Herd Effect Research Articles

Herding the crowds: how sentiment affects crowdsourced earnings estimates

AbstractThis study investigates the impact of firm-level investor sentiment derived from Twitter and news media on herding behavior among contributors on Estimize, a leading platform for crowdsourced earnings forecasts. The findings show that sentiment gleaned from tweets and news media content positively influences herding among Estimize contributors. Notably, herding intensifies when Twitter and news sentiment polarities align, while divergent sentiment polarities diminish this herding effect. Additionally, the analysis indicates that firms with investment-grade ratings and those characterized by low valuation uncertainty are particularly prone to sentiment-driven herding. Importantly, positive sentiment is identified as having a more potent influence on herding behavior than negative sentiment. By focusing on Estimize contributors, this study offers insights into how firm-level sentiment cues shape the crowd’s herding behavior, offering new perspectives on how different media sources shape the wisdom of the crowd.

Following the herd? An empirical investigation into the adoption of mobile payment systems

PurposeThe adoption of mobile payment remains low in certain regions, highlighting the need to identify the factors that enable and inhibit its adoption. This study aims to address this gap by investigating the role of information security, loss aversion and the moderating influence of the herd effect on Inertia and behavioral intentions in the adoption of mobile payment systems.Design/methodology/approachA structural equation model was developed and tested with 332 valid questionnaires to examine the proposed hypotheses.FindingsThe empirical results reveal that information security plays a significant role as an enabler, while loss aversion acts as an inhibitor of mobile payment adoption. Furthermore, the study uncovers the moderating influence of the herd effect on the relationship between Inertia and behavioral intentions.Research limitations/implicationsThis study was conducted in a specific region and may not be generalizable to other regions. Future studies could expand the sample size and scope to enhance the external validity of the findings.Practical implicationsThis study offers practical implications for mobile payment service providers. Understanding the key enabling and inhibiting factors identified in this study can guide providers in designing and improving their services. Strengthening information security measures can help build trust among potential adopters, while offering incentives can mitigate the impact of loss aversion and encourage early adoption.Social implicationsThe findings of this study have social implications as they contribute to promoting the adoption of mobile payment systems. Increased adoption can enhance financial inclusion and stimulate economic development.Originality/valueThis study provides novel insights into the enabling and inhibiting factors of mobile payment adoption and highlights the moderating role of the herd effect. By shedding light on the influence of social norms on individual behavior in the context of mobile payment adoption, this study contributes to the existing literature and advances our understanding of this phenomenon.

Does war spread the herding effect in stock markets? Evidence from emerging and developed markets during the Russia-Ukraine war

This paper investigates the influence of the Russia-Ukraine conflict on herding behavior in global stock markets. Examining MSCI World and MSCI Emerging indexes alongside Russia, the study explores imitation tendencies before the invasion, immediately after commencement of war, and during an extended war period. Findings reveal that emerging markets facing heightened geopolitical risk, either due to their proximity to the conflict or to commercial interests in energy markets, exhibit herding during the initial war phase. This analysis sheds light on the impact of geopolitical events on financial market dynamics, particularly in emerging economies.

Estimation of the nutrient variation in feed delivery and impacts on lactating dairy cattle

