Advanced Finance and Risk Analysis Skills - @RISK Edition (24hrs Remote Online)
OVERVIEW
Forecasting, simulation, real options and optimization techniques are increasingly popular tools that provide Financial Analysts with analytic power well beyond the traditional toolset. Through workshops, case examples and practical @RISK learning models, participants will actively learn and practice essential skills and techniques to obtain accurate estimates from subject matter experts, test & validate planning assumptions, leverage historical data in planning/estimating scenarios, assign a probability of realizing an objective, maximize benefits using optimization, etc.
This workshop is designed for both the beginner and advanced financial analyst and we will fully cover the A to Z of applying risk analysis techniques. – A must for executives, managers, consultants and analysts who can’t afford to be wrong!
WORKSHOP CONTENT
MODULE 1 – ENHANCING THE MODELING PROCESS WITH SIMULATION
Why is Risk Analysis SO important in today’s world?
Challenges in corporate finance
The flaw of averages
Understanding risk analysis key concepts and definitions
Workshop: What does 90% confidence really mean?
Modeling vs. Simulation
Overview and history of Monte-Carlo Simulation
Advantages and Disadvantages of simulation
How and Where simulation and risk analysis can have a positive impact on the organization
The Modeling Process
Understanding how the modeling process works in the business
Obtaining and using historical or published data
Discussion on using the Monte-Carlo Method for properly scoping the need, building assumptions and establishing model constraints with Subject Matter Experts
Workshop: Using risk analysis to develop a New Compensation Model
Using and Configuring @RISK for Risk Analysis: Toolbar, Basic Terminology, Sampling (Latin HyperCube vs. Monte-Carlo), Reporting and Data Extraction
MODULE 2 – BUILDING AND RUNNING MODELS
Essential Statistics For Risk Modeling
Workshop: Understanding how probabilities work with the DICE model
Basic probability statistics (Mean, Standard Deviation, Kurtosis, Skewness)
Overview of principal distributions and when to use them
How to analyze existing models to apply risk analysis: Tornado Charts and One Way Sensitivity Analysis to identify inputs with the greatest impact
Working with Distributions and Model Inputs
Best practices for defining model inputs in Excel and @RISK
Making sure your model behaves correctly using correlation
Workshop: Portfolio Allocation Model
Defining, Analyzing and Communicating results to the business
Setting up model outputs and visualizing results and charts (Sensitivity, Forecasts, Assumptions and Overlays)
Establishing Confidence Intervals and configuring precision control to optimize the number of trials
Generating @RISK Reports
Techniques to effectively and simply communicate your analysis to your peers, clients and superiors
Question handling
Risk identification and Assessment using Simulation
Interpreting Forecasts and Sensitivity Analysis
Identifying Risks and Potential Mitigation Strategies
Model Calibration using Risk Management Mitigation Solutions
Workshop: ROI Analysis and business growth analysis using historical data to build ROI Scenarios and compare them using Overlay Charts (DuPont Model)
MODULE 3 – INCORPORATING HISTORICAL DATA AND TRENDS INTO YOUR MODEL
Correlation and Regression
What are correlations and their impact on results
Overview of regression and its basic applications, including LogReturns
Workshop: How to calculate rank correlation and use it to correlate model assumptions
Workshop: Correlation Matrices
Aggregate Assumptions
Data/Distribution Fitting
How to fit a distribution using historical data
Analyzing fit results and selecting the RIGHT distribution for both univariate and multivariate data.
Time-Series Forecasting
Overview of the components and applications of time-series forecasting
Time-series projections using @RISK to easily incorporate Seasonality, Smoothing algorithms, Growth Projections using historical data
Workshop: Projecting Next Year’s Sales
MODULE 4 – OPTIMIZATION AND SCENARIO MODELING
Simulation Optimization
Introduction to Simulation - Optimization with @RISK
Everyday Optimization applications and examples
How does Simulation Optimization Work
Portfolio Optimization Techniques : With the help of several integrated financial models, this workshop will provide financial analysts with a complete understanding of why, where and how to apply spreadsheet forecasting, simulation, real options and optimization within their analyses.
Project Portfolio Selection: Use OptQuest to pick the best projects based on Organizational Budget Constraints
Portfolio & Resource Allocation Optimization: Allocate resources or budgets among various investments to maximize NPV or ROI or minimize risk or expense.
Modeling Efficient Frontier Analysis to optimize risk against benefit for projects and investments. (Portfolio Allocation)
Decision Tables to compare complex 2 dimensional problems
Workshop: Inventory Options
Creating 3D solution plots
MODULE 5 – ADVANCED DECISION MODELING TECHNIQUES
Decisions under uncertainty:
Overview of Bayes’ Theorem and its analytical applications
Bayes applied to medical testing
Workshop: How to improve profitability with additional information
Bayes applied to Quality Control
Value of Information
How much should you invest to collect additional information using Hubbard’s VOI approach with a UNIFORM rule.
Perfect versus imperfect information
Using VOI to constrain or optimize portfolios
Decision Trees
Overview of decisions trees
Methodology for documenting strategic options using decision trees
Conventional NPV versus Expanded NPV
Workshop: Using Bayes Theorem and Decision Trees to decide whether to hire a reserves expert (oil and gas / mining) or not and the decision’s impact on NPV
Real Options Analysis
Overview or Real Options Theory
Discounting Assets over time using lattices
Workshop: Integrated DCF and valuation using a 2 Phased Sequential Real Option
BENEFITS
At the end of this 3 day workshop, participants will be able to:
Understand and apply Monte-Carlo simulation and optimization in their day-to-day activities
Make better and more informed decisions
Quickly build effective models or customize existing ones with @RISK
Apply simple and effective @RISK Risk analysis techniques
Pick and manage project more effectively
Use historical data to forecast future revenues and how to use those forecasts to create better predictive Discounted Cash Flow (DCF) models
Perform a DCF analysis and determine ROI on a specific project using Monte Carlo simulation to identify and evaluate risk and uncertainty in your model
Apply real options techniques to accurately account for the impact of positive uncertainty in estimating your project’s value
Use a portfolio optimization model where the efficient allocation of resources is analyzed to improve the quality of your business decisions.