Herding Model 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.

Growth and addition in a herding model with fractional orders of derivatives

This work involves an investigation of the mechanics of the herding behavior using a non-linear timescale, with the aim to generalize the herding model which helps to explain frequently occurring complex behavior in the real world, such as the financial markets. A herding model with fractional orders of derivatives was developed. This model involves the use of derivatives of order α where 0<α⩽1 . We have found the generalized result which indicates that number of groups of agents with size k increases linearly with time as nk=p(2p−1)(2−α)p(1−α)+1Γ(α+2−α1−p)Γ(k)Γ(k−1+α+2−α1−p)t for α∈(0,1] , where p is a growth parameter. The result reduces to that in a previous herding model with a derivative order of 1 for α = 1. The results corresponding to various values of α and p are presented. The group-size distribution at long time is found to decay as a generalized power law, with an exponent depending on both α and p, thereby demonstrating that the scale invariance property of a complex system holds regardless of the order of the derivatives. The physical interpretation of fractional calculus is also explored based on the results of this work.

News Information and Investor Behaviour: A Conceptual Model of Herding in Green Investments

News Information and Investor Behaviour: A Conceptual Model of Herding in Green Investments

Understanding consumer herding behavior in online purchases and its implications for online retailers and marketers

Herding behavior refers to the imitation of the behavior of the masses. The primary purpose of this study is to determine how herding affects digital purchases. It is an under-researched area in the marketing and consumer behavior context. First, one needs to understand the circumstances under which one is likely to herd. Second, it is yet to be understood how the effect of observing others' buying behavior transmits into herding. Third, there needs to be a better understanding of the role of price in one's tendency to herd. An online experiment with 541 participants was conducted using a 3 (consumer ratings/sales volume/control) × 2 (high involvement product/low involvement product) between-subjects design. LED TV was selected as a high-involvement product and ball pen pack was selected as a low-involvement product. This research study combined three theories to propose a holistic herding model. It used the social impact theory to capture its persuasive impact, the information adoption model to analyze the acceptance of herding information, and the cue utilization theory to assess the inference from herding information in consumer decision-making. Results indicated significant negative moderating effects of perceived price and product knowledge and the positive moderating effect of perceived uncertainty between the relationship of herding cues (star rating and the number of products sold) and herding behavior. In addition, the mediating results of perceived information usefulness and perceived quality were also significant. This herding model provides a holistic view of the herding process beyond limiting it to an attempt to follow others blindly. Therefore, this study has both theoretical as well as managerial implications.

Herd Design

The classic herding model examines the asymptotic behavior of agents who observe their predecessors’ actions as well as a private signal from an exogenous information structure. In this paper, we introduce a self-interested sender into the model and study her problem of designing this information structure. If agents cannot observe each other, the model reduces to Bayesian persuasion. However, when agents observe predecessors’ actions, they may learn from them, potentially harming the sender. We identify necessary and sufficient conditions under which the sender can nevertheless obtain the same utility as when the agents are unable to observe each other. (JEL D82, D83, D91)

HERDING BIAS INVESTOR MASA NEW NORMAL PANDEMI COVID-19

This study examines the determinants of herding bias in investors in Indonesia during the Covid-19 New Normal period. This study was conducted for six months to survey the behavior of 232 investors with a single investor identification in the Indonesian capital market who were haphazardly selected to test the herding bias model. The results of Confirmatory Factor Analysis show that reputational factors, social proof, lack of awareness, volatility in global markets, underconfidence, optimism, bandwagon effect, and speculation are the determinants of investor herding bias in Indonesia. The results of this study also prove that the reputation factor is the dominant factor determining herding behavior, where investors will follow the behavior of their community to maintain their reputation. The second factor that contributes highly to determining herding bias is social proof, which means that Indonesian investors consider social roles in the decision-making process. We hope the results of this study can provide researchers and practitioners with a better understanding of herding behavior which is part of the theory of financial behavior.

