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Review of Accounting Studies

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18-07-2020

Does the media spotlight burn or spur innovation?

Authors: Lili Dai, Rui Shen, Bohui Zhang

Published in: Review of Accounting Studies | Issue 1/2021

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Abstract

We examine the effect of media coverage on firm innovation. Using a comprehensive sample of corporate news coverage and patenting over the period from 2000 to 2012, we find a negative relation between media coverage and firm innovation. We further document the two offsetting economic mechanisms underlying the impact of media coverage on innovation: the media’s role of short-term pressure on managers relates negatively to innovation, while its role of mitigating financial constraints is positively associated with innovation. Our findings provide new insights into the effect of news coverage on firms’ long-term growth.

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See, for example, Klibanoff et al. (1998); Chan (2003); Tetlock (2007); Tetlock et al. (2008); Fang and Peress (2009); Bushee et al. (2010); Blankespoor et al. (2014); Rogers et al. (2016); and Blankespoor et al. (2018) for the media’s impact on information asymmetry and therefore on the cost of capital. See, for example, Miller (2006); Core et al. (2008); Dyck et al. (2008); Joe et al. (2009); Dyck et al. (2010); Liu and McConnell (2013); Dai et al. (2015); and You et al. (2018) for the media’s impact on corporate governance.

Firm innovation has long been established by economists as one of the most important drivers of firms’ long-term economic growth and competitive advantage (Solow 1957; Romer 1987; Hall et al. 2005).

Porter (1992) also notes: “[t]he U.S. system first and foremost advances the goals of shareholders interested in near-term appreciation of their shares—even at the expense of the long-term performance of American companies.”

To informally test this conjecture, we randomly draw upon 100 performance-related full news articles in 2010 from RavenPack’s News Discovery Service to manually check their news content in relation to the performance horizon. Among these 100 articles, 88 (10) report quarterly (annual) performance, while only two articles discuss the firm’s long-term performance prospect.

Dow Jones Newswires is one of the leading news providers in the world. During our sample period from 2003 to 2012, Dow Jones Newswires was named the “Best News Provider” or the “News Provider of the Year” by Inside Market Data five times, followed by Thomson Reuters/Reuters News named three times and Bloomberg named two times. In the internet appendix, we employ the Thomson Reuters News Analytics database to re-conduct our analyses and find similar results. We do not use the web edition dataset from RavenPack, because this dataset only begins in 2007 and includes coverage from internet-based information intermediaries (Drake et al. 2017; Blankespoor et al. 2018).

The two major types of news alerts are performance news alerts and insider trading news alerts, which account for 35.79% and 32.02% of news alerts, respectively. See Appendix A for the examples of Full Article and News Flash of performance-related news coverage reported by Dow Jones Newswires as well as the examples of Full Article of product-, financing- and governance-related news coverage reported by Dow Jones Newswires.

The NBER Patent Citation database covers the period 1976–2006. By using information after 2006, Kogan et al. (2017) provide more accurate data for patents and citations, especially after the year 2000. For example, in their database, there are 1808 unique listed firms filing 80,967 patents in the year 2003. In contrast, there are only 1338 unique listed firms filing 42,315 patents in the NBER Patent Citation database in the year 2003. The online appendix of Kogan et al. provides more details regarding the two databases.

We choose the firms that have been covered at least once in the RavenPack data, because we are concerned that some firms not included in RavenPack may be reported by media outlets other than those news providers in RavenPack. Coding the news measures to zero for these firms may lead to an underestimate of media coverage. This filter is consistent with the literature, such as Dai et al. (2015); Blankespoor et al. (2018); and Blankespoor et al. (2019).

Consistent with the majority in the literature, we include all firms except for those in utilities or financial industries in our main analysis (e.g., He and Tian 2013; Fang et al. 2014; Cornaggia et al. 2015; Mukherjee et al. 2017). In the internet appendix, we provide consistent results with different sampling filters. These filters include firms with positive R&D expenditures, firms with non-missing R&D expenditures, manufacturing firms only, and patenting firms only.

Each news article is categorized into one content-based news type by RavenPack.

Performance-related news articles include, for example, those related to “earnings-above-expectations,” “earnings-meet-expectations,” “revenue-estimate,” and “revenue-volume,” which highlight the media’s focus on short-term performance. Product- and innovation-related news articles cover both “product” news and “patent” news. Financing-related news articles are related to “debt,” “debt restructuring,” and “credit rating.” Governance-related news articles include articles related to “insider trading,” “fraud,” “board-meeting,” “executive appointment,” “executive compensation,” and so on.

The number of citations per patent is adjusted for truncation, where the raw value is divided by the sample annual mean (Hall et al. 2001).

Kogan et al. (2017) estimate the patent value based on market-adjusted return in a window [0, 2] following patent grant day zero, taking into account the number of multiple patents filed on the same day, the firm market capitalization, and the volatility of firm returns. See more details regarding the estimation of the patent value in Section 2.4 of Kogan et al. (2017).

There are two potential concerns regarding lifecycle effects. First, it is likely that mature firms tend to gain greater media coverage and, at the same time, have a lower level of innovation investments, which leads to fewer innovation outputs in the future. Second, the mean reversion effect of innovation predicts that firms with more current projects tend to attract a higher level of attention from the media, and these firms may then have mean reversions to a lower level of innovation outputs. Therefore, to mitigate these concerns, we control for the change in innovation (∆Innovation _t). Both concerns are also alleviated to some extent when we control for firm age (Age _t) and current research and development expenses (R&D _t).

