FINITE-RANGE CONTACT PROCESS ON THE MARKET RETURN INTERVALS DISTRIBUTIONS

2010 ◽  
Vol 13 (05) ◽  
pp. 643-657 ◽  
Author(s):  
JUNHUAN ZHANG ◽  
JUN WANG ◽  
JIGUANG SHAO
Keyword(s):  
Stock Price ◽  
Statistical Physics ◽  
Scaling Function ◽  
Contact Process ◽  
Composite Index ◽  
Finite Range ◽  
Price Model ◽  
Simulation Data ◽  
The Mean ◽  
The Scaling Function

Stochastic system is applied to describe and investigate the fluctuations of stock price changes in a stock market, and a stock price model is developed by the finite-range contact process of the statistical physics systems. In this paper, the scaling behaviors of the return intervals for SSE Composite Index (SSE) and the simulation data of the model are investigated and compared. The database is from the index of SSE in the 6-year period for every 5 minutes, and the simulation data is from the finite-range contact model for different values of the range R. For different values of threshold θ, the statistical analysis shows that the probability density function Pθ(τ) of the return intervals τ for both SSE and the simulation data have similar scaling form, that is [Formula: see text] ([Formula: see text] is the mean return interval), where the scaling function h(x) can be approximately fitted by the function h(x) = ωe-a(ln x)γ, and ω, a, γ are three parameters. Further, with different values of R and θ, the statistical comparison of SSE Composite Index and simulation data are given.

STATISTICAL ANALYSIS BY STATISTICAL PHYSICS MODEL FOR THE STOCK MARKETS

2009 ◽  
Vol 20 (10) ◽  
pp. 1547-1562 ◽  
Author(s):  
TIANSONG WANG ◽  
JUN WANG ◽  
BINGLI FAN
Keyword(s):  
Stock Markets ◽  
Stock Price ◽  
Statistical Physics ◽  
Stock Exchange ◽  
Contact Process ◽  
Composite Index ◽  
Statistical Properties ◽  
Kolmogorov Smirnov ◽  
Physics Model ◽  
Power Law Distributions

A new stochastic stock price model of stock markets based on the contact process of the statistical physics systems is presented in this paper, where the contact model is a continuous time Markov process, one interpretation of this model is as a model for the spread of an infection. Through this model, the statistical properties of Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) are studied. In the present paper, the data of SSE Composite Index and the data of SZSE Component Index are analyzed, and the corresponding simulation is made by the computer computation. Further, we investigate the statistical properties, fat-tail phenomena, the power-law distributions, and the long memory of returns for these indices. The techniques of skewness–kurtosis test, Kolmogorov–Smirnov test, and R/S analysis are applied to study the fluctuation characters of the stock price returns.

Multiscale entropy and asynchronies of percolation-based price model and Chinese stock market

2021 ◽  
pp. 2150073
Author(s):  
Hongli Niu ◽  
Yunfan Lu
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Statistical Physics ◽  
Percolation Cluster ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Chinese Stock Market ◽  
Two Dimensional ◽  
Price Model ◽  
Simulation Data

In recent years, the concept of entropy is widely used to measure the degree of uncertainty or complexity of dynamics system. In our work, we utilize the composite multiscale entropy (CMSE) and the composite multiscale cross-sample entropy (CMSCE) which are two modified algorithms of SampEn and Cross-SampEn by considering multiscale factors, to, respectively, investigate the multiscale complexities and asynchronies (correlations) in the Chinese stock market (SSZ, SZSE and HSI) as well as in our established financial stock price model. The price model is given based on a greatly important statistical physics system, the two-dimensional continuum percolation system. In the model, the fluctuations of stock price changes are assumed to be attributed to the market information interactions among the traders, and the percolation cluster is taken to represent the traders holding the same investment attitude. The empirical CMSE and CMSCE results display and meanwhile make comparisons of abundant complexities and correlations properties about Chinese stock indices and simulation data on both overall and the upwards, downwards trend of their returns.

NEW AMY AND DELPHI MULTIPLICITY DATA AND THE LOGNORMAL DISTRIBUTION

1991 ◽  
Vol 06 (03) ◽  
pp. 245-257 ◽  
Author(s):  
R. SZWED ◽  
G. WROCHNA ◽  
A.K. WRÓBLEWSKI
Keyword(s):  
Energy Dependence ◽  
Lognormal Distribution ◽  
Scaling Function ◽  
Average Multiplicity ◽  
Linear Functions ◽  
The Mean ◽  
Multiplicity Distributions ◽  
The Scaling Function ◽  
Skewness And Kurtosis

Multiplicity distributions for e+e−→ hadrons recently reported by the AMY and DELPHI collaborations are compared with the data obtained at lower energies. It is proven that the new data obey the KNO-G scaling and the scaling function can be described by the lognormal distribution. The dispersions are linear functions of the mean as for the data measured at lower energies and the standardized moments (such as skewness and kurtosis) are independent of the energy. The energy dependence of the average multiplicity is described by <nch>=β sα−1.

scholarly journals Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wuyang Cheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Statistical Physics ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Contact Process ◽  
Composite Index ◽  
Return Time ◽  
Time Series Model ◽  
Financial Time

We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI) and Hang Seng Index (HSI) are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

