Financial Modeling for Biotechs

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The average publicly listed biotech generates neither profit nor revenue. As such, so-called “quarterly earnings” announcements are typically non-events. Instead, management teams use quarterly releases and occasional “earnings calls” to update The Street on strategic events or pipeline development. While other companies and their following analysts fret over sales growth, gross margins, and market penetration, the world of development stage biotechs is almost entirely focused on proving efficacy and safety. Biotechs are typically about the promise of cake tomorrow, rather than the size of today’s slice, its flavor, and whether or not it comes with ice cream.

That being said, biotech managements need to have a comprehensive in-house financial model, not just of their administrative and development cost projections, but also of the potential market opportunity for each of their pipeline assets. This is especially important around the time of your IPO, when you will need to hold a teach-in for your prospective covering analysts. At this time, and while you are still a private company, you can share the details of your model to guide analysts who are thinking about the commercial prospects for your potential therapies. Analysts are free to agree or disagree with your numbers and market assumptions, but the more logical and transparent you are, the more likely analysts are to share your views, and the more successful you will be in building a consensus around numbers similar to your own.

Here are a few points to consider when putting together your biotech financial model:

DON’T get too complex.

Any Wall Street analyst can tell you that the models built to simulate companies can get very complex; however, they pale in complexity compared to the in-house models created by some biotechs. There are a number of reasons for this. An analyst may have 20-30 models, one for each issuer they cover, while a biotech will only have one – their own. As such, time will constrain the intricacy of analyst models. Biotechs also have access to far more data than analysts (including the true inner workings of the company), and they tend to overcomplicate things by including too much information. One of the largest mutual fund companies has a rule that their internal company models, which exist as Excel files, aren’t allowed to be bigger than a single page of letter paper. Keeping models this small requires major simplification.

So, keep it simple. Have valid reasons for the assumptions you make, state them clearly, but don’t be afraid to simplify.

DON’T be discouraged by the “crystal-ball” nature of what you’re doing.

If your development assets are early stage, perhaps even preclinical, it may seem extremely theoretical to hypothesize about the price, market, and potential use of therapies that are years away from approval and commercialization. The world will likely have changed a lot by the time your drugs are approved, but that doesn’t invalidate your model as the best possible prediction for what the world might look like when or if you get there. Be logical, and work within the constraints of what you know now.

DON’T worry as much about secondary programs.

As biotech entrepreneurs, you might think it is prudent to develop multiple potential therapies to diversify risk and/or provide a series of potential milestones around which you can raise capital. When it comes to the public markets, however, investors and analysts are fairly short-term and will focus heavily on your lead asset, ascribing little (if any) value to your secondary assets.  Some 80-90% of your company’s perceived value is likely to come from your lead asset. Typically, secondary assets are much earlier in development than your lead program. As such, when building your financial model, it is reasonable to allocate fewer lines to secondary programs, though the lines you do include should be justifiable.

DON’T be too conservative.

Good analysts will use your numbers as a reference point. If they believe your projections for market penetration are too high, or that your development timeline is too aggressive, they will apply their own discounts and edits to the data you make available, or come up with their own estimates. Lazy analysts will simply use your numbers and arbitrarily apply a discount. The point is, it is typically unheard of for a covering analyst to be more aggressive or optimistic than management, so the numbers and or/guidance you provide from your model will likely form the upper bound of projections for your company. Consequently, if management is too conservative when creating the internal model, your company could be undervalued.

DON’T worry too much about your commercialization plans.

If you’re still in early development, chances are you won’t have the individual(s) in place who will eventually oversee your commercial infrastructure. Knowing the exact number of salespeople required to sell your drug(s), your cost of goods sold, or the exact price of your product isn’t necessary since such details are likely to change in the years that follow. In addition, the average biotech typically sells out before they ever commercialize a product. Make some assumptions based on existing drug products on the market and real-world numbers generated by your peers. The investment community won’t typically expect you to go into further detail on your commercial strategy until the months leading up to a potential drug’s regulatory approval.

Finally, DO get feedback from your IR advisor and bankers.

Unless this is your second, third, or more public venture, chances are you haven’t done this kind of modeling before. By contrast, your IR advisor and the lead bank in your syndicate have been through this process countless times. Seek counsel from your advisors and rely on them to give you greater bandwidth at a time when management’s time is likely stretched thin.

Also, don’t get angry or defensive when other people (like your advisors or your covering analysts) disagree with you. So much of the future is unknowable, and outsiders will usually have less faith than you do in your drug’s potential. Additionally, models and discounted cash flow (DCF) calculations are incredibly sensitive to tiny changes in certain parameters, and innumerous valuations and results are possible even when parties agree on a majority of the “facts.”

Do you have any questions on building your biotech financial model, or are you ready to get started? Contact us today!

Laurence Watts, Managing Director

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