Vol:.(1234567890) Review of Accounting Studies (2025) 30:352–383 https://doi.org/10.1007/s11142-024-09820-0 1 3 New product announcements, innovation disclosure, and future firm performance Jenny Chu1  · Yuan He2  · Kai Wai Hui3 · Reuven Lehavy4 Accepted: 8 January 2024 / Published online: 15 February 2024 © Crown 2024 Abstract This study examines the properties of innovation disclosures contained in new product announcements, a form of voluntary, nonfinancial disclosure. We analyze these properties using a novel, text-based measure of the extent of product innova- tion disclosed in new product announcements. We find that stock prices react more positively to announcements with more extensive innovation disclosure. In our main analyses, we first find that a higher level of innovation disclosure predicts a greater increase in future sales. We further find that this predictive ability falls when manag- ers have stronger incentives to maximize their wealth and when the corporate gov- ernance structure and customers’ bargaining power weaken. Our research enhances the understanding of the properties of managerial voluntary, nonfinancial disclo- sures and contributes a text-based measure of innovation that captures managerial assessment of the extent of product innovation. This new measure is more generaliz- able and incrementally informative for firm value and future performance than con- ventional innovation measures that depend on the existence of patents or research and development expenses. Keywords New product announcements · Innovation · Voluntary disclosure · Incentives · Performance · Qualitative disclosure · Narrative disclosure JEL Classification M41 · G34 · M13 * Jenny Chu j.chu@jbs.cam.ac.uk Reuven Lehavy rlehavy@umich.edu 1 University of Cambridge, Cambridge, UK 2 Accounting Department, Xiamen University, Xiamen, China 3 Faculty of Business and Economics, University of Hong Kong, Hong Kong, China 4 Stephen M. Ross School of Business, University of Michigan, Ann Arbor, MI, USA http://orcid.org/0000-0001-8674-3224 http://orcid.org/0000-0001-5255-7749 http://crossmark.crossref.org/dialog/?doi=10.1007/s11142-024-09820-0&domain=pdf 353 1 3 New product announcements, innovation disclosure, and future… 1 Introduction Innovation occurs across all levels of an organization, including product design, trial production, market analysis, employee training, and fixed asset investments that translate into new or improved products (Kleinknecht et al. 2002). Firm stakeholders typically learn about product innovations through a firm’s new product announce- ments. Disclosure in these announcements is important, as studies link product innovation to firm success and prosperity (Cooper 1998; Drucker 2007; Schumpeter 1942). Managerial discussions in new product announcements provide information that helps customers appreciate the quality and value of a new product (Van de Ven 1986) and provide investors with an understanding of the implications of the product for firm value (Bayus et  al. 2003; Chaney et  al. 1991; Chen et  al. 2002). Product announcements also cohesively depict the links between research and development (R&D) expenses, patent development, and final product innovations. (See the recent literature review by Glaeser and Lang 2023.) We first examine whether the extent of innovation disclosed in new product announcements is associated with incremental improvements in future firm perfor- mance beyond the expected level, given innovation inputs and past innovation suc- cesses. We then study the impact of several managerial disclosure incentives on the ability of a firm’s level of innovation disclosure to predict firm performance. Our findings provide insight regarding the properties and usefulness of managerial vol- untary, nonfinancial disclosures of new product innovation. This insight is especially useful, given the central role innovation plays in a firm’s success. To capture the degree of managerial innovation disclosure in new product announcements, we develop a novel measure that counts the number of words in each announcement that appear in our constructed dictionary of innovation words. Specifically, in our empirical analysis, we employ an innovation disclosure measure based on the residuals from a regression of the innovation word count on variables that capture innovation inputs and innovation successes (e.g., patent number, value, and citations, R&D expenses, trade secrets, and the number of recent new product announcements). This approach allows us to capture the extent to which managers use innovation-related words to describe the new products beyond the level pre- dicted by innovation inputs.1 To validate that our measure captures economically meaningful disclosure, we show that a higher level of innovation disclosure in an announcement is associated with a significantly greater stock market reaction to the disclosure. For example, we find that a one standard-deviation increase in the extent of innovation disclosure is associated with a 23% higher market reaction in a three- day window around the announcement. 1 Studies rely primarily on R&D or patents to measure corporate innovation. However, these findings may not be generalizable to industries without R&D or patent development, nor can they fully explain how technical innovations ultimately improve product features (Griliches et al. 1987; Kleinknecht et al. 2002). By contrast, our measure is based on new product announcements reflecting managerial innova- tion assessments issued across a diverse set of industries, controlling for R&D expenses and patent devel- opments. As such, our study may enable firm stakeholders and scholars to better understand the perfor- mance and valuation implications of these voluntary, nonfinancial innovation disclosures. 354 J. Chu et al. 1 3 In our main analyses, we find that greater innovation disclosure is associated with higher future sales, consistent with innovation disclosure capturing the value of the new product to customers. Specifically, we find that a one standard-devia- tion increase in the extent of innovation disclosure is associated with an increase of approximately 1.22% in a firm’s next year’s sales deflated by assets, representing an average increase of $328 million in sales. Importantly, we find that our measure has greater power to predict future sales than traditional innovation input measures, such as R&D expenses and patents, as it directly quantifies the level of innovation in new products that make it to market. These findings support our prediction that new product announcements provide new information about product innovation over and above that contained in observable innovation inputs and successes. In other tests, we document that the degree of innovation disclosure is positively correlated with future earnings but negatively correlated with future selling, general, and adminis- trative (SG&A) expenses. We find no relation to a firm’s future costs of goods sold (COGS). These findings are consistent with prior findings that innovative products are less costly to market but may not lower product or inventory costs (Gatignon and Xuereb 1997; Shields and Young 1994). In addition to examining the predictive power of our innovation disclosure meas- ure, we explore the impact of managerial incentives on the ability of innovation disclosures to predict firm performance. In this analysis, we expect a weaker rela- tion between innovation disclosure and future sales performance when managers have greater incentives to disclose for strategic reasons.2 To address this question, we conduct a series of cross-sectional analyses partitioned according to managerial disclosure incentives. First, we examine whether managers have stronger incentives to strategically disclose product innovation information either before engaging in insider trading (Rogers and Stocken 2005) or when their compensation is positively tied to risk-taking (Armstrong et al. 2013). Second, we examine whether the predic- tive power of innovation disclosure for firm sales is lower when board members are busier (Fich and Shivdasani 2006; Hauser 2018) or when CEOs have golden para- chutes (Bebchuk et al. 2009). Finally, we examine whether our main finding differs with several customer-related measures, including firms’ vertical integration scores (Frésard et al. 2020) and the extent of product market competition (the number of close rivals, as defined by Hoberg and Phillips 2010, 2016). These measures proxy for customers’ ability to monitor and verify innovation disclosure quality. Across all these measures, we empirically document that stronger strategic reasons for disclo- sure reduce the ability of innovation disclosures to predict sales performance. Together, our findings validate our text-based innovation measure and indicate that innovation disclosure in new product announcements predicts sales performance 2 The relation between innovation disclosure and future performance relates to the degree to which a disclosure reflects the value of the innovation embedded in the product. A weaker relation represents a larger differential between the innovation stated in new product announcements and the actual amount of innovation in the product. We argue that managers, on average, are aware of the true level of product innovation and that the relation between disclosure and future performance reflects managerial disclosure choices. 