Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2008. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 84-85). / Valuing medical device companies and technologies is a complex process. Several different approaches and models are often used in combination to determine a transaction valuation. This research uses the Enterprise Value to Forward Sales model as a tool for valuing mergers and acquisitions in the device industry. This model was selected for its transportability across industry segments, ease of calculation, broad acceptance, and lack of detailed forecasting assumptions. This research seeks to: 1) explore the importance of 20 commonly cited factors in determining a medical device company EV/Sales multiple, 2) develop a model for forecasting the EV/Sales multiple of medical device transactions, and 3) assess the explanatory power of these factors in determining the enterprise value (measured in dollars) of pre-revenue transactions. For purposes of this analysis valuation was approached from a sector neutral or portfolio diversification perspective. Multivariate regression analysis was performed on a database of 352 M&A transactions announced between January 1, 1996 and December 31, 2007 to assess the importance of various factors and develop a model for forecasting EV/Sales multiples. Consistent with our expectations, supernormal growth, industry growth, market size, sector beta, position in market, venture funding, and IPO status were all significant factors in determining the multiple. Based on these factors, we developed a model that was 95% accurate in forecasting the EV/Sales multiple of medical device transactions that occurred between January and May of 2008. / (cont.) Based on the success of this model, we then explored the utility of these factors in determining the gross enterprise value of pre-revenue M&A transactions. As expected, this approach was not successful. Varying discount rates, timing assumptions, difficult to determine value synergies, and emotion are confounding factors which make it difficult to reliably forecast absolute dollar transaction valuations. / by Jason S. Robins. / S.M.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/55276 |
| Date | January 2008 |
| Creators | Robins, Jason S |
| Contributors | T. (Teo) Forcht Dagi and Carl Berke., Harvard University--MIT Division of Health Sciences and Technology., Harvard University--MIT Division of Health Sciences and Technology. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 85 p., application/pdf |
| Rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission., http://dspace.mit.edu/handle/1721.1/7582 |
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