We use cookies to give you the best possible experience on ResearchGate. Read our cookies policy to learn more. Here are the instructions how to enable JavaScript in your web browser. We analyze the statistics of daily price change of stock market in the framework of a statistical physics model for the collective fluctuation of stock portfolio.

Ordered phase and non-equilibrium fluctuation in stock market (PDF Download Available)

In this model the time series of price changes are coded into the sequences of up and down spins, and the Hamiltonian of the system is expressed by spin-spin interactions as in spin glass models of disordered magnetic systems. Through the analysis of Dow-Jones industrial portfolio consisting of 30 stock issues by this model, we find a non-equilibrium fluctuation mode on the point slightly below the boundary between ordered and disordered phases.

The remaining 29 modes are still in disordered phase and well described by Gibbs distribution. The variance of the fluctuation is outlined by the theoretical curve and peculiarly large in the non-equilibrium mode compared with those in the other modes remaining in ordinary phase. Citations Citations 8 References References We analyze the cross-correlation between stock returns of the constituent issues of FTSE index listed on London Stock Exchange for the two period: As a result of the day-to-day principal component analysis of the time series sampled at the 1minute time interval during the continuous auction of the daytime, we find the long range up to a couple of month auto-correlation of the maximum eigenvalue of the correlation matrix.

It also correlates with the drawdowns of each issue, which are the cumulative values of successive losses. Using those results, we propose, as a risk measurement, the probability of large drawdowns conditioned on the maximum eigenvalue threshold as a market signal which notices the intensification of the herding behavior of the prices. Statistical Mechanics and its Applications. Jun-ichi Maskawa Wataru Souma.

On Some Processes and Distributions in a Collective Model of Investors' Behavior. This article considers a model for alternative processes for securities prices and compares this model with actual return data of several securities.

The distributions of returns that appear in the model can be Gaussian as well as non-Gaussian; in particular they may have two peaks. We consider a discrete Markov chain model. This model in some aspects is similar to well-known Ising model describing ferromagnetics. Namely we consider a set of N investors, each of whom has either bullish or bearish opinion, denoted by plus or minus respectively.

The probability of a plus becoming a minus and the probability of a minus becoming a plus depends only on the bullish sentiment described as the number of bullish investors among the total of N investors.

The number of bullish investors then forms a Ordered phase and nonequilibrium fluctuation in stock market chain whose transition matrix is calculated explicitly.

The transition ordered phase and nonequilibrium fluctuation in stock market of that chain is ergodic and any initial distribution of bullish investors converges to stationary. Stationary distributions of bullish where can i buy unsalted chicken stock in this Markov chain model are similar to continuous distributions of the "theory of social imitation" of Callen and Shapero.

Distributions obtained this way can represent 3 types of market behavior: Kyrylo Shmatov Mikhail Smirnov. Multivariate Markov chain modeling for stock markets. We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation.

The time series of price changes are coded into the sequences of up and down spins according to their signs. Questrade selling stocks start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation.

The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.

Gibbs measure and Markov chain modeling for stock markets. We reviewed the recent work on Gibbs measure statistical physics model describing the collective price jumps in stock markets. We started with the study of a multivariate Markov chain model as a. The time series of price changes were coded into the sequences of up and down spins according to their signs. As the stationary state of the Markov chain, Gibbs measure was naturally derived, which formally coincides with spin glass model of disordered magnetic systems.

The linear response of the system to external fields was examined to prove the fluctuation response theorem, Finally, the analysis of actual portfolios based on this model was briefly summarized. Domain Walls, Droplets and Barriers in Two-Dimensional Ising Spin Glasses. This chapter is devoted to spin glasses, a vast class of disordered magnetic materials [1, 2, 3, 4, 5]. Spin glasses are prototypical systems widely studied in condensed matter physics and statistical mechanics.

A lot of research has been devoted to such systems, more than 10 scientific publications about spin glasses exist.

The reason for this strong interest is that spin glasses exhibit a very puzzling behavior at low temperatures, which is still not completely understood. Furthermore, the theoretical treatment of these systems has led to many advances, with considerable impact on other cross-disciplinary applications such as neural networks [6], error-correcting codes [7], or optimization problems [8].

Here, we start with a general introduction, mentioning some fundamental experiments. Then we describe the standard model for spin glasses, the Edwards—Anderson Ising model, and mention some results of theoretical treatments, in particular the opposing mean-field and droplet pictures.

The main part of this contribution is devoted to the numerical treatment of two-dimensional spin glasses with short-range interactions. These systems are special because they do not exhibit an ordered phase at low temperature. This means that the behavior for finite-size systems and small temperatures can be probably very well described by the droplet picture.

Characteristic Sign Change of the Magnetoresistance of Strongly Correlated GaAs Two-dimensional Holes. The sign of the magnetoresistance is found to be charge density dependent: Discover more publications, questions and projects in Stock Markets. Characteristic Sign Change of the Magnetoresistance of Strongly Correlated GaAs Two-dimensional Hole Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchang Correlation of coming limit price with order book in stock markets.

Phys. Rev. B 93, () - Nonequilibrium and nonhomogeneous phenomena around a first-order quantum phase transition

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