Download PDFOpen PDF in browserTime Dependence of Stock Price Studied by a Statistical Physics ApproachEasyChair Preprint 54274 pages•Date: April 29, 2021AbstractIn the present work, we study by Monte Carlo simulations the evolution during the time of the price in a commodity market by examining the effects of several parameters: the majority of the neighbors, the market atmosphere, the variation of the price and some specific measure applied at a given time. Each agent is represented by a spin having a number of discrete states $q$ or continuous states, describing the tendency of the agent for buying or selling. The market atmosphere is represented by a parameter $T$ which plays the role of the temperature in physics: low $T$ corresponds to a calm market, high $T$ to a turbulent one. We show that there is a critical value of $T$, say $T_c$, where strong fluctuations between individual states lead to a disordered situation in which there is no majority: the numbers of sellers and buyers are equal, namely the market clearing. We will show in particular that a specific measure taken by the government or an economic organization during a short lapse of time to boost or to lower the market price can have a longlasting effect. Meanfield theory is also used to study the time dependence of the stock price. Keyphrases: Econophysics, Meanfield theory, Monte Carlo simulations, Price variatio, Statistical physics models, market dynamics
