Certainly there is merit to the analytical process of financial modeling: combing through public data, interviewing management teams, learning about industry trends and comparing different companies on specific empirical data.

The words of economist and model-sceptic, Ariel Rubinstein accentuate the point: Models can be denounced for being simplistic and unrealistic, but modeling is essential because it is the only method we have of clarifying concepts, evaluating assumptions, verifying conclusions and acquiring insights that will serve us when we return from the model to real life.

Unfortunately, models are often misunderstood (and misused) as accurate or precise predictors. Exactness in forecasting does not exist and over-reliance on model estimates is a dangerous mistake. In this vein, sceptical and selective use of financial models is suggested:

Models can serve as a checklist & reminder to investigate published disclosure and market data, but they can never produce exact estimates. Such precision in a dynamical, complex and interconnected business environment is an illusion.

Usefulness of financial projections deteriorates rapidly, the further out the forecast is made. As such, a financial estimate looking 12-18 months out is more useful than gazing 3-10 years into the future (rendering most DCF valuations falsely accurate and totally misleading).

Models can sometimes be useful to give broad directional signals – e.g. Company A looks much more expensive or risky than Company B – but they should never be relied on for a specific or accurate answer, like Company A is worth €8.57 per share.

Given the inherent inaccuracies associated with financial projections, buy/sell decisions (or any other decision, for that matter) should never be exclusively based on such projections. Again, a broad directional signal is the best one can hope for. And even then, common sense always trumps model outputs.