Financial Modelling for mining projects including project evaluation and finance.
Mining projects face many unique challenges: high capital costs, long-term projects, and high variability in commodity prices.
This course focuses on providing delegates with an understanding of both the principles of financial modelling for mining projects and the practical application of these to real-world situations through the use of Excel.
The course then builds upon the principles and practices learned to apply these to build sophisticated analytical models that are robust, flexible and user-friendly.
Duration Learning Credits
4 days – 32 hours of 32 PDU’s
Session
Public Classroom Pricing:
Early Bird Price: AUD 3555.00
Regular Price: AUD 3999.00
Instructor-Led Virtual Live Pricing:
Early Bird Price: AUD 2995.00
Regular Price: AUD 3555.00
Private Group/ In-House Learning:
Have a group of 3 or more people?
Register yourself with a special pricing and
request the training exclusively
Day 1: Advanced Excel
Excel is the tool of choice for most financial modellers so before we start building financial models, it’s critical that we have a good understanding of the key functions and tools in Excel that can help us build models that are robust, easy-to-use, and flexible.
Here are just some of the areas we cover in our in-depth, hands-on workshop:
● Working with large data sets
● Summarising and aggregating data
● Extracting data that matches criteria using flexible formulas
● Error handling
● Ways of improving structure
● Techniques for keeping formulas constant yet flexible
● Combining and splitting cells according to the content they contain
● Advanced techniques for creating automatically expanding charts and ranges
● Introduction to macros & when to use these
We then apply and practise these functions during the remainder of the course.
3-4 Days: Financial Modelling and Project Evaluation
Participants will build up a full financial model for a mining project from scratch. The example used is an iron ore project however the same financial modelling principles can be applied to any mining project model.
As such, this course is designed to be as close to real life as possible.
Lastly, the course describes how to both detect and prevent errors from occurring.
Part 1: Financial Modelling Principles & Practices
Introduction to Financial Modelling
● What is a model?
● Spreadsheet models
● Types of mining sector models
Principles of Financial Modelling
● Steps in model building
● Best practice guidelines and spreadsheet modelling standards
● Designing for flexibility and scalability
● Designing for consistency
● Designing for usability and functionality
● Designing checks and balances
● Optimising your environment for rapid financial modelling
Part 2: Applications of Financial Modelling
Principles of Risk and Return
● Volatility
● Capital Structures
● Weighted Average Cost of Capital (WACC)
● Cost of Equity Capital: What does this mean and how do you calculate it?
Project Evaluation: Theory and Application
● Recap of mining accounting principles
● Role of project evaluation: how do you evaluate a project? NPV, IRR, payback, VIR
● Approaches to project evaluation & feasibility studies: discover different alternatives
● Why free cash flow is critical for project evaluation and how to calculate this
● Economic limit & life of mine calculation
● Discover different techniques for forecasting future performance
● Learn how to model capital expenditure and include this in the financial model
● Learn new techniques for modelling uncertainty and including this in your calculations
● Learn the principles of discounted cash flow modelling and why this is so popular in financial modelling
● Sensitivity analysis and an introduction to Monte Carlo simulation and stochastic modelling: how and when would these techniques be most useful
● Introduction to alternative project evaluation methodologies such as real options
● Interpreting & presenting the results of your analysis to key decision-makers
Case Study: Income Model
Design and build an income statement using historical data and assumptions about future performance.
Case Study: 3-way Financial Models
Building on the income model developed, design and build a forecast that includes the balance sheet and cash flow. Learn how to resolve circular references in interest calculations.
Case Study: Financial Ratios
Review the financial performance of a company using financial ratios.
Case Study: Using Historical data to Forecast Future Performance
Use a variety of Excel functionality and techniques to forecast future performance based on historical data.
Case Study: Company Valuation
Perform a discounted cash flow valuation of a sample business.
Case Study: Scenarios & Sensitivities
Identify key risk areas and then incorporate sensitivity factors for these. Build a sophisticated scenario manager to run multiple scenarios and highlight the results.
Case Study: Monte Carlo Simulation & Stochastic Modelling
Incorporate Monte Carlo simulation and techniques into the financial models to simulate multiple sources of uncertainty.
Case Study: Model Optimisation
Use Excel’s tools to optimise the models results and perform breakeven analysis.
Part 3: Control of Financial Models
Financial Model Auditing/Review
● Why audit?
● Excel’s auditing tools
● Automated testing tools: role, purpose and limitations, demonstration
● Model documentation
Financial Modelling Software
● Overview: purpose, advantages, disadvantages, demonstration
Related Courses
Financial Modelling for Oil & Gas (4 days).
Excel VBA Training Course (3 days)
● Business & Finance Analysts
● Investment & Corporate Bankers
● Finance staff
● Mine Accountants
● Project Managers
● Business Development Managers
● Mining Engineers
● Mine Managers
● To provide participants with a good working knowledge of the most useful functions in Excel for financial modelling in the mining industry.
● To provide participants with a sound understanding of mining financial modelling and project evaluation concepts.
● To provide participants with experience in designing solutions to real-world financial modelling challenges.