31. Development and evaluation of a sow herd model coupled with a bioclimatic model of gestation rooms

31. Development and evaluation of a sow herd model coupled with a bioclimatic model of gestation rooms

Anchor investors and equity crowdfunding for entrepreneurs

PurposeThis empirical study uses herd behavior model to explore the role of anchor investors in ensuring fundraising success and overfunding of crowdfunded ventures.Design/methodology/approachQualitative comparative analysis (QCA) is applied to find the configurational patterns describing how anchor investors' information disclosure leads to successful financing and overfunding.FindingsEven when the anchor investor's resume is not detailed or the anchor investor has little experience in entrepreneurial investment, success or overfunding can be achieved, provided the anchor investor is a corporation rather than an individual. For individual anchor investors, a detailed resume matters. Overfunding can be achieved even when an individual anchor investor makes a small relative investment, if this small relative investment is compensated for by a detailed resume. Experience in entrepreneurial investment is crucial when individual anchor investors have few previous investments. Regardless of the anchor investor's identity, investment in absolute terms is crucial for crowdfunding success when experience in entrepreneurial investment is low. Such experience must be extensive if the anchor investor's resume is not detailed.Practical implicationsBoth entrepreneurs and crowdfunding platforms can benefit from the findings in relation to the design of campaigns that use anchor investors' informational cues to achieve success and overfunding.Originality/valueThe study examines the importance of anchor investors' information disclosure in digital crowdfunding environments, differentiating between individual and corporate anchor investors.

Using a Novel Hybrid Krill Herd and Bat based Recurrent Replica to Estimate &lt;br /&gt; the Sentiment Values of Twitter based Political Data

Big data is an essential part of the world since it is directly applicable to many functions. Twitter is an essential social network or big data replicating political information. However, big data sentiment analysis in opinion mining is challenging for complex information. In this approach, the Twitter-based political datasets are taken as input. Furthermore, the sentiment analysis of twitter-based political multilingual datasets like Hindi and English is not easy because of the complicated data. Therefore, this paper introduces a novel Hybrid Krill Herd and Bat-based Recurrent Replica (HKHBRR) to evaluate the sentiment values of twitter-based political data. Here, the fitness functions of the krill herd and bat optimization model are initialized in the dense layer to enhance the accuracy, precision, etc., and also reduce the error rate. Initially, Twitter-based political datasets are taken as input, and these collected datasets are also trained to this proposed approach. Moreover, the proposed deep learning technique is implemented in the Python framework. Thus, the outcomes of the developed model are compared with existing techniques and have attained the finest results of 98.68% accuracy and 0.5% error.

Organizational Herding and Business Model Innovation Adoption

Organizational Herding and Business Model Innovation Adoption

Population, biomass, and economic value of small ruminants in Ethiopia.

Ethiopia has a large population of small ruminants (sheep and goats) which are mostly kept in traditional subsistence production systems that are poorly described. Understanding these different systems, their population structure, biomass, production, and economic value is essential for further analysis and effective policy making. The objective of this study was to quantify these parameters for small ruminant production systems in Ethiopia to use them as a basis for analysis of disease burden within the Global Burden of Animal Diseases program. Population structure and trends of small ruminants were analyzed using data from ten annual national agriculture surveys. A stochastic herd model was used to simulate the small ruminant population, biomass, and economic value. The model was parameterised stochastically using data from statistical databases and the literature, and sensitivity analysis of main model outputs to the stochastic inputs was done. Small ruminants are held across the country mainly managed under two major production systems: the crop-livestock mixed system and the pastoral system. The small ruminant population has grown in the past 10 years with an average annual growth rate of 4.6% for sheep and 6.7% for goats. The national average small ruminant population for 2021 was projected at 96.4 (range 95.3–97.7) million heads and the mean stock biomass was about 2,129 (range 1,680–2,686) million kilograms. The monetary value of the small ruminant population was estimated at USD 5,953 (range 4,369–7,765) million. The annual monetary value of small ruminant production outputs was estimated at USD 1,969 (range 1,245–2,857) million. Although the small ruminant population is large and rapidly growing, contributing about 2% of national annual GDP, the sub-sector is characterized by low productivity, low offtake rates, and a limited range of production outputs with no signs of intensification. Efforts should be made to reduce small ruminant mortality, improve fertility, and better utilize products such as milk to improve the livelihoods of rural households and to benefit the national economy. The approaches developed in this study can be replicated in other systems and countries to reveal trends in the size and value of livestock systems, providing a better understanding of its economic importance and performance.

