Assumptions Assumptions…


MS Excel is a fairly powerful tool that is very commonly used by professionals ranging from data entry staff, to analysts, to corporate executives right up to the C-level. It is ironic, however, that most of those who use MS Excel in one way or another quite regularly don’t know the power of the language. According to an estimate, only around 20% of the users actually realize the functionality and power of this software.

Assumptions Make a Difference

In proforma financial models, where linkages are spread all across the various sheets in order to make sense of the numbers in the end, one of the most important things to check first are: Assumptions.

Sometimes, these are integrated within different sheets in the model in a hap-hazard fashion, however, most properly-built models have a separate tab for Assumptions. I have seen people commit three major blunders as far as model assumptions are concerned:

Ignoring or undermining the importance of assumptions and trying to concentrate on the numbers instead: I have seen executives that just want the numbers in place. These are the kind that are often lost in the numbers, forgetting the logic behind them. They feel that by looking at the numbers hard enough, they can realize how the situation would be like in the coming years and when they want some number changed, for instance sales, they just ask to change it without realizing that if they don’t look into and change the assumptions, someone else under them would.

Being too involved in some assumptions and not caring about most: These executives often have a hard time figuring out which areas to concentrate on. Pressed for time, they just want to look into some assumptions and leave the rest. The problem, however, comes up when they get too much involved in one particular assumption to the extent that they lose sight of the big picture. For example, the amount of Capital Expenditure (CapEx) to be done in the fifth year of projection to be increased by 20% rather than 15% YoY basis. This is problematic, as all this does is to lose the connection with the overall big picture of the model.

Not carefully checking the model linkages to the assumptions: As simple as it sounds, few analysts, bankers and evaluators forget to check model assumptions’ linkages to the actual model. I have often encountered cases where certain assumptions listed in the model are not actually linked anywhere in the financial model. For analysts, that is where they need to remain careful and make sure there is no such error because in certain cases, the implications for such a loophole could mean a disaster in decision making.

So, make sure you don’t fall prey to these basic flaws when you’re handling proforma models.

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