Diets formulated for dairy cattle are designed to supply nutrients, but rations delivered to the animal may deviate from original formulations resulting in implications on milk production, composition, and even pregnancy rate. The objective of this study is to evaluate retrospective feed mixing records collected from 8 commercial dairy farms over 52 weeks. Farms ranged from 2,982 – 29,783 animals, producing 31.8 ± 5.93 kg milk with 4.14 ± 0.461% fat and 3.26 ± 0.218% protein. Data collected included daily TMR nutrient deviation; calculated as diet nutrient content minus the observed divided by the formulated diet concentrations of CP, fat, NDF, and starch. Data were analyzed with fixed effects of time (modeled as 28-d periods) and days of positive nutrient deviation in fat, CP, NDF and starch with each nutrient alone and in combination with the other nutrients, random effects of herd, and repeated measure of time within herd. The nutrient deviation by farm averaged (±SD) 1.68 ± 2.655, 2.28 ± 4.473, 1.75 ± 2.621, and 1.47 ± 2.147% for CP, fat, NDF, and starch. Days of positive nutrient deviation per period averaged (±SD) 25.5 ± 3.47, 25.5 ± 3.72, 25.6 ± 3.49, and 25.9 ± 2.61. Dry matter intake (DMI; 17.2 ± 1.78 kg/d) decreased with increasing positive deviation days in starch (−0.0483 ± 0.01265; P < 0.01) and increased with increasing positive deviation days in CP (0.0211 ± 0.00942; P = 0.03). Milk yield (31.4 ± 2.50 kg/d) increased with positive deviation days in starch (0.0486 ± 0.02110; P = 0.02) and decreased with increased positive deviation days in NDF (−0.0298 ± 0.02202; P = 0.18). Finally, pregnancy rate (21.7 ± 4.34) increased with increasing positive deviation days in fat (0.385 ± 0.1635; P = 0.02) and decreased with increasing positive deviation days in CP (−0.420 ± 0.1879; P = 0.03). For these models, period significantly affected milk yield (P < 0.01) and but was not observed to affect pregnancy rate and DMI (P ≥ 0.12). Results suggest farm and nutrient type were significant sources of variation and overall, farms overfeed nutrients CP, fat, NDF, and starch 92% of the days of a 28-d period.

The association between time-series milk β-hydroxybutyrate dynamics and early reproductive performance of dairy cows

The DeLaval Herd Navigator is an on-farm sensor system that measures on a frequent basis milk progesterone (P4) and BHB in individual cows to closely monitor reproductive performance and energy balance. This information provides the opportunity to investigate the dynamics of BHB measured in milk (mBHB) and study the association between mBHB and early reproductive performance. The objectives of the study were (1) to describe mBHB dynamics within the first 20 DIM, and (2) to evaluate the association between mBHB dynamics and early reproductive performance at cow level. Two-year time-series data from 4,133 dairy cows in 38 Dutch dairy farms were available for analysis. Data included information on mBHB, daily milk yield, and the indicators of early reproductive performance, days from calving to resumption of cyclicity, days from calving to first estrus, and days from calving to first insemination. The following mBHB dynamic parameters were defined based on the first 20 DIM for each individual cow: average mBHB (AvgBHB), DIM when mBHB was for the first time ≥80 μmol/L (OnsetKeto), the total number of consecutive days a cow had mBHB concentration ≥80 μmol/L, and the number of measurements mBHB concentration was ≥80 μmol/L. Three Cox proportional hazard regression models with random herd effect were developed to evaluate the association between cow-level mBHB dynamics and days from calving to resumption of cyclicity, first estrus, and first insemination. Results showed that the mean AvgBHB within 20 DIM among all cows was 73 μmol/L. The mean OnsetKeto within 20 DIM, was 8 DIM. Among all cows having hyperketolactia, 55.8% (1,350/2,419) had OnsetKeto in the first week of lactation. In total, 41.5% (1,714/4,133) of the cows did not have OnsetKeto in the first 20 DIM. An early onset of hyperketolactia was associated with delayed fertility events. Cows with higher AvgBHB have a prolonged time interval from calving to resumption of cyclicity and first estrus. Information on mBHB dynamics and the association with early reproductive performance provides insights that might be helpful to improve reproductive performance of individual dairy cows.

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.