355 1 3 New product announcements, innovation disclosure, and future… but that this predictive ability decreases when managers have stronger disclosure incentives. Our study contributes to the literature on the impact of voluntary, nonfinancial disclosures as well as the literature on the impact of corporate innovation on firm outcomes. Within the area of voluntary, nonfinancial disclosure research, stud- ies generally focus on corporate social responsibility (Dhaliwal et  al. 2011), envi- ronmental impact disclosures (Blacconiere and Patten 1994), and product market reporting, such as levels of customer satisfaction (Ittner and Larcker 1998). We extend this literature by focusing on the informativeness of managerial innovation disclosures for future firm performance as well as the impact of managerial incen- tives on a disclosure’s informativeness.3 Our study also contributes to the growing literature on the role of innovation in a firm’s success by developing a text-based measure of innovation that draws on managerial narratives in a large sample of new product announcements. By contrast, the literature on innovation typically uses proxies constructed from either R&D expenses (e.g., Chan et al. 2001; Hsu 2009) or patents (e.g., Griliches 1990; Lerner and Seru 2022; Matolcsy and Wyatt 2008). However, these proxies are limited in the conclusions that can be drawn about the benefits of technical innovations for customer-focused product features or streamlined marketing costs (Griliches et  al. 1987; Kleinknecht et  al. 2002). Moreover, these studies tend to focus on firms in R&D-intensive or patent-focused industries, excluding innovation in a broader swath of industries.4 Within this area, Bellstam et al. (2021) apply an unsupervised topic modeling approach to a sample of sell-side analysts’ reports to measure innovation. However, their approach requires an ex-ante, subjective determination of the total number of topics in their sample and yields an abstract (i.e., untitled) list of topics. Their sample is also limited by the availability of sell-side analyst research reports (in comparison, one-third of our sample firms are not covered by sell-side analysts). By contrast, our focus on the narratives in new product announcements allows us to explicitly quantify the extent of innovation discussed by corporate managers, who have the most precise information regarding a product’s true innovation. Thus, we can study the economic phenomenon of the determinants of managerial voluntary innovation disclosure. Moreover, our innovation-word dictionary is based on syno- nyms from an English-language dictionary, providing a more objective list of inno- vation terms. Finally, our innovation disclosure measure is based on a large archival database of new product announcements issued by the universe of Compustat firms. 3 Our paper also complements and extends Merkley’s (2014) study of managerial R&D disclosure in 10-Ks by investigating voluntary rather than regulatory disclosure, by including firms in non-R&D-inten- sive industries and by identifying situations in which managers may have incentives to strategically dis- close new product innovation. 4 Studies suggest that R&D expenses and patents are noisy measures of innovation value. Various firm characteristics can affect the market’s valuation of R&D investments, such as industry concentration (Doukas and Switzer 1992), firm size (Connolly and Hirschey 2005), ownership concentration (Hall and Oriani 2006), and the overall financial environment (Booth et al. 2006). Using the implicit valuation of patents in mergers and acquisitions, Belenzon and Patacconi (2013) find that the value of American pat- ents has declined substantially from 1985 to essentially zero near the end of their sample period in 2007, further limiting the usefulness of this measure. 356 J. Chu et al. 1 3 Overall, our use of an objective-based measure applied to firms beyond those tied to R&D, patents, or analyst coverage yields findings with greater generalizability to a diverse set of firms. The rest of the paper proceeds as follows. Section  2 develops our hypotheses. Section 3 describes the sample and our definitions of the empirical variables. Sec- tion 4 details our findings. Section 5 concludes. 2 Hypothesis development 2.1 Innovation disclosure and future sales performance Product innovation is an important signal of firm value for capital market inves- tors and also helps firms secure share in the product market (Van de Ven 1986). To enhance transparent communication between informed managers and stakeholders, managers use new product announcements to provide crucial contextual informa- tion about new product innovation and benefits. These narrative disclosures provide customers and investors with a foundational understanding of both the existence and features of new products. For example, managers may use the announcements to explain why a new product is innovative, position the product within the portfolio of the firm’s other products, or educate customers about technological advancements that can cater to their evolving needs. These disclosures can also clarify how new products compare with competing products to facilitate customers’ buying deci- sions. Innovation disclosures can also provide investors with insights into potential future revenue streams and profits from a new product, enhancing their understand- ing of a firm’s position in the competitive landscape and informing their investment decisions. Based on this discussion of the usefulness informativeness of new product announcements, we predict that innovation disclosures in these announcements are incrementally informative about future firm performance. This leads to our first hypothesis: H1: Future sales increase with the extent of innovation information disclosed in new product announcements. 2.2 Disclosure incentives, innovation disclosure, and future firm performance In addition to understanding the relation between innovation disclosures and future sales, we investigate the effect of managerial disclosure incentives on this relation. Managers may have conflicting incentives when disclosing value-relevant informa- tion to outsiders (Shleifer and Vishny 1989). On the one hand, truthful disclosures can improve firms’ information environment, help investors assess firm value, and potentially lower the cost of capital (Botosan 1997). On the other hand, manag- ers may have incentives to bias their firms’ disclosures to bolster compensation 357 1 3 New product announcements, innovation disclosure, and future… and gains from stock sales, enhance job security, increase operational flexibility, or gain greater control (e.g., Rogers and Stocken 2005; Dechow et al. 2010; Kothari 2001). In the context of our study, outsiders are at an information disadvantage in assessing how new product innovation translates into future financial perfor- mance (Rijsdijk and Hultink 2009). Thus, managers may exploit this information disadvantage by strategically disclosing product innovation for personal gain. To test for this possibility, we identify three settings in which managers are more likely to strategically disclose new product innovation. First, managers may dis- close innovation information to maximize their personal wealth, specifically to reap personal gains via insider trading (Rogers and Stocken 2005). The account- ing literature further suggests that managers may strategically disclose informa- tion when their wealth is sensitive to changes in stock price volatility (Armstrong et  al. 2013). Thus, in our setting, we expect managers with such incentives to strategically disclose information about product innovation. In turn, this strategic disclosure is likely to reduce the predictive ability of innovation disclosures for future firm performance. Motivated by this discussion, we state our next hypoth- esis as follows: H2A: The positive relation between the extent of innovation disclosed and future sales is reduced when managers’ personal wealth is more sensitive to firm value. In addition to examining managerial personal wealth incentives for disclosure, we examine whether our observed predictive effect changes with the strength of corpo- rate governance within a firm. Studies show that weak corporate governance exacer- bates the agency problem, leading to lower quality managerial voluntary disclosures (Karamanou and Vafeas 2005). Studies also suggest that the earnings of firms with weaker governance are less informative (Irani and Oesch 2013). Following these findings, we predict a lower relation between innovation disclosure and future per- formance for firms with weaker governance. H2B: The positive relation between the extent of innovation disclosed and future sales is reduced for firms with weaker corporate governance. Finally, we examine whether our observed predictive effect differs with the abil- ity of customers to assess the quality of innovation disclosures. On the one hand, managers may have incentives to report an inflated level of innovation in their new products to impress capital market participants and reduce the cost of capital (Boto- san 1997). On the other hand, users of the product have first-hand information that can be used to assess the true innovativeness of a product (Nelson 1970). Therefore, firms that strategically disclose information about product innovation to increase their firm value may suffer a resulting decrease in reputation in the product market if their claims are not borne out. Consequently, we predict that innovation disclosures are less predictive of future sales when customers have less ability to monitor firms’ disclosure quality (i.e., when managerial capital market incentives dominate product market incentives). Formally, we hypothesize: 358 J. Chu et al. 1 3 H2C: The positive relation between the extent of innovation disclosure and future sales is reduced when customers have a weaker ability to monitor disclosure quality. 3 Sample selection and empirical methodology 3.1 Sample Panel A of Table 1 details our sample selection methodology. To obtain our initial sample, we begin with the complete list of US firms in the Capital IQ Key Develop- ments database listed on the New York Stock Exchange, NASDAQ, or the Ameri- can Stock Exchange between January 2001 and December 2016. This database contains summaries of material news and events, such as mergers and acquisitions (M&A), management changes, regulatory issues, and product development. From this list, we select any announcements identified as product-related and ensure that the announcement headline contains words that describe the introduction of new products (“unveil,” “launch,” and “new product”).5 From this curated list, we delete observations that cannot be read by our text-processing algorithm as well as any observations where the innovation keywords appear only in the company name. Next, we ensure that each firm in our sample has daily stock returns data in the Center for Research in Security Prices (CRSP) database and financial information in Compustat. Finally, because the Capital IQ database contains only summaries of the new product announcements, we manually search and download the full text of each of the new product announcement press releases from Factiva. This pro- cedure results in a sample of 30,663 announcements. Requiring non-missing data for the control variables reduces our sample to 17,800 firm-quarter (28,076 firm- day) observations that contain at least one announcement for the future performance (market reaction) tests.6 All continuous variables are winsorized at the top and bot- tom one percent levels. Panel B of Table 1 reports the industry classifications for our sample firms.7 A comparison of our sample distribution (% in sample) with that in the Compustat universe (% in Compustat) indicates that our sample has a higher proportion of firms from the business equipment industries, which include computers, software, and electronic equipment. This finding is not surprising, as these industries experi- enced an increase in innovation during our sample period. Interestingly, our sample 5 We manually check a random sample of 800 announcements and find that all of them are new product announcements. 