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Managers’ Discussion of Competition in the 10-K and Firms’ Financing Policies and Investing Activities Kyle Peterson† University of Oregon Lundquist College of Business Eugene, OR 97403 541-346-3329 [email protected] Nam Tran Melbourne Business School 200 Leicester St. Carlton VIC 3053, Australia [email protected] September 2014 PRELIMINARY. Please do not cite or distribute without permission. Abstract: We examine whether managers’ perceived competition, based on firms’ disclosures in their 10-K filings, influences firms’ financing policies and investments. We document that firms with high perceived competition have more conservative financing policies by holding more cash, having lower leverage and lower dividend payout ratio. We also find that perceived competition is positively related to firms’ investments in R&D and acquisitions over the subsequent three years. Finally, we examine whether conservative financing policies and investments in R&D and acquisitions help mitigate the impact of perceived competition on the rate of diminishing marginal returns established in prior literature. We find that the impact of perceived competition on the rate of diminishing marginal return is more severe for firms with more conservative financing policies and firms with more investments in R&D and acquisitions, suggesting that perceived competition combined with costly financing and investing activities provides a more credible signal of the actual level of competition. Keywords: competition, financing policies, investment, research and development JEL classification: † Corresponding Author. We thank workshop participants at the University of Oregon and Melbourne Business School for their helpful comments and suggestions. 1. INTRODUCTION Understanding a firm’s competitive environment and its strategies for competing in that environment is a critical component of standard equity valuation models (Healy and Palepu 2007, Lundholm and Sloan 2007, Penman 2009). Li, Lundholm and Minnis (2013) provide evidence that greater managerial perceived competition, measured based on firms’ disclosures in 10-K filings, is associated with higher rates of diminishing marginal returns for the firms, suggesting that these disclosures signal real threats of competition. This raises questions about whether firms with high perceived competition engage in actions to mitigate the negative effects of competition on their profitability, and whether those actions are effective. We extend Li et al. (2013) by examining these questions. Specifically, we examine how perceived competition affects firms’ financing policies including cash holdings, leverage, and dividend payout ratios. Second, we examine whether perceived competition predicts future investments in research and development (R&D) and acquisitions. Finally, we examine whether the financing policies and investments that firms with high perceived competition choose to undertake help curtail the negative impact of strong perceived competition on the rate of diminishing marginal returns found in Li et al. (2013). With respect to financing policies, theories suggest that firms faced with strong market competition should have high cash holdings, low financial leverage and low dividend payout ratios (Bates et al. 2009; Haushalter et al. 2007; Benoit 1984; Booth and Zhou 2009). These conservative financing policies help firms respond to negative cash flow shocks and financial distress, which are more likely to occur in more competitive markets. More conservative financing policies also provide financial flexibility, allowing firms to act quickly when a profitable investment opportunity 1 arises before their competitors take the opportunity away. High cash reserves and low leverage can also signal the possibility of aggressive behavior, which is useful in deterring rivals’ entry and expansion. Prior empirical studies, using various proxies for market competition, find that firms in more competitive markets hold more cash (Morellec et al. 2013; Haushalter et al. 2007; Hoberg et al. 2014), have lower leverage (MacKay and Phillips 2005; Kovenock and Phillips 1995) and pay less dividends (Booth and Zhou 2009; Hoberg et al. 2014).1 Consistent with these existing theories and empirical evidence, we find that firms with more frequent references to competition in their 10-K filings have significantly higher cash holdings, lower financial leverage and lower dividend payout ratio. Univariate analysis shows that, compared to firms in the bottom decile of perceived competition, firms in the top decile have 16.4% higher cash holdings, 13.0% lower leverage, and 11.1% lower dividend payout ratio. These relations between perceived competition and financing choices continue to hold after controlling for risk, size, performance, investment opportunities, industry concentration, and even for lagged dependent variables (i.e., prior year cash holdings, leverage and dividend payout ratio). We predict that firms will also respond to competitive threats by altering their investment strategies. We test this prediction by examining the relation between firms’ perceived competition and R&D expenditures and acquisition activities. Prior theoretical and empirical studies suggest that high market competition generally 1 Proxies for market competition in these studies include price-cost margin, industry concentration, product market fluidity, the degree of import penetration, and the similarity of a firm’s technology with its rivals. Two common characteristics of these measures are: (1) they only capture the competitive pressures from existing competitors (perhaps with errors) but not threats from potential competitors; (2) they do not necessarily reflect managerial perception of competition. Dedman and Lennox (2009) survey managers of UK firms and find no relation between industry concentration and managers’ perceived competition. Our measure of competition is capable of capturing managerial perception of both existing competitive pressures and threats of entry, thus offering incremental explanatory power beyond what is captured by existing measures of competition. 