1. Impact of Innovation on Financial Results in the Pharmaceutical Industry Benjamin Jonen Kevin Mabe

2.  Primary motivation: Exploring relationship between innovation efforts and stock price  Innovation efforts reflected by number of patents (reason: industry specific problem of reverse innovation)  Granted patent  Success in the R&D race  Monopolistic market structure (USA) for a limited time  Expiration allow for generic copycats to enter the market  Stock Price = PV of future profits  Approval of patent increases expected future profits  Idea: Due to industry peculiarities, patents are significant driving force behind stock price Introduction

3.  Interpretation of results necessitates basic understanding of patenting process  Important fact to notice: Effective patent life does not begin until 8 years after discovery of compound Background Info

4. Hypotheses  Hypothesis One: Stock Price t = ƒ(Patents t-h )  Granted patents in year ‘t-h’ are primary driver of stock price in ‘t’  Lag ‘h’ should mimic the expected empirical lag seen in industry between a patent’s grant and the compound’s introduction to market  Intuition: Enhanced future profits are ensured only after FDA approval and successful introduction to the market  Hypothesis Two: R&D Intensity t = ƒ(Profitability t-1 )  R&D expenditures this year should positively correlate with last year’s financial performance  Hypothesis Three: Patent Growth t ≠ ƒ(Patent Growth t-1 )  Competitive environment creates difficulty for a firm to maintain prolonged innovative growth relative to the industry

5. Data  Group of seven major pharmaceutical firms  Pfizer, Merck, Johnson & Johnson, Bristol-Myers Squibb, Wyeth, Eli Lilly, and Schering-Plough  All in top 15 of world industry  Count of granted patents by firm, annual,1988 to 2005  Study uses the ratio of granted patents for a firm relative to the arithmetical mean for the group for a given year  Data transformation allows for inter-firm comparisons  Stock price by firm, annual, 1988 to 2005  Similar data transformation as patents  Mitigates stock bubble effects and measures relative performance

6. Data (cont.)  Measure of commitment to innovation, 1994 to 2005  Ratio of R&D expenditures to total annual sales  Measure of financial success, 1994 to 2005  Ratio of profit to total annual sales (profit margin)  Methodology  Time series analysis  Autocorrelation for a variable  Cross-correlation between two variables

7. Analysis – Hypothesis One  Rise in relative patent grants for Johnson and Johnson between 1993 and 1998 appears related to surge in relative stock price in the 1990’s.  Six of seven firms show statistically significant cross-correlation at lags of seven to nine years, corroborating the empirical time between the granting of patents and eventual drug introduction on the market  Little evidence to reject Hypothesis One

8. Analysis – Hypothesis Two  Johnson and Johnson appears to adjust R&D expenditures based on previous year’s profitability  Mix of positive and negative cross-correlations for a one-year lag implies conflicting evidence against supporting Hypothesis Two

9. Analysis – Hypothesis Two (cont.)  Furthermore, the lag associated with the highest cross-correlation for each firm ranges from -1 to 1 year.  Evidence shows sufficient support to reject Hypothesis Two

10. Analysis – Hypothesis Three  Johnson and Johnson’s patent growth oscillates about a zero mean, and doesn’t appear to remain positive or negative for long lags  Five of the seven firms show low autocorrelation for a one-year lag  Mild evidence to uphold Hypothesis Three Table 5. Autocorrelation values for one-year lag for first differences of patent performance relative to the industry FirmAutocorrelation Value Pfizer0.243 Merck0.224 J&J0.065 Bristol-Myers -0.276 Wyeth0.002 Lilly0.380 Schering -0.396

11. Conclusions  Hypothesis One  Past innovations positively impact future stock performance  Lag between granted patents and future stock price movements consistent with the lag between patents and drug introduction  Implication: Relative patent performance today can be used to help explain tomorrow’s movements in stock price  Hypothesis Two  This year’s profitability does not necessarily drive next year’s commitment to innovation and R&D intensity in the short run  Low sensitivity of R&D expenditures to profits most likely caused by high costs of altering R&D capacity (firms plan years ahead)  Implication: Exploring the important policy issue of excessive profits in the pharmaceutical industry relies on a larger data set

12. Conclusions (cont.)  Hypothesis Three  Relative patent growth in one year does not necessarily translate into continued superior performance above the industry average  Lagging firms have ability to catch up to industry in R&D race  Implications:  Winning the lottery today does not guarantee winning tomorrow  Fiercely competitive environment implies a pharmaceutical firm must continuously fight to innovate  Overall  Commitment to R&D drives measurable financial reward  Link between R&D and profitability not strongly established  Patent race highly competitive and drives continued innovation

13. Relevance to Industrial Organization  R&D race is an important research area in IO  Stock price (value of a firm) does not react significantly until patent is granted and drug is sure to enter the market (i.e. patents are the lifeblood of a company)  Winning the lottery is random, confirming Oz Shy’s assumption of model of innovation race which assigns the same probability of discovery for both firms participating in the modeled race  After patent expires, competitors flood the market  Reverse innovation is a cheap way to enter the market: combination of low sunk costs (low barriers to entry) and initial prices above marginal cost  Patents are important tools to stimulate R&D behavior  Patent count remains a convenient measure of innovation in the pharmaceutical industry