6 In the market reaction tests, we eliminate announcements made within five days of a firm’s earnings announcement date. The nearest earnings announcement to a new product announcement is on average 24.5 days away (untabulated). Therefore, it is unlikely that our market reaction results are driven by earn- ings announcements rather than by the innovation disclosures in new product announcements. 7 Industry classification is based on the Fama-French 12 industry classification obtained from Ken French’s website: https:// mba. tuck. dartm outh. edu/ pages/ facul ty/ ken. french/ Data_ Libra ry/ det_ 49_ ind_ port. html. https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_49_ind_port.html https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_49_ind_port.html 359 1 3 New product announcements, innovation disclosure, and future… Ta bl e 1 S am pl e Se le ct io n an d Su m m ar y St at ist ic s Pa ne l A : S am pl e Se le ct io n St ep Sa m pl e Se le ct io n N um be r of O bs er va tio ns 1 N um be r o f p ro du ct -r el at ed a nn ou nc em en ts fr om C ap ita l I Q (J an ua ry 2 00 1 to D ec em be r 2 01 6) w he re th e he ad lin es co nt ai n at le as t o ne o f t he th re e ke yw or ds (n ew p ro du ct , l au nc h, u nv ei l) an d ha ve th e fir m id en tifi er a nd a nn ou nc em en t da te av ai la bl e. 98 ,8 49 2 K ee p an no un ce m en ts th at c an b e re ad b y th e te xt -p ro ce ss in g al go rit hm . 97 ,2 96 3 K ee p an no un ce m en ts m ad e by c om pa ni es w ho se n am es c on ta in n o in no va tio n ke yw or ds . 96 ,5 20 4 K ee p an no un ce m en ts m ad e by c om pa ni es w ith n on -m is si ng re tu rn s i n C R SP a nd C om pu st at . 31 ,4 67 5 D ow nl oa d th e fu ll pr es s r el ea se s o f e ac h ne w p ro du ct a nn ou nc em en t f ro m F ac tiv a. 30 ,6 63 M er ge th e an no un ce m en ts w ith fi rm d ay s a nd q ua rte rs to g en er at e th e te sti ng sa m pl es . 6 Fi rm d ay s w ith n on -m is si ng d at a fo r t he m ar ke t r ea ct io n te sts . 28 ,0 76 7 Fi rm q ua rte rs w ith n on -m is si ng d at a fo r t he fu tu re sa le s p er fo rm an ce te sts . 17 ,8 00 Pa ne l B : I nd us tr y C la ss ifi ca tio n In du st ri es N % o f S am pl e % in C om pu st at % n ot r ep or tin g pa te nt s % n ot r ep or tin g R & D % n ot r ep or tin g ei th er pa te nt s o r R & D C on su m er N on du ra bl es 68 8 3. 9 3. 1 50 .4 53 .1 68 .5 C on su m er D ur ab le s 51 0 2. 9 1. 6 14 .7 9. 4 20 .2 M an uf ac tu rin g 1, 10 0 6. 2 5. 8 13 .7 9. 2 18 .3 En er gy 68 0. 4 4. 9 17 .6 22 .1 25 .0 C he m ic al s 32 1 1. 8 1. 6 17 .8 3. 7 18 .4 B us in es s E qu ip m en t 8, 44 9 47 .5 12 .4 23 .3 5. 9 25 .2 Te le co m m un ic at io ns 1, 10 1 6. 2 2. 3 47 .3 65 .6 74 .2 U til iti es 28 0. 2 2. 6 85 .7 10 0. 0 10 0. 0 Sh op s 91 6 5. 1 5. 4 71 .5 81 .9 87 .0 H ea lth ca re 1, 89 7 10 .7 8. 5 23 .6 3. 6 24 .2 Fi na nc e 1, 45 0 8. 1 37 .6 64 .8 83 .0 90 .2 O th er 1, 27 2 7. 2 14 .4 58 .0 66 .4 80 .0 To ta l 17 ,8 00 10 0. 0 10 0. 0 33 .3 26 .1 41 .6 360 J. Chu et al. 1 3 Pa ne l A p re se nt s th e sa m pl e se le ct io n pr oc ed ur e. P an el B s ho w s th e in du str y cl as si fic at io n (b as ed o n th e Fa m a- Fr en ch 1 2 in du str ie s) o f t he n ew p ro du ct a nn ou nc em en t sa m pl e co m pa re d w ith th e in du str y di str ib ut io n of th e en tir e C om pu st at d at ab as e fo r t he sa m e sa m pl e pe rio d. P an el C re po rts d es cr ip tiv e st at ist ic s o f e ac h in no va tio n w or d qu ar til e so rte d on th e nu m be r o f i nn ov at io n w or ds in n ew p ro du ct a nn ou nc em en ts Ta bl e 1 (c on tin ue d) Pa ne l C : S um m ar y St at ist ic s o f t he F re qu en cy o f I nn ov at io n W or ds in N ew P ro du ct A nn ou nc em en ts Q ua rt ile o f I nn ov at io n W or ds In no va tio n W or ds p er A nn ou nc em en t Q ua rt er ly su m o f I nn ov at io n W or ds M ea n M ed ia n St an da rd D ev ia tio n M ea n M ed ia n St an da rd D ev ia tio n Lo w 0. 55 5 1. 00 0 0. 49 7 1. 09 5 1. 00 0 0. 79 8 2 2. 44 9 2. 00 0 0. 49 7 3. 45 7 3. 00 0 0. 49 8 3 4. 42 8 4. 00 0 0. 49 5 6. 23 7 6. 00 0 1. 10 3 H ig h 9. 17 7 8. 00 0 3. 57 0 17 .4 30 14 .0 00 10 .1 70 To ta l 3. 84 8 3. 00 0 3. 69 5 6. 17 3 4. 00 0 7. 69 7 361 1 3 New product announcements, innovation disclosure, and future… shows significant representation of firms in industries that do not tend to report R&D expenses or patents such as finance, consumer durables and nondurables, and shops. From the last column of Panel B, we see that 41.6% of our sample firms do not report either patent or R&D data, particularly those in the utilities (100.0%) and shops (87.0%). Therefore, we conclude that our findings and inferences are general- izable to firms and industries that innovate but are associated with little or no R&D or patent development. 3.2 Measuring innovation in new product announcements We begin the process of constructing our text-based innovation disclosure measure by identifying our list of words related to innovation. Using “innovative” and “innovation” as seed words, we implement an iterative process that identifies derivative and synony- mous words according to the Oxford Thesaurus of English. (See the details of this pro- cedure in Panel A of Appendix Table 9) This process results in 61 words and inflections that describe innovation that are contained in our new product announcements (Panel B of Appendix Table 9).8 The online appendix (A.1) provides three examples of new product announcements that illustrate the use of words from our innovation word list. Using this list, we count the number of times each word appears in each new product announcement. From Table 1 Panel C, we see that the average number of innovation words in a given announcement is 3.848, ranging from an average of 0.555 in the bot- tom quartile to 9.177 in the top quartile. The right three columns of this panel show that the number of innovation words in all new product announcements issued in each firm- quarter ranges from 1.095 to 17.430 across the top and bottom quartiles. After validating our selection of words, we use this list to construct our empirical measure that captures the degree of managerial innovation disclosure, which is either above or below the expected degree of disclosure, given the level of innovation inputs and innovation successes. We do so by estimating a regression of the innovation word count (either at the announcement level or aggregated by firm at the quarterly level) on variables that capture innovation inputs and innovation successes. We then use the resid- uals from this regression as our primary measure of innovation disclosure in our sample of new product announcements. Specifically, we estimate the following regression: The dependent variable INNOVCOUNTQTR is equal to the total number of innova- tion words divided by the total number of words in all new product announcements 8 To validate our list, we confirm that each innovation word appears in a random sample of 100 new product announcements in the context of benefits to customers, how the new product is positioned within the existing product portfolio, and how the new product compares with other products in the market. (1) INNOVCOUNTQTR or INNOVCOUNT = � + �1R&DCapital + �2Patent + �3PatCitation + �4PatValue + �5Trade Secret + �6Industry R&DCapital + �7Industry Patent + �8Industry PatCitation + �9Industry PatValue + �10NUMNPA12Q + �11Industry NUMNPA12Q + Year × Quarter Dummies + �. 362 J. Chu et al. 1 3 issued by a firm in a given quarter, whereas INNOVCOUNT is equal to the num- ber of innovation words divided by the total number of words in each new product announcement. The explanatory variables for these regressions include proxies of innovation inputs and innovation successes. The proxies for our innovation inputs are R&D capital (R&D Capital), patent count (Patent), forward citations received by the patents (PatCitation), the monetary value of the patents (PatValue), and trade secrecy (Trade Secret). We control for industry-level technological progress by including the industry R&D capital (Industry R&D Capital), the number of patents (Industry Pat- ent), patent citations (Industry PatCitation), and patent value (Industry PatValue) of all firms in each three-digit SIC industry.9 The proxies for firm- and industry-level innovation success include the natural logarithm of one plus the number of new prod- uct announcements made by a firm in the past three years (NUMNPA12Q) as well as the sum of NUMNPA12Q for all firms in each three-digit SIC industry (Industry NUMNPA12Q). All variables are defined in Appendix Table 10 and 11. Table 2 presents the estimation results of Eq. (1). The dependent variable for the results in column (1) is the quarterly measure of the innovation word count (INNOV- COUNTQTR), whereas that for column (2) is the announcement-level word count (INNOVCOUNT). The results reported in both columns are qualitatively similar. For example, the results in column (1) show a positive and significant relation between INNOVCOUNTQTR and R&D capital (coefficient 0.007 with t-statistic 2.26), number of patents (coefficient 0.012 with t-statistic 1.70), and number of patent citations (coef- ficient 0.007 with t-statistics 2.74), suggesting our innovation word count measure reflects firms’ innovation fundamentals. We further find that industry-level patent cita- tions positively predict innovation word count, whereas industry-level patent value and innovation successes relate negatively to our firm-level innovation word count. We next use the residual components from the above regressions to test the rela- tion between managerial innovation disclosure in new product announcements and future performance (INNOVDISQTR) as well as that between innovation disclosure and stock market reactions to new product announcements (INNOVDIS). Doing so allows us to control for the effect of innovation inputs and successes on the expected use of innovation-related words in new product announcements. It also allows us to better distinguish those words that reflect managerial disclosure of innovation from those that describe innovation inputs or innovation successes.10 9 Untabulated tests suggest that our findings remain qualitatively similar if we replace R&D capital and patents variables (both at the firm and industry levels) with combined innovation efficiency measures that use the number of patents or citations as the numerator and R&D capital as the denominator. 