The Financial Modelling: Backward Thinking and Forward Calculation Processes

The modelling process is essentially two-directional (see Figure):

◾ A "backward thought process", in which one considers a variable of interest (the model output) and defines its underlying, or causal, factors. This is a qualitative  process, corresponding to reading Figure from left to right. For example, cash flow may be represented as being determined from revenue and cost, each of which may be determined by their own causal factors (e.g. revenue is determined by price and volume). As a qualitative process, at this stage, the precise the nature of the relationships may not yet be made clear: only that the relationships exist.
◾ A “forward-calculation process”, in which one which starts with the assumed values of the final set of causal factors (the “model inputs”) and builds the required calculations to determine the values of the intermediate variables and final outputs. This is a numerical process corresponding to reading Figure from right to left. It involves defining the nature of the relationships sufficiently precisely that they can be implemented in quantitative formulae. That is, inputs are used to calculate the intermediate variables, which are used to calculate the outputs. For example, revenue would be calculated (from an assumed price and volume), and cost (based on fixed and variable costs and volume), with the cash flow as the final output.

Note that the process is likely to contain several iterations: items that may initially be numerical inputs may be chosen to be replaced by calculations (which are determined from new numerical inputs), thus creating a model with more input variables and detail. For example, rather than being a single figure, volume could be split by product group. In principle, one may continue the process indefinitely (i.e. repeatedly replacing hard-coded numerical inputs with intermediate calculations). Of course, the potential process of creating more and more detail must stop at some point:

◾ For the simple reason of practicality.
◾ To ensure accuracy. Although the creation of more detail would lead one to expect to have a more accurate model, this is not always the case: a detailed model will require more information to calibrate correctly (for example, to estimate the values of all the inputs). Further, the capturing of the relationships between these inputs will become progressively more complex as more detail is added.

It may be of interest to note that this framework is slightly simplified (albeit covering the large majority of cases in typical Excel contexts):

◾ In some applications (notably sequential optimisation of a time series, and decision trees), the calculations are required to be conducted both forward and backward, as the optimal behaviour at an earlier time depends on considering all the future consequences of each potential decision.
◾ In econometrics, some equations may be of an equilibrium nature, i.e. they contain the same variable(s) on both sides of an equation(s). In such cases, the logic flow is not directional, and will potentially give rise to circular references in the implemented models.