See, for example, the papers related to the impact of media on executive compensation (Core et al. 2008; Kuhnen and Niessen 2012) and on limited voting shares (Braggion and Giannetti 2013).

Based on the summary statistics in Table 2, the economic impact of News_t on Log (Patent_t + 2 + 1) = (−0.060 × 0.990) / log (1.424 + 1) = −6.7%, where the standard deviation of News_t is 0.990 (divided by 100) and the mean value of Patent_t + 2 is 1.424.

Although we find a strong relation between News_t and Innovation_t + 2, the incremental R²s due to news coverage are not large (0.09%, 0.58%, and 0.05% increases in Adjusted R² for CitaPat_t + 2, PValue_t + 2, and Patent_t + 2, respectively). This suggests that news coverage does not explain the majority of innovation variation and provides comfort that our model does not omit a critical variable.

The economic impact of News_t on Log (PValue_t + 2 + 1) = (−0.126 × 0.990) / log (1.375 + 1) = −14.4%, where the standard deviation of News_t is 0.990 (divided by 100) and the mean value of PValue_t + 2 is 1.375. Similarly, the economic impact of News_t on Log (Patent_t + 2 + 1) = (−0.061 × 0.990) / log (7.674 + 1) = −2.8%, where the standard deviation of News_t is 0.990 (divided by 100) and the mean value of Patent_t + 2 is 7.674.

The literature on corporate governance suggests the potential excessive monitoring effects, for example, by shareholders (e.g., Burkart et al. 1997; Pagano and Röell 1998) and by boards of directors (e.g., Almazan and Suarez 2003; Adams and Ferreira 2007).

Firms are required to describe and update information about their significant products by the U.S. Securities and Exchange Commission (SEC) in Item 101 of Regulation S-K. The data for Fluidity is obtained from Professor Gerard Hoberg’s website.

The average VIF value is 2.82 for the sentiment components of news coverage, the sentiment components of the press release, and other control variables in Table 6, which is below the 5.0 cutoff and therefore suggests no severe multicollinearity problem (Sheather 2009).

We use ESS rather than another sentiment score, Composite Sentiment Score (CSS), provided by RavenPack, for the following reasons. First, the distribution of ESS spreads across all the values between 0 and 100, while the CSS score is only coded for 30 values out of the range from 0 to 100. Therefore, it is more feasible to decompose news coverage into multiple groups based on ESS. Second, CSS is constructed partly based on stock price reactions to news articles, not solely based on news content, which may induce measurement noise into sentiment metrics. Nevertheless, in untabulated analyses, we find consistent evidence using CSS score for news decomposition.

Clarke et al. (2015) show that the effect of financial analysts on innovation, documented by He and Tian (2013), is subject to innovator quality.

The eight Dow Jones offices are at Boston, Chicago, Minneapolis, New York, Princeton, San Francisco, Waltham, and Washington. See http://www.dowjones.com/contact.

Using alternative airports always results in an increase in travel times for the Dow Jones offices.

Following Giroud (2013), we use ramp-to-ramp time from the U.S. Department of Transportation T-100 segment data to measure flight duration and assume a 60-min layover time for a transfer flight. In addition, we also add 60 min to all flight journeys to account for boarding time.

The economic impact of TravelTime_t on News_t = (−0.018× 1.086) / 0.687 = −2.85%, where the standard deviation of TravelTime_t is 1.086 and the mean value of News_t is 0.687 in the sample for instrumental variable analyses (both scaled by 100).

The critical value of the Stock-Yogo test is 16.38 for 10% maximal IV size (Stock and Yogo 2005).

Given that the two-stage estimator is biased and inefficient but consistent (Wooldridge 2006), it is not surprising that the coefficients on News_{Predicted, t} are much larger than, but have similar levels of significance as, the coefficient estimates for the un-instrumented News _t in Table 3.

We choose the discretionary SG&A and R&D expenses to capture mangers’ expenditure and investment decisions rather than firms’ fundamentals that can influence the inputs of innovation (Roychowdhury 2006). For example, in a recent study, Chen et al. (2018) show that the increases in noncompete enforceability make managers behave myopically and focus on short-term profitability by cutting discretionary spending, such as SG&A and R&D expenses, which, in turn, lead to lower future innovation outputs.

We follow Roychowdhury (2006) to conduct the following regressions using two-digit-SIC-industry-year observations: SG & A (R & D) = α₀ + α₁(1/A_t − 1) + β(S_t − 1/A_t − 1) + ε_t. SG&A (R&D) is firm’s selling, general, and administrative (research and development) expenses. A_t-1 is total assets in year t-1, and S_t-1 is net sales in year t-1. ε_t is our measure of discretionary SG&A (R&D). We require firms to have non-missing SG&A expenses when calculating the discretionary expenses and replace missing R&D with zero.

Detailed discussions about these robustness tests are presented in the internet appendix.

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Title: Does the media spotlight burn or spur innovation?
Authors: Lili Dai
Rui Shen
Bohui Zhang
Publication date: 18-07-2020
Publisher: Springer US
Published in: Review of Accounting Studies / Issue 1/2021
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-020-09553-w

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