10 This approach resembles that of Huang et  al. (2014), who estimate the abnormal tone in earnings press releases based on the “residuals of a tone model that controls for firm quantitative fundamentals such as performance, risk, and complexity.” They refer to the normal component of tone as the part that “reflects a neutral description of current available information about fundamentals” and the abnormal component as the one that proxies for “managerial strategic choice of tone either to inform or misinform investors.” 363 1 3 New product announcements, innovation disclosure, and future… 3.3 Validation test of the innovation disclosure measure To validate the ability of our innovation disclosure measure to capture value-relevant infor- mation, we investigate the stock market’s reaction to innovation disclosures in individual new product announcements.11 Specifically, we estimate the following regression model: Table 2 Expected Innovation Disclosure Model This table reports the coefficients and t-statistics of Eq. (1). Standard errors are double-clustered at the fis- cal quarter and firm levels. Variable definitions are provided in Appendix Table 10. ***, **, and * indicate two-tail significance at the 1%, 5%, and 10% levels, respectively (1) (2) VARIABLE INNOVCOUNTQTR INNOVCOUNT Intercept 1.150*** 1.201*** (5.87) (7.32) R&D Capital 0.007** 0.010*** (2.26) (3.48) Patent 0.012* 0.013* (1.70) (1.89) PatCitation 0.007*** 0.006** (2.74) (2.53) PatValue -0.006 -0.006* (-1.52) (-1.68) Trade Secret -0.001 0.004 (-0.10) (0.36) Industry R&D Capital -0.003 -0.006* (-0.77) (-1.86) Industry Patent 0.002 0.001 (0.18) (0.11) Industry PatCitation 0.022*** 0.024*** (3.71) (4.35) Industry PatValue -0.020*** -0.020*** (-4.33) (-4.49) NUMNPA12Q -0.003 -0.008 (-0.58) (-1.65) Industry NUMNPA12Q -0.017*** -0.013*** (-4.04) (-2.98) Year-Quarter FE Yes Yes Observations 17,938 28,117 Adj. R2 0.015 0.014 11 Studies in marketing document a positive market reaction to firms announcing new products and a negative one for their rivals (Bayus et al. 2003; Chaney et al. 1991; Chen et al. 2002, 2005). However, these studies do not investigate the cross-sectional variation in the extent of innovation disclosed in new product announcements. 364 J. Chu et al. 1 3 where the dependent variable, CAR , is equal to the size-adjusted cumulative abnor- mal return in various windows around a new product announcement and INNOVDIS captures the level of managerial innovation disclosure incremental to innova- tion inputs and innovation successes within each individual NPA. To control for announcement characteristics, we include the number of new product announce- ments (NUMNPA), measured as the natural logarithm of the number of new product announcements made by a firm on the announcement day; the tone of the announce- ment (TONE), based on the word list proposed by Loughran and Mcdonald (2011); and the readability of the announcement (FOG), as suggested by Li (2008). Finally, we include firm size (Size), market-to-book ratio (MTB), leverage ratio (Leverage), sales growth (Salesgrowth), cash holdings (Cash), and momentum (Momentum) as additional control variables in Eq. (2). Figure 1 depicts the stock price reaction around new product announcement dates for our full sample as well as two subsamples partitioned by whether INNOVDIS is above or below the sample median. Figure 1 shows a positive market reaction on the announcement date, with a greater reaction for those firms with more innova- tion disclosure (our High subsample). The figure further shows that this market reac- tion starts a few days prior to the announcement date, consistent with information leakage. Finally, the positive market reaction does not revert within 10 days of the (2)CAR = � 0 + � 1 INNOVDIS + ∑J j=2 �jControls + �, Fig. 1 Cumulative abnormal returns around new product announcement dates. Notes. This figure depicts sample average cumulative abnormal returns in the (-10, +10) window around New product announce- ment dates (day 0). New product announcements are assigned to the High group when INNOVDIS is above the sample median and to the Low group otherwise 365 1 3 New product announcements, innovation disclosure, and future… announcement date. In summary, Fig. 1 suggests that the disclosure of innovation words in new product announcements elicits a positive, lasting market reaction. Table 3 Panel A reports the average abnormal returns (CAR ) for different win- dows around new product announcement dates. From Panel A, we see that firms announcing new products experience statistically significant and positive abnormal returns during the announcement periods. For example, announcing firms experi- ence an average positive return of 0.3% during the three-day (-1, +1) announce- ment window and an average positive return of 0.5% during the 21-day (-10, +10) announcement window. An untabulated analysis suggests that these sample averages differ statistically from 0 at 1% confidence levels. These results are consistent with those of previous studies indicating that new product announcements are associated with an increase in an announcing firm’s value.12 Panel B of Table  3 reports the results of estimating Eq. (2) across different announcement windows. From Panel B, we see that INNOVDIS is positively and significantly related to an announcing firms’ cumulative abnormal returns across all windows: the coefficients on the (-1, +1), (-2, +2), (-5, +5), and (-10, +10) windows are 0.10% (t-statistic of 3.45), 0.10% (t-statistic of 2.86), 0.10% (t-statistic of 1.73), and 0.20% (t-statistic of 2.03), respectively. We also find a positive and significant reaction to the readability of new product announcements (a higher FOG score indi- cates lower readability) but a similar effect for tone (TONE) only across the shorter announcement windows. Overall, these results suggest that greater innovation dis- closure in a new product announcement is associated with higher announcement returns. They further validate the ability of our innovation disclosure measure to capture value-relevant information. 4 Empirical analysis of innovation disclosure and future firm performance 4.1 Research design To examine the relation between innovation disclosure and future sales (H1), we estimate Eq. (3) as follows: In the above specification, the dependent variable is future sales, measured as (a) sales in each of the next four quarters or (b) the rolling sum of future sales over the next four quarters. The variable of interest, INNOVDISQTR, is our meas- ure of quarterly innovation disclosure, as developed in Section  3.2. We further (3) Future Sales = γ 0 + γ 1 INNOVDISQTR + γ 2 NUMNPAQTR + γ 3 FOGQTR + γ 4 TONEQTR + γ 5 Size + γ 6 MTB + γ 7 Leverage + γ 8 ROAVOL + γ 9 Age + γ 10 Instown + γ 11 ADV + γ 12 HHI + γ 13 Salesgrowth + γ 14 Cash + Industry Dummies + Year × Quarter Dummies + σ. 12 The mean value of INNOVDIS (the residual component from Eq. (1)) is not identical to zero due to winsorization of the top and bottom one percent of the raw values. 366 J. Chu et al. 1 3 Table 3 Stock market reaction to innovation disclosure in individual new product announcements Panel A: Descriptive Statistics Variable Mean SD P10 P25 P50 P75 P90 CAR (-1, +1) 0.003 0.042 -0.038 -0.016 0.001 0.019 0.046 CAR (-2, +2) 0.003 0.054 -0.051 -0.022 0.001 0.025 0.060 CAR (-5, +5) 0.004 0.082 -0.081 -0.033 0.002 0.039 0.092 CAR (-10, +10) 0.005 0.113 -0.117 -0.050 0.002 0.055 0.126 INNOVDIS -0.001 0.686 -0.800 -0.497 -0.121 0.364 0.923 FOG 3.084 0.141 2.901 2.998 3.089 3.174 3.255 TONE 0.524 0.494 -0.130 0.200 0.667 1.000 1.000 LENGTH 5.922 0.642 5.056 5.595 5.989 6.337 6.663 Size 8.016 2.633 4.440 5.910 8.073 10.120 11.540 MTB 3.572 4.418 0.926 1.542 2.625 4.388 7.434 Leverage 0.182 0.186 0.000 0.005 0.146 0.281 0.439 Salesgrowth 0.004 0.037 -0.027 -0.006 0.003 0.015 0.035 Cash 0.248 0.199 0.031 0.086 0.196 0.369 0.541 Momentum 1.056 0.344 0.672 0.863 1.033 1.198 1.422 Panel B: Innovation Disclosure and Market Reaction (1) (2) (3) (4) VARIABLE CAR (-1, +1) CAR (-2, +2) CAR (-5, +5) CAR (-10, +10) INNOVDIS 0.001*** 0.001*** 0.001* 0.002** (3.45) (2.86) (1.73) (2.03) NUMNPA 0.002 0.003 0.005 0.002 (0.89) (0.86) (0.94) (0.37) FOG -0.003* -0.005* -0.006 -0.009 (-1.80) (-1.95) (-1.38) (-1.65) TONE 0.001* 0.001* 0.002 0.002 (1.95) (1.79) (1.27) (1.02) Size -0.001*** -0.001*** -0.001*** -0.002*** (-7.04) (-6.28) (-4.51) (-3.66) MTB -0.000 -0.000 -0.000*** -0.001*** (-0.37) (-1.27) (-2.92) (-3.43) Leverage -0.001 0.002 0.006* 0.008 (-0.63) (0.71) (1.71) (1.55) Salesgrowth 0.031*** 0.031** 0.073*** 0.134*** (3.11) (2.38) (3.74) (4.60) Cash 0.002 0.004* 0.011** 0.014* (1.10) (1.69) (2.18) (1.95) Momentum -0.002 -0.003 -0.003 0.003 (-1.35) (-1.51) (-0.96) (0.75) Constant 0.021*** 0.026*** 0.030* 0.036* (3.20) (3.22) (1.96) (1.96) Observations 28,076 28,076 28,076 28,076 367 1 3 New product announcements, innovation disclosure, and future… include control variables for the following: the number of new product announce- ments issued by a firm in a given quarter (NUMNPAQTR), to account for firms’ propensity to issue these announcements; the tone and readability of new