Lester Leong – CFI Education – Modeling Risk with Monte Carlo Simulation

Description

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Lester Leong – CFI Education – Modeling Risk with Monte Carlo Simulation

Modeling Risk with Monte Carlo Simulation

Quantify and model uncertainty with Monte Carlo Simulation, using random sampling in Python to support better decision-making.

• Generate statistical insights by using historical data to estimate future events
• Calculate value at risk to summarize risk exposure
• Visualize the results of your simulation to better communicate your recommendations

Overview

In this course, you’ll learn how to quantify and model uncertainty by using Monte Carlo simulation.

Traditional scenario analysis relies on 2 or 3 “best case” or “worst case” situations that are rarely scientific in nature. Businesses can benefit greatly from improved modeling of risk and uncertainty, by using even basic Monte Carlo simulation.

Using this technique, we can quantify and simulate scenarios that include multiple uncertainties at the same time.

This course will start from the basics, and work through five scenarios that will help you master the basics of Monte Carlo Simulation.

Using these scenarios, you’ll learn how to quantify uncertain scenarios in a more meaningful way to help make business decisions.

Modeling Risk with Monte Carlo Simulation learning objectives

Upon completing this course, you will be able to:

• Explain the main concepts of Monte Carlo simulation
• Use historical observations to estimate the probability distributions of data
• Simulate many possible outcomes of uncertain variables using Python
• Summarize the distribution of scenarios using confidence intervals
• Interpret the output of Monte Carlo simulation results and use it to guide business decisions

Who should take this course?

Business Intelligence derives value from descriptive, backward-looking metrics. To provide the next level of value we must start to consider future scenarios. Modeling uncertainty and scenarios is a key part of this forward-looking skillset, and this Monte Carlo course is a perfect introduction to that world.

What you’ll learn

Monte Carlo Simulation Introduction
Course Introduction
Learning Objectives
Download Course Materials
Monte Carlo Simulation Overview
Random Sampling and the Law of Large Numbers
Monte Carlo Simulation Process
Distributions
Monte Carlo Simulation Applications

Coin Flipping Example
Coin Flipping Simulation Overview
Coin Flipping Simulation in Practice
Coin flipping Simulation in Excel Part 1
Coin flipping Simulation in Excel Part 2
Coin flipping Simulation in Python Part 1
Coin flipping Simulation in Python Part 2
Coin Flipping Simulation Recap

Stock Price Prediction
Stock Price Prediction Case Overview
Daily Returns
Stock Price Monte Carlo Overview in Python
Extract Stock Data
Calculate Historical Returns and Statistical Measures
Simulate Future Daily Returns
Including Drift
Examine Scenarios and the Probability Distribution
Stock Price Prediction Case Recap

Value at Risk Assessment
Value at Risk Case Overview
Parametric Simulation
Set Up Stock Parameters
Calculate Investment Returns
Identify Value at Risk
Value at Risk Case Recap

Net Income Forecast
Net Income Forecast Case Overview
Simulate Sales, COGS, and Net Income
Examine Net Profit Simulation Results
Net Income Forecast Recap

Capital Investment (NPV) Forecasting
Net Present Value (NPV) and Free Cash Flow (FCF)
NPV Case Overview – Assumptions
NPV Case Overview – Simulations
Set Up the Financial Assumptions
Simulate Variables
Simulate Sales
Set Up All Financial Items
Simulate NPV
Analyze the Profitability of the Investment
Run the Model with Different Assumptions
NPV Case Recap
Course Summary

Qualified Assessment
Qualified Assessment+;