Note from Towards Data Science’s editors:_ While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details._

In this article I will try to briefly explain a method for simulating stock prices, which is the result of studies related to financial modelling processes in the search to reduce exposure and risk in financial investments.

In this case, I’m utilizing the  Geometric Brownian Motion (GBM) process to emulate the random path of an asset’s returns, particularly a stock.

Understanding the model’s principles:

• According to this model, volatility remains constant through the successive trading periods in which the stock trades**.**
• Expected return has an independent behavior to the performance of the stock.
• In addition, returns are normally distributed, which means that calculated in great scale (meaning hundreds of thousands or millions), returns tend to average 0 and have a standard deviation of 1.

#python #statistics #programming #stocks #finance