GENESIS OF THE LOGNORMAL MULTIPLICITY DISTRIBUTION IN THE e+e− COLLISIONS AND OTHER STOCHASTIC PROCESSES

1990 ◽  
Vol 05 (23) ◽  
pp. 1851-1869 ◽  
Author(s):  
R. SZWED ◽  
G. WROCHNA ◽  
A. K. WRÓBLEWSKI
Keyword(s):  
Limit Theorem ◽  
Multiplicity Distribution ◽  
Scaling Function ◽  
Average Multiplicity ◽  
Linear Functions ◽  
Scale Invariant ◽  
The Central Limit Theorem ◽  
The Mean ◽  
Multiplicity Distributions ◽  
The Scaling Function

It has been observed that the e+e− multiplicity distributions exhibit the following properties: the dispersions are linear functions of the mean and the distributions obey the KNO-G scaling with the scaling function of the lognormal shape. In this paper the scale invariant branching is assumed as a mechanism within which all these properties could be derived. It is shown that the lognormal shape of the scaling function can be obtained within proposed mechanism by using the generalization of the Central Limit Theorem. The dependence of the average multiplicity on energy is also derived within the postulated framework. It is also shown that many other phenomena encountered in nature have the similar statistical properties.

Directed Polymers in Restricted Geometries

1992 ◽  
Vol 290 ◽  
Author(s):  
G. Zumofens ◽  
J. Klafter ◽  
A. Blumen
Keyword(s):  
Scaling Function ◽  
Random Potential ◽  
Potential Fields ◽  
Directed Polymers ◽  
Transverse Displacement ◽  
Fractal Nature ◽  
One Dimensional ◽  
Characteristic Exponents ◽  
The Mean ◽  
The Scaling Function

AbstractWe study numerically directed polymers in random potential fields for one-dimensional and fractal substrates. For fractal substrates the time evolution of the mean transverse fluctuations depends besides on the randomness of the potential also on the fractal nature of the substrate. The two effects enter in a subordinated way, i.e. the corresponding characteristic exponents due to the potential and the substrate combine multiplicatively. For a one-dimensional substrate the propagator P(x, t), the probability distribution of the transverse displacement x(t), follows the scaling form P(x, t) ∼ 〈x2(t)〉-1/2f (ξ), where ξ is the scaling variable ξ = x/〈x2(t)〉1/2. The numerical results support the scaling function f (ξ) ∼ exp (-cξδ) with δ > 2 which indicates an “enhanced” Gaussian behavior. These results are compared with those of a related “toy model”.

scholarly journals Fluctuations of stock price model by statistical physics systems

2010 ◽  
Vol 51 (5-6) ◽  
pp. 431-440 ◽  
Author(s):  
Jun Wang ◽  
Qiuyuan Wang ◽  
Jiguang Shao
Keyword(s):  
Stock Price ◽  
Statistical Physics ◽  
Price Model

scholarly journals Simulation and Statistical Analysis of Market Return Fluctuation by Zipf Method

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Yalong Guo ◽  
Jun Wang
Keyword(s):  
Statistical Analysis ◽  
Stock Markets ◽  
Stock Price ◽  
Composite Index ◽  
Price Model ◽  
Financial Model ◽  
Stock Indices ◽  
Global Stock Markets ◽  
Global Stock ◽  
Different Time Scales

We investigate the fluctuation behaviors of financial stock markets by Zipf analysis. In the present paper, the empirical research is made to describe ensembles and specifics of stock price returns for global stock indices, and the corresponding Zipf distributions are given. First we study the fluctuation behavior of global stock markets by(m,k)-Zipf method. Then we consider a dynamic stock price model, and we analyze the absolute frequencies and the relative frequencies for this financial model. Further, the Zipf distributions of returns for SSE Composite Index are studied for different time scales.

Analisis Kinerja Keuangan Perusahaan dengan Menggunakan Pendekatan Economic Value Added (EVA) dan Market Value Added (MVA) (Studi Kasus pada PT Astra International, Tbk. Periode Tahun 20082012)

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdul Hamid
Keyword(s):  
Financial Performance ◽  
Financial Services ◽  
Stock Price ◽  
Economic Value ◽  
Composite Index ◽  
Value Added ◽  
Secondary Data ◽  
Study Approach ◽  
Market Value Added ◽  
The Interest Rate

This study is a qualitative study using a case study approach to the PT. Astra International, Tbk. The object of this research is PT. Astra International, Tbk. PT. Astra International, Tbk is a company engaged in six business sectors, namely: automotive,financial services, heavy equipment, mining and energy, agribusiness, information technology, infrastructure and logistics. Researchers chose PT. Astra International, Tbk as research objects due in the year 2012, PT. Astra International, Tbk managed to rank first in the list of 100 Best Companies to Go Public by the 2011 financial performance of Fortune magazines Indonesia. The data used in this research is secondary data, the financial statements. Astra International, Tbk 20082012. Other secondary data used is the interest rate of Bank Indonesia Certificates (SBI), the Jakarta Composite Index (JCI), and thecompanys stock price began the year 20082012. This study aims to determine the companys financial performance by the use of EVA and MVA approach, therefore the data analysis technique used is the EVA and MVA. Based on the value EVA of the year 2008 2012, PT. Astra International, Tbk has good financial performance that managed to meet the expectations of the company and the investors. Based on the value of MVA during the years 20082012, PT. Astra International, Tbk managed to create wealth and prosperity for companies and investors. It concluded that financial performance. AstraInternational, Tbk for five years was satisfactory.

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