A hierarchical deep model integrating economic facts for stock movement prediction

Accurate stock movement prediction is essential to profit from the stock market. However, this task is challenging due to the complexity and non-stationary nature of the market. Deep learning methods have obtained more attention and success in mining price movement patterns.However, some limitations affect their performances. In general, the stock market is ever-changing, and many factors affect stock movement, so capturing the stock movement patterns is hard without enough prior information. To tackle it, we consider employing economic facts to help improve the deep learning method. In this paper, we propose a novel Hierarchical Deep learning Model that fuses Economic Facts (HDMEF) to predict stock movement from the micro to the macro tiers: the individual, industry, and whole market tiers. Specifically, we present three well-designed modules to separately model them based on the Capital Asset Pricing Model (CAPM), the herding effects, and the holiday effects in the stock market. Experiments on the A-share CSI300 and CSI500 indexes demonstrate that our proposed method performs best on all test phases compared with previous competitive baselines, even an absolute improvement of 2%–3% on some test phases where all the baselines act poor, proving our method is more efficient and robust in different market conditions. In addition, we do an ablation study to analyze the role of various economic effects used in our model, and the results prove that each module is helpful for prediction.

Selecting an appropriate statistical model for estimating genetic parameters: A case study of pig maternal breeds in Czechia

A three-trait statistical model was designed for backfat thickness (BFT) and loin eye muscle depth (LMD) with accompanying variable average daily gain (ADG). Data from 82,507 Czech Large White and 37,556 Czech Landrace pigs were collected during on-farm performance testing from 2013 to 2022. Several animal models were tested with different combinations of effects and complexity. Model performance was evaluated by the linear regression (LR) method and predictivity. In Czech Large White, the preferred model comprised fixed effects of sex, birth year, herd, and ultrasound device, as well as random effects of herd-year-season (HYS), litter, and animal. In Czech Landrace, HYS was treated as fixed instead. The inclusion of maternal effects was not supported due to questionable impact on the main statistics. The selected model yielded mean absolute bias of 0.11 and 0.19, mean determination of 0.39 and 0.21, mean population accuracy of 0.36 and 0.31, and mean predictivity of 0.13 and 0.08 in Large White and Landrace, respectively. Heritability estimates were overall lower than those reported by other authors: 0.23, 0.10, and 0.10 in Large White and 0.26, 0.10, and 0.09 in Landrace for ADG, BFT, and LMD, respectively. Genetic correlations, on the other hand, reached relatively high values: 0.61, 0.61, and 0.66 in Large White and 0.71, 0.92, and 0.70 in Landrace for ADG-BFT, ADG-LMD, and BFT-LMD, respectively.

Analysis of Undergraduate Decision-making for Further Education Based on Behavioral Economics Theory

With the economic development and improvement of education in China, the scale of China's postgraduate education has become larger and larger. The choice of further education or direct employment is increasingly a major concern for most undergraduates. Therefore, this paper analyzes the different effects of irrational factors in the decision-making process of college students for further education at various stages under the framework of behavioral economics such as Prospect Theory, Psychological Account Theory, Procrastination Theory, and Herd Effect. Based on the analysis results, this paper puts forward corresponding suggestions from the school, society and the students themselves, all parties working together to guide college students to find their own career planning.

Digital transformation and the herd effect of corporate green investment

Based on China's unique institutional context, this paper empirically examines the relationship between corporate digital transformation and the herd effect of green investment. The findings indicate that digital transformation significantly promotes the herd effect in corporate green investments. Heterogeneity tests reveal that the enhancing impact of digital transformation on this herd effect varies with the nature of ownership and industry characteristics. The results of this study not only enrich the understanding of the economic consequences of corporate digital transformation but also offer a new research perspective on the herd effect at the organizational level.

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.

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.

A Brief Review of Conformity: Bandwagon Effect and Bystander Effect

Human beings, as representative creatures of social life, exhibit herd effects in all aspects of life. In this paper, we summarize the manifestation of the bandwagon effect and the bystander effect in people’s behavior and summarize their causes and commonalities, namely why people choose to make the same choices as everyone else. This review aims to discuss the conformity behavior from bandwagon effect and bystander effect. Both of these effects account for a large part of the factors that contribute to conformity behavior. People can get rid of the conformity effect and stick to their ideas and beliefs, and become more independent. Meanwhile, policies should be taken to avoid it in the case of running a red light. We conducted the demonstration by collating relevant experiments. People behave differently in groups than on their own. This is attributed to conformity, doing the same as others.

