Chapter 1 - Introduction To Enhanced Financial Modeling

1.00 - Introduction (6 min.) Sample Lesson
1.01 - Setting The Scene (17 min.)
1.05 - Course Roadmap (10 min.) Quiz: 1.05 - Course Roadmap

Chapter 2 – Finance Function & Modeling Approach Used

2.01 - Finance Function Overview (9 min.)
2.02 - Finance Function Models and Roles (12 min.)
2.04 - Financial Model Outputs (15 min.)
2.07 - Building Better Financial Models (5 min.) Quiz: 2.07 - Building Better Financial Models

Chapter 3 – Probabilistic Approach To Financial Modeling

3.01 - Benefits of a Probabilistic Approach (11 min.)
3.03 - Using @RISK from Lumivero (12 min.)
3.04 - Probabilistic Model Inputs (17 min.) Quiz: 3.04 - Probabilistic Model Inputs

Chapter 4 - Building Probabilistic Financial Models

4.01 - Introduction to E&P Models (21 min.)
4.05 - Discount Rates (19 min.)
4.08 - Industry Probabilistic Inputs (6 min.) Quiz: 4.08 - Industry Probabilistic Inputs

Chapter 5 – Probabilistic Financial Model Outputs & Insights

5.01 - Introduction to Transaction Outputs (10 min.)
5.03 - M&A/A&D Analysis (27 min.) Quiz: 5.03 - M&A/A&D Analysis

Chapter 6 - Benefits of Upgrading the Finance Function

6.01 - Finance function overview (26 min.) Quiz: 6.01 - Finance function overview

Building Better Energy Companies Through Enhancing the Finance Function With a Probabilistic Approach / Chapter 1 - Introduction To Enhanced Financial Modeling

Lesson 1.00 - Introduction

Access All SAGA Wisdom Training content Subscribe

Already a member? Sign in

Access All SAGA Wisdom Training content Subscribe

Already a member? Sign in

Transcript

01. Lesson 1.00: Introduction 02. Course Introduction 03. Important Definitions

01. Lesson 1.00: Introduction

Enhancing Energy Finance Using a Probabilistic Approach, a risk and economics course for the SAGA Wisdom platform.

02. Course Introduction

Good morning. My name is Lachlan Hughson and I'm the presenter for this course. I'm also the founder of 4D Resources Advisory, LLC, an advisory firm focused on helping investors, executives, and boards build better energy and mining companies through enhancing the finance function with a probabilistic approach. Hence, also teaching this course. The purpose of this course is to highlight how valuable a probabilistic approach to finance will be across the global energy industry, in fact, all industries. As the energy industry, both traditional and renewable companies, seek better ways to meet the increasing demand for low-cost, reliable and environmentally friendly energy. It is important we all consider how the finance function can be upgraded to support the energy transition to net zero, in whatever form that ultimately takes.
While their operational colleagues have fundamentally driven the historical evolution of the industry, the finance function has barely changed from using the same tool, Excel, for the past 30 years for basically all of my career. Is there a way to enhance the finance function through upgrading this tool to fundamentally increase the quality of the decisions we make? Both the company and at industry level. And the answer is yes, through using a probabilistic approach to our financial models. This course is designed to show how the finance function can be upgraded using a probabilistic approach to its financial models, and how this will resolve many of the data input, output, and sensitivity analysis limitations currently experienced using a deterministic approach.
It will also show how to apply this probabilistic approach to a range of widely used corporate finance models such as valuation, M&A/A&D, capital raising, leverage analysis, board risk analysis, and will highlight how valuable this approach is to generating more insightful data to enhance the decision-making process. Its ultimate and critical role. And very importantly, we will also focus on how this approach can be made commercial from day one. The best way to ensure that the industry incorporates it in a far bigger and more helpful way than it does today. So who will this course be useful to relate to all finance professionals, including analysts, investors, executives and boards, and anybody else involved in the finance function? It will also be really helpful to our operational colleagues, the engineers, and scientists who were often tasked with providing data from the operating models fromreports into a form that best works for a deterministic financial model. This course will also really help all finance professionals who are trying to understand what does the finance function look like as we evolve technologically, as AI becomes more a part of our industry, and it seems to me that a probabilistic approach is at minimum, a halfway step to an even better approach, or really is the next evolutionary stage. And AI really then supports how we use a probabilistic approach even better.

03. Important Definitions

There are several definitions I would like to walk through before we start the presentation. You're going to hear me use these words continuously and so having a good understanding of what they mean I think will be very helpful. When we speak about deterministic or static financial models. That's basically a modeling approach whereby data value are included in as one value per input cell in Excel. The output then is run using these data points. But because there's no variability involved per cell the output data value is always the same, no matter how many times we run the model i.e. random chance is not involved.
The other approach, a probabilistic or a stochastic or dynamic approach, is where a range of data values is included per Excel cell such that we now have a range of values in the output cell. And when we run that analysis, the value of that output range will change marginally each time we run the model, because now random chance is involved as part of the process. How do we use this probabilistic approach? What is the algorithm behind it? And that's where acomes in. It's a tool, basically a broad class of computational algorithm that relies on repeated random sampling to obtain the numerical results we see in our outputs. And as we walk through this presentation, the difference between a deterministic approach and a probabilistic approach will become a lot more evident. Now let us move to the start of the course in the presentation. Chapter one.