Short sellers and innovation: Evidence from a quasi-natural experiment
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`Jie (Jack) He
` Terry College of Business
`University of Georgia
`jiehe@uga.edu
`(706) 542-9076
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`Xuan Tian
`Kelley School of Business
`Indiana University
`tianx@indiana.edu
`(812) 855-3420
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`This version: March, 2014
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` We thank Russell Investments for providing us the list of Russell 3000 index used in this paper. We remain
`responsible for any remaining errors or omissions.
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` Electronic copy available at: http://ssrn.com/abstract=2380352
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`Coalition for Affordable Drugs IV LLC - Exhibit 1041
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`Short sellers and innovation: Evidence from a quasi-natural experiment
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`Abstract
`We examine the causal effect of short sellers on innovation. Using exogenous variation in short-
`selling costs generated by a quasi-natural experiment, Regulation SHO, which randomly assigns
`a subsample of the Russell 3000 index firms into a pilot program, we show that short sellers have
`a positive, causal effect on firm innovation. The positive effect of short sellers on innovation is
`more pronounced when firms are subject to a larger agency problem and a higher degree of
`information asymmetry. Our paper provides new insights into an under-explored and possibly
`unintended real effect of short sellers – their encouragement for firm innovation.
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`Key words: Innovation; Short selling; Regulation SHO; Pilot program
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`JEL number: G14; G18; O31; O32
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` Electronic copy available at: http://ssrn.com/abstract=2380352
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`Coalition for Affordable Drugs IV LLC - Exhibit 1041
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`1. INTRODUCTION
`There has been an intensive debate about the economic impact of short sellers among
`academics, practitioners, and regulators in the past few decades. Critics of short sellers claim that
`they affect security prices adversely, lead to high market volatility, and undermine investors’
`confidence in the economy because of panic selling. Moreover, some anecdotes suggest that
`short sellers, given their strong incentives to profit from their short positions, may engage in
`opportunistic or even unethical behavior by disseminating pessimistic, false rumors about a firm.
`However, advocates of short selling take an opposite stand and argue that short sellers actually
`help improve market efficiency, facilitate price discovery, and prevent financial misconducts due
`to their active information production and intensive monitoring of the corporate management.1
`While there might be an element of truth in both sides of these arguments, in practice it is
`hard to identify the causal effect of short sellers on the real economy due to the endogenous
`nature of short sales: short selling activities could give rise to or result from the underlying
`characteristics of the corporate sector. For instance, a drop in stock prices following a period of
`active short sales may imply that short sellers depress the price level via their trading, but it
`could also reflect the fact that short sellers merely predict an upcoming decreasing trend in the
`stock market and thus trade on their expectation.
`In this paper, we exploit a quasi-natural experiment, Regulation SHO, to tackle the above
`endogeneity problem and provide the first empirical study that examines the causal effect of
`short sellers on technological innovation, which is perhaps the most important driver of
`economic growth. The impact of short sellers on innovation is of particular interest to policy
`makers and firm stakeholders not only because innovation is a crucial driver of a nation’s
`economic growth (Solow, 1957) and competitive advantage (Porter, 1992), but also because
`short selling activities in the U.S. are highly regulated and can be altered by a series of security
`laws and regulations over time.
`We rely on existing literature and the prevailing views of short selling to propose two
`competing hypotheses regarding the effect of short sellers on firm innovation. Our first
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`1 In a highly publicized case, short seller Muddy Waters LLC discovered that Sino-Forest, a Canadian company that
`had its operations in China, exaggerated the level of its principal assets (i.e., trees) that were not even owned by the
`company. In contrast, Sino-Forest’s auditor, Ernst & Young, failed to detect the accounting fraud but still claimed
`“we are confident that Ernst & Young Canada’s work… met all professional standards. … Ernst & Young Canada
`did extensive audit work to verify ownership and existence of Sino-Forest’s timber assets.” (New York Times,
`December 6, 2012)
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`hypothesis conjectures that short sellers impede firm innovation. Short sellers are often accused
`of creating tremendous price pressure on a firm’s stock (e.g., Mitchell, Pulvino, and Stafford,
`2004), which leads to excessive pressure on managers to focus on short-term activities,
`exacerbating the managerial myopia problem (e.g., He and Tian, 2013; Fang, Tian, and Tice,
`2014). Manso (2011) theoretically shows that tolerance for failure is necessary for effectively
`motivating and nurturing innovation due to the long-term, risky, idiosyncratic, and unpredictable
`nature of technological innovation.2 However, short sellers have an innate distaste for tolerance
`towards short-term failures, because their main job is to identify underperforming firms, sell
`short these stocks to reflect their unfavorable information, and make trading profits. As a
`consequence, firm managers who care more about short-term stock prices as well as operating
`performance may sacrifice long-term firm value by cutting their investments in long-term, risky,
`but innovative projects to keep their stock prices high in the presence of short selling pressure.