prod- uct announcements, aggregated over the quarter (TONEQTR and FOGQTR, respec- tively); and variables related to future firm performance, measured as firm size (Size), market-to-book value (MTB), leverage ratio (Leverage), institutional own- ership (Instown), firm age (Age), advertising expenses (ADV), cash ratio (Cash), industry competition (HHI), and sales growth rate (Salesgrowth). Finally, we include fixed effects for Fama-French 49 industries and fiscal year-quarters to control for industry-specific and time-invariant unobservable variables. To examine whether the relation between the degree of innovation disclosure and future firm performance is affected by the presence of disclosure incentives (H2A–H2C), we use the following modified version of Eq. (3): where D_Incentive is an indicator variable that takes the value of one when our empirical proxies for managerial disclosure incentives are classified as strong and zero otherwise. In Eq. (4), γ 1 captures the impact of managerial disclosure incen- tives on the relation between innovation disclosure and future firm performance. In this analysis, we expect that stronger disclosure incentives will reduce the predictive power of innovation disclosure for future sales (i.e., a negative coefficient for γ 1 ). Table 4 provides the descriptive statistics for the variables used in this analy- sis. From Table 4, we see that the average of the natural logarithm of one plus the average number of new product announcements per quarter is 0.891 (representing 1.707 new product announcements issued per quarter). We further see that the average of the natural logarithm of one plus the average Fog Index of new product announcements per quarter is 3.093 (21.251 prior to taking natural logarithm). Comparing this with a mean Fog Index of 24 for 10-K R&D disclosures (Merkley 2014) shows that new product announcements are associated with easier read- ability, appropriate for their intended broader audience. Finally, we see that new product announcements exhibit a positive tone (mean and median of 0.537 and (4) Future Sales = γ0 + γ1INNOVDISQTR × DIncentive + γ2INNOVDISQTR + γ3DIncentive + ∑J j=2 γjControls + σ, Table 3 (continued) Adjusted R-squared 0.008 0.007 0.009 0.014 Industry FE Yes Yes Yes Yes Year-Quarter FE Yes Yes Yes Yes Panel A provides descriptive statistics for the variables used in the market reaction tests. Panel B reports the coefficients and t-statistics from the ordinary least squares regressions of cumulative abnormal returns (CAR ) on INNOVDIS and the control variables. The t-statistics based on standard errors clustered at both the firm and fiscal quarter levels are displayed in parentheses below the coefficients. Variable definitions are provided in Appendix Table 10 and 11. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, using two-tailed tests 368 J. Chu et al. 1 3 0.636, respectively), consistent with their goal of promoting new products. The descriptive statistics of the other control variables are generally consistent with those in prior studies (e.g., Bellstam et al. 2021; Merkley 2014). Table 5 reports the correlation coefficients among the variables. Consistent with our expectations, we see that INNOVDISQTR relates positively and significantly to future sales in the next fiscal quarter. The correlation coefficients among the control variables suggest that multicollinearity is not a concern for our tests. 4.2 Innovation disclosure and future sales Table 6 reports the results of the regressions of future sales performance on innova- tion disclosure using sales in the next one to four quarters (rolling sum of sales in the next four quarters) as the dependent variable in columns (1) through (4) (col- umn (5)). The results in Table  6 show a positive and significant relation between the extent of innovation disclosure in new product announcements and future sales across all future windows. For example, using rolling sales as our dependent vari- able, we find that the coefficient on INNOVDISQTR is 0.017 (t-statistic of 2.26), indi- cating that a one standard deviation increase in INNOVDISQTR is associated with a Table 4 Descriptive statistics of variables used in the future performance tests This table reports the summary statistics for the variables used in the future performance tests. Variable definitions are provided in Appendix Table 10 and 11 Variable Mean SD P10 P25 P50 P75 P90 INNOVDISQTR 0.000 0.623 -0.739 -0.442 -0.101 0.323 0.822 NUMNPAQTR 0.891 0.363 0.693 0.693 0.693 1.099 1.386 LENGTHQTR 6.246 0.783 5.347 5.778 6.186 6.690 7.257 FOGQTR 3.093 0.128 2.930 3.015 3.095 3.174 3.249 TONEQTR 0.537 0.452 -0.037 0.289 0.636 0.975 1.000 Size 7.297 2.513 4.053 5.395 7.214 9.098 10.600 MTB 3.493 5.166 0.855 1.446 2.485 4.189 7.132 Leverage 0.185 0.195 0.000 0.005 0.141 0.290 0.453 ROAVOL 0.040 0.108 0.004 0.007 0.016 0.039 0.087 Age 2.857 0.747 1.946 2.303 2.833 3.434 3.989 Instown 0.605 0.282 0.143 0.424 0.668 0.817 0.917 ADV 0.016 0.032 0.000 0.000 0.002 0.016 0.046 HHI 0.134 0.145 0.038 0.052 0.076 0.147 0.305 Salesgrowth 0.004 0.042 -0.029 -0.006 0.003 0.015 0.038 Cash 0.252 0.210 0.029 0.079 0.197 0.376 0.570 t+1 Qtr. Earnings -0.007 0.079 -0.057 -0.007 0.009 0.021 0.036 t+1 Qtr. Sales 0.217 0.152 0.069 0.121 0.185 0.273 0.394 t+1 Qtr. COGS 0.619 1.022 0.198 0.336 0.508 0.693 0.822 t+1 Qtr. SG&A 0.482 0.761 0.128 0.225 0.356 0.531 0.756 369 1 3 New product announcements, innovation disclosure, and future… Ta bl e 5 C or re la tio n m at rix fo r v ar ia bl es u se d in fu tu re p er fo rm an ce te sts Th is ta bl e re po rts th e Pe ar so n co rr el at io ns fo r t he v ar ia bl es u se d in th e em pi ric al a na ly se s. Va ria bl e de fin iti on s ar e pr ov id ed in A pp en di x Ta bl e  10 a nd 1 1. ** *, * *, a nd * in di ca te si gn ifi ca nc e at th e 1% , 5 % , a nd 1 0% le ve ls , r es pe ct iv el y, u si ng tw o- ta ile d te sts IN N O VD IS Q TR N U M N PA Q TR FO G Q TR TO N E Q TR Si ze M TB Le ve ra ge RO AV O L Ag e In sto w n AD V H H I Sa le sg ro w th C as h 1 Q tr. Sa le s IN N O VD IS Q TR 1 N U M N PA Q TR 0. 00 0 1 FO G Q TR 0. 01 4* -0 .0 41 ** * 1 TO N E Q TR 0. 16 3* ** 0. 00 8 0. 00 5 1 Si ze -0 .0 27 ** * 0. 33 5* ** -0 .1 77 ** * -0 .0 54 ** * 1 M TB -0 .0 08 0. 01 3* -0 .0 38 ** * -0 .0 22 ** * -0 .0 37 ** * 1 Le ve ra ge 0. 00 0 0. 01 6* * -0 .1 10 ** * 0. 01 6* * 0. 29 3* ** -0 .0 11 1 RO AV O L -0 .0 15 ** -0 .0 54 ** * 0. 03 7* ** 0. 01 3* -0 .2 70 ** * 0. 04 3* ** -0 .0 10 1 Ag e 0. 01 6* * 0. 17 5* ** -0 .0 77 ** * -0 .0 37 ** * 0. 53 5* ** -0 .0 50 ** * 0. 19 8* ** -0 .1 79 ** * 1 In sto w n -0 .0 12 * 0. 07 2* ** -0 .0 32 ** * -0 .0 36 ** * 0. 37 9* ** -0 .0 10 0. 08 1* ** -0 .2 28 ** * 0. 22 1* ** 1 AD V 0. 05 6* ** 0. 01 1 -0 .1 84 ** * 0. 02 7* ** -0 .0 26 ** * 0. 06 6* ** -0 .0 01 0. 08 0* ** -0 .0 71 ** * -0 .0 33 ** * 1 H H I 0. 00 6 -0 .0 43 ** * -0 .0 81 ** * 0. 03 5* ** 0. 10 5* ** 0. 00 8 0. 05 8* ** -0 .0 55 ** * 0. 15 0* ** 0. 04 6* ** 0. 04 6* ** 1 Sa le sg ro w th -0 .0 08 -0 .0 10 -0 .0 02 0. 00 7 -0 .0 33 ** * 0. 05 6* ** -0 .0 26 ** * 0. 00 8 -0 .0 55 ** * -0 .0 16 ** 0. 01 2 -0 .0 09 1 C as h -0 .0 23 ** * -0 .0 25 ** * 0. 10 3* ** -0 .0 23 ** * -0 .4 21 ** * 0. 11 2* ** -0 .4 02 ** * 0. 15 7* ** -0 .3 74 ** * -0 .1 13 ** * 0. 05 6* ** -0 .1 98 ** * 0. 02 0* ** 1 t+ 1 Q tr. S al es 0. 05 2* ** -0 .0 55 ** * -0 .0 66 ** * 0. 08 6* ** -0 .1 85 ** * 0. 07 4* ** -0 .0 54 ** * -0 .0 08 0. 01 5* * -0 .0 14 * 0. 05 2* ** 0. 19 7* ** 0. 10 9* ** -0 .1 25 ** * 1 370 J. Chu et al. 1 3 1.22% percentage increase in the next year’s sales deflated by assets, translating to an increase of $328 million in sales for an average firm. Examining the coefficient estimates on the control variables, we find that more new product announcements per quarter (NUMNPAQTR) and a more positive announcement tone (TONEQTR) are each associated with higher future sales, while poorer readability is associated with lower future sales. These results are consistent with findings related to tone and readability in the literature, which shows a positive (negative) relation between tone (readability) and firms’ financial performance (e.g., Merkley 2014). We also find that smaller (Size), higher growth (MTB), and older (Age) firms are associ- ated with higher future sales. Finally, we find that future sales are positively associated with greater industry concentration (HHI), greater past sales growth (Salesgrowth), and lower liquidity (Cash).13 Taken together, the results in Table 6 support our hypoth- esis H1 and suggest that greater innovation disclosure in new product announcements is associated with more product purchases and thus higher future sales. 4.3 The effect of managerial incentives on the predictability of future sales We next examine our results related to the impact of strategic disclosure incentives on the predictive power of innovation disclosures for future firm performance. First, we examine the impact of managerial personal wealth incentives (H2A). Empiri- cally, we proxy for this type of incentive using net insider sales (Insider Trading) as well as the sensitivity of managerial wealth to changes in stock price volatility (Executive Vega).14 The choice of these proxies is motivated by research that shows firm disclosures are more likely to be biased when there is greater managerial net sales of shareholdings (Rogers and Stocken 2005) and greater sensitivity of execu- tive wealth to changes in stock price volatility. Accordingly, we expect that the exist- ence of these incentives will diminish the quality of the innovation disclosures in new product announcements, reducing their ability to predict sales. Second, we examine the impact of the strength of corporate governance (H2B). Following prior research, we proxy for the strength of a firm’s corporate governance by measuring how busy its board members are as well as whether CEO compensa- tion agreements include golden parachutes. Busy boards, defined as the total number of directorships held by all the members of a firm’s board, are less effective at mana- gerial monitoring (Brown et  al. 2019; Fich and Shivdasani 2006; Hauser 2018).15 13 The variance inflation factors of the regressions are lower than three, suggesting that multicollinearity is not a concern. In addition, an untabulated analysis suggests that including regressors from Eq. (1) does not change our interpretation of the results in Table 6. 