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