And here's how it would work using your company financial statement.
I discovered at Business School that I could calculate some powerful forecasts using my quite limited initial mathematical knowledge. And this surely is a fairly simple, yet highly effective way to value a company.
Perhaps if start-ups went back to this 'old school' valuation, they may begin to tone down some of their outlandish expectations of IPO's. After all, sure there are cases of companies that aren't producing profit, smashing it in their IPO or being acquired for mega-bucks, but that is a bit like hoping to win the lottery. For most companies, profits, cash flows, margins and future growth are all crucial.
Going back into the real world for my Summer internship, I was struck by 2 key facts:
1. Most company data is not that good - In a recent study it was found that only 3% of company data sets are up to a standard necessary to make really accurate future predictions.
2. Even when you have good data, you as an analyst have to make many judgement calls on it - what periods to look at, what parts of the data to accentuate, which to leave out, how to display the data, and so on
This is where I have to move to presenting of forecasting information itself. There are numerous examples of even highly educated and highly intelligent professionals making basic cognitive errors.
In a study of MIT students, their valuation of a random good was highly correlated to the last number they had heard about (Ie if they heard $100, they would value a bottle of wine in the 100s of dollars, if they heard $10, they would value it in the 10s). This is called anchoring.
Confirmation bias - the tendency to interpret new evidence as confirmation of one's existing beliefs or theories. Misinterpreting statistics is quite common. For example, in a recent survey, doctors are more likely to recommend surgery if they are told that there is a '90% chance of success' than if they are told that there is a '10% chance of fatality' - even though this is describing the same situation.
Other judgement calls you have to make with forecasting, is when you get new data in, sometimes from the same time frame of the data you had before, that enables you to make more accurate predications. Do you change the prediction? Which may cause people to question your competence. Or do you stick with the less reliable old forecast?
52% of Senior executives say that they have altered forecasts to make them more politically palatable within the organisation. I think this says more about the culture of the organisation than the executives. However you can see how this can happen.
I ended up getting quite into Forecasting at Business school. My final term I created a Monte Carlo Simulation using Oracle's Crystal Ball Software. This is a random number generator that creates 1000 different outcomes of your forecast by slightly varying a set of parameters you create.
The chart here is describing the final Net Present Value of an investment (Real Estate, but it could be anything - a Marketing Campaign, a New product launch, even an employee) in terms of probability (therefore in this one most likely is a little under a $2,000,000 profit).
This one happened to be a Real Estate Investment project. So I varied key profit drivers like occupancy rates, tax and interest payments.