Friday, December 19, 2014

Our New Client's Technology Has Unlimited Upside Potential



Powerful Drug Discovery Engine for a Drug Repositioning Platform. This remarkable technology is a Quantum Leap above anything prior in speeding new cures to patients and discovering new uses for existing drugs. Because our selection universe is drugs already approved for other uses, we take several years and hundreds of millions of dollars out of the Drug Discovery, Development and Delivery process. They combine Natural Language Processing, Artificial Intelligence, Network Graph Theory, Computational Biology and several predictive algorithms that can accurately identify existing approved drugs that can be used to effectively treat additional diseases.

Please review this presentation on YouTube
  




Dave Kauppi is a Merger and Acquisition Advisor and Managing Director of MidMarket Capital, providing business broker and investment banking services to owners in the sale of information technology companies. To view our lists of buyers and sellers click to visit our Web Site MidMarket Capital

Tuesday, September 2, 2014

Valuing The Growth Rate in the Sale of a Technology Company

If you are selling a rapidly growing business, especially one based on technology, using an EBITDA multiple will not provide an accurate valuation metric. This article presents an argument that these businesses should more appropriately be measured like the concept of Price to Earnings to Growth (PEG) Ratio that is used for rapidly growing public companies.

In the sale of privately held businesses there seems to be no mechanism and certainly no attempt on the part of buyers to account for the selling company's growth rate. In the public market this factor is widely recognized and is accounted for with an improvement on the PE multiple, the PEG or Price Earnings Growth multiple.

 Because there is no exact translation between EBITDA multiple (the primary valuation metric for privately held companies) and Earnings Per Share and PE multiple (the primary valuation metric for publicly traded stocks), the purpose of this article is to try to calculate an adjustment factor that can be applied against the EBITDA valuation metric in order to present a more accurate accounting for differences in growth rate for the valuation of privately held companies.

Experienced business buyers are masters of setting the rules for how they calculate the value of a business they are attempting to acquire. You may think that a 5 X multiple of EBITDA or 1 X Sales would be pretty cut and dried, but in practice it is open for creative interpretation. For example, if you just had your best year ever and your EBITDA was $2 million and the market valuation was 5 X, then you would expect a $10 million offer. Not so fast. The buyer may counter with, "That last year was an anomaly and we should normalize EBITDA performance as an average of the last three years." That average turns out to be $1.5 million and like magic your purchase offer evaporates to $7.5 million. On the flip side, if you just had your worst year at $1 million EBITDA, you can bet the buyer will use that as your metric for value.

The three owners paid themselves $100,000 each in salary, but the buyer asserts that the fair market value salary for a replacement for each senior manager is really $150,000. They apply this total $150,000 EBITDA adjustment and your valuation drops by another $750,000. If the family owns the building separately and rents it to the business for an annual rent of $200,000 when the FMV rental rate is $300,000, the resulting adjustment costs the seller another $500,000 in lost value.

Another valuation trap for a seller is that they want to hire additional sales resources to pump up their sales just prior to the sale. This is almost always a bad move. Most technology sales reps take a year or longer to ramp up to productivity. In the interim, with salary and some draw or guarantee, they actually become a drain on earnings. The buyers do not care about the explanation, they just care about the numbers and will whack you with a value downgrade.

The least understood valuation trap, however, is there seems to be no mechanism and certainly no attempt on the part of buyers to account for the selling company's growth rate. In the public market this factor is widely recognized and is accounted for with an improvement on the PE multiple, the PEG or Price Earnings Growth multiple. The rule of thumb is that if the stock is valued with a PEG of less than 1 then it is a good value and if it is over 1 it is not as good.

Because there is no exact translation between EBITDA multiple (the primary valuation metric for privately held companies) and Earnings Per Share and PE multiple (the primary valuation metric for publicly traded stocks), please allow me a measure of imprecision in my analysis. My purpose is to try to calculate an adjustment factor that can be applied against the EBITDA valuation metric in order to present a more accurate accounting for differences in growth rate for the valuation of privately held companies.

I have chosen two stocks for my analysis, Google and Facebook. The reason I choose these two is that they are widely known, very successful, in the same general market niche, and are at different stages of their growth cycle. Google sells at a PE multiple of 33.37 while Facebook sells for a PE multiple of 113.71. The PEG of Google which = PE Multiple/5 year growth rate is 33.37/16.85 for a PEG of 1.98. I actually backed into the growth rate using the readily available PE multiple and the PEG from my Fidelity account.

Facebook sells at a PE multiple of 113.71 and has a PEG ratio of 3.62 (may be some irrational exuberance here), which translates into a 5 year growth rate of 31.41%. For our comparison we should also include the average PE multiple for the S&P 500 of about 15. Let's make the assumption that on average, this assumes that these companies will grow at the growth rate of the U.S. Economy, say 3%.

So to calculate a normalized PE ratio for these two companies, we are going to create an adjustment factor by dividing the 5 year compound growth rate of Google and Facebook versus the anticipated 5 year compound growth rate of the S&P 500. For Google the 16.85% growth rate over 5 years creates a factor or 2.178 or a total of 217.8% total growth over the next 5 years. The S&P factor is 1.16. So if you divide the Google factor by the S&P factor you get 1.878. If you multiple the market PE multiple of 15 by the Google factor, the result is a PE of 28.2. Not too far off from the current PE multiple of 33.37

Facebook is a little off using this method resulting in a normalized calculated PE of 50.65 versus their current rate of 113.71. This will appropriately seek a level over time and settle into a more rational range. My point here is that the public markets absolutely account for growth rates in the value of stocks in a very significant way.

