• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.27-34
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• 2020

The study investigates herding behavior in cryptocurrencies in different situations. This study employs daily returns of major cryptocurrencies listed in CCI30 index and sub-major cryptocurrencies and major stock returns listed in Dow-Jones Industrial Average Index, from 2015 to 2018. Quantile regression method is employed to test the herding effect in market asymmetries, inter-dependency and intra-dependency cases. Findings confirm the presence of herding in cryptocurrency in upper quantiles in bullish and high volatility periods because of overexcitement among investors, which lead to high volume trading. Major cryptocurrencies cause herding in sub-major cryptocurrencies, but it is a unidirectional relation. However, no intra-dependency effect among cryptocurrencies and equity market is observed. Results indicate that in the CKK model herding exists at upper quantile in market that may be due when the market is moving fast, continuously trading, and bullish trend are prevailing. Further analysis confirms this narrative as, at upper quantile, the beta of bullish regime is negative and significant, meaning the main source of market herding is a bullish trend in investment, which increases market turbulence and gives investors opportunity to herd. Also, we found that herding in cryptocurrencies exits in high volatility periods, but this herding mostly depends on market activity, not market movement.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.322-329
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• 2005

This paper considers the application of mobile robot to the herding problem. The herding problem involves a ‘pursuer’ trying to herd a moving ‘evader’ to a predefined location. In this paper, two mobile robots act as pursuer and evader in the fenced area, where the pursuer robot uses a fuzzy cooperative decision strategy (FCDS) in the herding algorithm. To herd evader robot to a predefined position, the pursuer robot calculates strategic herding point and then navigates to that point using FCDS. FCDS consists of a two-level hierarchy: low level motion descriptors and a high level coordinator. In order to optimize the FCDS, we use the multi­thread evolutionary programming algorithm. The proposed algorithm is implemented in the real mobile robot system and its performance is demonstrated using experimental results.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.147-158
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• 2020

We apply Return Dispersion Model by calculating CSAD (Cross-sectional standard deviation of return) and State Space Model to identify herding behavior in the period of pandemic (H1N1 and COVID-19). Employing data from TEJ and Data Stream, this paper examines whether the herding behavior is existing in Vietnam and Taiwan stock market, especially during pandemic influenza. We compare the differences in herding behavior between frontier and emerging markets by examining different industries across Vietnam and Taiwan stock market approaches. The results indicate solid evidence for investor herd configuration in the various industries of Vietnam and Taiwan. The herding impact in the industries will be greater than with the aggregate market. The different industries respond differently to influenza pandemic panics through uptrend and downtrend demonstrations. Up to 12 industries were found to have herding in Vietnam, while Taiwan had only 5 of 17 industries classified. Taiwan market, an emerging and herding-level market, has changed due to the impact of changing conditions such as epidemics, but not as strongly as in Vietnam. From there, we see that the disease is a factor that, not only creates anxiety from a health perspective, but also causes psychological instability for investors when investing in the market.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.207-218
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• 2021

The present research intends to examine the herding aspect during the COVID-19 outbreak. The study is conducted to achieve specific objectives, so the underlying sampling technique is purposive sampling. The considered data source is the Pakistan Stock Exchange (PSX). Daily stock prices of 528 listed companies in PSX have been taken from the official website of PSX from 1998 to 2021. The current study envisions investigating the herding aspects for pre-pandemic and the time covering the pandemic period. The study has also targeted ten sectors of PSX. The present study's motive is to investigate investors' herding prospects before and during the pandemic in the Pakistan Stock Exchange (PSX) and its selected sectors. Daily closing stock prices of listed companies have been collected from the official website of PSX to calculate the stock returns. The Cross-Sectional Absolute Deviation (CSAD) has been used as a herding measure. Findings revealed that herding has not been observed in PSX during both time spans and even not during the bullish and bearish trends. However, robust sectoral evidence has been observed during the pandemic. It implies that investors in PSX tend to follow the crowd irrespective of making their own decisions to avoid further losses.

