Biotech Analyst Optimism: Price Targets Post-IPO

Posted in Biotech investment themes, Exits IPOs M&As | 7 Comments

Getting high quality analyst coverage of your company as a recently minted IPO is important for communicating the rationale and excitement around your story. Thoughtful analysts can evangelize (or punish) companies they believe in (or not). But understanding the relationship between a forecasted “price target” of a stock and its current share price has always puzzled me.

Before reviewing some recent data, here’s some background: most analysts build their valuation models to reflect the disease area’s market size, share of patients addressable, price per therapy etc, and then discount these back in various ways to incorporate pipeline attrition expectations and such: NPVs with high discount rates, forward P/E ratios, etc…  These models vary in their sophistication, and there is wide heterogeneity in analyst quality.  In general, the early analysts that cover a stock are related to the investment banks that helped underwrite and manage the offering, though the linkage is now more tightly regulated post-Sarbanes-Oxley.

Analysts have to adjust their price targets upon any “new” news: a positive clinical readout or other good event (and a risk-related discount on the value of a stock has been removed), they typically go up, and vice versa.  Since there aren’t real revenues and earnings to go off of, a lot of “future value” and sentiment is baked into these price targets.

To try and understand the relationship of price targets and current prices in biotech today, I examined data from ~60 or so therapeutic VC-backed biotechs that went public since January 2013, and have at least three analysts covering them with price targets.

The striking, although not surprising, summary data conclusion is that the differential between the average analyst 12-month price target and the current stock price is often quite considerable: for all the IPOs since January 2013, its 90%.  So the mean target price is nearly double the current price.

Below is the breakdown of various IPO cohorts plotted against the ratio of mean analyst target price versus current stock price (TP/P ratio as of 8/27/2014) , compared to a basket of larger Biotechs (Gilead, Celgene, Biogen, Alexion, Vertex, Biomarin, Pharmacyclics, and Amgen).

Analyst optimism_Biotech_Aug2014

A few observations:

  • Most of the biotech offerings since June 2013 have a 12-month price target that is 2x higher than the current price, with a few companies (Max) having mean targets as high as 3x higher than the current price.
  • As you might expect, the ratio for more seasoned larger cap biotech companies is small – on average just 11% above the current stock price.  More coverage, deeper understanding, less price target differential – a rather obvious point, but nice to see in the data.
  • Interestingly, even over rather short time periods of 2011-2014, a meaningful trend exists that the longer a stock “seasons” as a public company the closer the target price to current price ratio becomes.

Why is this last point the case? 

Could be a few things.  Either analysts get smarter on these stocks over time (and adjust their forecasts appropriately up or down), or companies’ stocks perform and approach their price targets, the latter being a function of the market valuing the company “more in line” with the analyst’s forecast.  I don’t have the longitudinal data to understand how analyst price targets have changed over time, but given the outperformance of biotech in general over the past three years, I suspect that, while analysts undoubtedly get smarter every day watching these stories progress, it is probably a reflection of both.

How does all this compare to the past? 

A 2006 paper by Mark Bradshaw of Harvard and Lawrence Brown of Georgia State reviewing analyst price target performance is of interest (here).  They reviewed 100,000 12-month price targets by analysts across all industries from 1997-2002.  I realize this is an old sample set and not specific to biotech, but their findings were interesting.  The aggregate dataset across the period showed that the ratio of target price to current price was rather tight around 135% (i.e., targets were 35% higher than current price).  Further, 24% of stocks hit their target at the end of the forecast horizon, and 45% hit the price target at some point during the 12-month period.  Analysts were, therefore, more optimistic on average than they otherwise should be, and most were unable to persistently perform well in forecasting.

Reflecting back then on the Biotech IPO dataset, one thing is very clear: analyst price targets are much higher than for more seasoned stocks.  A ~100% premium on average is well above the large Bradshaw dataset (which was during the first bubble, btw), and well above larger cap Biotech stories.  This premium presumably reflects several things:

  • Analysts are overly optimistic about “shiny new toys” (as we all are, especially VCs), and adjust their forecasts over time as management teams and their drug candidates perform.  It is fair to say, however, that a good analyst who has done their homework knows a company and its drug portfolio far better than many public investors (especially retail investors lacking institutional support).
  • Given the short trading histories, the overall market has had less time to find an equilibrium price point accounting for expectations of performance, a more fulsome understanding of the drug candidates, and a “permission to believe” that what a management team is saying is truly likely to happen.  All these things affect market sentiment
  • Lastly, these stocks are very illiquid and presumably trade below their fair value because of it – the concept of the illiquidity discount – which keeps lots of potential investors out of the market for their shares.  The average trading volumes, even after the lock-up expires, remain very thin until “big event” days when huge amounts of shares can move.  This makes price targets and market equilibrium concepts challenging.

I’ll close with a thought experiment as an optimist: if, in line with historic data, 45% of the current IPO class hits their price targets at some point over the next 12-months, which implies a doubling of many recent biotech IPO’s stock prices, that would certainly be quite the year ahead for the biotech market.  There are, of course, other possible futures, but that one is particularly intriguing.


