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Radical Innovation and Stock Price Volatility:

Radical Innovation and Stock Price Volatility: patent citation dynamics and idiosyncratic risk in pharma-biotech. Mariana Mazzucato (Open University) Massimiliano Tancioni (University of Rome) Workshop on Finance, Innovation and Inequality London, November 9, 2007.

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Radical Innovation and Stock Price Volatility:

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  1. Radical Innovation and Stock Price Volatility: patent citation dynamics and idiosyncratic risk in pharma-biotech Mariana Mazzucato (Open University) Massimiliano Tancioni (University of Rome) Workshop on Finance, Innovation and Inequality London, November 9, 2007

  2. Questions arising from some old and new work TODAY Mazzucato, M. and Tancioni, M. (2006),“Stock Price Volatility & Patent Citations: the case of pharma-biotech”,work in progress BACKGROUND Mazzucato, M. and Tancioni, M. (2006), “Idiosyncratic Risk & Innovation: a Firm and Industry Level Analysis,” OU Discussion Paper, 50-06. Mazzucato, M. (2003), “Risk, Variety and Volatility: Innovation, Growth and Stock Prices in Old and New Industries,”Journal of Evolutionary Economics, Vol. 13 (5): 491-512. Mazzucato, M. (2002), “The PC Industry: New Economy or Early Life-Cycle,” Review of Economic Dynamics, Vol. 5: 318-345. Mazzucato, M. and W. Semmler (1999), “Stock Market Volatility and Market Share Instability during the US Automobile Industry Life-Cycle,”Journal of Evolutionary Economics, Vol. 9: 67-96.

  3. Aim and motivation • To link stock price volatility dynamics to innovation, using firm level innovation data. • Very little empirical work on this. • Provide link between industry dynamics and financial dynamics. • Contribute to understanding impact of real activity underlying stock price bubbles (vs. more ‘irrational’ stories).

  4. Innovation = ‘Knightian’ uncertainty • The starting point for any financial model is the uncertainty facing • investors, and the substance of every financial model involves the • impact of uncertainty on the behaviour of investors, and ultimately, on • market prices.” (Campbell, Lo and MacKinlay, 1997) Innovation →uncertainty about future growth (high hopes/failures): • R&D can lead to dry hole • Persistence: one off success or new industry leader • Effect on industry structure: competence destroying innovations → shake-up status quo • Idiosyncratic (albeit cumulative) and tacit evolution of capabilities Uncertainty about expected growth → Volatility.

  5. Some facts on volatility • No increase in trend of market volatility (using monthly value weighted composite indices NYSE/AMEX/Nasdaq) between 1926-2000 (Schwert 1989; 2002). • Peaks in late 20’s, 1970’s oil shock, 1987 crash. Annual std (monthly data): 1990-97=11%, 1970s= 14%, 1980s= 16%. • However, firm specific volatility (idiosyncratic risk) has increased over the last 40 years (Campbell et al. 2001). Doubled since 1962. Declining correlation between individual stocks and decreased explanatory power of CAPM model for a ‘typical’ stock. Recent papers relate firm specific volatility to technological change in a vague sense (Campbell et al. 2000; Shiller 2000). No data.

  6. Innovation → Stock PriceVolatility Most volatility studies don’t use innovation data (just broad assumptions around impact of innovation on uncertainty). 1. “Excess Volatility” (Shiller 1981, 1989, 2000) Animal spirits, herd behaviour, bandwagon effects. 2. “Idiosyncratic Risk” (Campbell et al. 2000) Increase in idiosyncratic risk since the 1960’s. Why? Effect of IT revolution on speed of information. 3. “Rational bubbles”(Pastor and Veronesi 2006): Volatility rises before idiosyncratic risk becomes systematic risk.

  7. Excess Volatility and Technological Revolutions Shiller, R.J. (1981). “Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends,” AER, 71.

