Concluding Remarks
The case presented here started with a rather simple balance sheet. From there, we have taken you step-by-step from a static world to a stochastic one. There, not series of single values in time (vectors) reign but distributions in time.
We have introduced first one and then several uncertain variables as well as their correlations. Then we deepened the model by linking the variables to observable external drivers.
Finally, we have shown you how we can handle uncertainty in the balance sheet as well.
By going through this example, you now have seen the principles on how we use Opexar to build up complex financial statements. You are now well prepared to follow the discussion.
This case here was kept as simple as possible in order to flatten the learning curve.
Simulated real world financial statements typically are built with of a large number of such building blocks that connect the business variables to their key drivers. We have for example built models with more than 750 elements that extend an existing industrial-grade financial plan.
Their is a library with a continuously growing number of pre-constructed elements that can be used to efficiently build new Stochastic Financial Statements.
But what can be gained by modelling a project or a financial statement stochastically?
There are a number of important advantages:
- Identify the most like scenario in a systematic manner.
Typically, the expected, most likely project path is guessed very early on in a project. Much effort is then spent building out that expected case.
In our experience, the guesstimated expected project path is neither especially interesting (it will always work) nor is it the true expected median case. Given a number of uncertain variables, it is very hard to identify the most like outcome without further analysis. Quantifying and estimating the distribution of outcomes systematically will result in a much richer picture of the project.
- Estimated uncertainty is risk. Uncertainty is endured, Risks can be managed
Estimating and quantifying uncertainty, as imperfect this may be, will give you an idea about the range of outcomes that will be realised. It is a strange thing: knowing the range of potential outcomes instills security. You can judge whether you can live with a bad outcome or no. If not you can do something about it. The earlier you identify potentially damaging scenarios, the more time you have to do something against them.
- Identify the business drivers first, understand their nature second, better manage third.
Modelling a project stochastically will let you identify your true business drivers. Some of them are external (exogenous) and you have to accept them as they come. Some drivers are under your control and influence (endogenous). It is important to know what you can influence and what not. You will think and act more strategically!
- Liquidity matters
Even if a project’s cash flows work over the project as a whole, it is not assured that you become illiquide while executing the project. Identify and estimate worst case scenarios early and get adequate funding before it is too late!
- Get rid of project myopia and get hooks into a project
By forcing the planer to think about uncertainty and risk he has to widen his perspective from the adored and always promising looking base case. Project myopia – the urge to present a liked project better than it is, the urge to ignore and suppress potential risks – is always a danger. It is hard to abandon a project where you have invested so much time! This is understandable but dangerous. To plan a project stochastically significantly reduces project myopia. Strengths and weaknesses of a project become clearly visible and discussions gain in depth and width.
Thank you for taking your time to read and study this intro.