2 motivates firms to invest in R&D (see Gilbert [2006] for an excellent review of both theoretical and empirical literature on this topic). Thus, we expect a positive association between managers’ perceived competition and future R&D expenditures. In addition, we argue that firms facing strong competition are more likely to undertake acquisitions. Acquisition is an effective growth strategy when firms want to eliminate existing or potential competitors and quickly gain market share (Gaughan 2005). Acquisition is also preferable to organic growth if the firm wants to expand into a new market (Coad 2009), which is likely the case as the existing market has become too competitive to offer any growth opportunity. Finally, acquisition allows firms to quickly gain access to innovations, which help them “escape competition.” As a result, we expect a positive association between firms’ perceived competition and future acquisition activities.2 Our test results reveal a strong positive association between managers’ perceived competition and R&D expenditures over the subsequent three years. Univariate analysis shows that, compared to firms in the bottom decile, firms in the top decile of perceived competition have 23.4% higher R&D expenditure (as percentage of total assets) over the subsequent three years. The positive association between perceived competition and R&D expenditures remains highly significant after controlling for other factors known to explain R&D activities such as firm size, cash flow risk, profitability, growth opportunities, and industry concentration. We also find that firms with higher perceived competition invest more through acquisitions in the subsequent three years, both in terms of the likelihood to engage in acquisitions, the number of acquisitions undertaken and total dollar value of 2 For completeness we also examine how perceived competition relates to capital expenditures although we have no ex ante prediction about this relation. We find an insignificant relation between perceived competition and capital expenditure after controlling for past capital expenditure. 3 acquisitions undertaken. The positive associations between perceived competition and future R&D expenditures and acquisitions persist even after controlling for current period R&D and acquisition activities, respectively. Given our findings that firms alter their financing policies and investments in response to high perceived competition, we revisit the main finding in Li et al. (2013) that firms with higher perceived competition exhibit higher rates of diminishing marginal returns. We examine whether more conservative financing policies and more aggressive investments in R&D and acquisitions help firms mitigate the negative impact of competition on marginal returns. We posit three possible effects of financing policies and investments on the relation between perceived competition and the rate of diminishing marginal returns. First, the financing and investing policies taken by managers could be effective at counteracting the effects of competition, leading to lower rates of diminishing marginal returns for companies that follow these strategies. Second, the real competitive threats for firms with high perceived competition are strong enough such that firms’ financing and investment strategies have no significant effect on the rate of diminishing marginal returns. Finally, if managers are making costly financing choices and investments in response to high perceived competition, this may be a signal of the strength of the competition they are facing. In this case, we should observe that a combination of high perceived competition and conservative financing policies and/or high investments in R&D and acquisitions is associated with higher rates of diminishing marginal returns. Our tests reveal that the negative impact of perceived competition on the rate of diminishing marginal returns is much stronger for firms that adopt conservative financing policies and firms that invest more in R&D and acquisitions. This result suggests that managers’ disclosures about competition are more credible and serious if 4 accompanied by costly financing and investment strategies that attempt to contest the perceived competition.3 Our finding that firms faced with high perceived competition tend to increase future investments in R&D also suggests an alternative explanation for why firms with high perceived competition experience higher rates of diminishing marginal returns (Li et al. 2013). U.S. accounting standards require R&D to be expensed immediately, which could result in mechanically lower future operating returns for firms with more R&D expenditures. However, additional tests where we adjust for the effects of R&D expenditure on operating returns reveal that although increased expenses from R&D do contribute to lower future returns, this does not fully explain the higher rates of diminishing marginal returns for firms with high perceived competition. We conclude that economic competitive pressure is still a fundamental reason for the association between perceived competition and diminishing returns documented in Li et al. (2013). In additional analysis we also find that our main results hold for market followers but not market leaders, which suggests that