  8. Excess Volatility and Technological Revolutions Shiller, R.J. (1981). Do stock prices move too much to be justified by subsequent changes in dividends? AER 71(3): 421-36 Stock prices are 5 x more volatile than can be justified by changes in fundamentals Efficient market model: real price = expected value of discounted future dividends V* = the ex-post rational or perfect-foresight price D = the dividend stream γ = real discount factor = r = short (one-period) rate of discount where

  9. Idiosyncratic Risk and IT Revolution Campbell, J.Y., Lettau, M., Malkiel, B.G., and Yexiao, X. (2000), Have Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk, Journal of Finance, 56. ------------------------------------------------------------------------------------------- Use high-frequency time series data on daily stock returns for the general market, industries and firms for the period 1963-1997. Using variance decomposition of a CAPM equation, decompose return of a typical stock into market wide return, industry specific residual, and firm-specific residual (sum to total volatility). Result: positive deterministic time trend in stock return variances for individual firms; not for market and industry returns. Why? (a) IT effects on speed of information (b) Companies have begun to issue stock earlier in their life cycle, when there is more uncertainty about future profits.

  10. Yet none of these studies actually use innovation data. Just assume that volatility is a sign of uncertainty and that this is highest during periods of technological change.

  11. Firm/Industry Innovation and Stock Price Volatility • Uncertainty is better studied at the micro level, i.e. related to the • firm’s environment. Evidence that most shocks are • idiosyncratic to the firm or plant (Davis and Haltiwanger 1992). • Look at IR and EV over the industry life-cycle • Mazzucato, M. (2002), “The PC Industry: New Economy or Early Life-Cycle,” Review of Economic Dynamics, Vol. 5: 318-345.

  12. Industry Life-Cycle (Mazzucato 2002)

  13. Quality Change Autos & PCs (auto: Raff /Trajtenberg 1997 [Abernathy et al. 1983]; PC: Berndt /Rappaport 2000)

  14. Movement of 28 Leading Auto Producers Ranked According to Places in Production (Epstein, 1928)

  15. “Excess Volatility” in Autos(Mazzucato 2002; 2004)

  16. Excess Volatility in PCs(Mazzucato 2002; 2004)

  17. Conclusions • Co-evolution of industrial and financial volatility over the industry life-cycle. • Volatility may look ‘irrational’ but tied to real changes in technology. • More volatility of stock prices in phase of “competence-destroying” innovations.

  18. New work using firm level innovation data • Test for relationship between volatility and innovation, using firm • level innovation data. Test across different industries. • Mazzucato, M. and Tancioni, M. (2005), “Idiosyncratic Risk & • Innovation: a Firm and Industry Level Analysis,” OU wp, 50-06. • Is “idiosyncratic risk” higher for “innovative” industries and firms • (higher R&D intensity)? Sectoral taxonomy of innovation (Pavitt • 1984) → Sectoral taxonomy of stock price dynamics? • __________________________________________________________ • 2. Mazzucato, M. and Tancioni, M. (2006),“Stock Price Volatility & Patent • Citations: the case of pharma-biotech”,work in progress • Do firms with patents with higher citation intensity experience more • idiosyncratic risk? • Relation between P/E and innovation. Implications for bubbles.

  19. Financial Data • Industry level data: Standard and Poors Analysts Handbook • quarterly stock price, dividends, earnings, R&D • 34 industries • 1976-1999 • sectoral taxonomy (R&D intensity) • ______________________________________________________________________________________________ • Firm level data: Compustat • monthly stock price, dividend, earnings, R&D • 1974-2003 • annual volatility via standard deviation of 12 month terms • unbalanced panel: • Biotechnology (435 firms) • Computers (129 firms) • Pharmaceutical (282 firms) • Textile (78 firms) • Agriculture (45 firms)

  20. R&D Intensity by sector, avg 1980-1992

  21. Idiosyncratic Risk Stock return for firm i: IR=Volatility of firm i (or industry j)returns vs. market M returns S&P 500) Proxy for Idiosyncratic risk for firm i:

  22. Methodology • Is idiosyncratic risk higher in innovative firms and industries? • INDUSTRY LEVEL • 1. Develop 34 bivariate VAR representations of the industry-level • and market-level stock returns, and perform a Forecast Error • Variance Decomposition (FEVD) analysis in order to capture the • degree of idiosyncratic risk of the series. • If growth more uncertain in innovative industries, expect that % of • industry level predictive error variance is mostly explained by • idiosyncratic (industry) shock. Expect to find that the forecast error • variance explained by the market shock (i.e. S&P500) should be • lower in innovative sectors and higher in less innovative sectors.

  23. 2. CAPM model. Pool the industry-level data obtaining a balanced panel with time dimension T (88 observations) and sectional dimension N (34 observations). Regress the industry-level stock returns on industry specific dummies (Fixed Effects) and the S&P500 returns. Allows test of (a) EMM, and (b) heterogeneity in section. Expect the variability explained by the regression to be higher for the ‘low innovative’ industries and lower for the ‘high innovative’ industries.

  24. b. FIRM LEVEL Employ panel of 822 firms belonging to 5 industries- 1974-2003 – and directly test the existence of a positive relationship between idiosyncratic risk and innovative effort (R&D intensity). Estimate panel regressions in which firm-level IR depends on R&D effort and the firm’s relative weight in terms of market capitalization. Analysis conducted both employing the whole panel sample and the five different industry-specific panels of firms.

  25. Part 1: Conclusion Industry level analysis: mixed results (similar to Campbell et al.). Expectations hold only for some industries in extremes of the taxonomy (e.g. very innovative semiconductors, very low innovative public utilities). Firm level analysis: strong relationship between idiosyncratic risk and R&D intensity. Most interesting result: Relationship not stronger for firms in more innovative industries. Relationship holds stronger in textiles (low-innovative) than in pharmaceuticals (highly innovative). Perhaps because the low average R&D intensity in textiles makes innovative firms in that industry ‘stick out’. And holds stronger in biotech due to higher uncertainty than in computers and pharma. Dynamic nature of volatility: computers (1989-1997) and biotechnology (1995-2003).

  26. Part 2: Patent Citation Data and Stock Return Volatility • Mazzucato, M. and Tancioni, M. (2006),“Stock Price Volatility & Patent • Citations: the case of pharma-biotech”,work in progress Citation weighted patents measure importance of innovation. • HJT question: how well do patents measure economic performance? • Our question: are firms with higher R&D intensity, more patents, and more “important” patents characterized by more uncertainty, and hence higher volatility? • If so, this provides some insights into the real aspects of volatility dynamics (rather than animal spirits, irrational exuberance). • Patents as signals of innovations

  27. Why focus on pharma-biotech? • Industry life-cycle approach • A sector with high R&D and high patenting rates • Changing knowledge regimes (Gambardella 1995): • Do volatility dynamics evolve over industry life-cycle with changing knowledge regimes? • Do stock prices react to innovation more in random or guided search regime?

  28. Data • GIC codes 352010 for Biotech and 352020 for Pharmaceuticals. • S&P Compustat • monthly stock price, dividend, earnings, R&D • compute annual volatility via standard deviation of 12 month terms • unbalanced panel: biotech (563 firms) and pharma (323 firms) • NBER patent citation data • Detailed patent related information on 3 million US patents granted between January 1963 and December 1999, and all citations made to these patents between 1975 and 1999 (over 16 million). • Annual data, using application date (more uncertainty!) • Merged sample: 126 pharma firms and 177 biotech firms.