`Therefore, our first hypothesis, the pressure hypothesis, argues that short sellers, by imposing
`short-term pressure on managers to prevent stock prices from falling, impede firm innovation.
`Alternatively, there are at least two plausible reasons why short sellers may, even
`unintentionally, encourage innovation. First, moral hazard models such as Grossman and Hart
`(1988) and Harris and Raviv (1988) suggest that managers who are not properly monitored will
`shirk or tend to invest more in unchallenging routine tasks to enjoy private benefits such as
`“quiet life” (Bertrand and Mullainathan, 2003). Value-destroying underinvestment in innovative
`projects due to agency problems could be mitigated by the threat of depressing stock prices from
`short sellers who have been shown to serve as an effective disciplinary force in the corporate
`setting (Karpoff and Lou, 2010; Massa, Zhang, and Zhang, 2013a, 2013b; Fang, Huang, and
`Karpoff, 2013). Whenever short sellers detect managerial slack such as shirking on long-term
`innovative projects, they could immediately short sell the company’s stock, leading to negative
`market reactions and potential disciplinary actions against the managers, including reduced
`bonuses and even forced managerial turnover. As a result, managers would be motivated to work
`hard and maximize firm value by making value-enhancing investment in innovative projects.
`Second, as Holmstrom (1989) points out, innovative activities involve exploring untested
`and unknown approaches that have a high probability of failure, which makes the innovation
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`2 Recent empirical papers such as Acharya et al, (2013, 2014), Ederer and Manso (2013), and Tian and Wang (2014)
`all find supporting evidence for the implications of the failure tolerance theory.
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`process risky and long. Therefore, firms investing more heavily in innovative projects may be
`subject to a greater degree of information asymmetry (Bhattacharya and Ritter, 1983), are more
`likely to be undervalued by equity holders, and have a greater exposure to hostile takeovers
`(Stein, 1988). To minimize the chance of such expropriation, managers tend to reduce their
`investment in long-term innovation projects (in many cases sub-optimally) and exert more effort
`on routine tasks that offer faster and more stable returns, resulting in a typical managerial myopia
`problem. Since short selling is a costly trading strategy to implement, short sellers, relative to
`other stock market participants, generally spend more resources in information gathering and
`processing. Consequently, they offer a potential remedy for the above underinvestment problem
`in innovation by actively producing information about firm fundamentals, short selling over-
`valued stocks, and making stock prices more efficient.3 While the actual trading actions by short
`sellers reveal bad news (i.e., overvaluation) about the short-sold stocks, the fact that short sellers
`do not take actions after producing information about a firm’s fundamentals itself conveys good
`news to the equity market. In other words, the possibility of short selling a stock (rather than the
`actual trading of it) can potentially reduce the information asymmetry (as well as the associated
`stock undervaluation problem) for innovative firms. Since firm-specific information, including
`that concerning innovative activities, can now be better incorporated into current stock prices,
`firms exposed to a larger number of potential short sellers would be more willing to engage in
`value-enhancing innovation activities.4
`Taken together, the alternative hypothesis argues that short sellers, by reducing
`information asymmetry of innovative firms and actively disciplining managers, encourage firm
`innovation. We term this view the disciplining and information hypothesis.
`We test the above two hypotheses by examining the effect of short sellers on firm
`innovation. Obtaining patent information mainly from the National Bureau of Economic
`Research (NBER) Patent Citation database, we use the number of patents granted to a firm and
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`3 Boehmer, Jones, and Zhang (2008, 2013) show that short sellers are important contributors to efficient stock
`prices. Boehmer and Wu (2013) find that stock prices are more accurate when short sellers are more active.
`4 It is conceivable that information revelation and transparency may sometimes reduce firms’ incentives to innovate
`due to strategic concerns. If most of the information that short sellers could produce is about firms’ innovation-
`related business secrets, then firms exposed to a larger number of short sellers may actually reduce their innovation
`activities, especially when these activities need to be hidden from industry and product market competitors.
