# Simulate stock prices with Geometric Brownian Motion motion with mu and signa based on 'normal' or continuous compounding?

I have written a simple script for modelling stock prices using Geometric Brownian Motion. The time series I am downloading are daily adjusted closing prices. My aim is to be able to change the prediction period and all other variables.

However, I am trying to understand the price prediction differences between calculating the mu (average returns) and sigma (variance) using a linear or standard methods versus using a log approach. The log approach consistently generates a higher predicted stock price. My code is below.

I have scavenged the internet and read whatever I could find. There are several helpful articles on this forum as well, such as here and here. But nothing really covers my question.

My question is, which approach is the most appropriate?

(I am using Python 3.)

from math import log, e
import matplotlib.pyplot as plt
from datetime import date, timedelta
import datetime

stock = 'AAPL' # Enter the name of the stock
start = '2015/1/1'

# This is the 'normal' way of calculating mu and sigma
mu = (close[-1]/close[1])** (252.0/len(close)) - 1
sigma = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)

# This is the 'log' way of calculating mu and sigma
apple['log_return'] = apple['log_price'].diff()
mu = apple.log_return.sum() -1
sigma = np.std(apple.log_price)

s0 = close[-1]
T = 18/12
delta_t = 0.001
num_reps = 1000
steps = T/delta_t
plt.figure(figsize=(15,10))
closing_prices = []
for j in range(num_reps):
price_path = [s0]
st = s0
for i in range(int(steps)):
drift = (mu - 0.5 * sigma**2) * delta_t
diffusion = sigma * np.sqrt(delta_t) * np.random.normal(0, 1)
st = st*e**(drift + diffusion)
price_path.append(st)
closing_prices.append(price_path[-1])
plt.plot(price_path)
plt.ylabel('stock price',fontsize=15)
plt.xlabel('steps',fontsize=15)
plt.axhline(y = s0, color = 'r', linestyle = '-') # print latest price TW
plt.show()

mean_end_price = round(np.mean(closing_prices),2)