Consider the publicly traded biotechnology company, Science Project Inc. (SPI)—a real company that I am disguising with a made-up name and ticker symbol. SPI announced its quarterly results yesterday. The five investment bank research analysts who cover the company each published a research report interpreting the announcement and each reaffirmed 12-month price targets which ranged from $8 to $14 a share, significantly higher than the $5.35 market price at the time of the announcement.
SPI has a proprietary drug discovery platform and partnerships with five larger pharmaceutical companies, and it also plans to develop drug candidates independently. None of the partnered or internal drug candidates have been tested on humans yet, and none will generate sales until 2018 at the earliest.
The analysts’ derive their price targets by estimating the future cash generation for each product in development and then what that flow of money would be worth as a lump sum today. In doing this “present-value” calculation, the analysts adjust future revenue estimates by guessing a peak sales year, multiplying that year’s sales by 15 to 20 and then discounting this figure by 35 to 50 percent to reflect the risk that the predictions will be inaccurate. The analysts then add together the present values for a sum-of-the-parts target price.
When performed on a portfolio of low-risk bonds, this exercise—formally called a discounted cash-flow analysis or DCF—is precise and accurate. When applied to company earnings, the analysis, while still precise, becomes increasingly inaccurate as inputs into the equation become more variable. DCF is most helpful when analyzing companies with fixed prices, steady earnings and predictable growth. The opposite is true for companies with uncertain pricing, difficult-to-predict markets, long development stages and huge optionality. Development-stage biotechnology is the most difficult sector for this type of analysis.
Let’s return to SPI. Between now and the predicted arrival of sales revenue in 2018, each of the company’s products will face a series of clinical and regulatory tests. Failure during any of these stages can drop the value of that drug candidate to zero. SPI’s product pipeline could ultimately be worth nothing.
DCF price targets trivialize the complexity of placing a value on this type of company. Analysts can justify any number they wish with their sums of estimates, guesses and discretionary variables force-fed into an otherwise straightforward formula.
As a life sciences portfolio manager, I know many biotechnology equity analysts. I frequently rely on their reports to answer the who, what, when and where in my own due diligence—and often the why too. They work tirelessly, addressing the often-conflicting pulls from colleagues in banking, clients on the buy-side and management teams from the companies they cover. They are superbly trained, many with Ph.D.’s in the sciences or engineering and are all smart enough to recognize the futility of building a seemingly precise model on guesstimates and almost random variables. Price targets on companies like SPI are exercises in precise inaccuracy, diluting the integrity of the main body of the analysts’ work.