  29. Variables (all in logs) Idiosyncratic (IDRISK) = stdev RET firm i / stdev RET market Price earnings ratio (PE) R&D intensity (RDREV) = R&D/Revenues (both real) Patent count (PAT) = annual number of patents for firm i divided by average number of patents per firm in industry j. Weighted patents (PATW) = number of citations received by firm i divided by number of patents for firm i, all divided by same ratio for industry (avg for firm) Patent yield (PATY) = Patents/R&D SIZE= Control for firm size (market share) CAPSHARE= Control for firm’s share of market capitalization

  30. Hypotheses (plus all the controls and dummies) 1. Market value is related to innovation indicators (R&D, Patents). (Trajtenberg 1990; Hall, Jaffe, Trajtenberg 2001) 2. Idiosyncratic risk is related to innovation indicators. (Campbell et al. 2001; Mazzucato and Tancioni 2005)

  31. Hypotheses 3. Bubbles: Price-Earnings related to “Idiosyncratic Risk” (Pastor and Veronesi 2004) • 4. P/E related to innovation indicators.

  32. Periods and samples tested • Periods: • a) Full dates: 1975-1999 • b) Truncation: re-run the estimates fixing the end date: 1995 (find no difference). • c) Test for possible structural breaks (e.g. institutional changes after Bayh-Dole) using post 1985 dummy. (significant). • Test 3 different samples: • 1. Whole sample (with biotech dummy) • 2. Pharma sample • 3. Biotech sample

  33. Results for Model 1 (MV on innovation) R&D intensity and un/weighted patents coefficients are both positive and statistically significant. When add patents, R&D less significant, signaling possible correlation between R&D (-2) and patents (- 1), especially biotech. Flows are just as important as stocks (used in HJT). Citation weighted patents not more significant than un-weighted. Higher lag on R&D (3) than patents (1). Firm size control: positive and significant (pharma and bio). Dummy post 1985: positive and significant (pharma and bio). Bets fit of all models.

  34. Results for Model 2 (IR on innovation) Coefficients on R&D intensity and patents (count and weighted) are positive and statistically significant (but less than in Model 1) Lag on R&D falls to 1, i.e. volatility reacts quicker than levels of MV and PE (3 lags in model 1 and 4), Lag on patents also falls to 1 (only model where same lag for R&D and patents). Firm size control:negative and (as expected) and significant Post 1985 (dummy): positive and significant only in 2a (higher volatility post 1985 as in Campbell et al. 2000, but not when include patents).

  35. Results for Model 3 (PE on IR) • Positive and statistically significant relation between P/E and IR. • Best estimates are obtained when measure of IR is entered • with 2 lags. • Suggesting that • volatility leads levels (supported in previous results) • innovation leads volatility • Some evidence for rational bubble hypothesis

  36. Results for Model 4 (PE on innovation variables) R&D intensity and weighted patents coefficients are both positive and statistically significant, providing support to ‘rational bubble’ hyp Better fit than Model 3. Un-weighted patents not significant. Only model in which patent yield is is significant! Best fit obtained with: 3 lags R&D intensity, 2 lags patents. Firm size (control): negative and significant Dummy post 1985: positive and significant

  37. Biotech differences… Biotech firms have on average: 10% less MV 30-35% more IR 5% higher P/E R&D significant only for model 4 (perhaps because biotech is so R&D intensive—don’t stick out?) Patents insignificant in model 4 (except patent yield). Slightly lower lags (market reacts quicker to new segments of the industry?). Stronger correlation between R&D (-2) and Patents (-1) (supported by higher mean patent yield in biotech). Post 1985 lower P/E (unlike pharma).

  38. Conclusions • Volatility of firm specific returns related to firm innovation. • MV and PE levels also related to firm innovation. • Lags: IDRISK reacts more quickly to innovation than MV, PE quicker in Biotech than in pharma quicker to patents than to R&D • Dynamic correlations between R&D (-2) and Patents (-1) • Smaller firms have lower market value, but higher volatility and PE. • Possibility of structural break post 1985 (explore further)

  39. Future extensions • Innovation characteristics: general vs. original • Temporal dimension: recent vs. old citations • Does volatility react to patents more so in periods of high or low tech opportunity?

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