the Nairobi Securities Exchange. In addition, the study assessed the moderating effect of market information on the relationship between herding behaviour and individual investor decision-making at the Nairobi Securities Exchange. The study was anchored on behavioural finance theory and was guided by a correlational research design. The study's target population was 2.03 million individual investors who traded at the Nairobi Securities Exchange through the 17 licensed brokerage firms in Kenya. A structured questionnaire was used to collect data and Internal consistency of the instruments was measured using Cronbach’s alpha coefficient, where a coefficient of 0.865 was obtained. The obtained data was analyzed descriptively using frequencies, means, and standard deviation and inferentially by correlation and multiple regression models. The findings revealed that herding behaviour (r = 0.235; β =0.180 p<0.05) had a positive and significant relationship with individual investor decision-making. Jointly, the study established that herding behaviour explains a 59.6% change in individual investor decision-making. In addition, the study established that market information had a positive significant moderating effect on the relationship between herding behaviour and individual investor decision-making as the moderator increased the R2 from 59.6% to 61.0%, which is an R-square change of 1.4%. The study concluded that herding behaviour had a significant relationship with individual investor decision-making at the Nairobi Securities Exchange. The study recommended that investors understand market trends, financial statements, and economic indicators and continue seeking advice from financial experts. NSE should also provide regular market analysis, ensure transparency in market operations to build investor confidence and introduce investment products that cater to different risk profiles, such as low-risk mutual funds. The findings of the study could be of significance to policymakers, financial experts, academicians, scholars, and theoretical developments.

Factors affecting traditional micro business (MB) to continue using E-marketplace from habit and social impact perspective

Purpose This study aims to proposes a theoretical model to understand the intention of Indonesian traditional micro businesses (MB) to continue using an e-marketplace. Design/methodology/approach This research involved the utilization and combination of habit formation and social impact theories to develop the model. A total of 162 responses were retrieved from the owners of traditional MB and used to validate the proposed model. Findings The results showed subjective norms to be the most substantial direct predictor of continuous usage, followed by usefulness, satisfaction and promotion proneness. Meanwhile, compatibility was found to have only an indirect effect through usefulness as a mediator. Moreover, moderating effect analysis also showed that the impact of herd behaviour was only significant for male and younger traditional MB owners. Originality/value The emphasis on the seller perspective using habit formation as well as the social impact theory to explain continuance behaviour is an alternative approach observed not to have been widely explored, especially for traditional businesses such as indigenous industry rather than organizations.

The influence of psychological factors on investment decision-making: Psychological features of economic relations formation

The article aimed to analyse the influence of psychological factors on the investment decision-making process, which shapes the economic behaviour of investors in Ukraine. The research involved surveys and experimental tasks conducted among investors, with the data analysed using statistical methods to identify the impact of psychological factors on investment decisions. Fear and greed have been found to be most significant emotions affecting investment decisions, whereas fear leads to excessive caution and risk avoidance. In contrast, greed drives investors to take on excessive risks. Overconfidence in one’s knowledge and skills results in an overestimation of opportunities and an increased risk of losses, while confirmation bias leads investors to seek information that supports their previous decisions, ignoring conflicting data. Expert opinions and group behaviour significantly influence investment decisions, particularly under conditions of uncertainty, often resulting in herd behaviour and the formation of economic bubbles. The experimental tasks demonstrated that investors frequently make irrational decisions under the influence of emotions and cognitive biases, even when they can access complete information about risks and opportunities. The survey results also indicated that investors with higher levels of financial literacy are less susceptible to the influence of emotions and cognitive biases, highlighting the importance of financial education in improving the quality of investment decisions. These findings illustrate how psychological factors can distort the investment decision-making process and underscore the necessity of considering these factors when developing investment strategies

Price delay and herding: evidence from the cryptocurrency market

PurposeThe purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.Design/methodology/approachThe paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).FindingsOur findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.Practical implicationsThe study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.Originality/valueThis paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.

Did grain futures prices overreact to the Russia–Ukraine war due to herding?

We study the impact of the 2022 Russian invasion of Ukraine on grain futures prices. The war allows us to evaluate whether commodity futures markets at the time were driven by investor herding. Using event study methods, we find that wheat futures prices rose by 35 percent above the counterfactual until the EU Solidarity Lanes were announced, more than corn futures prices, which were up 16 percent. This relative price response cannot be explained by herding behavior. Furthermore, prices for control commodities did not respond to the war at all, contradicting the herding theory. There is no statistical evidence of abnormal speculative pressure in the market around the time of the invasion, and we conclude the markets put a fair price on the wartime risk of Black Sea grain shipment disruptions.

Herd behavior in U.S. bank stocks

This study investigates investor herd biases in U.S. banks' stock returns before, after, and during the COVID-19 pandemic by focusing on the differential impacts of monetary policy cycles and bank regulatory framework. Our results display fundamental herding for both large stress-tested banks and non-stress-tested regional banks during the pre-pandemic tightening monetary policy period in which bank fundamentals were improved through regulatory changes after the global financial crisis. However, we find evidence of nonfundamental herding during the post-pandemic tightening monetary policy period in which bank fundamentals were weakened. This situation might have contributed to the value irrelevance of bank fundamental information to stock prices. Our results have important implications for regulators, policymakers, and investors.