Coupled biophysical and decision-making processes in grassland systems in East African savannahs – A modelling framework

Increasing livestock densities on limited grazing areas in African savannahs lead to resource degradation through overgrazing, aggravated by drought. Assessing herd management strategies over longer periods at landscape scale is important to propose options for sustainable land use. This requires an understanding of processes related to hydrology, nutrient cycling, herd movement, pasture degradation, and animal resilience that involve dynamic soil-plant-animal interactions and human decisions about stocking rates, livestock purchases and sales.We present the coupled model system MPMAS-LUCIA-LIVSIM (MLL), the combination of a spatially explicit agent-based model for human decision-making (MPMAS), a spatially distributed landscape model for water flows, nutrient cycles and plant growth (LUCIA), and a herd model (LIVSIM) representing grazing, body weight, nutrition and excreta of individual animals. MLL represents daily vegetation growth in response to grazing and organic inputs, monthly animal performance influenced by forage availability and quality, and herders’ management in response to resource status. New modules for selective grazing, resprouting of pasture, herd movement and model coupling were developed for MLL.The test case of a pastoral system in the Ethiopian Borana region demonstrates the capabilities of MLL to simulate key soil-plant-animal-human interactions under climate-related management scenarios with varying access to grazing land, changing cattle prices and different spending / saving behaviour of herders. 20-year simulations showed the negative impact of consecutive drought years on vegetation biomass, on herd development and movement and how reserving grazing areas for dry seasons could mitigate overgrazing and improve income. Seasonality and drought response of vegetation growth, selective grazing of different plant parts, resprouting after grazing, calving intervals, milk yields and lactation in response to forage supply and quality as well as herder reactions to shocks were plausibly represented.Building upon this successful proof-of-concept, MLL can be used to identify robust management options for improved grazing systems in savannahs in follow-up research.

Do investors herd in a volatile market? Evidence of dynamic herding in Taiwan, China, and US stock markets

This article investigates whether investors exhibit herd behavior in a high market volatility state. A modified herding model with the Kalman filter and GARCH methodology is used to estimate the time-variation herding corresponding to each influential event. Our proposed model provides comprehensive results for the relationship between investor herding and the market state, which has been argued in the previous literature. We find that investors indeed herd in volatile markets, including the 2001 dot.com bubble and the 2009 global financial crisis. However, in recent years, anti-herding is prevalent and herding is slight even in turbulent markets, such as the 2020 Covid-19 pandemic.

A principal component analysis required in technical assistance guidance for chilled raw milk producers

The purpose of the present study was to evaluate the principal component analysis (PCA) to guide technical assistance regarding several dairy farms’ issues, which includes improving microbiological quality and physical-chemical composition of raw refrigerated milk. Data of monthly analysis of fat, protein, lactose, dry defatted stratum, somatic cell count, total bacterial count, milk temperature of 8,101 samples of milk from expansion tanks and production of 78 farms located in the northern region of Minas Gerais, Brazil were processed. Descriptive statistical measures and Pearson correlation coefficient were estimated involving all evaluated traits during the dry and rainy seasons. In addition, multivariate analyses were performed using PCA. The results showed that two farm sites were negatively related to milk quality in both seasons. One farm stood out positively, being able to be used as a herd management model to drive technical assistance actions. Thus, PCA is efficient in simplifying large amounts of data, allowing simpler and faster technical herd management interpretation.

A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions

The inconsistent irradiance, temperature, and unexpected behavior of the weather affect the output of photovoltaic (PV) systems, classified as partial or complex partial shading conditions. Under these circumstances, obtaining the maximum output power from PV systems becomes problematic. This paper proposes a population-based optimization model, the horse herd optimization algorithm (HOA), inspired by natural behavior, to solicit the maximum power under partial or complex partial shading conditions. It is an intelligent strategy inspired by the surprise pounce-chasing style of the horse herd model. The proposed technique outperforms the standard in different weather conditions, needs less computational time, and has a fast convergence speed and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the HOA is achieved with conventional techniques, such as “perturb and observe” (P&amp;O), the bio-inspired adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), and the dragonfly algorithm (DA). The following comparison of the presented scheme with the other techniques shows its better performance with respect to fast tracking and efficiency, as well as stability under disparate weather conditions and the ability to obtain maximum power with negligible oscillation under partial and complex shading.