market leaders may be using competition disclosures strategically. Our study contributes to the literature by providing large sample evidence on the actions managers take in response to perceived competition as disclosed in 10-K filings. Li et al. (2013) provide important evidence that firms’ disclosures about competition in the 10-K filings can help predict changes in firms’ operating returns. We document that on average managers respond to perceived competition by altering their financing policies and future investing activities. Consistent with Li et al. (2013), 3 We cannot rule out the possibility that the diminishing marginal returns for firms that engage in costly financing and investing strategies could have been even worse had these firms not implemented such strategies. Therefore, the relatively higher rates of diminishing marginal returns for these firms may represent the best alternative given their competitive environment. 5 our results suggest that firms’ discussion of competition in 10-K filings is not merely boilerplate as some (including the SEC) have suggested, but are useful indicators of the firm’s competitive threats. Our findings suggest this is especially true when competition disclosures are coupled with firms engaging in costly financing policies and investments. Understanding the relation between firms’ disclosure of competition in the 10-K and financing and investment strategies could enhance shareholders’ and information intermediaries’ ability to evaluate firms’ financial statements (i.e., understanding financing choices) and forecast their future cash flows through forecasting future investments. In the wake of recent concerns expressed by the SEC and FASB about too much disclosure in financial reporting (Gallagher 2014, Seidman 2012), our research provides additional evidence that disclosures about competition are potentially useful to financial statement users.4 Our second contribution relates to the measurement of competition. Prior studies in economics and accounting rely primarily on industry-based measures of competition such as the Herfindahl Index or Lerner Index. While these measures have their merits, they generally only capture the competitive threats from existing rivals (perhaps with noises). However, as Schumpeter (1942, p. 85) states: “[Competition] disciplines before it attacks. The businessman feels himself to be in a competitive situation even if he is alone in his field.” Measuring competition through managers’ eyes captures a broader set of competitive threats like the threat of new entrants. One way to infer managers’ perceived competition is to rely their own disclosures about competition 10-K filings. We find that this measure of perceived competition explains 4 In 2012 remarks by FASB Chairman Leslie Seidman, he said “Many of those stakeholders tell me that financial reports are just too long…[y]et investors continue to say they want more information, particularly when there is a business downturn or failure. Often the information that these investors want is available in the financial statements — but it is hidden in plain sight.” Our evidence indicates this is the case, suggesting regulators should use caution as they address this disclosure issue. 6 managers’ important financing and investing choices beyond that explained by the Herfindahl Index, the most traditional measure of competition. Our results provide additional support for the use of the disclosure-based measure of competition as a proxy for the competitive environment of the firm. The remainder of this paper is organized as follows: Section 2 reviews the related literature and develops hypotheses. Section 3 describes the data, empirical tests and results. Additional analyses are provided in Section 4. Finally, Section 5 concludes. 2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Throughout this paper we assume that on average the intensity of firms’ discussion of competition in the 10-K generally reflects managers’ perception of the level of competitive threats facing the firm. One could argue that managers make extensive reference to competition in the 10-K filing to overstate the level of competition facing the firm to achieve certain goals. While certainly there are firms that provide misleading disclosures in their 10-K filings, we expect that litigation and reputational risks mitigate this behavior, making competition disclosure a credible signal of managers’ true belief at least on average. Our assumption is consistent with the battery of empirical tests in Li et al. (2013) that provide robust evidence that firms that discuss competition more frequently experience higher rates of diminishing marginal returns. As competition will drive down returns and potentially threaten the existence of the firm, we expect that managers that perceive high levels of competition will take actions that can help them remove, or at least mitigate, the impacts of competition on the company’s profitability and existence. In the sections that follow, we develop 7 hypotheses about how managers’ perceived competition affects some of the firm’s financing and investing policies. We also discuss how the financing and investing policies that firms engage in might affect the relation between perceived competition and the rate of diminishing marginal returns. 