`However, as outsider information producers, short sellers are unlikely to access the core technology or business
`secrets of the firms they sell short, so the possible leakage of strategic information through their information
`production activities does not appear to be a serious concern for our study. Moreover, this “adverse” consequence of
`information production by short sellers actually predicts the opposite to our main findings below.
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`the number of future citations received by each patent to measure innovation output.
`Specifically, the former captures the quantity of innovation and the latter proxies for the quality
`of innovation. Our use of patenting to capture firms’ innovation output has become standard in
`the innovation literature (e.g., Acharya et al., 2014; Aghion et al., 2013; Nanda and Rhodes-
`Kropf, 2013).
`As we argued before, identifying the causal effect of short sellers on firm innovation is
`challenging because of the endogenous nature of short selling activities. There may exist
`unobservable firm characteristics that are correlated with both short selling and innovation,
`introducing biases to our causal inferences. It is also possible that the causality goes the other
`way around: firms’ innovation potential could affect their attractiveness to short sellers.
`Therefore, in this paper, we use a quasi-natural experiment, Regulation SHO, to identify the
`causal effect of short sellers on firm innovation.
`Short selling activities in the U.S. have been largely constrained historically. For
`example, the uptick rule, which was established in 1935, prohibits short sales when stock prices
`are declining, imposing significant costs on short sellers. In July 2004, the Security and
`Exchange Commission (SEC) announced a new regulation on short-selling activities in the U.S.
`equity market, Regulation SHO, which removed the uptick rule restriction for an ex-ante
`randomly selected pilot group of firms (about one third of the Russell 3000 firms listed on
`NYSE, NASDAQ, and AMEX). Meanwhile, the uptick rule remained in effect for the non-pilot
`Russell 3000 firms (i.e. the rest two thirds of the Index). This sudden regulatory change, by
`significantly reducing the cost of short selling only for pilot firms but not for non-pilot firms,
`provides us a nice quasi-laboratory setting to observe the causal impact of short sellers on firm
`innovation, as it was not initiated to alter firms’ investment behavior in anyway. Another crucial
`advantage of this experiment is that it does not require pilot firms to experience an actual
`increase in short selling activities (and the corresponding price pressure) after the regulatory
`shock. The mere threat (or possibility) of becoming more likely to be shorted will influence
`managerial behavior and affect their incentives to innovate. Hence, we adopt a difference-in-
`differences (DiD) method to analyze how firms’ innovation outputs are affected by this
`exogenous shock to short-selling constraints.
`After performing various diagnostic tests to ensure that the parallel trends assumption,
`the key identifying assumption of the DiD test, is satisfied, we show a positive, causal effect of
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`short sellers on firm innovation. According to our multivariate DiD analysis, a reduction in short
`selling costs due to Regulation SHO leads to a larger increase in patent counts and patent
`citations for the treatment (pilot) group compared to the control (non-pilot) group. The evidence
`is consistent with the implication of the disciplining and information hypothesis.5
`We next perform two robustness tests for the baseline DiD analysis. First, to address the
`concern that our DiD results could have been driven by chance, we run simulations that
`randomize the inclusion of pilot firms in our analysis, and find that the DiD estimators obtained
`from this randomization test are on average close to zero. Second, to address the concern that
`unobservable shocks which are unrelated to Regulation SHO could have driven both the
`inclusion into the pilot list and firm innovation, we conduct a placebo test by artificially picking
`a “pseudo-event” year when we assume a regulatory shock reduced short selling costs for the
`pilot firms. We find no significant difference between the innovation activities of pilot firms and
`those of the non-pilot firms around such “pseudo-event” years. Thus, the identified positive
`effect of short sellers on firm innovation is unlikely to be driven by chance or by other earlier
`unobservable shocks, which further supports our causal inference.
`We further attempt to identify possible underlying mechanisms through which short
`sellers encourage firm innovation. To this end, we examine how cross-sectional variation in
`agency problems and information asymmetry alters our main results. We find that the positive
`effect of short sellers on innovation is more pronounced when firms have a larger exposure to
`agency conflicts between shareholders and management, i.e., when institutional ownership is
`lower, when product markets are less competitive, when corporate governance is poorer, and
`when CEO compensation is more sensitive to short-term performance. We also show that the
`positive effect of short sellers on innovation becomes stronger when firms are subject to a greater
`degree of information asymmetry, i.e., when firms are followed by a smaller number of financial
`analysts and when firms are smaller in size. The evidence suggests that both external disciplining
`and information production by short sellers are possible underlying mechanisms through which
`short sellers encourage firm innovation.