Financial impact of sheeppox and goatpox and estimated profitability of vaccination for subsistence farmers in selected northern states of Nigeria

Sheeppox and goatpox (SGP) are important transboundary diseases, endemic in Nigeria, causing severe clinical manifestations, impacting production, and resulting in economic losses. Vaccination is an effective control measure against SGP in endemic countries but is not currently implemented in Nigeria. This study aimed to estimate SGP financial impact and assess economic viability of SGP vaccination at the herd and regional level under different scenarios in Northern Nigeria. Integrated stochastic production and economic herd models were developed for transhumance and sedentary herds. Models were run for two disease scenarios (severely and slightly affected) and with and without vaccination, with data parameterisation from literature estimates, field survey and authors’ experience. Herd-level net financial impact of the disease and its vaccination was assessed using gross margin (GM) and partial budget analyses. These were then used to assess regional financial impact of disease and profitability of a 3-year vaccination programme using a cost-benefit analysis. The regional-analysis was performed under 0 %, 50 % and 100 % government subsidy scenarios; as a standalone programme or in combination with other existing vaccination programmes; and for risk-based and non-risk-based intervention.Median SGP losses per reproductive female were £27 (90 % CI: £31-£22), and £5 (90 % CI: £7-£3), in sedentary, and £30 (90 % CI: £41-21), and £7 (90 % CI: £10-£3), in transhumance herds, for severely and slightly affected scenarios respectively. Selling animals at a reduced price, selling fewer young animals, and reduced value of affected animals remaining in the herd were the greatest contributors to farmer’s SGP costs. SGP-affected herds realised a GM reduction of up to 121 % in sedentary and 138 % in transhumance. Median estimated regional SGP cost exceeded £24 million. Herd-level median benefits of vaccination per reproductive female were £23.76 (90 % CI: £19.28-£28.61), and £4.01 (90 % CI: £2.36-£6.31), in sedentary, and £26.85 (90 % CI: £17.99-£37.02) and £7.45 (90 % CI: £3.47-£15.14) in transhumance herds, in severely and slightly affected scenarios, respectively. Median benefit: cost ratio (BCR) for severely affected herds at 50% subsidies was 6.62 (90% CI: 5.30-8.90) for sedentary, and 5.14 (90% CI: 3.31-13.81) for transhumance herds. The regional SGP vaccination standalone programme BCR: 7–27, regional SGP vaccination with existing vaccination programme BCR: 7–228 and vaccinating high-risk areas BCR: 19–439 were found to be economically viable for all subsidy levels explored. Vaccinating low-risk areas only realised benefits with 100 % of government subsidies.This study further increases understanding of SGP’s impact within Northern Nigeria and demonstrates vaccination is an economically viable control strategy at the herd-level and also regionally, depending on the strategy and government subsidy levels considered.

The measure of spatial position within groups that best predicts predation risk depends on group movement.

Both empirical and theoretical studies show that an individual's spatial position within a group can impact the risk of being targeted by predators. Spatial positions can be quantified in numerous ways, but there are no direct comparisons of different spatial measures in predicting the risk of being targeted by real predators. Here, we assess these spatial measures in groups of stationary and moving virtual prey being attacked by three-spined sticklebacks (Gasterosteus aculeatus). In stationary groups, the limited domain of danger best predicted the likelihood of attack. In moving groups, the number of near neighbours was the best predictor but only over a limited range of distances within which other prey were counted. Otherwise, measures of proximity to the group's edge outperformed measures of local crowding in moving groups. There was no evidence that predators preferentially attacked the front or back of the moving groups. Domains of danger without any limit, as originally used in the selfish herd model, were also a poor predictor of risk. These findings reveal that the collective properties of prey can influence how spatial position affects predation risk, via effects on predators' targeting. Selection may therefore act differently on prey positioning behaviour depending on group movement.