Artificial intelligence and big data tokens: Where cognition unites, herding patterns take flight

Artificial intelligence (AI) and big data tokens have emerged as unique investment options, garnering interest due to their connectedness with other assets and financial markets. Utilizing Chang et al. (2000)'s cross-sectional absolute deviation (CSAD) model, we investigate static and time-varying herding in the AI and big data token markets. This research contributes to the growing discourse on AI and big data token investment through the lens of behavioral finance, with a particular focus on examining investor herding. The study's findings confirm market-wide herding of AI and big data tokens. The results suggest that investors exhibit herding in up markets, low volatility, and low volume days. Conversely, anti-herding is more prevalent in down markets, high volatility, and high volume days. Our analysis shows that herding is time-varying and emerges during a crisis period. The finding carries robust regulatory and policy implications to mitigate systemic risk and safeguard investor interests, ensuring market stability and resilience. The provided insights offer a valuable understanding of investors’ behavior across various market scenarios.

Herding behavior in NFT Auction: The role of visual complexity and familiarity

Despite the growing popularity of non-fungible tokens (NFTs) in the market, they still lack established valuation guidelines. Invoking herding and auction theories, this study investigates how NFT collectors’ bidding tendencies are influenced by other collectors’ bidding decisions (herding behavior). This study also examines how NFT visual characteristics, such as complexity and familiarity, moderate herding behavior. We use a computer vision technique to quantify the visual complexity of an NFT and apply transfer learning to capture its visual familiarity. Examining data from a widely used NFT platform, we find evidence of herding behavior in such auctions. Furthermore, our results show that there are nonlinear moderating effects of visual characteristics on herding behavior. Specifically, we find that consumers exhibit a stronger herding tendency at the two extremes of complexity distribution and at the lower extreme of familiarity distribution. Further analysis of the NFT resale market suggests that auction winners consider herding in the primary sale of an NFT as a signal of high demand; thus, they are more willing to resell it and set a higher price premium in the resale. These findings can be taken as input by the NFT market platforms in their NFT display decisions to boost primary market sales and promote resale market participation. The findings can also help content creators formulate creative strategies that lead to higher bidding probability or sales performance.

Unintended consequence of high bid price exclusion in IPO auctions: Evidence from China

Using a reform to relax the exclusion rate of highest bids in the book-building process as an exogenous shock, we find that IPO underpricing decreases, which indicates that excluding a certain percentage of the highest bids impairs IPO pricing efficiency. Further tests reveal that relaxing the mandate increases investors’ valuation of an IPO, thereby decreasing IPO underpricing. Our results also suggest that the relaxation of the stipulation motivates investors to provide more information, as revealed by fewer anchoring bids and reduced herding behavior, as well as higher opinion divergence and better predictive power for investor bids on post-IPO prices.

Herding towards pygmalion: Examining the cultural dimension of market and bank based systems

The study aims to not only detect the presence of herd behavior in the countries studied, but also to examine the effect of cultural dimensions and market/bank-based systems on the herding behavior of financial market investors. The study employs the Cross-Sectional Standard Deviation and Cross-Sectional Absolute Deviation methods to analyze daily data from public companies traded in the capital markets in Emerging Seven and Group of Seven economies. The results suggest that being a member of E7-G7, a Future Oriented (FO), and a Performance Oriented (PO) cultures are the most important factors in explaining herd behavior. Additionally, the study found that the Ridge Classifier and CatBoost Classifier algorithms arethe most superior model for estimating herd behavior periods determined by the CSSD and CASD models, respectively. The feature selection results show that the Assertiveness (A) in-group collectivism (GC) are the three most important explanatory factors of the herd behavior.

Impact of Herding Behavior on Investment Decisions in the Nepalese Stock Market with Mediation and Moderation Effects

This research examines the influence of herding behavior on investment decisions made in Nepal, with the moderating role of financial literacy and overconfidence as a mediating factor. Traditional financial theories suggest that rational investors make decisions based on available information to maximize their capital. In contrast, behavioral finance argues that psychological factors significantly impact financial decisions. Data were collected from 384 investors trading on the Nepal Stock Exchange using a structured survey. The findings indicate that herding behavior significantly enhances both overconfidence and investment decisions. Overconfidence has a positive impact on investment decisions. Financial literacy acts as a positive moderator, while overconfidence serves as a positive mediator between herding behavior and investment decisions. SMART-PLS was employed for data analysis.

Digital transformation and the herd behavior of corporate investment

Digital technology is a widespread integration of the benefits of digital transformation into all aspects of modern company operations. This paper examines how digital transformation affects firms’ investment herd behavior. Based on listed firms from 2012 to 2022, this paper adopts a two-fixed model to explore the effects and underlying mechanisms. The findings indicate that digital transformation can effectively inhibit corporate investment herd behavior, and the impact is achieved by enhancing internal information disclosure and external information intermediation. The findings offer empirical proof of how to effectively regulate the herd behavior observed in financial markets in the digital era.

