Sierpinski Lattice Percolation Stock Price Model And Predictive Analysis

Econophysics,widely concerned by scholars,can be described as another revolution in the history of financial research,which deepens the comprehension of the financial securities market.The purpose of this paper is to construct a financial pricing model to analyze the complexity of financial market volatility and provide the forecast reference to a certain extent.In this paper,the statistical physical model is used in financial field as an interdisciplinary tool,which constructs financial pricing model on fractal carpet by applying filtration theory and analyzes the complexity of real market and simulation data,to explore financial market characteristics,verify the effectiveness of this model,evaluate the stochastic interactive financial pricing model practically and provide relevant forecasting guidance subsequently.This paper firstly introduces the research background of econophysics and discusses the significance of this paper from the perspective of financial researchers and financial market participants,elaborating the scholars related research and demonstrating the innovative content from model building to statistic selection to statistical analysis.Secondly,this paper introduces and evaluates all kinds of commonly used forecasting methods and discusses the common problems that may exist in economic forecasting to provide the readers with reference.The basic and innovation statistics are introduced and compared in detail.Thirdly,the theoretical knowledge needed to build the model is described,viz.agent-based financial market theory,percolation theory and fractal market theory.And the applicable volatility duration statistic is selected before constructing the certain model.After improving the initial fixed setting of the information dissemination mechanism,a specific financial pricing model is established and the relevant parameters in the model are discussed and the innovation points in the model are further elaborated.Fourthly,after constructing the model,fluctuation complexity analysis of real market and simulation data is performed.The first analytical method is LZC analysis,which characterized as a quantitative calculation of the complexity of the financial market volatility duration.This paper makes the complexity analysis of major financial markets in the world,discovers the similar complexity between China's financial markets and simulation data and determinates the parameters by fitting the situation.The second analytical method is MF-DCCA analysis.This paper inspects the multiple fractal of financial market and simulation data in China and proves that there are multiple tractals in the real market and simulated data market through calculation and comparison of some multiple indicators.The complexity analysis above demonstrates the validity of the the price model.Finally,taking into account the practical value of the study,this paper also conducts a sample test on the practicableness and obtains a relatively acceptable result and then provides 30 predictions based on the current economic situation.Although actual forecast requires the users to combine the market situation with their own judgments to set the initial values and adjust the parameters in the model,such forecast is intended to provide a reference for researchers,participants and regulators in financial markets.