Introduction to Financial Modelling

Principles of Excel as a Modelling Tool

Chapter 2: Excel Foundations

8 Topics | 1 Quiz
Chapter 3: Sensitivity Analysis

10 Topics | 1 Quiz
Excel Operations, Structures and Short-Cuts

Chapter 4: Operations and Short-cuts (I)

6 Topics | 1 Quiz
Chapter 5: Common Structures within Financial Models (I) – Arithmetic Based

7 Topics | 2 Quizzes
Chapter 6: Operations and Short-cuts (II)

15 Topics | 1 Quiz
Introduction to Excel Functions

Chapter 10: Dynamic Arrays and Array Functions

4 Topics | 1 Quiz
Applications of Lookup and Reference Functions

Chapter 14: INDEX and XLOOKUPs

5 Topics | 1 Quiz
Planning and Building Models for Optimal Decision Making

Chapter 17: From Planning to Practice

16 Topics
The examples shown in the previous sections were very simple. Nevertheless, they illustrate some of the underlying skills that a well-rounded financial modeller needs. These include:

- The ability to analyse the business context.
- Some knowledge of core aspects of decision sciences.
- A solid understanding of general economics, and of financial measures and criteria.
- A good knowledge of the specific area of application, or of the behaviour and relationships in the situation.
- Sufficient knowledge of Excel functionality and functions.
- The ability to use Excel best practices.
- The skills to design models around sensitivity analysis principles.
- The ability to manipulate data, conduct statistical analysis and integrate datasets.
- A knowledge of automation and the use of algorithms, VBA and macros.

These are briefly described in more detail below, whilst the materials of the overall CertFM Program is structured to develop these skills and knowledge in an effective and focussed way.

Decision science is the study of the ways that decisions are made. It is a very large topic, and in a sense modelling is simply a sub-set of this (as the aim of models is to support the development and selection of appropriate courses of action).

From a modelling perspective, key aspects of decision sciences that one needs knowledge of include the processes by which decisions can be made, the challenges to making good decisions, and same ways to deal with such challenges. An understanding of these can help to ensure that the right models are built and which provide output that is as valuable as possible to the final decision process.

Decision sciences also includes the criteria by which decisions can be made. In general, this requires a knowledge of economic criteria and the concepts behind these.

There is also an important role for sensitivity and scenario analysis, since decision-makers will typically want to consider these within the process. More generally, the presence of risks/uncertainties may need to be taken into account. Further, there may be optimisation aspects (in terms of making the best decision from several or many possible ones): At least in theory, the presence of risk/uncertainty and optimisation possibilities are inherent in almost all situations, so in a sense their inclusion in the analysis should be a fundamental part of “trying to model reality”. In practice, such factors may be complex to treat in full detail, and decision-makers may wish for simpler forms of analysis (such as sensitivity analysis). However, when de-emphasising risks/uncertainties or optimisation aspects of the analysis, one needs to take care to not oversimplify the analysis either (“as simple as possible, but no simpler).

(On the assumption that the modelling platform is Excel), the knowledge of Excel needs to be sufficient for the situation being addressed: Although quite limited Excel is needed for simple models, very quickly it often becomes clear that a wide knowledge allows for better models to be built and for more complex situations to be dealt with. In the CertFM Program, the essential aspects of of Excel are covered in the course I.2 Essentials of Excel for Modelling, whilst a huge range of topics are covered at the first relevant points throughout the other courses.

In addition to knowing what is possible with Excel, one also needs to use this knowledge to create models which are “as simple as possible, but no simpler”. This includes the appropriate design and layout of the model and its logic flow, as well as the disciplined and skilled us of formatting, choice of functions, and so on. The use of best practices does not (in principle) change the numerical results of a model (although they reduce the chance of error). However, they enhance transparency and thereby make a model more credible and ultimately more useful in decision support. Throughout the CertFM Program, we aim to generally show models that are in-line with best practice principles, as well as pointing out variations of the interpretation of the principles. The course II.1 Model Planning, Principles and Best Practices has a detailed discussion about many best practice principles.

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