Abstract
Recent finance literature highlights the role of technological change in increasing firm specific (idiosyncratic) and aggregate stock return volatility, yet innovation data is not used in these analyses, leaving the direct relationship between innovation and stock return volatility untested. The paper investigates the relationship between volatility and innovation using firm level patent data. The analysis builds on the empirical work by Mazzucato (Rev Econ Dyn 5:318–345, 2002; J Evol Econ 13(5):491–512, 2003) where it is found that stock return volatility is highest during periods in the industry life-cycle when innovation is the most ‘radical’. In this paper we ask whether firms which invest more in innovation (more R&D and more patents) and/or which have more important innovations (patents with more citations) experience more volatility in their returns. Given that returns should in theory be higher, on average, for higher risk stocks, we also look at the effect of innovation on the level of returns. To take into account the competition between firms within industries, firm returns and volatility are measured relative to the industry average. We focus the analysis on firms in the pharmaceutical industry between 1974 and 1999. Results suggest that there is a positive and significant relationship between volatility, R&D intensity and the various patent related measures—especially when the innovation measures are filtered to distinguish the very innovative firms from the less innovate ones.
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Notes
“The practical difference between the two categories, risk and uncertainty, is that in the former the distribution of the outcome in a group of instances is known (either from calculation a priori or from statistics of past experience). While in the case of uncertainty that is not true, the reason being in general that it is impossible to form a group of instances, because the situation dealt with is in a high degree unique...” (Knight 1921, pp. 232–233)
Gambardella (1995) documents that although the guided regime did not increase the number of new molecules discovered, it did decrease the failure rate of those tested (hence making the process more efficient).
Pisano (2006) reports that it takes an average of 10–12 years for a company to get a drug out on the market. Only 10 %–20 % of drug candidates beginning clinical trials have been approved by the FDA.
CUSIP, operated by Standard and Poor’s, refers to the Committee on Uniform Security Identification Procedures, which identifies any North American security for the purposes of facilitating clearing and settlement of trades. It serves as the National Securities Identification Number for products issued from both the United States and Canada.
On average, nearly 95 % and 97 % of the merged sample is available when financial variables are matched with, respectively, R&D intensity and patents weighted by citations received.
Other sample selection criteria have been used in the literature. For example, in a related study on spill-overs and market value, Deng (2005) omits firms with less than 3 years in the Compustat database.
The return of a firm’s stock is defined as: \(\frac{\left( {P_t -P_{t-1} } \right)+D_t }{P_{t-1} }\).
The patent application date is listed by year, while patent grant date is listed by month.
Hall et al. (2001) notes that the significance of the R&D flow is reduced when cash flow is included as a regressor suggesting that at least part of the R&D flow effect arises from its correlation with cash flow. In contrast, the R&D stock variable is not sensitive to the inclusion of the cash flow variable. We test for this below and find that the cash flow variable is less significant than it is in Hall et al. (2001).
Another problem regarding citations is that since the propensity to cite is not constant, it is important to distinguish when an increase in the number of citations (e.g. technological impact of the patent) is “real” as opposed to “artefactual”. The latter includes the possibility that in some periods there was “citation inflation”, e.g. due to institutional factors (e.g. USPTO practices) and/or differences across fields.
To remove year and/or field effects, the number of citations received by a given patent are divided by the corresponding year-field mean, or only by yearly means to remove only year effects. The justification for the correction is to remove factors of time variability that are not related to substantial innovation, as in the case of legislative interventions which affect number of patents and citations (e.g. the Bayh–Dole act), or by the truncation issue. The problem with this method is that it does not distinguish between differences that are real and those that are artefactual (e.g. if patents in the 1990’s really did have more technological impact, removing the year effects ignores this real factor).
Given the distribution, which is assumed stationary and independent of the overall citation intensity, the authors estimate the total citations of any patent for which a portion of its citation life is observed. This is done by dividing the observed citations by the fraction of the population that lies in the time interval for which citations are observed (Hall et al. 2005, p. 13).
The number of firms that are contemporaneously present in the whole sample goes from 31 in 1980 to 187 in 2003, while the average number of patent applications per firm is (only) doubled in the same period.
Furthermore, the FE approach suggested in Jaffe and Trajtenberg (2002) removes the time series variability, since the evolution of innovative intensity over time is substantially extracted by the correction.
The market share instability index is defined in Hymer and Pashigian (1962): \(I=\sum\limits_{i=1}^n {\left[ {\left| {s_{it} -s_{i,t-1} } \right|} \right]} \), where s = market share of firm i, and n = number of firms.
This assumption is questionable, since it is likely that the omitted factors that are relevant for the dependent variable are also relevant in determining the explanatory variable (Mundlack 1978). As regards our specific analysis, the omitted factors no doubt include tacit knowledge and managerial capabilities, factors that have relevant effects on both innovative activity and the market performance of a given firm.
However, out of interest we ran the same equation with Return-Earnings as the dependent variable, the sign is again negative as would be expected. Small innovative firms tend to have higher P/E both because their earnings are lower but also because the growth expectations driving returns is higher.
This is one of the reasons for its low R&D productivity, a delusion for those that hoped that biotech’s more nimble structure would save pharma’s low turnout of new drugs.
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The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under Socio-economic Sciences and Humanities, grant agreement n° 217466 (www.finnov-fp7.eu). The authors are also grateful to the ESRC Innogen centre for support.
Comments from participants at the 2010 International Schumpeter Conference and at seminars (Kites-Bocconi), and workshops (CERF Cambridge workshop on ‘Technology and Bubbles’) were very useful. All errors remain our own.
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Mazzucato, M., Tancioni, M. R&D, patents and stock return volatility. J Evol Econ 22, 811–832 (2012). https://doi.org/10.1007/s00191-012-0289-x
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DOI: https://doi.org/10.1007/s00191-012-0289-x