Finance and Risk Analysis Skills - @RISK Edition (16hrs Remote Online)
OVERVIEW
Forecasting, simulation, 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 Excel based 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,
- Forecast market conditions probabilistically
- Etc.
TARGET AUDIENCE
- This workshop is designed for both the beginner and advanced financial analyst who want to learn how to simulate and stress test their forecasts and projections using @RISK.
- Banking, Credit & Insurance Professionals
- Forecasting and Planning Professionals
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)
BENEFITS
At the end of this 2 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 through the understanding of the factors driving financial performance.
- 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