Cognitive Biases Impact on Investor’s Investment Decision of Retail Mutual Fund Investor

The purpose of this research is to study the effect of Cognitive bias on Investor’s investment decision of Retail Mutual Fund Investor.&#x0D; Methodology Adopted:Structured questionnaire was distributed to 1012 Indian mutual fund investors of four states considered financial hubs of India (Delhi/NCR, Mumbai, Bangalore, and Kolkata). Multiple regressions were used to test the model. Validity of instrument was established through CFA.&#x0D; Findings: Cognitive Bias (Overconfidence, over-reaction, Herd Effect and Regret Aversion) significant with investor’s investment decision making.&#x0D; Research Implications:Results are significant on Indian investors but with different geographic results might different. &#x0D; Originality/ Value: Academic research is still needed to understand the impact of mutual fund investors' cognitive biases on their investment decisions, Biases studied on Equity investors in south India, due to inconsistency in literature the work found to be original.&#x0D;

How to promote the transition of fuel vehicle enterprises under dual credit policy? An improved tripartite evolutionary game analysis with time delay

Under the context of carbon peak and carbon neutrality, new energy vehicle (NEV) is considered as ultimate objective in development of automotive industry. To facilitate sustainable growth of automotive market and ensure a smooth transition for fuel vehicle (FV) enterprises, this paper constructs a tripartite evolutionary game model based on tripartite Hoteling model, which involving NEV enterprises, FV enterprises and governments. The herd effect and time-delay effect are considered to improve the tripartite evolutionary game. Results indicate that: (1) Fund feedback to NEV enterprises will counterintuitively decrease probability of active assistance from NEV enterprises. (2) Governments’ willingness for strict involvement positively correlates with supervision costs but negatively correlates with additional benefits. (3) Time delay in funds leads to weak effect of high default penalties in promoting active assistance of NEV enterprises in early stage, but opposite situation appears in later stage. (4) Higher NEV production proportion counterintuitively stimulates FV enterprises to give up transition. (5) High rewards drive diffusion of herd effect, further increasing competition among vehicle enterprises leading to FV enterprises giving up transition. (6) Herd effect and time-delay effect increase dependence of FV enterprises on funds results in higher sensitivity of credit trading price compared to credit ratio requirement.

Consumers or infrastructure firms? Who should the government subsidize to promote electric vehicle adoption when considering the indirect network and herd effects

At present, two subsidy policies, which are based on electric vehicle (EV) consumers and EV charging station firms, have been widely implemented in different areas in China to promote EV adoption. However, the effectiveness of subsidy policies is significantly influenced by the indirect network and herd effects, depicting the positive impacts of the numbers of energy infrastructure firms and the sales of vehicles on consumers’ utilities, respectively. Taking the aforementioned effects into consideration, this study examines the optimal subsidy policies by developing a Stackelberg game model. The game model analyzes the interaction and decisions of multiple stakeholders (i.e., consumers, energy infrastructure firms, EV and gasoline vehicle manufacturers, and the government). Interestingly, we find that a stronger herd effect will promote EV sales when the unit production cost of EVs is low; however, it will hinder EV sales when the unit production cost is high. Besides, we compare the effectiveness of the two subsidy policies for promoting EV adoption, reducing environmental impacts, and increasing the social welfare. Findings show that the effectiveness of subsidy policies highly depends on the extent of the indirect network and herd effects. Specifically, compared with EV consumers-based subsidy policy, EV charging station firms-based subsidy policy will be more effective in only two conditions: a weak indirect network effect and a strong herd effect. Otherwise, the EV consumers-based subsidy policy will be more effective. The findings of the paper are helpful in enhancing the effectiveness of subsidy policy in China.

The Influence Of Financial Socialization, Overconfidence And Mental Accounting On Investment Decisions

This study examines the effect of financial socialisation, herding, overconfidence and mental accounting on investment decisions among stock investors in Jakarta. The sample in the study is stock investors who reside in Jakarta. The purposive sampling method collected 205 respondents through Google Forms distributed online on social media. The research data were analysed with a structural equation system using SmartPLS.3.2.9 software. The results showed that financial socialisation positively and significantly influences Generation Z's investment decisions in Jakarta. Overconfidence positively and significantly influences Generation Z's investment decisions in Jakarta. Mental accounting has a positive and significant influence on the investment decisions of Generation Z in Jakarta.