COVID-19 Pandemic and Investor Herding in International Stock Markets

The aim of this study is to understand the effect of the recent novel coronavirus pandemic on investor herding behavior in global stock markets. Utilizing a daily newspaper-based index of financial uncertainty associated with infectious diseases, we examine the association between pandemic-induced market uncertainty and herding behavior in a set of 49 global stock markets. More specifically, we study the pattern of cross-sectional market behavior and examine whether the pandemic-induced uncertainty drives directional similarity across the global stock markets that cannot be explained by the standard asset pricing models. Utilizing a time-varying variation of the static herding model, we first identify periods during which herding is detected. We then employ probit models to examine the possible association between pandemic-induced uncertainty and the formation of herding. Our findings show a strong association between herd formation in stock markets and COVID-19 induced market uncertainty. The herding effect of COVID-19 induced market uncertainty is particularly strong for emerging stock markets as well as European PIIGS stock markets that include some of the hardest hit economies in Europe by the pandemic. The findings establish a direct link between the recent pandemic and herd formation among market participants in global financial markets. Considering the evidence that herding behavior can drive security prices away from equilibrium values supported by fundamentals and further contribute to price fluctuations in financial markets, our findings have significant implications for policy makers and investors in their efforts to monitor investor sentiment and mitigate mis-valuations that might occur as a result. Furthermore, the evidence on the behavioral pattern of stock investors in relation to infectious diseases uncertainty can be useful in studying price discovery in stock markets and might help market participants in forming hedging strategies to mitigate downside risk in their investment portfolios.

Critical Linkages Between Livestock Production, Livestock Trade and Potential Spread of Human African Trypanosomiasis in Uganda: Bioeconomic Herd Modeling and Livestock Trade Analysis.

Background: Tsetse-transmitted human African trypanosomiasis (HAT) remains endemic in Uganda. The chronic form caused by Trypanosoma brucei gambiense (gHAT) is found in north-western Uganda, whereas the acute zoonotic form of the disease, caused by T. b. brucei rhodesiense (rHAT), occurs in the eastern region. Cattle is the major reservoir of rHAT in Uganda. These two forms of HAT are likely to converge resulting in a public health disaster. This study examines the intricate and intrinsic links between cattle herd dynamics, livestock trade and potential risk of spread of rHAT northwards.Methods: A bio-economic cattle herd model was developed to simulate herd dynamics at the farm level. Semi-structured interviews (n = 310), focus group discussions (n = 9) and key informant interviews (n = 9) were used to evaluate livestock markets (n = 9) as part of the cattle supply chain analysis. The cattle market data was used for stochastic risk analysis.Results: Cattle trade in eastern and northern Uganda is dominated by sale of draft and adult male cattle as well as exportation of young male cattle. The study found that the need to import draft cattle at the farm level was to cover deficits because of the herd structure, which is mostly geared towards animal traction. The importation and exportation of draft cattle and disposal of old adult male cattle formed the major basis of livestock movement and could result in the spread of rHAT northwards. The risk of rHAT infected cattle being introduced to northern Uganda from the eastern region via cattle trade was found to be high (i.e. probability of 1).Conclusion: Through deterministic and stochastic modelling of cattle herd and cattle trade dynamics, this study identifies critical links between livestock production and trade as well as potential risk of rHAT spread in eastern and northern Uganda. The findings highlight the need for targeted and routine surveillance and control of zoonotic diseases such as rHAT.

Bandit cascade: A test of observational learning in the bandit problem

I conduct an experimental investigation of observational (social) learning in a simple two-armed bandit framework where the models are based on Bayesian reasoning and non-Bayesian count heuristics providing different predictions. The agents can choose between two alternatives with different probabilities of providing a reward. They must make their choice in order to see the outcome and act in a sequence. They can base their decision on the choices of the predecessors and the outcomes of their own choice. The results of the experiment follow neither Bayesian Nash Equilibrium nor Naïve herding model (BRTNI): Subjects follow and cascade on choices that contain no information about the state of the world, and, therefore, sustain losses when learning from others. I also test the Quantal response equilibrium and the robustness of this theory.