Impact of Innovation on Financial Results in the Pharmaceutical Industry

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

2. Introduction • 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

3. Background Info • 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

4. Hypotheses Hypothesis One: Stock Pricet = ƒ(Patentst-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 Intensityt = ƒ(Profitabilityt-1) R&D expenditures this year should positively correlate with last year’s financial performance Hypothesis Three: Patent Growtht ≠ ƒ(Patent Growtht-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 Table 5. Autocorrelation values for one-year lag for first differences of patent performance relative to the industry Firm Autocorrelation Value Pfizer 0.243 Merck 0.224 J&J 0.065 Bristol-Myers -0.276 Wyeth 0.002 Lilly 0.380 Schering -0.396 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

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