• Korea Real Estate Review
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• v.28 no.1
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• pp.91-104
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• 2018

In this study, the occurrence and degree of herding behavior as a market participant behavior in a housing market were analyzed. For the analysis method, the actual sales price was applied in the CSAD (Cross-sectional Absolute Deviation) model, which has been used the most of late for herding behavior analysis. For the analysis contents, these were subdivided into region, elapsed year, size, and market condition to analyze the regionality and the internal and external factors. For the study results, first, there was no herding behavior in the entire region of Seoul. By region, herding behavior occurred in the downtown, southeast, and northwest regions, which coincided with the results of the precedent study (Ngene et al., 2017). Second, in the market analysis by elapsed year, herding behavior was captured in dilapidated dwellings. By size, herding behavior was observed in small-scale ($60m^2$ or less) apartments and in $85m^2$ or higher and less than $102m^2$ national housing units. Third, during the time of the global financial crisis, herding behavior was not observed in all the regions, whereas when the market situations were in a boom cycle, it was observed in the northwest region. These results suggest that there is a difference from the stock market, where in a period of recession, herding behavior occurs intensively with the expanding fear of incurring losses. This study is significant in that it analyzed the market participant behaviors in the behavioral economic aspects to better understand the abnormal phenomenon in a housing market, and in that it additionally provides a psychological factor - market participant behavior - in market analysis.

• Management & Information Systems Review
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• v.32 no.3
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• pp.83-105
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• 2013

There are many literatures about the herding behavior of institutional investors but there is lack of literatures about the relation among several investor groups consisting of institutional investors. So we investigate the relation among sub-institutional investor groups like bank, insurance companies, pension funds using KRX intraday trading data of 2009. As the result, we find that foreign, individual, and securities firm investors trade in the opposite direction of other investor groups including pension funds. And pension, insurance, asset management, private equity funds, other companies, government, and banks are cross-mimicking each other, so we conclude that these investors make herding behavior. In 2009 institutional investors except securities firms make herding in a short period, and insurance, asset management, pension funds and other companies make herding and self-mimicking in all period, but there is no herding and mimicking after foreign investors.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.53-59
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• 2021

This research aims to examine the model of investor herding behavior in making investment decisions in the Indonesian capital market, which is influenced by social and information impacting on the value of the Book Value Per Share (BVPS). The latest stock market conditions show that most investors make the same error pattern in making investment decisions that result in losses. The experiment involves two independent variables, namely, information about BVPS and social influence. This study used a 2×2 factorial design laboratory experimental method. Data collection was carried out through treatment of a sample of 100 individual investors listed on the Indonesia Stock Exchange. Univariate Two-Way Analysis of Variance (ANOVA) statistical tool was used to test the independent variable on the dependent variable. Research results showed that the social influence originating from expert investors is more influential than the Book Value Per Share (BVPS) information on the behavior of herding investors in making investment decisions. These findings suggest that investors know their psychological factors, thereby increasing self-control and investment analysis skills. Further research can use psychological bias and other indicators of accounting relevant information such as Earning Per Share (EPS) to test herding behavior in investment decision making in the capital market.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.845-853
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• 2021

The stock market shows the current health of an economy, and investment performance represents it. This study aims to clarify the relationship between financial behavior and investment decisions as well as its impact on investment results. Determine the influence of behavioral factors on individual investors' investment decisions and investment performance on the Vietnam stock market. The study surveyed 250 investors. The main analytical methods used are Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). Research results show that Heuristic, Prospect, Market, and Herding directly and positively affect investment decision-making. Besides, the above factors have a direct and positive effect on investment performance. In particular, the Prospect factor has the strongest influence on investment decision-making and investment performance. The major findings of this study suggested that the important role of Heuristic, Prospect, Market, and Herding on Investment Decision-making and Investment Performance. Prospect had the strongest impact on Investment decision-making (β = 0.275). Heuristic had the second strongest impact (β = 0.257), then Herding (β = 0.202), and finally Market (β = 0.189) had the weakest effect. Regarding Investment Performance, the Prospect factor has a higher degree of impact than Heuristic Herding and Market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.47 no.4
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• pp.327-337
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• 2011

This study was conducted to develop energy-efficient LED lamps with an excellent fishing performance for an anchovy scoop net by comparing the functions of 6 different lamps- incandescent, blue LED, green LED, white LED, yellow LED and red LED lamp. We used incandescent and red LED lamps only for the initial test and then excluded because those showed the lowest herding capacity. According to the result, yellow LED showed lower herding capacity in comparison with the blue, green and white one. Although the herding performance of the blue, green and white LED was similar in almost tests, herding speed to the each light was different. The anchovies were gathered into the blue LED as the speed of 39.88cm/s that was the fastest. Green LED was the second as the speed of 33.28cm/s. White LED was the slowest as the speed of 26.73cm/s. We will have field tests because we found the result that yellow LED's herding performance was better than green LED's for 5 seconds comparing after starting in some tests.