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`5 The pilot program ended on August 6, 2007 when the tick restriction was removed for all stocks. This feature of
`the experiment provides us a potential opportunity to check whether the effect of short sellers on innovation outputs
`reverted after the pilot program ended. Unfortunately, we do not have enough reliable information about firms’
`patenting activities after the end of the pilot program to carry out this analysis.
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`The rest of the paper is organized as follows. Section 2 discusses the related literature.
`Section 3 describes sample selection and reports summary statistics. Section 4 presents the main
`results. Section 5 discusses possible mechanisms. Section 6 concludes.
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`2. RELATION TO THE EXISTING LITERATURE
`Our paper contributes to two strands of literature. First, our paper is related to the
`emerging literature on finance and innovation. Holmstrom (1989) shows that innovation
`activities are inherently different from and may not mix well with routine tasks in an
`organization. Aghion and Tirole (1994) argue that the organizational structure of firms matters
`for innovation. Manso (2011) suggests that corporate contracting environment plays an important
`role in the innovation process. He theoretically demonstrates that managerial contracts that
`tolerate failure in the short run and reward success in the long run are best suited to motivate
`managers to engage in innovation activities.
`Empirical evidence shows that various firm characteristics and economic forces affect
`managerial incentives of investing in innovation. For example, a larger institutional ownership
`(Aghion, Van Reenen, and Zingales, 2013), corporate rather than independent venture capitalists
`(Chemmanur, Loutskina, and Tian, 2013), debtor-friendly bankruptcy laws (Acharya and
`Subramanian, 2009), lower union power (Bradley, Kim, and Tian, 2013), and private instead of
`public equity ownership (Lerner, Sorensen, and Stromberg, 2011) all enhance managerial and
`employees’ incentives to innovate. Other studies have examined the effects of product market
`competition, general market conditions, firm boundaries, CEO overconfidence, banking
`competition, and failure tolerance on corporate innovation (e.g., Aghion et al., 2005; Nanda and
`Rhodes-Kropf, 2012, 2013; Hirshleifer et al., 2012; Cornaggia et al., 2013; Seru, 2014; Tian and
`Wang, 2014). However, existing literature has been silent on how short sellers affect firms’
`innovation activities. Our paper contributes to this line of research by filling in the gap.
`Our paper also contributes to the literature on short selling. There has been an intensive
`debate on the effects of short-selling constraints on asset prices. While many existing studies
`show that short-selling constraints inflate security prices above their fundamental levels, leading
`to overvaluation (e.g., Miller, 1977; Harrison and Kreps, 1978; Chen, Hong, and Stein, 2002;
`Hong and Stein, 2003), other studies find that short-selling constraints have little effect on the
`stock market (e.g., Battalio and Schultz, 2006; Diether, Lee, and Werner, 2009; Beber and
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`Pagano, 2013). However, the empirical literature that examines the effect of short sellers or
`short-selling constraints on corporate decisions is quite limited. Gilchrist, Himmelberg, and
`Huberman (2005) show that short-selling constraints distort firm investment and financial
`decisions. Karpoff and Lou (2010) and Hirshleifer, Teoh, and Yu (2011) find that short sellers
`are able to detect corporate financial misconduct and dump on suspicious firms. Henry, Kisgen,
`and Wu (2013) document that short sellers could identify firms that have significant changes in
`default probabilities and those whose credit ratings are about to downgrade. Massa, Zhang, and
`Zhang (2013a) show that short sellers reduce earnings management in a cross-country setting.
`Massa, Zhang, and Zhang (2013b) find that short sellers improve corporate governance.
`Two recent papers use the same quasi-natural experiment as ours to examine the real
`effect of short sellers on corporate finance activities. Grullon, Michenaud, and Weston (2013)
`show that an exogenous change in short-selling constraints causes stock prices to fall and
`financially constrained firms respond to the drop in prices by reducing equity issues and
`investment.6 Fang, Huang, and Karpoff (2013) find that an exogenous decrease in short-selling
`costs due to the SHO program reduces pilot firms’ propensity to engage in earnings management
`and that this pattern reverses when the difference in short-selling constraints between pilot and
`control firms disappears after the SHO program ends. Different from the above two studies, our
`paper focuses on the causal effect of the removal of short-selling constraints on firm innovation
`and provides the first empirical analysis to shed light on this important research question.