2.1. Perceived Competition and Cash Holdings, Financial Leverage and Dividend Payout Ratio There are multiple motives for firms to hold cash (see Bates et al. [2009] for an excellent summary of those motives). We posit that higher perceived competition motivates firms to hold more cash for precautionary reasons.5 Specifically, high competition increases the likelihood of adverse cash flow shocks. Therefore, firms with strong perceived competition should have an incentive to hold more cash to cover negative cash flow shocks and avoid financial distress. Moreover, in a competitive market, profitable investment opportunities are rare and do not last long. In such a market, firms must act quickly whenever an investment opportunity arises. Large cash reserves allow firms the financial flexibility to invest and avoid losing profitable opportunities to their competitors (Haushalter et al. 2007). Finally, large cash reserves can serve as a credible signal that the firm is willing to engage in aggressive behavior, which can help deter market entry or rivals’ expansion (Benoit 1984). Taken together, our first hypothesis is: H1: Managerial perceived competition is positively related to the firm’s cash holdings. The trade-off theory of capital structure posits that firms set the optimal capital structure by trading off the benefits, such as tax shield, versus the costs of debt 5 Other motives for holding cash include transaction costs to convert non-cash assets into cash, the tax motive for multinational firms, and agency conflicts. 8 financing such as financial distress or bankruptcy costs (Barclay et al. 1997; Ramalho and Silva 2009). We expect that firms with high perceived competition will choose lower levels of debt for two reasons. First, competition increases the likelihood of negative cash flow shocks, which increases the expected bankruptcy costs of debt. Second, as discussed above, in a competitive industry, profitable investment opportunities are rare and do not last long. In such an environment, high level of debt imposes financial constraints, limiting firms’ ability to take advantage of those opportunities. Therefore, our second hypothesis is: H2: Managerial perceived competition is negatively related to the firm’s financial leverage. Prior studies find that the market reaction to dividend changes is asymmetric in that the reward for dividend increases is smaller than the penalty for dividend cuts (Michaely et al. 1995), implying that managers prefer a stable stream of dividends to a volatile one. Consistent with this notion, survey results suggest that managers consider the stability of future earnings and cash flows among the most important determinants of the firm’s dividend policies (Brav et al. 2008, 2005; Lintner 1956). As competition increases the likelihood of negative cash flow shocks, we expect that firms with high perceived competition have an incentive to maintain a low dividend payout ratio to reduce the likelihood of future dividend cuts. Consistent with this intuition, Booth and Zhou (2009) find evidence that firms in more competitive industries, using proxies such as Herfindahl Index, Lerner Index and Import Penetration, tend to pay lower dividends. Therefore, our third hypothesis is: H3: Managerial perceived competition is negatively related to the firm’s dividend payout ratio. 9 2.2. Perceived Competition, R&D Expenditure and Acquisition Activities Intuition suggests that firms operating in more competitive markets have greater incentives to invest in R&D, as innovations can help them “escape competition” by utilizing more efficient technologies to reduce costs or introducing superior products to gain market share. Anecdotal evidence is generally consistent with this intuition. For example, Andrew Grove, former CEO of Intel Corporation, is well known for his motto “Only the paranoid survive” (Gilbert 2006, p.179). Similarly, when Steve Jobs took the role of CEO at Apple Inc., he said “The cure for Apple is not cost-cutting. The cure for Apple is to innovate its way out of its current predicament” (Gallo 2014). While the idea that competition promotes R&D investments is intuitive, prior research on this relationship is not necessarily conclusive. On the one hand, Arrow (1962) demonstrates analytically that, when intellectual property rights are exclusive, competition motivates firms to invest in R&D as the incremental benefits from the same innovation are higher for competitive firms than for a monopolist. Schmidt (1997) argues that competition increases the probability of liquidation, which creates a disutility for managers. This motivates managers to invest in R&D as innovations can make the firm more efficient, reducing the liquidation risk. Various other models also predict that competition creates patent races that would increase R&D investments (Fudenberg et al. 1983, Harris and Vickers 1985). On the other hand, a few theoretical models suggest a negative relationship between competition and R&D investment. For example, Schumpeter (1942) argues that monopolists have stronger incentives to invest in R&D than competitive firms because competition limits the ability of innovators to exploit economies of scale if R&D investment results in innovations (Schmidt [1997] also makes a similar argument). In addition, monopolies generally have more funds available to finance R&D activities. 10 Empirical studies generally provide evidence consistent with competition motivating R&D expenditure, although there is some evidence that the relation is not necessarily linear in the sense that “too much” competition might discourage firms from investing in R&D (see Gilbert [2006] for a review). Taken the literature as a whole, we predict that firms with higher perceived competition will invest more in R&D. H4: Managerial perceived competition is positively related to the firm’s future R&D expenditure. One well-known motive of firms to undertake acquisitions is to gain market share or eliminate potential and existing competitors (Gaughan 2005). This implies that firms that are more concerned about competition have a stronger incentive to undertake acquisitions. Moreover, in a competitive environment, firms likely prefer acquisitions to organic growth because acquisitions allow firms to rapidly acquire new production capacity, particularly for firms that choose to diversify into another industry as the existing industry has become too competitive (Coad 2009). Finally, acquisition is an alternative way for firms to quickly acquire innovations, which allow them to “escape competition” as discussed above (Ahuja and Katila 2001, Hitt, Hoskisson, Johnson, and Moesel 1996). Taken together, we predict a positive association between managers’ perceived competition and acquisition activities.6 H5: Managerial perceived competition is positively related to the firm’s future investment in acquisitions. 