Pick two stocks, one from a high growth industry and one from a low growth industry. The two stocks do not have to be related to each other. The goal of this assignment is to back-test a stochastic process for stock price using Monte Carlo simulation. Here is a basic set of instructions for the assignment. Follow the approach detailed in class. Using the geometric Brownian motion model as the stochastic process describing stock price behavior, simulate a path for the price of your chosen stocks at the end September 2, 2022. Use 10 observations based on the following time intervals: 1. Daily prices 2. Weekly prices 3. Monthly prices To estimate the input parameters for the model, use daily closing stock price for 250 trading days ending before the start of your time interval. For example, when you use daily time intervals, you will use closing stock prices ending on August 19, 2022 this will give you 10 daily prices ending on September 2, 2022. Using Monte Carlo simulation, with 100 trials, compute the probability of the stock price increasing by 10% as of the time period ending September 2, 2022 (increasing during the prior 10 days/weeks/months). Produce a one-page, single spaced, typed report, using the following structure: 1. What do you find when you compare the results for 1, 2, and 3? 2. What do you find when you compare the results of one stock versus that of the other stock? 3. How do the results compare against the actual stock price observed at the end of trading on September 2, 2022? 4. What can we conclude from your findings?