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`3. SAMPLE SELECTION AND SUMMARY STATISTICS
`3.1 Sample Selection
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`6 However, their empirical analysis only studies observable capital expenditures (including R&D expenses), which
`are easily verifiable and closely monitored by shareholders. Unlike ordinary investment activities that mainly rely on
`observable inputs, innovation activities involve the usage of many unobservable corporate resources (such as the
`allocation of talent, effort, and attention to innovative projects/divisions and internal incentive schemes including
`mental support and non-monetary awards), which cannot be easily verified by the investors and thus are subject to
`more managerial discretion. Hence, innovation investments are much more likely than ordinary capital expenditures
`to be affected by the incremental disciplining and information production role played by short sellers. To capture
`this unique feature of innovation investments, in this paper, we use patenting as the main innovation output measure,
`which encompasses the successful usage of all (both observable and unobservable) innovation inputs and is most
`likely to be influenced by the disciplining and information production activities of short sellers. Therefore, our use
`of patenting (as opposed to ordinary capital expenditures) as the main outcome variable helps explain why we
`observe a different, and probably an even more important, effect of short sellers on firms’ investment behavior from
`that reported in Grullon, Michenaud, and Weston (2013).
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`Our sample construction starts with the Russell 3000 index in June 2004. Following the
`SEC’s first pilot order issued on July 28, 2004 (Securities Exchange Act Release No. 50104),
`which describes in detail how the pilot and non-pilot stocks in the Regulation SHO program
`were chosen, we exclude stocks that were not listed on the NYSE, AMEX, or NASDAQ NM,
`and stocks that went public or had spin-offs after April 30, 2004. Out of the remaining 2,952
`stocks, we identify 986 pilot stocks according to the published list of the SEC’s pilot order and
`the rest 1,966 stocks comprise the initial non-pilot sample. The exchange distribution of these
`stocks shows that they are very representative of the Russell 3000 Index. For example, around
`50% of the pilot stocks are listed on the NYSE, 48% on the Nasdaq NM, and 2% on the AMEX.
`The exchange distribution of the non-pilot stocks is almost the same.
`To examine the dynamics of innovation output around the implementation of Regulation
`SHO in July 2004, we extract firm characteristics from various data sources four years before
`and after the event year (i.e., 2004). Specifically, we examine innovation outcomes of firms
`whose fiscal year ending dates are between December 31, 2000 and December 31, 2008 (which
`essentially covers all firm activities taken place during the calendar year period of 2000 to 2008).
`We further require all firms to have non-missing Compustat records to calculate firm
`characteristics across the above sample period. The resulting final sample consists of 643 pilot
`firms and 1,261 control firms.7, 8 We collect firm-year patent and citation information from three
`sources. First, we retrieve our patent and citation data between 2000 and 2006 from the latest
`version of the National Bureau of Economic Research (NBER) Patent Citation database. The
`NBER database provides information for all utility patents granted by the US Patent and
`Trademark Office (USPTO) over the period of 1976-2006. Second, we obtain information on
`patents granted over the period of 2007-2008 that is provided by Kogan et al. (2012) (available at
`https://iu.box.com/patents). Third, we construct a dataset for patent citations over the period of
`2007-2008 using
`the Harvard Business School (HBS) patent database (available at
`http://dvn.iq.harvard.edu/dvn/dv/patent).
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`7 If we relax this requirement and only retain firms with non-missing Compustat records in any year during our
`sample period, the resulting full sample contains 909 pilot firms and 1,836 control firms in the year immediately
`before the announcement of the pilot program (i.e., 2003). Although all results reported in the paper are based on the
`restricted sample, they are very similar based on the full sample and available upon request.
`8 Note that this sample is balanced in the sense of calendar year activities but unbalanced in the sense of fiscal year
`activities. For example, a small set of firms in our sample have nine observations to cover their fiscal year activities
`while the majority others only have eight observations.
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`To calculate the control variables used in our study, we collect financial statement
`information from Compustat, stock price information from CRSP, institutional holdings data
`from Thomson’s CDA/Spectrum database (form 13F), anti-takeover provision information from
`the RiskMetrics database, analyst coverage data from the Institutional Brokers Estimate Systems
`(I/B/E/S) database, and CEO wealth-performance sensitivity data from Alex Edmans’ website
`(available at http://faculty.london.edu/aedmans/data.html).
