0/ I decided to do a tweet storm about DCF models and their usefulness re crypto assets for those valuation diehards out there.
1/ I have come across some analysts who mistakenly equate building a DCF model with doing fundamental valuation analysis. Most of these analysts don’t understand the shortcomings of DCF and naively think it is a robust predictor of price.
2/ Just because a company or platform has cashflow doesn’t mean a DCF model is the best way to value it. Often for startups in high growth phase, a DCF analysis leads to a very wrong outcome and more often than not ends up undervaluing an asset.
3/ Those who have spent years doing valuation analysis know that a DCF model can spit out whatever NPV you want it to be because there are so many subjective variables in a DCF model that you can tweak. Your outcome is only as good as your input.
4/ Even in traditional industries where companies have proper cashflows, DCF is rarely applied for this reason. DCF is useful only in industries that are ex-growth and with stable cashflows such as utilities.
5/ For industries that are in growth phase or for startups, DCF is rarely useful as a price predictor. Hence even as fundamental investors we don’t rely on it very much.
6/ Worse I have seen some crypto analysts do 5 year DCF models. Come on, are you kidding me? Even for mature low growth industries people do 15-20 year DCF forecasts. You think your 5 year forecast for a crypto asset is any good?
7/ Where a DCF can be useful is informing one about price extremities ie. If something is trading at x, and our most conservative assumptions spit out a value that is higher than x indicating a distressed valuation.
8/ Likewise in a bubbly market, if something trades at y, and our most aggressive assumptions can not justify the valuation, it suggests the asset is overvalued.
9/ However, notwithstanding this stress test, asset prices can and do often deviate from fundamental value for long periods especially in a bubble. He who doesn’t understand this market reality is a fool and will not make a good analyst.
10/ In bull markets you will often see analysts raising their DCF-based target prices significantly even though their cash flow projections have not changed much.
11/ How did that happen? There is something called equity risk premium in your discount rate. As markets creep higher, the implied equity risk premium drops lowering the WACC leading to a higher price target.
11/ This is no different than an analyst valuing Apple at 30x PE a few months ago, who now decides to raise his target price by raising his target PE to 40x PE (without changing his estimates) because the overall PE for the market has risen.
12/ So if you thought DCF models gives you an absolute price target, you couldn’t be more wrong.
13/ I hope I have dispelled the delusions of grandeur that young crypto analysts have about the usefulness of DCF models. At best they have limited applicability to crypto. At worse they lead you down the wrong path.
14/ When it comes to valuation, usually the simplest models are the most useful. Complexity truly does not equate to reliability and valuation is as much art as it is science.
15/ What I do advocate is using multiple metrics to triangulate a fair value range for an asset. If you still want to do a DCF, so ahead but do a sensitivity analysis rather than rely on a point estimate.