14 Executive vega measures the sensitivity of executives’ wealth to a 1% change in stock return volatil- ity (Coles et al. 2006). Armstrong et al. (2013) document a positive relation between executive vega and earnings management. They further show that the disclosure incentives associated with a higher execu- tive vega subsume those of executive delta, which measures the sensitivity of executives’ wealth to a 1% change in stock price. 15 Using an M&A setting to improve identification, Brown et  al. (2019) find that busy directors of acquired firms devote more time to the remaining boards and that the monitoring gain on average domi- nates the loss in connections. In our setting, the monitoring role of boards matters more than their advi- sory role. 371 1 3 New product announcements, innovation disclosure, and future… Table 6 Innovation disclosure and future sales performance This table reports the coefficients and t-statistics from the ordinary least squares regressions of sales on INNOVDISQTR and the control variables. Columns (1)–(4) use quarterly sales as the dependent variable. Column (5) uses the rolling sum of the future four quarters of sales as the dependent variable. Variable definitions are provided in Appendix Table 10 and 11. The t-statistics based on standard errors clustered at both the firm and fiscal quarter levels are displayed in parentheses below the coefficients. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, using two-tailed tests (1) (2) (3) (4) (5) VARIABLE 1st Qtr. 2nd Qtr. 3rd Qtr. 4th Qtr. 1 Yr. INNOVDISQTR 0.004* 0.004** 0.004** 0.005** 0.017** (1.90) (2.23) (2.19) (2.45) (2.26) NUMNPAQTR 0.018*** 0.020*** 0.019*** 0.018*** 0.077*** (3.28) (3.53) (3.43) (3.43) (3.52) FOGQTR -0.040*** -0.037*** -0.032** -0.032** -0.140*** (-3.13) (-2.83) (-2.48) (-2.61) (-2.79) TONEQTR 0.006** 0.006* 0.005* 0.006* 0.023* (2.04) (1.89) (1.76) (1.95) (1.95) Size -0.019*** -0.019*** -0.019*** -0.019*** -0.077*** (-11.84) (-12.24) (-12.01) (-12.58) (-12.45) MTB 0.002*** 0.002*** 0.002*** 0.002*** 0.009*** (4.88) (4.51) (4.68) (4.37) (4.66) Leverage -0.057*** -0.049*** -0.049*** -0.047*** -0.196*** (-3.63) (-3.06) (-3.12) (-3.00) (-3.13) ROAVOL -0.034 -0.028 -0.025 -0.021 -0.103 (-1.54) (-1.11) (-1.02) (-0.76) (-1.01) Age 0.010** 0.010** 0.010** 0.011** 0.041** (2.44) (2.47) (2.38) (2.60) (2.55) Instown 0.011 0.008 0.007 0.004 0.030 (1.29) (0.99) (0.88) (0.53) (0.92) ADV 0.086 0.117 0.057 0.081 0.352 (1.10) (1.43) (0.72) (1.01) (1.11) HHI 0.075*** 0.068*** 0.072*** 0.068*** 0.286*** (3.61) (3.25) (3.42) (3.25) (3.47) Salesgrowth 0.342*** 0.185*** -0.036 0.476*** 0.959*** (8.74) (4.14) (-0.76) (10.40) (6.22) Cash -0.133*** -0.127*** -0.121*** -0.121*** -0.502*** (-9.98) (-9.43) (-9.11) (-9.04) (-9.44) Constant 0.448*** 0.439*** 0.423*** 0.427*** 1.736*** (10.54) (10.26) (10.11) (10.50) (10.47) Observations 17,800 17,799 17,800 17,796 17,794 Adjusted R-squared 0.409 0.393 0.395 0.407 0.419 Industry FE Yes Yes Yes Yes Yes Year-Quarter FE Yes Yes Yes Yes Yes 372 J. Chu et al. 1 3 Golden parachutes provide executives with substantial monetary compensation in the event of a change of control, thus shielding them from discipline by the market for corporate control (Bebchuk et  al. 2009). Accordingly, we expect managers of firms with busy boards and managers with golden parachutes in their compensation agreements to have fewer constraints that prevent them from extracting private bene- fits from their firm. In turn, these managers may be more likely to behave opportun- istically, resulting in innovation disclosures that are less predictive of future sales. Third, we examine the impact of the ability of customers to monitor and disci- pline managerial disclosure quality (H2C). We proxy for customer bargaining power using the degree of vertical integration (Frésard et al. 2020) as well as the level of product market competition (the number of product market rivals, as defined by Hoberg and Phillips 2010, 2016). Vertically integrated firms are better protected against the loss of existing customers, given their enhanced supply chain capabilities and resources (Forbes and Lederman 2010).16 Similarly, a less competitive product market means customers have higher switching costs and lower bargaining power. In both cases, customers’ lower bargaining power makes them less able to monitor innovation disclosure quality, opening the possibility for strategic disclosure. Table 7 reports the results of our cross-sectional tests of the impact of manage- rial incentives on the predictive power of innovation disclosure for future sales. Consistent with our prediction in H2A, the negative and significant coefficients on the interactions between INNOVDISQTR and insider trading in column (1) (-0.025, t-statistic -1.78) and executive vega in column (2) (-0.039, t-statistic-2.19) indicate that our observed positive relation between innovation disclosure and future sales is significantly lower when managers’ personal wealth is more sensitive to firm value. Consistent with our prediction in H2B, the negative and statistically significant coef- ficients on the interaction terms between INNOVDISQTR and board busyness in col- umn (3) (-0.032, t-statistic -2.21) and INNOVDISQTR and golden parachutes in col- umn (4) (-0.047, t-statistic -3.08) indicate that innovation disclosure by firms with weaker governance has lower predictive ability for future sales than that of other firms (holding the level of innovation constant). Finally, consistent with our prediction in H2C, the negative and statistically sig- nificant coefficient on the interaction terms between INNOVDISQTR and Vertical Integration (-0.023, t-statistic of -1.69) and INNOVDISQTR and Low Competition (-0.051, t-statistic of -3.26) in columns (5) and (6) indicate that the relation between innovation disclosure and future sales weakens when customers have less power to monitor disclosure quality. 16 Vertical integration provides a competitive advantage through increased economics of scope (resources and capabilities), reduced transaction costs, decreased costs arising from corporate complex- ity, and greater ability to capture upstream and downstream profit margins (Grant 2015). These attributes reduce customer bargaining power and thus their ability to discipline poor quality disclosures. 373 1 3 New product announcements, innovation disclosure, and future… Ta bl e 7 In no va tio n di sc lo su re a nd fu tu re o ne -y ea r-a he ad sa le s p er fo rm an ce p ar tit io ne d ba se d on m an ag er ia l d is cl os ur e in ce nt iv es (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) D _I nc en tiv e is e qu al to : VA R IA B LE S In si de r T ra di ng Ex ec ut iv e Ve ga Bo ar d Bu sy ne ss G ol de n Pa ra ch ut e Ve rt ic al In te gr at io n Lo w C om pe tit io n IN N O VD IS Q TR × D _ In ce nt iv e -0 .0 25 * -0 .0 39 ** -0 .0 32 ** -0 .0 47 ** * -0 .0 23 * -0 .0 51 ** * (- 1. 78 ) (- 2. 19 ) (- 2. 21 ) (- 3. 08 ) (- 1. 69 ) (- 3. 26 ) IN N O VD IS Q TR 0. 02 1* ** 0. 03 5* * 0. 02 3* * 0. 03 6* ** 0. 02 9* * 0. 04 2* ** (2 .7 0) (2 .5 7) (2 .4 4) (2 .8 4) (2 .6 2) (3 .6 8) D _ In ce nt iv e 0. 02 7* 0. 01 3 0. 01 2 -0 .0 28 0. 03 9* 0. 09 5* ** (1 .8 2) (0 .5 8) (0 .4 7) (- 1. 25 ) (1 .7 3) (3 .8 8) N U M N PA Q TR 0. 07 6* ** 0. 07 2* ** 0. 06 7* ** 0. 06 6* ** 0. 07 1* ** 0. 07 6* ** (3 .4 7) (3 .1 1) (3 .1 7) (2 .9 3) (3 .1 8) (3 .5 1) FO G Q TR -0 .1 39 ** * -0 .1 24 ** -0 .1 37 ** -0 .0 94 -0 .1 51 ** * -0 .1 44 ** * (- 2. 77 ) (- 2. 07 ) (- 2. 59 ) (- 1. 67 ) (- 2. 87 ) (- 2. 80 ) TO N E Q TR 0. 02 2* 0. 02 2 0. 02 3* 0. 01 7 0. 01 8 0. 01 8 (1 .9 5) (1 .6 0) (1 .9 1) (1 .2 9) (1 .4 6) (1 .4 6) Si ze -0 .0 78 ** * -0 .0 84 ** * -0 .0 76 ** * -0 .0 84 ** * -0 .0 78 ** * -0 .0 74 ** * (- 12 .4 2) (- 8. 29 ) (- 11 .0 4) (- 9. 44 ) (- 11 .8 5) (- 10 .8 7) M TB 0. 00 8* ** 0. 01 2* ** 0. 00 8* ** 0. 01 3* ** 0. 00 9* ** 0. 00 8* ** (4 .5 0) (4 .0 3) (4 .2 0) (5 .3 2) (4 .5 1) (4 .2 7) Le ve ra ge -0 .1 95 ** * -0 .2 55 ** * -0 .2 33 ** * -0 .2 86 ** * -0 .1 86 ** * -0 .1 87 ** * (- 3. 12 ) (- 2. 92 ) (- 3. 32 ) (- 3. 79 ) (- 2. 90 ) (- 2. 87 ) RO AV O L -0 .1 02 0. 02 6 -0 .0 78 0. 09 7 -0 .1 68 * -0 .1 62 * (- 1. 00 ) (0 .2 2) (- 0. 52 ) (0 .5 0) (- 1. 95 ) (- 1. 80 ) Ag e 0. 04 2* * 0. 03 1 0. 03 4* 0. 05 6* * 0. 04 3* * 0. 03 3* (2 .6 5) (1 .3 6) (1 .9 4) (2 .3 9) (2 .4 7) (1 .9 4) 374 J. Chu et al. 1 3 Th is ta bl e re po rts th e co effi ci en ts a nd t- st at ist ic s fro m th e or di na ry le as t s qu ar es re gr es si on s of th e ro lli ng s um o f t he fu tu re fo ur q ua rte rs o f s al es o n IN N O VD IS Q TR , t he in te ra ct io n te rm s of I N N O VD IS Q TR a nd th e in ce nt iv e va ria bl es , a nd th e co nt ro l v ar ia bl es . T he v ar ia bl e D _I nc en tiv e is a lte rn at iv el y eq ua l t o In si de r Tr ad in g, E xe cu tiv e Ve ga , B oa rd B us yn es s, G ol de n Pa ra ch ut e, V er tic al In te gr at io n, a nd L ow C om pe tit io n. T he c on tro l v ar ia bl es a re th e sa m e as th os e in T ab le  6 . V ar ia bl e de fin iti on s ar e pr o- vi de d in A pp en di x Ta bl e  10 a nd 1 1. T he t- st at ist ic s b as ed o n st an da rd e rr or s c lu ste re d at b ot h th e fir m a nd fi sc al q ua rte r l ev el s a re d is pl ay ed in p ar en th es es b el ow th e co ef - fic ie nt s. ** *, * *, a nd * in di ca te si gn ifi ca nc e at th e 1% , 5 % , a nd 1 0% le ve ls , r es pe ct iv el y, u si ng tw o- ta ile d te sts Ta bl e 7 (c on tin ue d) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) D _I nc en tiv e is e qu al to : VA R IA B LE S In si de r T ra di ng Ex ec ut iv e Ve ga Bo ar d Bu sy ne ss G ol de n Pa ra ch ut e Ve rt ic al In te gr at io n Lo w C om pe tit io n In sto w n 0. 02 7 -0 .1 40 ** -0 .0 22 -0 .0 96 * -0 .0 05 -0 .0 12 (0 .8 1) (- 2. 28 ) (- 0. 57 ) (- 1. 69 ) (- 0. 14 ) (- 0. 31 ) AD V 0. 34 8 0. 18 9 0. 39 5 -0 .0 62 0. 28 3 0. 12 1 (1 .1 0) (0 .4 0) (1 .1 6) (- 0. 16 ) (0 .8 8) (0 .3 6) H H I 0. 28 6* ** 0. 35 5* ** 0. 35 4* ** 0. 37 3* ** 0. 29 7* ** 0. 26 4* ** (3 .4 8) (3 .6 1) (4 .0 0) (3 .7 8) (3 .5 0) (3 .0 5) Sa le sg ro w th 0. 95 1* ** 0. 78 2* ** 1. 05 4* ** 0. 84 4* ** 0. 96 0* ** 0. 99 3* ** (6 .1 6) (4 .3 2) (6 .3 1) (5 .0 1) (6 .0 1) (6 .3 4) C as h -0 .5 08 ** * -0 .2 37 ** * -0 .5 18 ** * -0 .2 89 ** * -0 .4 85 ** * -0 .4 33 ** * (- 9. 