DCF values are dependent on the accuracy of forecasts. For early stage companies, with zero or no track record, and being likely to fail, these forecasts are usually far from accurate. There are however, startup specific adjustments to DCF methods that can soften these limitations of forecast accuracy and make DCF for startups different from normal DCF. Illiquidity discount The first big difference, illiquidity, is inherent to private investments. Early stage shareholders cannot sell the 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 simply finding a buyer. The impossibility of selling the shares on a short notice, means that, if something is going wrong with the company, the investor has to bear the consequences without the possibility that other players, more prone to risk, would step in in his position. For this reason, the investor bears more risk. This risk needs to be accounted for in the valuation, by lowering it. You could 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%.
Failure rate 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. From Eurostat Data, we can see that about 60 to 80% of newborn companies fail in the first 3 years of operation. This value is much higher compared to <10% for public companies. These failure rates strongly influence financial forecasts. Indeed, from the standpoint of an investor that has average knowledge of the business, there is no reason to believe that this particular company has higher success rate than others. Thus, the application of average failure probabilities is the safest option. If these represent the likelihood of the project to fail, they should also be the likelihood of the forecasts not to be realized, and of the company to realize no revenues. In Equidam models, we account for these possibilities by weighting the financial projections according to their likelihood of being realized. So, if the company projects one million revenues, but has only 35% possibility of surviving until that year, the projection revolves 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 normal DCF 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 already understand how a better use of DCF can not only expand its usefulness in early stage valuation but also make it a reliable check when investing in early stage companies and when evaluating the possible return of single investments and portfolios. Stay tuned on our blog for future articles! If you enjoyed this post, feel free to share it with your friend or fellow entrepreneur who is struggling with valuation issues. Help us spread the word!