Now let's try to apply this same logic to the EBITDA multiple for valuing a privately held technology company. If the rule-of-thumb multiple for your company's valuation is 5 X EBITDA but you are growing at 10% compounded, shouldn't you receive a premium for your company. Using the logic from above we assign a 3% compound growth rate as the norm in the 5 X EBITDA metric. So the 10% grower gets a factor of 1.61 versus the norm of 1.16. Dividing the target company factor by the normalized factor results in a multiple acceleration factor of 1.39. Multiply that by the Standard 5 X EBITDA multiple and you get a valuation metric of 6.95 X EBITDA.

A little sobering news, however, you will have a real challenge convincing a financial buyer or a Private Equity Group to veer to far away from their rule of thumb multiples. You will have a better chance of moving a strategic technology company buyer with this approach.  A discounted cash flow valuation technique is superior to the rule of thumb multiple approach because it accounts for this compound growth rate in earnings. If the technique produces a higher value for the seller, the buyer will keep that valuation tool in his toolbox.

Perhaps the best way to negotiate a projected high growth rate and translate that into transaction value is with a hybrid deal structure. You might agree to a cash at close valuation of 5 X EBITDA and then create an upside kicker based on hitting your growth targets.

 So for example, your EBITDA is $2 million and your standard industry metric is 5X EBITDA. You believe that your 10% growth rate (clearly above the industry average) should provide you a premium value of 6 X.  So the value differential is $10 million versus $12 million. You set a target of a 10% compounded growth in Gross Profit over the next 4 years and you calculate an earn out payment methodology that would provide an additional $2 million in transaction value if you hit the targets. It is a contingent payment based on actual post closing performance, so if you fall short of targets you fall correspondingly short on your earn out. If you exceed target you could earn more.

Successful buyers do not remain as successful buyers if they over pay for an acquisition. Therefore, the lower the price they pay, the greater their odds of chalking up a win. This is a zero sum game in that each dollar that stays in their pocket is one less dollar in your pocket. They will utilize every tool at their disposal to convince the seller that "this is market" or this is "how every industry buyer values similar companies."  It is to your advantage to help move them toward your value expectations. That is a very hard thing to accomplish unless you have other buyers and can walk away from a low offer. Believe me, if they are looking at you, they are doing the same dance with at least a couple of others. You must match their negotiation leverage by having your own options.



Dave Kauppi is a Merger and Acquisition Advisor and Managing Director of MidMarket Capital, providing business broker and investment banking services to owners in the sale of information technology companies. To view our lists of buyers and sellers click to visit our Web Site MidMarket Capital

Friday, August 15, 2014

New Engagement - Revolutionsry Big Data Clinical Decision Support Analysis Engine and Database



Big Data Analysis Engine Optimizes Disease Treatment & Speeds Drug Discovery      


  • Initial "Freemium" version has 15,336 registered users with the highest concentration coming from (in order) The US National Institute of Health, Harvard’s Dana Farber Cancer Research Center, Stanford Medical School, Johns Hopkins, The Mayo Clinic, M.D. Anderson, and Mt. Sinai

  • Super computational AI systems biology machine has read the entire US National Library of Medicine Medline Archive, extracted all key clinical outcome data from over 200 years of research and integrated it into real time treatment optimization and new cure discovery solutions.

  • Database tracks: 11,600 diseases and rates247,600 drugs and biological agents1,224 therapeutic techniques and 1,416 pharma companies for achieved successful clinical outcomes
  • Commercialized Products and Services: Company solutions are productized as 1. evidence-based publications of best treatment options for patients,  2. Outcome-centric on line real time Clinical Decision Support Systems for physicians, and 3. Contract Services for new drug discovery real time drug repurposing for pharma manufacturers.
The Company's Systems Biology Machine was designed to meet the NIH "Bench to Bedside Program" goal for rapid translation of pure research into an Evidence-Based Medicine System usable in real time at the point of clinical care, and the FDA "Critical Path Initiative" goal for rapid discovery of new cures using built in CA-DDD: Computer Aided Drug Discovery and Development models.

Operationally, Company products are  web browser-accessible applications easily integrated  into all software as service solutions from leading health information/EMR Cloud providers seeking to improve patient outcomes through Personalized Medicine, evidence-targeted therapies, and side by side cost  to efficacy comparative data.  The company’s clinical outcome database is the largest in the world based solely on proven clinical efficacy and is directly integrated with Network Graph Theory algorithms for new cure discovery. Its use preempts futile path investments in the hundreds of millions of R&D dollars.  The ability of the Company's Machine to autonomously discover new cures for human disease—in 3-4 minutes of computation--has been formally demonstrated to the NSF, NIH, and FDA.  Early adopter sales have been made for each product line segment.

Our Client has engaged MidMarket Capital to locate a Strategic Buyer / Investor that could capitalize on their world class technology solution, leverage their intellectual property and scale into a very large and receptive market space.

We are exclusively representing this Company to your firm as part of an offering to a select group of qualified investors.

Dropbox Link for Profile and NDA

https://dl.dropboxusercontent.com/u/12507505/%23%20140811%20%20PROFILE%20and%20NDA.pdf
 

 

Dave Kauppi is a Merger and Acquisition Advisor and Managing Director of MidMarket Capital, providing business broker and investment banking services to owners in the sale of information technology companies. To view our lists of buyers and sellers click to visit our Web Site MidMarket Capital