Of course, if you use python/R, you don't need to manually tweak a few assumptions in a spreadsheet and look at a few outcomes. You can instead just assign them a probability distribution and run a monte carlo simulation.
PS: I think excel probably has some support for monte carlo too. should look into it.
If you want graphs you can spit out not just one, but multiple representative ones, credible intervals, etc.
First because the probablity distribution itself would have to be pulled out of thin air, and make the results questionable.
Second because writing the assumption->outcome formulas is simply quicker than running an MC sim even if doing the sim is a one-liner (simply ensuring that the data is in appropriate format and testing once will take longer than needed).
Third (and main) because you don't particularly need the actual outcomes at all, the hands-on-tweaking of these assumptions and 'interactive learning' is the whole point of doing it all, you need to personally learn and feel the relation between these assumptions and financial results, and the 'result' numbers and graphs are just a side-effect and notes/docs to remind you later.
And if you need to convince someone else afterwards of your conclusions, then for 99% of audiences you anyway want to use 'specific plausible scenario story' (or comparisons of such scenarios) instead of a probability distribution coming out of a solid mathematical simulation of all possibilities; since it's well researched that the first kind of evidence works better in convincing homo sapiens about anything at all.
Of course. It's just a more systematic version of manually tweaking parameters in a spreadsheet (which are also pulled out of thin air).
With libraries like PyMC, it's one extra step beyond writing an assumption->outcome formula. You write the same formula, but then set variables to probability distributions rather than floats .
And if you need to convince someone else afterwards of your conclusions, then for 99% of audiences you anyway want to use 'specific plausible scenario story'...since it's well researched that the first kind of evidence works better in convincing homo sapiens about anything at all.
Fair enough. This is solid dark arts, but useful.
Convenience matters in such things, and simply reading a 20x20 data table into any programming language already takes more time than just writing the formulas next to this table in excel.
outputs = []
for x in monte_carlo_input(param1_dist, param2_dist, ...):
outputs.append( (x, f(x)) )
histogram/whatever(outputs)
> f(10, 3, 0.07)
12
> f(12, 5, 0.09)
-3.9All aspects of financial forecasting in the initial stages are pulled out of thin air and are hence questionable. This is one of the big uncertainties one lives with when starting a business.
> Third (and main) because you don't particularly need the actual outcomes at all,
Bingo. That's the key. What you are looking for is not "will my business succeed" but rather "what do I have to do in order to succeed?" The tweaking of the model is the point, not the outcomes.