54 ) (- 2. 82 ) (- 9. 25 ) (- 3. 41 ) (- 8. 86 ) (- 7. 86 ) C on st an t 1. 73 5* ** 1. 87 7* ** 1. 78 5* ** 1. 69 5* ** 1. 78 9* ** 1. 73 0* ** (1 0. 48 ) (7 .9 8) (1 0. 14 ) (7 .9 9) (1 0. 31 ) (1 0. 21 ) O bs er va tio ns 17 ,7 94 10 ,9 57 14 ,8 82 9, 68 7 16 ,5 92 16 ,1 15 A dj us te d R- sq ua re d 0. 42 0 0. 52 6 0. 42 9 0. 56 6 0. 42 6 0. 43 6 In du str y FE Ye s Ye s Ye s Ye s Ye s Ye s Ye ar F E Ye s Ye s Ye s Ye s Ye s Ye s Q ua rte r F E Ye s Ye s Ye s Ye s Ye s Ye s 375 1 3 New product announcements, innovation disclosure, and future… Taken together, our findings in Table 7 are uniformly consistent with our predic- tion that managers with disclosure incentives will incorporate a strategic component in their innovation disclosure, reducing the predictive ability of their innovation dis- closures for future sales.17 4.4 Additional future performance measures In this section, we examine whether innovation disclosures yield similar predictive power for other measures of firm performance, namely COGS, SG&A, and earn- ings. Unlike the relation with future sales, the relation between innovation disclo- sure and future COGS is unclear. On the one hand, improved engineering and manu- facturing technology embedded in product innovation may help a firm reduce its production costs, as reflected in COGS. On the other hand, innovative new products may require significant additional production and inventory costs or may lead to a loss of previous cost efficiencies derived from economies of scale if these product lines replace obsolete brands or product lines. Given these opposing forces, the rela- tion between innovation disclosure and future COGS is an open question. Regarding the relation between innovation disclosure and SG&A expenses, inno- vative products may enable firms to better target their existing customers, instead of incurring additional marketing expenditures to find new customers. For instance, Bayus et al. (2003) find that new products in the personal computer industry contrib- ute to a firm’s current and future profitability mainly due to a reduction in SG&A expenses as a proportion of sales, since new products require less marketing support than old ones. We expect the relation between innovation disclosure and earnings to be determined jointly by the earnings components examined above. Regarding additional measures of firm performance, the results in Panel A of Table 8 indicate that innovation disclosure does not reliably predict COGS, consistent with the notion that production costs scaled by sales are not necessarily reduced by innovations in product features. However, the results in Panel B show that the degree of innovation disclosure in negatively correlated with SG&A expenses, consistent with the findings of Bayus et al. (2003). Finally, the results in Panel C suggest that innovation disclosure is positively correlated with future earnings. Overall, these find- ings are consistent with our inference that innovation disclosure helps predict multiple measures of firms’ performance, including sales, SG&A expenses, and earnings. 4.5 Robustness checks We conduct several additional tests to ensure the robustness of our main find- ings. First, we re-run our main analysis using INNOVCOUNTQTR (the sum of all 17 In untabulated results, we find that managers with stronger incentives to disclose strategically (defined as above-median values based on an equal-weighted index of our incentive measures in Table  7) use more innovation words in their new product announcements and yield announcements with a higher innovation disclosure value. This finding suggests that managerial disclosure incentives manifest them- selves in the form of more innovation words in the respective firm new product announcements com- pared to those issued by firms with little or no disclosure incentives. 376 J. Chu et al. 1 3 Table 8 Innovation disclosure and future earnings, COGS, and SG&A performance Panel A: Future COGS (1) (2) (3) (4) (5) VARIABLE 1 Qtr. 2 Qtr. 3 Qtr. 4 Qtr. 1 Yr. INNOVDISQTR -0.011 -0.012 -0.003 -0.006 -0.042 (-0.92) (-1.17) (-0.30) (-0.67) (-1.05) NUMNPAQTR 0.044* 0.031 0.036* 0.030 0.145* (1.86) (1.52) (1.78) (1.65) (1.81) FOGQTR -0.042 0.039 0.005 0.048 0.025 (-0.49) (0.60) (0.08) (0.89) (0.10) TONEQTR -0.031 -0.019 -0.003 -0.003 -0.080 (-1.52) (-1.12) (-0.21) (-0.27) (-1.22) Controls Yes Yes Yes Yes Yes Observations 17,697 17,704 17,696 17,696 17,534 Adjusted R-squared 0.136 0.134 0.130 0.134 0.136 Industry FE Yes Yes Yes Yes Yes Year-Quarter FE Yes Yes Yes Yes Yes Panel B: Future SG&A (1) (2) (3) (4) (5) VARIABLE 1 Qtr. 2 Qtr. 3 Qtr. 4 Qtr. 1 Yr. INNOVDISQTR -0.019** -0.032*** -0.025*** -0.028*** -0.073** (-2.16) (-3.66) (-2.84) (-3.22) (-2.43) NUMNPAQTR 0.022 0.017 0.021 0.012 0.052 (1.11) (0.83) (1.17) (0.77) (0.77) FOGQTR 0.131** 0.106* 0.052 0.075* 0.384** (2.23) (1.98) (1.14) (1.72) (2.07) TONEQTR -0.030* -0.036** -0.028* -0.020 -0.084 (-1.94) (-2.05) (-1.81) (-1.46) (-1.66) Controls Yes Yes Yes Yes Yes Observations 16,165 16,184 16,199 16,197 16,048 Adjusted R-squared 0.220 0.205 0.220 0.221 0.251 Industry FE Yes Yes Yes Yes Yes Year-Quarter FE Yes Yes Yes Yes Yes Panel C: Future Earnings (1) (2) (3) (4) (5) VARIABLE 1 Qtr. 2 Qtr. 3 Qtr. 4 Qtr. 1 Yr. INNOVDISQTR 0.002** 0.002** 0.002*** 0.002* 0.009*** (2.35) (2.11) (2.70) (1.90) (2.83) NUMNPAQTR -0.003* -0.002 -0.003 -0.004* -0.012 (-1.77) (-0.74) (-1.64) (-1.84) (-1.53) FOGQTR -0.013** -0.013*** -0.015*** -0.010** -0.052*** (-2.45) (-2.70) (-2.89) (-2.04) (-2.89) TONEQTR 0.001 0.002* 0.001 0.002 0.006 (1.06) (1.87) (1.00) (1.26) (1.40) 377 1 3 New product announcements, innovation disclosure, and future… innovation words in new product announcements made by a firm in a given quarter deflated by the total number of words in these announcements) as the dependent variable and including the regressors in Eq. (1) as additional control variables in Eq. (3) and find similar results (Table A2 in the online appendix). Next, we replace all the quarterly measures in our current tests with annual measures and obtain similar inferences (Table A3 of the online appendix). We further find our main results are robust to using alternative industry fixed effects definitions, separating year and quarter fixed effects, and clustering standard errors at firm, firm quarter, or industry levels (Table A4). Lastly, we re-run our main analysis for subsamples of firms with or without R&D or patent data and obtain similar findings (Table A5). 5 Conclusion Innovation has long been recognized as a critical aspect of business success, offering value to both customers and investors. We examine the ability of innovation disclo- sure to predict future firm performance. We first construct a dictionary of innovation words to identify the information content and properties of voluntary, nonfinancial managerial disclosures of production innovation. We use this dictionary to develop an innovation disclosure measure that is more generalizable and incrementally informative for firm value and future performance than conventional innovation measures that depend on the existence of R&D expenses and patents. We then employ our measure to conduct a comprehensive empirical investiga- tion and find a positive relation between the extent of innovation disclosure in new product announcements and future firm performance. Further analyses find that the predictive ability of innovation disclosures weakens when managers have incentives to disclose innovation information strategically. As the first archival study to quantify the extent of managerial innovation disclo- sure in new product announcements and investigate its implications, our research This table reports the coefficients and t-statistics from the ordinary least squares regressions of cost of goods sold (Panel A), SG&A expenses (Panel B), and earnings (Panel C), on INNOVDISQTR and the control variables. In Panels A–C, columns (1)–(4) use quarterly performance as the dependent variable. Column (5) uses the rolling sum of the future four quarters of performance as the dependent variable. Variable definitions are provided in Appendix Table 10 and 11. The t-statistics based on standard errors clustered at both the firm and fiscal quarter levels are displayed in parentheses below the coefficients. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, using two-tailed tests Table 8 (continued) Controls Yes Yes Yes Yes Yes Observations 17,801 17,801 17,801 17,801 17,801 Adjusted R-squared 0.230 0.213 0.211 0.204 0.276 Industry FE Yes Yes Yes Yes Yes Year-Quarter FE Yes Yes Yes Yes Yes 378 J. Chu et al. 1 3 provides insights into how managers voluntarily reveal qualitative private informa- tion to both product and capital market audiences. While innovation disclosure in new product announcements is informative, our analysis shows that careful interpre- tation is needed to effectively link innovation disclosure to future firm performance. Appendix 1 Table 9 Innovation disclosure measure Panel A. Word List Construction Procedure Step Procedure 1 Identify the synonyms of “innovative” accord- ing to the Oxford Thesaurus of English. These synonyms are called “level 1 synonyms” (seven synonyms, total eight words). 2 Identify synonyms of the “level 1 synonyms” according to the Oxford Thesaurus of English. Keep a synonym if it has at least two synonyms that are included in the eight words in step 1, i.e., “innovative” and “level 1 synonym”; drop the word otherwise. These synonyms are called “level 2 synonyms” (added 14 new words for a total of 22 words). 3 Find the synonyms of the “level 2 synonyms” according to the Oxford Thesaurus of English. Keep a synonym if it has at least two synonyms that are included in the eight words in step 1, i.e., “innovative” and “level 1 synonym”; drop it otherwise. These synonyms are called “level 3 synonyms” (added two new words for a total of 24 words). 4 Repeat the same procedure (from steps 1 through 3) to find synonyms of “innovation” (added two “level 1 synonyms,” one “level 2 synonym,” and four “level 3 synonyms” for a total of eight words). 5 The word list consists of “innovation” and “innova- tive,” “level 1 synonyms,” “level 2 synonyms,” and “level 3 synonyms,” with 32 words in total. 