The Stages of Financial Modelling

The modelling process can be considered as consisting of several stages, as shown in Figure

The key characteristics of each stage include:

◾ Specification: This involves describing the real-life situation, either qualitatively or as a set of equations. In any case, at this stage one should also consider the overall objectives and decision-making needs, and capture the core elements of  the behaviour of the real-world situation. One should also address issues relating to the desired scope of model validity, the level of accuracy required and the trade-offs that are acceptable to avoid excessive complexity whilst providing an adequate basis for decision support.

◾ Implementation: This is the process to translate the specification into numerical values, by conducting calculations based on assumed input values. For the purposes of this text, the calculations are assumed to be in Excel, perhaps also using additional compatible functionality (such as VBA  macros, Excel add-ins, optimisation algorithms, links to external databases and so on).

◾ Decision support: A model should appropriately support the decision. However, as a simplification of the real-life situation, a model by itself is almost never sufficient. A key challenge in building and using models to greatest effect is to ensure that the process and outputs provide a value-added decision-support guide (not least by providing insight, reducing biases or correcting invalid assumptions that may be inherent in less-rigorous decision processes), whilst recognising the limitations of the model and the modelling process.

Note that in many practical cases, no explicit specification step is conducted; rather, knowledge of a situation is used to build an Excel workbook directly. Since Excel does not calculate incorrectly, such a model can never truly be “(externally) validated”: the model specification is the model itself (i.e. as captured within the formulae used in Excel). Although such “self-validation” is in principle a significant weakness of these pragmatic approaches, the use of a highly formalised specification stage is often not practical (especially if one is working under tight deadlines, or one believes that the situation is generally well-understood). Some of the techniques discussed in this text (such as sensitivity-driven model design and the following of other best practices) are particularly important to support robust modelling processes, even where little or no documented specification has taken place or is practically possible.