Introduction to Financial Modelling

Principles of Excel as a Modelling Tool

Chapter 2: Excel Foundations

8 Topics | 1 Quiz
Chapter 3: Using Sensitivity Analysis

10 Topics | 1 Quiz
Chapter 4: Core Operations and Shortcuts

7 Topics | 1 Quiz
Common Modeling Structures (I)

Chapter 5: Common Structures in Financial Models (I)

7 Topics | 2 Quizzes
Further Operations and Shortcuts

Chapter 7: Formatting, Presentation and Graphs

7 Topics | 1 Quiz
Excel Functions (I): Information and Numerical Aggregation

Excel Functions (II): Conditionality, Aggregations and Arrays

Chapter 11: Logic and Conditionality

7 Topics
Chapter 12: Conditional Aggregation

4 Topics | 1 Quiz
Chapter 13: Array Functions and Dynamic Arrays

5 Topics | 1 Quiz
Common Modelling Structures (II)

Excel Functions (II): Lookups and Referencing

Chapter 15: MATCH and XMATCH

5 Topics
Chapter 16: CHOOSE and SWITCH

6 Topics
Chapter 17: INDEX and XLOOKUPs

6 Topics | 1 Quiz
Model Planning and Best Practices

Chapter 18: Model Planning

11 Topics
CUTZ or ... intrducton to rest of program....and clisoing remarks

We noted earlier that the modelling process in general consists of three stages (even if in a specific situation, one or other of these may be the dominant focus of activity):

- Concept or specification.
- Implementation.
- Usage, results and conclusions.

The skill set of a well-rounded modeller needs to encompass the skills required at each stage. Some skills are required more intensively at one stage or the other (for example, Excel skills are essential for implementation).

In addition, the modeller needs to be able to treat the stages in an integrated fashion, with each stage taking into account the effect on the others. For example, the way the model will be used will affect its ideal specification and design, whilst the design requirements will affect what is implemented, as well as how the model can be used.

In addition, the modeller needs to be able to treat the stages in an integrated fashion, with each stage taking into account the effect on the others. For example, the way the model will be used will affect its ideal specification and design, whilst the design requirements will affect what is implemented, as well as how the model can be used.

The main skills and capabilities fall into the following areas:

**Identifying relevant decisions, options and alternatives**. For a model to be effective when used, it must address the relevant decision possibilities. In particular, it is important to ensure that the decision options that are considered are the most relevant. That is a modeller should have some skills to identify appropriate decision options before planning and building the model. For example, when designing forecasting model for a business, the ability to understand the core aspects of the strategy, competitive position, macro-economic or political context of the business could be important: These factors would likely impact the possible decision options and their variations, and therefore have implications for the model design and use. In particular, it is important to avoid the “fallacy of choice”, which is the (all-too-frequent) situation in which the most relevant course of action is not even considered as a possibility.**Decision-making processes**. The modeller should have some understanding of the processes by which decisions can be made, the challenges to making good decisions, and same ways to deal with such challenges. Part of this involves ensuring that the decision criteria are understood (so that these can be reflected in the model as far as possible), whilst another aspect involves managing the challenges (such as stakeholder involvement, dealing typical biases, optimizing communication, and so on).- General concepts, metrics and criteria in economics and finance. A good knowledge of economics and finance is required in order to plan, implement and interpret the economic analysis required to support the decision. There are many core concepts which are very frequently needed, and should not only be well understood, but also be able to be selected and implemented as appropriate to each situation.
- Application-specific knowledge: Behaviours, metrics and criteria. Whilst some models may require only a knowledge of general or common topics in economics and finance, others require more specific knowledge. For example, topics such as cash flow valuation, financial statement modelling, project finance, optimisation, the valuation of options and derivatives, and statistical analysis each have specific features or behaviours that a modeller will need to be aware of.
**Excel skills, including VBA and selected add-ins**. Although it can be possible to build quite large models with a rather limited set of Excel features and knowledge, a well-rounded modeller who can deal with many types of situations will need a much wider knowledge of Excel. Ideally, a modeller should have knowledge and capabilities that extend beyond those which are strictly necessary in any area, since this allows the modeller to consider more possibilities of how to best design and implement the model, so that the optimal structure can be used (rather than one which may be technically correct, but may be highly inefficient to update, or lack transparency). In addition, a knowledge of the automation (and other) possibilities available by using VBA (macros) can be indispensable in some cases. Finally, some of the core add-ins which are shipped with Excel can be useful to provide enhanced functionality.- Data Analysis and manipulation. Models may need to be driven by (or linked to) large datasets or databases, requiring integration, manipulation, cleaning or transformation of the input data or output calculations. The “theoretical” part of this topic is rather broad, varying from the simplest forms of calculations (e.g. calculations of sums and averages) to advanced topics in statistics. Similarly, the practical implementation ranges from the very simple (e.g. adding up the values in several cells of Excel) to quite advanced (such as where several large data sets need to be integrated and live-linked into a models). Excel (combined with VBA and selected add-ins) provides a wide set of functionality that meets the full spectrum of requirements; the well-rounded modeller needs to be aware and capable in these areas.
**Integration, structuring, simplification and problem-solving skills**. A core challenge to designing and building high quality models is to be able to integrate all the required aspects together, in a way that is “as simple as possible, but no simpler”. At the implementation stage, much is related to the use of Excel best practices, which are chiefly concerned with creating transparency by using the appropriate design, layout, logic flow, choice of functions, and formatting. However the phrase (generally attributed to Einstein) can be applied to the approach used in each stage of the process (i.e. at the concept, implementation, and usage stages).

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