There are however, two startup-specific adjustments that can be made to DCF methods which can soften the limitations of forecast accuracy and make DCF work for startups.
The first big difference, illiquidity, is inherent to private investments. Early stage shareholders cannot sell shares with little anticipation, as they are not freely traded on a public market. Selling these shares usually entails changes in shareholder agreements as well as finding a buyer.
The impossibility of selling shares on a short notice, means that, if something goes wrong with a company, the investor has to bear the consequences without the possibility that other players, more prone to risk, would step in and take his/her position. For this reason, the investor bears more risk.
This risk needs to be accounted for in the valuation, by lowering it. You can incorporate this risk in the general discount rate. At Equidam we prefer, given its importance, keeping it separate and use statistical models to calculate it for each specific company. The risk is then applied to the DCF value and has the result of lowering the valuation by a certain percentage, usually between 10 and 30%.
Indeed, startups are much more prone to fail than accomplished public companies. For this matter, valuing early stage companies requires the incorporation of this risk in the calculations in a much more prominent way compared to the valuator of public.
Eurostat Data suggests 60-80% of newborn companies fail in the first 3 years of operation. This value is much higher compared to < 10% for public companies. In the U.S., according to the BLS, about two-thirds, or 66 percent last past the first two years. Extended to four years, the number of surviving businesses decreases to only 44 percent, meaning that about 56 percent of businesses fail at the five-year mark.
These failure rates strongly influence financial forecasts. From the standpoint of an investor that has average knowledge of the business, there is no reason to believe that one particular company has a higher success rate than others. Thus, the application of average failure probabilities is the safest option.
If these statistics represent the likelihood of a project to fail, they are often tied to assumptions that forecasts will not to be realized, and the company may not realize revenue. In Equidam models, we account for these possibilities by weighting the financial projections according to their likelihood of being realized.
So, if a company projects one million in revenues, but has only 35% possibility of surviving until that year, the projection resolves to be 350,000. Smaller adjustments need to be made regarding the discount rates, the industry multiples, comparable companies, etc; however, none of them is as important as illiquidity and failure rate.
For entrepreneurs and investors, understanding these factors is pivotal and can lead to a more reasonable valuation. Forecasts are normally inflated to reflect the dreams of the entrepreneur, and this usually leads to values that are not acceptable in the market. This, in turn, spurs the idea that DCF cannot be applied to startups.
However, when considering these two main factors, we can readily understand how a better use of DCF cannot only expand its usefulness in early stage valuation, but also make it a reliable measure when investing in early stage companies and evaluating possible returns on single investments and portfolios.
This article has been edited and condensed.
Daisy de Vries is the Marketing & Communication manager at Equidam, an online value management tool for small businesses. Her passions are startups, writing and technologies. Keep investors up-to-date in a consistent and efficient way with the Equidam valuation report. The report gives a clear overview of the performance of your company and enables you and your capital providers to understand and manage the value of your business. Connect with @equidamtweets on Twitter.
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