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`3.2 Variable Measurement
`3.2.1 Measuring Innovation
`We construct two measures to gauge a firm’s innovation output. The first measure is the
`total number of patents filed in a given year (and eventually granted), which captures the
`quantity of innovation. Hall, Jaffe, and Trajtenberg (2001) find that there is an average lag of two
`to three years between patent application year and grant year, though there is significant
`variation in the approval time. We use the application year instead of the grant year to determine
`a firm’s innovation output in a given year because the patent application year has been shown to
`better align with the actual time when the innovation activities take place (Griliches, Pakes, and
`Hall, 1988).
`Despite its straightforward intuition and easy implementation, a simple measure of patent
`counts hardly distinguishes groundbreaking innovations from incremental technological
`improvements. Hence, we construct the second measure of innovation output, the total number of
`citations each patent receives in subsequent years, which captures the quality (impact) of
`innovation.
`Nevertheless, both innovation measures are subject to truncation problems. Since we only
`observe patents that are eventually granted by the end of 2009, patents filed in the last few years
`of our sample period may still be under review and not granted by 2009. Similarly, patents tend
`to receive citations over a long period after its grant date, but we observe at best the citations
`received up to 2009. To deal with these truncation problems, we adjust the patent and citation
`data by using the “weight factors” first developed by Hall, Jaffe, and Trajtenberg (2001, 2005)
`and estimating the shape of the application-grant distribution and the citation-lag distribution,
`respectively.
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`The patent databases used in our study are unlikely to be affected by survivorship bias.
`As long as a patent application is eventually granted, it is attributed to the applying firm at the
`time of application even if the firm later gets acquired or goes bankrupt. Moreover, since patent
`citations are attributed to the patent rather than the applying firm, the patent granted to a firm that
`later gets acquired or goes bankrupt can still keep receiving citations long after the firm ceases to
`exist.
`We merge the patent data with the Russell 3000 index sample. Following the innovation
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`literature, we set the patent and citation counts to zero for Russell-3000 firms not matched to the
`patent database, because our patent sample covers the entire universe of publicly-traded firms
`that have filed with the U.S. Patent Office. The distribution of patent grants in our final sample is
`right skewed, with its median at zero. Due to the right skewness of patent counts and citations
`per patent, we winsorize these variables at the 95th percentiles and then use the natural logarithm
`of one plus patent counts (LnPatent) and the natural logarithm of one plus the number of
`citations per patent (LnCitePat) as the main innovation measures in our analysis.
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`3.2.2 Measuring Control Variables
`Following the innovation literature, we control for a vector of firm and industry
`characteristics that may affect a firm’s innovation output in our analysis. We compute all
`variables for firm i over its fiscal year t. Our control variables include firm size (the natural
`logarithm of book value assets), firm age (the natural logarithm of a firm’s age since its IPO
`year), profitability (ROA), investments in intangible assets (R&D expenditures over total assets),
`asset tangibility (net PPE scaled by total assets), leverage, capital expenditures, growth
`opportunities (Tobin’s Q), financial constraints (the Kaplan and Zingales (1997) five-variable
`KZ index), industry concentration (the Herfindahl index based on sales), and institutional
`ownership. To control for non-linear effects of product market competition on innovation outputs
`(Aghion et al., 2005), we also include the squared Herfindahl index in our regressions. We
`provide detailed variable definitions in the Appendix.
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`3.3 Summary Statistics
`To minimize the effect of outliers, we winsorize all control variables at the 1st and 99th
`percentiles. Table 1 provides summary statistics of the variables. On average, a firm in our
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`sample has 6.08 granted patents per year and each patent receives 0.38 citations. Regarding other
`variables, an average firm has book value assets of $5.58 billion, R&D-to-assets ratio of 3.9%,
`ROA of 9.0%, PPE-to-assets ratio of 47.6%, leverage of 17.2%, capital expenditure ratio of
`4.8%, Tobin’s Q of 1.9, and is 21.9 years old since its IPO date.
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`4. EMPIRICAL RESULTS
`4.1 Baseline Difference-in-differences Results
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`In our baseline analysis, we use a quasi-natural experiment, Regulation SHO, to identify
`the causal effect of short sellers on firm innovation. Before July 2004, short selling activities in
`the U.S. equity market were constrained by a regulation commonly referred to as the “uptick
`rule”, which prohibited short sales when stock prices were declining. On July 28, 2004, however,
`the SEC announced a new policy experiment, Regulation SHO, to remove all short sale
`restrictions for a randomly selected group of firms (the pilot group), which include 968 stocks.
`The selection of pilot firms followed a Rule 202T program, which first ranked all Russell 3000
`stocks listed on NYSE, NASDAQ, and AMEX according to their average trading volume, and
`then picked every third stock within each of the