6 Form a final word list with the adjectival and adverbial forms of the synonyms of “innovative” and the noun and verb forms of the synonyms of “innovation,” with 61 words in total. Panel B. List of Innovation Words “advanced, avant-garde, brilliant, brilliantly, clever, cleverly, create, created, creates, creating, creation, creations, creative, creatively, creativeness, different, fresh, groundbreaking, ground-breaking, imagi- native, ingenious, ingeniously, innovate, innovated, innovates, innovating, innovation, innovations, innovative, innovatively, innovativeness, innovatory, invent, invented, inventing, invention, inven- tions, inventive, inventively, inventiveness, invents, new, newest, newness, novel, novelties, novelty, offbeat, original, originalities, originality, pioneering, precedent-setting, progressive, revolutionary, seminal, unconventional, uniqueness, unprecedented, unprecedentedly, way-out”. 379 1 3 New product announcements, innovation disclosure, and future… Appendix 2: Variable definitions Table 10 Variables used to estimate the innovation disclosure measure (Table 2) Variable Name Definition INNOVCOUNTQTR The sum of innovation words deflated by the total number of words in all new product announcements made by a firm in a given quarter, multiplied by 100. INNOVCOUNT The sum of innovation words deflated by the total number of words in each new product announcement, multiplied by 100. R&D Capital Natural logarithm of 1 plus the sum of five-year cumulative R&D expenses, assuming an annual depreciation rate of 20% in the fiscal year ending in year t-1. Missing values are replaced by 0. Patent Natural logarithm of 1 plus the sum of the number of patents filed by a firm in the past three years. Missing values are replaced by 0. PatCitation Natural logarithm of one plus the sum of the number of forward citations of patents filed by a firm in the past three years. Missing values are replaced by zero. PatValue Natural logarithm of one plus the sum of the monetary value of patents filed by a firm in the past three years. Missing values are replaced by zero. Trade Secret Natural logarithm of one plus the total number of “trade secret” or “trade secrecy” phrases mentioned in the 10-K filing in the last year, following Glaeser (2018). Industry R&D Capital Natural logarithm of one plus the sum of five-year cumulative R&D expenses, assuming an annual depreciation rate of 20% in the fiscal year ending in year t-1 in a three-digit SIC code. Missing values are replaced by zero. Industry Patent Natural logarithm of one plus the sum of the number of patents filed by all firms in a three-digit SIC code in the past three years. Missing values are replaced by zero. Industry PatCitation Natural logarithm of one plus the sum of the number of forward citations of patents filed by all firms in a three-digit SIC code in the past three years. Missing values are replaced by zero. Industry PatValue Natural logarithm of one plus the sum of the monetary value of patents filed by all firms in a three-digit SIC code in the past three years. Missing values are replaced by zero. NUMNPA12Q Natural logarithm of one plus the number of new product announcements made by a firm in the past 12 quarters. Industry NUMNPA12Q Natural logarithm of one plus the number of new product announcements made by all firms in a three-digit SIC in the past 12 quarters. INNOVDISQTR The residuals from model (1) when INNOVCOUNTQTR is the dependent vari- able (used in the future performance tests). INNOVDIS The residuals from model (1) when INNOVCOUNT is the dependent variable (used in the market reaction tests). 380 J. Chu et al. 1 3 Table 11 Variables used in Tables 3, 4, 5, 6, 7 and 8 Variable Name Definition Sales Sales divided by average total assets (Sales = SALEQ / (ATQ + lag (ATQ)) / 2). Earnings Net quarterly income divided by average total assets (Earnings = NIQ / (ATQ + lag (ATQ)) /2). SG&A Quarterly selling, general, and administrative expenses deflated by total sales (SG&A = SGAQ/SALEQ). COGS Quarterly cost of goods sold deflated by total sales (COGS = COGSQ /SALEQ). CAR(-t,+t) Size-adjusted cumulative abnormal return for the three-, five-, 10-, 21- day window around the new product announcement date. The size-adjusted abnormal return is equal to the firm’s stock return minus the index return of firms in the same size portfolio formed at the end of the previous calendar year. LENGTHQTR Natural logarithm of the total number of words in new product announcements made by a firm in a quarter. TONEQTR NPA Tone is calculated as (quarterly sum of positive words − quarterly sum of negative words) / (quarterly sum of positive words + quarterly sum of negative words), following Loughran and Mcdonald (2011). FOGQTR Natural logarithm of the quarterly average Fog Index, which is calculated as (words per sentence + percentage of complex words) × 0.4, following Li (2008). NUMNPAQTR Natural logarithm of the number of new product announcements made by a firm in a quarter. Insider Trading This variable is equal to one if net insider sales (insider sales minus purchases in a fiscal quarter) are in the top quintile of the sample in a fiscal year and zero otherwise. Insider trading data is obtained from the Thomson Reuters Insider Filing data. Executive Vega This variable is equal to one if the average pay sensitivity to a 1% change in stock volatility of the top five executives in a firm is greater than the sample median in a fiscal year and zero otherwise. Executive compensation data is from Execu- comp. The calculation method of Executive Vega follows Coles et al. (2006). Board Busyness This variable is equal to one if the total number of directorships held by all the members of the firm’s board is in the top quintile of the sample in a fiscal year and zero otherwise. Total number of directorships held by all the member of the firm’s board is calculated by aggregating individual board member’s number of board’s seats. This data is from the BoardEx database. Golden Parachute This variable is equal to one if a firm has a golden parachute for its CEO in a fiscal year and zero otherwise. This data is from the Institutional Shareholder Services database. Vertical Integration This variable is equal to one if a firm’s vertical integration score is greater than the sample median in a fiscal year and zero otherwise. The vertical integration score indicates the extent to which a given firm’s products are vertically related to the other products of the same firm (Frésard et al. 2020). We obtain the vertical inte- gration data from the Frésard-Hoberg–Phillips Vertical Relatedness Data Library. Low Competition This variable is equal to one if the number of all of the product market rivals is lower than the sample median by fiscal year group (i.e., low competition) and zero otherwise. The number of product market rivals is defined as the number of competitors that have similar products with a firm for a year using the firm-by- firm pairwise product similarity scores (Hoberg and Phillips 2010, 2016). We obtain the product similarity score variable from the Text-based Network Indus- try Classifications data provided by the Hoberg-Phillips Data Library. Size Natural logarithm of the book value of total assets (Compustat item ATQ), meas- ured at the beginning of the fiscal quarter. 381 1 3 New product announcements, innovation disclosure, and future… Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s11142- 024- 09820-0. Acknowledgements We thank our editor, Russell Lundholm, and an anonymous reviewer for their valu- able comments and guidance. We also gratefully acknowledge helpful comments and suggestions from Mary Barth, Anne Beatty, John Campbell, Patricia Dechow, Ross Garon, Igor Goncharov, Stephan Hol- lander, Peter Joos, Philip Joos, Wayne Landsman, Lubomir Litov, Shiwon Song, Jayanthi Sunder, Steven Young, and seminar participants at Cubist Systematic Strategies, INSEAD, Lancaster University, Nova Business School, Tilburg University, Wuhan University, University of Oklahoma, and University of Tech- nology Sydney. Yuan He acknowledges financial support from the National Natural Science Foundation of China (No. 72202189; No. 72232007) and Fundamental Research Funds for the Central Universities (No. 20720221055). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permis- sion directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/ licenses/by/4.0/. Table 11 (continued) Variable Name Definition MTB Market-to-book ratio, measured at the beginning of the fiscal quarter (MTB = CSHOQ*PRCCQ/CEQQ). Leverage Long-term debt plus debt in current liabilities divided by assets for the preceding quarter, measured at the beginning of the fiscal quarter (Leverage = (DLCQ + DLTTQ) / ATQ). ROAVOL Standard deviation of Earnings over the past 12 quarters. Age Natural logarithm of one plus the number of years since the first year entered in Compustat, measured at the beginning of the fiscal year. Instown Percentage of institutional ownership, measured at the beginning of the fiscal quarter. ADV Advertising expenses deflated by sales, measured at the beginning of the fiscal year (ADV = XAD / SALE). Missing values are replaced by zero. HHI Sum of the squares of the market share of all firms within each three-digit SIC industry, measured at the beginning of the fiscal year. A firm’s market share is calculated as its sales divided by the total sales of all firms operating in the three- digit SIC industry. Sales data is from Compustat. 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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. New product announcements, innovation disclosure, and future firm performance Abstract 1 Introduction 2 Hypothesis development 2.1 Innovation disclosure and future sales performance 2.2 Disclosure incentives, innovation disclosure, and future firm performance 3 Sample selection and empirical methodology 3.1 Sample 3.2 Measuring innovation in new product announcements 3.3 Validation test of the innovation disclosure measure 4 Empirical analysis of innovation disclosure and future firm performance 4.1 Research design 4.2 Innovation disclosure and future sales 4.3 The effect of managerial incentives on the predictability of future sales 4.4 Additional future performance measures 4.5 Robustness checks 5 Conclusion Appendix 1 Appendix 2: Variable definitions Acknowledgements References