6 There is some evidence that merger and acquisition activities do not have an adverse impact on the level of market competition (Goldberg 1973, 1974; Feinberg 1984). If managers believe that they cannot reduce competition by engaging in acquisitions, then we might not find that firms with high managerial perceived competition invest more in acquisitions. 11 2.3. Impacts of Financing and Investing Policies on the Relation Between Perceived Competition and the Rate of Diminishing Marginal Returns. We have argued that firms with high perceived competition will have more conservative financing policies and invest more in R&D and acquisitions. Li et al. (2013) find that firms with high perceived competition experience higher rates of diminishing marginal returns. This raises a natural question about whether having conservative financing policies and investing in R&D and acquisitions help firms mitigate the effect of competition on the rate of diminishing marginal returns. On the one hand, if these strategies are effective at contesting competition, we should observe that the negative effect of competition on marginal returns is weaker for firms with more conservative financing choices and/or more investments in R&D and acquisitions. On the other hand, it is possible that firms with more extensive discussion of competition in their 10-K filings and simultaneously implement costly financing and investing strategies to counteract the perceived competition are those that are really faced with strong competition. In other words, a combination of extensive discussion of competition in the 10-K and conservative financing choices and/or aggressive investments in R&D and acquisitions is a more credible signal of managerial strong perceived competition. In this case, we would expect the impact of competition on the rate of diminishing marginal returns to be stronger for firms with more conservative financing policies and/or more aggressive R&D and acquisition investments. Given the opposing possible effects of financing and investing policies on the relation between perceived competition and the rate of diminishing marginal returns, we examine this issue without making directional predictions. 12 3. EMPRICAL TESTS 3.1. Sample and Descriptive Statistics We use the Python software to obtain competition disclosures from 10-K filings using the EDGAR filings database for the period from 30 June 1994 through 31 March 2013. To obtain financial data, we merge our collected data with Compustat using the SEC’s Central Index Key (CIK). We eliminate firm-years with market value of equity or total assets less than $1 million, firm-years with negative or missing values for cash holdings and leverage. Finally, we remove all financial firms (SIC 6000-6999) because those firms’ cash holdings, leverage, and investment opportunities differ substantially from other firms. Our final sample includes 72,416 firm-years. The samples used in individual tests might be smaller due to additional data requirements. Table 1 presents the number of observations and descriptive statistics for our measure of perceived competition (Comp_Pct) by Fama-French 48 industry groups. Comp_Pct is the number of occurrences of competition related words in the 10-K filing, scaled by total number of words in the 10-K and multiplied by 1,000 so that the measure counts the competition references per 1,000 words. We follow Li et al. (2013), we use various forms of the word competition including competitor, competitive, compete, and competing (both singular and plural forms). We remove any cases where “not,” “less,” “few,” or “limited” precedes any form of the word “competition” by three or fewer words. We order industries by their mean Comp_Pct value in Table 1. One observation from Table 1 is that firms with high Comp_Pct are more likely to be in technology and research-related industries. Part of this high perceived competition reflects the ever-changing technology, but it also reflects the vast number 13 of firms in these industries in the U.S that are competing with each other. For example, the top 10 industries by Comp_Pct represent 52 percent of the sample. Given these strong industry level effects, we control for industry fixed effects in our tests. Another observation from Table 1 is that many of the commodity-related and regulated industries, such as Utilities, Agriculture, and Petroleum and Natural Gas, are generally low in perceived competition. This may seem counterintuitive since the commodities in these industries would generally be viewed as virtually perfect substitutes. However, what this highlights is that for price-taking firms in purely competitive markets, the competition they face is so pervasive that it is largely exogenous to their decision making. From a disclosure perspective, Li et al (2013) argue that because competition in these industries is so obvious and omnipresent, mentioning competition might be unnecessary unless these firms are facing some new threats of competition. Table 