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
Common Modeling Structures (I)

Chapter 4: Core Operations and Shortcuts

7 Topics | 1 Quiz
Further Operations and Shortcuts

Chapter 5: Common Structures in Financial Models (I)

7 Topics | 2 Quizzes
Excel Functions (I): Information and Numerical Aggregation

Chapter 7: Formatting, Presentation and Graphs

7 Topics | 1 Quiz
Excel Functions (II): Conditionality, Advanced 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)

Chapter 14 Measuring and Calculating Growth

6 Topics | 1 Quiz
Excel Functions (II): Lookups and Referencing

Chapter 16: MATCH and XMATCH

5 Topics
Chapter 17: CHOOSE and SWITCH

6 Topics
Chapter 18: INDEX and XLOOKUPs

6 Topics | 1 Quiz
Model Planning and Best Practices

Chapter 19: Model Planning

11 Topics
At this general level, models can exist in many forms, including:

**Physical**. Art, sculpture and objects made with Lego® may be considered as models: They express key elements and their relationships between the items in a system.**Graphical**or**Illustrative**. Examples include influence (or flow) diagrams, which can be used to depict and summarise the logic, dependency relationships, choices, and possible outcomes within a situation. Decision trees are of a similar nature. (Each of these may also contain numerical calculations, but in general, their use is for visual and conceptual purposes).**Descriptive**. A text-based document that describes the situation can also be considered as a model, so long as it captures the elements and their relationships in a reasonably precise way.**Mathematical**. The elements and their relationships in a situation could be expressed using formulas (which inherently represent relationships between the key elements or variables).**Numerical**. The relationships and behaviour of a system could be captured directly within a set of calculations (very often using Excel). The variables and their relationships are specified implicitly by these calculations (although some external documentation may also be created, containing descriptions, illustrations or mathematical formulas).

In general, models do not perfectly capture every aspect of a situation: Indeed, a “model” which did so would essentially no longer be a model! There are almost always features or behaviours of the real-life situation that cannot be captured exactly (even if these are unusual behaviours that could arise only rarely or in highly specific circumstances).

Part of the “art” of modelling is to capture the key elements in a way that is sufficient to fulfil the particular purpose. Some important reflections to bear in mind, in almost all situations (the the simplest to the most complex) include:

- “Every model is wrong, some are useful”.
- “Every model should be as simple as possible, but no simpler”.

- To facilitate a reaction or be thought-provoking (e.g. art, sculpture,…)
- To generate insights and understanding, by documenting, testing and developing hypotheses about the behaviour of the system.
- To support a decision process. This could include the economic evaluation of a possible course of action and the development of alternatives.
- To calculate a numerical quantity whose value is needed for some purpose, but is not known directly from measurement or other data sets.

There are many areas of application, including:

- In physics and engineering, models are used in the design of aircraft, cars and wind-turbines (e.g. modelling of airflows over the structures).
- In epidemiology, models are used to forecasting of the spread of infectious diseases.
- In finance, economics and business, models are used in business forecasting, project evaluation, investment decisions, valuation, credit assessment, risk quantification, and portfolio optimisation, and many other areas. In addition, data analysis is required not only as an input to models, but also (and increasingly) as an activity in its own right, including to make data-driven prediction, based on relationships that are inferred from the data.

In general, a modelling process consists of three stages:

- A concept or specification stage. This focusses on defining the objectives, the overall concept, architecture and structure, as well as identifying the key variables or elements, and defining the relationships between them.
- An implementation stage. This is the expression of the model in its “physical” form. (Within this, we include Excel workbooks and programming languages).
- A results or conclusions stage, where the models’ results are used in some way.

In some cases, not all stages are explicitly used or are given much emphasis. For example, quite often models are built in Excel using a “hands-on” approach i.e. the models are directly implemented in Excel, whilst the underlying conceptual or contextual basis for the model is de-emphasized, or not documented, or is assumed to be implicitly clear. (Needless to say, this is not an ideal situation in many cases!)

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