Data Insight: The Return Distribution Of BioPharma VC Financings

Posted in Exits IPOs M&As, General Venture Capital, VC-backed Biotech Returns | 1 Comment

Return distributions in venture capital across different sectors have been a frequent source of commentary, and new data from Correlation Ventures provides further substrate for that theme.

I’ve highlighted data from Correlation Ventures (CV) in the past, looking at top decile returns (here), returns by multiple or aggregate size (here), and their comparison to the S&P500 (here).  The team at CV has built an extensive data set, as a key part of their investment model, compiled via datasets like Dow Jones VentureSource.

CV just released a new dataset looking at 21,640 financings of U.S. VC-backed companies that closed between 2004-2013 into companies that have been acquired, IPO’d, or went out of business.  Approximately 10% of these financings were into BioPharma companies; this proportion is less than the share of dollars flowing into biotech over the decade (>15%) and likely reflects the multi-year tranched “financings” in biotech vs other sectors.  The bulk of the other financings were in technology-related sectors like software, information technology, eCommerce, etc… It’s important to emphasize these are financings, not companies, and so the returns for one series of financing could be positive while for other financings negative.  Kudos to Correlation for pulling this analysis together.

The distribution of returns in these financings is intriguing, and continues to support the thesis that BioPharma venture investing, in contrast to common perception, has actually outperformed most other non-biotech venture sectors at most of the percentiles of performance.  Here’s the summary data:

Returns by Financings- Correlation Ventures

Key observations:

  1. The rate of “significant winners” above 5x is greater for BioPharma financings than other sectors.   Based on more than 20K aggregate financings, the rate was 11.5% for BioPharma vs 9.8% for other sectors; given the huge size of the dataset, this is likely to be real difference – a 17% spread – though I have not run statistical significance analyses on these numbers (I only have the summary data). Further, this favorable margin for BioPharma financings themselves is consistent with prior analyses at the company (rather than financing) level (here), showing a higher rate of 5x+ outcomes in BioPharma.
  2. Frequency of returns above 20x are surprisingly similar across BioPharma and other sectors.  With frequency rates of 1.6% in BioPharma and 1.5% in other sectors, these data suggest that 1-out-of-60 financings lead to greater than 20x returns.  This makes intuitive sense if one considers the pricing of early “seed” rounds of financing and the eventual returns of winners off of those values.  In the past I have speculated that the distribution curves likely favored non-biotech sectors at return rates this high, but CV’s dataset suggests otherwise.  Only above 50x does BioPharma begin to numerically lag other venture sectors, where only 1-out-of-300 financings reach those return levels.  But the numbers begin to get very small and noisy: only 7 financings led to >50x returns in this BioPharma dataset over the decade.
  3. Loss rates for BioPharma financings are lower than the rest of venture capital.  These data show that 57% of BioPharma financings returned less than their invested capital (<1x) versus 66% of financings in other venture sectors.  This is inline with prior analysis showing more favorable loss ratios in biotech (here, here), both on a company/deal basis (% of deals that lose money) and on capital-weighted basis (% of dollars invested into loss-making deals).  The latter figures – dollar-weighted – are even more favorable towards biotech (e.g., Adams Street Partners data had a 36% loss ratio for biotech, vs Internet at 59%.).

These data are further confirmation of the relative attractiveness of biotech venture capital in the asset class.  Further, a recent report from Jon Norris at Silicon Valley Bank highlights the resurgence in returns in healthcare with over $12.5B in potential LP distributions in 2013 alone (3x higher than most years in the past decade; see Exhibit 12 in this link to the report).  This is solidly 2.5x more than the amount invested into new BioPharma deals on an annual basis.  Estimated distributions have outpaced investments in BioPharma for the past few years.

Given the nature of these return distributions, it’s worth pausing to consider why it is that biotech has historically been perceived as the “ugly stepchild” of the asset class (written about here and here).  We’ve speculated about the lack of halo deals (Facebook’s), the unrealized valuation problem of emerging biotechs (here), and the issue of biotech’s esoteric R&D models (vs simple-to-digest social media stories).  But on top of these, one big driver may just be the challenge of absolute vs relative numbers.  The reality is Tech represents 80%+ of the financings over the last  decade – some 18,000 financing events. At a 20x “hit rate” of 1.5%, that’s nearly 290 financings outside of BioPharma vs only 36 from the sector.  That’s 8x more noise, the sort of stuff that fills up TechCrunch, Primack’s columns, Xconomy, and even broader media (NYTimes etc).  Beyond the

With 2013-2014 continuing with strong M&A and IPO momentum, coupled with continued demand for innovation from Big Pharma/Big Biotech, returns and LP perception of the sector have strengthened and are likely to continue to improve; its not surprising that a number of Life Science focused venture firms are raising new funds in this environment.

Thanks again to Correlation Ventures for sharing the above analysis – shining more light on the opaque realm of venture capital data.