Time Dependence of Stock Price Studied by a Statistical Physics Approach

EasyChair Preprint no. 5427

Abstract

In 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 long-lasting effect. Mean-field theory is also used to study the time dependence of the stock price.

Keyphrases: Econophysics, market dynamics, Mean-field theory, Monte Carlo simulations, Price variatio, Statistical physics models

@Booklet{EasyChair:5427,
  author = {Hung T. Diep and Gabriel Desgranges},
  title = {Time Dependence of Stock Price Studied by a Statistical Physics Approach},
  howpublished = {EasyChair Preprint no. 5427},

  year = {EasyChair, 2021}}