Herd Behaviour and COVID-19 Intervention

The paper attempted to confirm the rational assets pricing model by examining the herd behaviour in extreme market returns and examining the COVID-19 intervention by comparing before COVID-19, during COVID-19 and after COVID-19 period. Cross Sectional Standard Deviation (CSSD) calculated as the index and further regressed by using dummy variables (Extreme upper tail market returns =1, otherwise = 0). For the better inferences stationarity of time series data were checked by both graphically and Augmented Dickey –Fuller test in all three states of COVID-19. Finally, the Cumulative-Sum (CUSUM) test was conducted to check the parameter stability of the regression model. The finding of the study supports the rational asset pricing models, meaning that there is no presence of herding in NEPSE in extreme upper tail returns. But it rejects the rational asset pricing models in case of extreme lower market returns and in COVID-19 period. For better affirmation of rational assets pricing models, future research can extend the work by addressing the market characteristics, such as: trading volume, volatility, and liquidity.

Chatgpt Usage in Academia: Extending the Unified Theory of Acceptance and use of Technology with Herd Behavior

This paper is motivated by the widespread availability and usage of AI technology such as ChatGPT in academia. The study adopted the Unified Theory of Acceptance and Use of Technology (UTAUT), herd behavior, and a mediator (website familiarity) to demonstrate behavioral intention to use ChatGPT among university students. A total of 202 valid sample sizes were used, all of which have access to ChatGPT. The structural equation model analysis's outcome indicates that performance expectancy, social influence, facilitating conditions, and imitating others positively affected behavioral intention to use ChatGPT. However, effort expectancy and discounting one's own information did not have a significant effect. Additionally, website familiarity mediated the relationship between performance expectancy, effort expectancy, and social influence but did not mediate the relationship between facilitating conditions and intention to use ChatGPT, respectively. The study suggests AI developers should ensure they offer technologies that can perform and possess the required features that aid users in adopting AI technologies.

Penetrating Language

In this paper, consideration is given to contemporary culture’s impact on language and its communicative potential. Drawing on psychoanalytic understanding of the human herd instinct, particularly in time of strife, this essay outlines what the “talking cure” has to offer as counterpoint to group think and as a means to enter into all that is idiosyncratic and individual in the use of language.

Is style drift informative? Evidence from mutual funds in China

In this study, we investigate whether the style drift of portfolio managers is informative. We establish that style drift is based on public information rather than the imitation of peers. Fund managers are found to adjust their portfolios to align with market expectations, particularly in normal market conditions. However, we observe a pronounced shift towards irrational herd behavior during market turbulence and style rotation periods. While style drift can offer benefits in contemporary periods, such as positive risk-adjusted returns, the funds that exhibit excessive drift can hardly beat their peers in subsequent periods. More notably, sophisticated investors tend to abandon funds with more considerable style drift in favor of those adhering to a consistent investment strategy. Our findings indicate that while style drift may be a strategic response to market conditions, its advantages are transient and often unaligned with the preferences of sophisticated investors.

Navigating the roadmap to meta-governance adoption

Purpose This study aims to investigate citizens' adoption intention towards meta-government (metaverse-based government) by proposing a dual-perspective technology acceptance model. Design/methodology/approach The validity of the proposed model was established by gathering and analysing 533 responses using structural equation modelling (SEM). Findings The study findings underscore the importance of perceptions of ease of use and usefulness in fostering the intention to adopt meta-government. However, perceived cyber risk and switching costs negatively impact these perceptions. On the contrary, herd behaviour and perceived immersion positively influence perceptions of ease of use and usefulness. Originality/value This study significantly contributes to the existing literature by offering valuable insights into the factors that influence citizens' adoption of meta-government. It introduces a novel perspective on the primary factors (both enablers and barriers) shaping citizens' adoption intention of meta-government. These insights serve as a foundation for scholars, governments and policymakers to develop effective strategies for promoting meta-government adoption.

Multi-Dimensional Herding Behavior of Mobile Payment Users

ABSTRACT The swift advancement of technology and the convenience of mobile payment use have significantly contributed to its growth, impacting financial inclusion in developing regions. However, continuity remains a challenge for users in these regions favoring cash. To address this, we investigate how users often exhibit herding behavior that influences mobile payment usage and not just techno-behavioral factors, as suggested by past literature. We propose that users frequently imitate others’ actions owing to the networked nature of mobile payment technology and design a research model comprising multiple dimensions of both rational and irrational herding behavior affecting its continued usage. With 507 user survey responses, we confirmed our model’s validity. Results reveal a balancing impact of both rational and irrational herding on continuance intention, providing insights for enhancing the technology’s popularity in financial inclusion efforts. This study enhances mobile payment literature by integrating theoretical aspects of herding behavior.