The authors have declared that no competing interests exist. Conceived and designed the experiments: Minsung Kim Minki Kim. Contribution to writing manuscript:

Of many crucial models, the main concentration is on the herding multiagent model [4, 5] and the related percolation models[6, 7], the democracy and dictatorship model [8], the crowd- anticrowd theory, the self-organized dynamical model [9], the cut and paste model, and the fragmentation and coagulation model [10].

One of microscopic models in the self-organized phenomena is the herding model [11, 12], in which some degrees of coordina- tion among a group of agents share the same information or the same rumor and make a common decision in order to create and produce the returns. Directly, we can discuss the dynamical herd bechavior via analyzing and simulating the real tick data.

There are three important reasons to be influenced into the herd behavior [13]: First, it exists the crash model that the herds may be occured by the biased information between investors.

Second, the return structure of fund managers may be sensitive to the herd behavior, since bank and stock company influence powerfully to investors. Lastly, fund manager and market analysist may play a crucial role to essentially determine the investment, in order to maintain their reputation and credit.

Particularly, it is of interest for the herd model to search for the bubbles and crashes in econophysical system.

The probability distribution of returns shows a power-law behavior for the herding parameter below a critical value, but the financial crashes yields an increase in which the probability of large returns exists for the herding parameter larger than the critical value.

Futhermore, it is well known that the distribution of normalized returns has almost the form of the fat-tailed distribution [14] and a crossover toward the Gaussian distribution in financial markets. The theoretical and numerical analyses for the volume of bond futures transacted at Korean futures exchange market have presented in the previous work [15].

This treated mainly with the number of transactions for two different delivery dates and found the decay functions for survival probabilities [16] in the Korean bond futures. In a recent paper [18], Skjeltorp has shown that there exists the persistence caused by long-memory in the time series on Norwegian and US stock markets.

The numerical analyses based on multifractal Hurst exponent and the price-price correlation function have used for the long-run memory effects. It is found that the form of the probability distribution of the normalized return leads to the Lorentz distribution rather than the Gaussian distribution [17].

In this paper, we investigate the dynamical herding behavior for the yen-dollar exchange rate in Japanese financial market.

Our obtained result will compared with other numerical calculation. First, we introduce the yen-dollar exchange rate for two delivery dates of tick data: One analyzes minutely tick data for the period 1st March - 8th Marchwhile the other analyzes daily tick data for the period 4th January - 30th June We show minutely time series of the yen-dollar exchange rate p t in Fig.

To describe the averaged distribution of cluster, let us suppose three return states com- posed by N agents, i. Assuming that it belongs to the same cluster between a group of agents sharing the same information and making a common decision, the active states of transaction can be represented by vertices in a network having links of time series.

Here P s means the probability of the cluster s for selling and buying herds. The charateristic feature of herds can be well described by the probability distribution of returns P R. The herding parameter is incorporated into the price return, whose elements are usually the random numbers proportional to the real data in Fig.

Lastly, let us calculate the distribution of normalized returns. As the time step takes the larger value, the probability distribution as a function of R t is expected to approach to the Gaussian form.

In summary, we have investigated the dynamical herding behavior for the yen-dollar exchange rate in Japanese financial market.Research Papers Does herding affect volatility? Implications for the Spanish stock market Does the stock market drive herd behavior in commodity futures markets?.

International Review of Financial Analysis 39, Journal of International Financial Markets, Institutions and Money 23, pages Transmission of Information and Herd Behavior: an Application to Financial Markets modelled by an evolving network, and herd behavior to account for the observed fat-tail distributionin returns of ﬁnancial-price data.

The only parameter of market price P(t), Fig. 1b shows the corresponding returns R(t), while the evolution of the.

What we have to remember is the financial market is a complex of rational and irrational behavior and we can barely categorize them before the disaster happens.

We have to be prepared of the consequence the herd behavior and be rational when the irrationality happens. Herd Behavior in Financial Markets: A Field Experiment with Financial Market In contrast to these papers, however, we assume that the market maker can post only one price, i.e., it is not allowed to post di ﬀerent prices at which traders can buy (the ask price) or sell (the bid price).

HERD BEHAVIOR IN FINANCIAL MARKETS. compensation scheme selected by an employer would seek to maximize the employer’s profits rather than society’s welfare.

We study herd behavior in a laboratory?nancial market with?- nancial market professionals. An important novelty of the experi- mental design is the use of a strategy-like method.

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Herd Behavior in Financial Markets, Vol. 6, No. 6 () | International Business Research | CCSE