Information Asymmetry in Financial Markets: A Theoretical Review of Its Impact on IPO Underpricing

Recent trends have highlighted a significant prevalence of IPO underpricing. The primary focus of this study will be on the influence of information asymmetry on IPO underpricing within the financial market, followed by an analysis of the causes and consequences of this trend. Firstly, it briefly introduces the overview of information asymmetry and links to its importance within the IPO market. Then, it reviews two important concepts from existing literature regarding information asymmetry and IPO underpricing: the market of “lemons” and the herding effect. However, the study does not merely dwell on illustrating these two concepts; it also critically evaluates their limitations while also addresses the pros and cons of IPO underpricing caused by information asymmetry. Finally, it offers potential solutions for the information asymmetry problem to lessen IPO underpricing. The findings and significance of this research are particularly significant for financial market participants and decision-makers, as they help to better address and manage concerns with information asymmetry in financial markets. Through this multi-faceted exploration, the paper aims to unravel avenues that foster transparency, equitable valuation, and sustainable long-term development within the IPO market.

The impact of perceived quality, advertising quality, and herding effect on purchase intention: Moderation effect of age

In recent years, with the rapid development of technology, the development trend of online game industry is good. This paper focuses on the online gaming industry and discusses the factors that affect the willingness of Chinese college students to purchase products on gaming platforms. This paper selects consumer perception quality, advertising quality, and herd effect on game platforms as the main research variables through literature review. This paper introduces age as a moderating effect, investigate the moderating mechanism of age groups on the three main research variables. This paper found that both perceived quality and herd effect have a positive impact on consumer purchase intention, while age has a positive moderating effect on herd effect and the combined variables of perceived quality and herd effect. However, advertising quality has no significant impact on consumers' purchase intention, and the moderating effect of age on the impact of advertising quality on purchase intention is not significant. The paper falls into six parts. Firstly, it introduces the background, purpose, significance, conceptual framework, and contribution. Secondly, it introduces the literature review and assumptions. Thirdly, it presents the methodology. Fourth, it introduces the discovery of the study. Fifth, it indicates the discussion and implication. Sixth, it concludes the main points. The innovation lies in the following points. Firstly, this paper focuses on the consumption behavior of Chinese college students in online games. Secondly, this paper incorporates multiple variables mentioned in existing literature into the research mode. Thirdly, this paper introduces age groups as moderating variables.

Socio-ecological model as a framework to understand the low participation of Earth Hour among Chinese college students: conflict between belief and practice.

Earth Hour, a global mass effort coordinated to show concern for green urban construction and sustainable development, was first organized by the World Wildlife Fund in Australia in 2007 with a growing trend of participation worldwide. However, analysis of participation in Earth Hour based on a large population are sparse, with only a few studies reporting details in positive results without a clear pattern that explains the potential low participation. This study focuses on the non-participants and analyzed the reasons for low participation in Earth Hour using a questionnaire with 401 college students based on the socio-ecological model. Two aspects are explored: (1) social-demographic features; (2) psychosocial traits (environmental awareness, acceptance for law, social support from family and friends and knowledge about the event). Barriers toward participation are included as mediators to explain how these basic features change students' decision on joining large-scale environmental campaign. A participation analysis method using binary logistic regression and one-way MANOVA is applied in data analysis. This study highlights that the irrelevance between students' belief and practice on environmental protection should not be overlooked, and that college students are inclined to join in groups in relevant activities-conversely, herd effect could greatly reduce their willingness to participation. The findings of this study have wider implications for school educators, practitioners and organizations involved in pro-environmental career. This paper highlights that, from an international perspective, the essence of collective action with a similar nature to Earth Hour and contributes to a global dialogue on fostering sustainable behaviors.