2 presents descriptive statistics for our sample. The median Comp_Pct is 0.93 words per one thousand words in the 10-K. To put this in perspective, the median number of words in the 10-Ks in our sample is 27,723. Thus, the average firm in our sample mentions competition approximately 26 times in its 10-K. The variation in the frequency of references to competition is quite large, ranging from virtually no mention of competition (minimum Comp_Pct is 0.07) to 2.73 times per thousand words. Cash holdings are roughly 10 percent of total assets for the median firm in the sample and median leverage is 19 percent. Less than half of our sample firms pay dividends. Mean (median) R&D expenditure over the subsequent three years is 19 percent (0 percent) of total assets, while mean (median) investment in acquisitions over the subsequent three years is 6 percent (0 percent) of total asset. 14 3.2. Relations between Perceived Competition and Financing Policies 3.2.1. Univariate Test Results Table 3 presents the mean and median cash holdings (Cash_Holding), leverage (Leverage) and dividend payout ratio (Div_Payout_Ratio) for the sample firm-years sorted into deciles based on the level of perceived competition (Comp_Pct). Both mean and median cash holdings increase monotonically from the 1st to 10th decile of perceived competition, suggesting that firms with higher perceived competition tend to hold more cash. The difference in mean (median) cash holdings between firms in Decile 10 and those in Decile 1 is 16.4 (18.5) percent of total assets. We consider this difference economically significant given the sample mean (median) cash holding is 19 (10) percent. Also consistent with our expectation, both leverage and dividend payout ratio decrease with the level of perceived competition. The difference in mean (median) leverage between Decile 1 and Decile 10 is 13.0 (21.1) percent, and the difference in mean (median) dividend payout ratio between Decile 1 and Decile 10 is 11.1 (0.00) percent. 3.2.2. Multivariate Test Results Although the univariate tests suggest a strong relation between perceived competition and financing policies, in this section we conduct multivariate tests to control for other firm characteristics that potentially explain the differences in financing policies between firms with high and low perceived competition. Specifically, we estimate the following regression: FinVarit = β0 + β1Comp_Rankit + ∑φControls + ∑δYear + ∑λIndustry + ζ (1) The dependent variable (FinVarit) is alternatively Cash_Holding, Leverage and Div_Payout_Ratio, each is defined as follows: 15 Cash_Holding: cash and cash equivalents (CHE) scaled by total assets (AT). Leverage: the ratio of long-term debt (DLTT) plus debt in current liabilities (DLC) to total assets. Div_Payout_Ratio: the ratio of total common dividends (DVC) to net income (NI). When the dependent variable is Div_Payout_Ratio, we exclude firmyears with negative dividend or negative net income. The independent variable of interest, Comp_Rankit, is the decile rank of the number of references to competition scaled by total number of words in the 10-K (i.e., Comp_Pct). To facilitate interpretation, we transform Comp_Rankit to be in [0, 1]. Following prior studies, we include the following control variables. Herfindahl_Index: sum of squared market shares of all firms in each industry. Industries are defined in terms of the two-digit SIC assigned by Compustat. We include this variable to mitigate the concern that our measure of competition merely captures the effects of industry concentration, a traditional measure of market competition. Size: natural logarithm of market value of equity (PRCC_F x CSHO) at the end of the current year. MB_Ratio: market value divided by book value of equity (CEQ) at the end of the current year. This variable controls for growth opportunities. We expect firms with more growth opportunities to hold more cash to prepare for future investments and to have lower leverage to mitigate the underinvestment problem. Sale_Growth: current year sales (SALE) minus prior year sales, divided by prior year sales. 16 CFO_Vol: standard deviation of operating cash flows (OANCF) scaled by total assets, calculated over 10 years up to the current year, inclusive. We require that firms have available operating cash flows for at least three years. This variable is included to control for cash flow risk. We expect firms with higher cash flow risk to hold more cash, have lower leverage and lower dividend payout ratio. ROA: net income (NI) divided by average total assets. This variable controls for the firm’s profitability. We expect more profitable firms to hold more cash and have lower leverage. PPE: net property, plant and equipment (PPENT) divided by total assets. This variable controls for borrowing capacity (as PP&E can serve as collateral against debts). Net_WC: working capital (WCAP) minus cash and cash equivalents (CHE), scaled by total assets at the end of the current year. We include this variable because non-cash working capital can be a substitute for cash holdings. Therefore, we expect this variable to be negatively correlated with cash holdings. Cap_Exp: capital expenditure (CAPX) for current year scaled by lagged total assets. We include this variable because we expect that engaging in capital expenditures will influence a firms’ financing choices as they may need more cash on hand, but also may have more debt to finance those expenditures. Acquisition: total cash paid for acquisitions (AQC) for current year scaled by lagged total assets. We include this variable because we expect that engaging in acquisitions will influence their financing choices. 17 RD_Exp: R&D expenditure (XRD) for current year scaled by lagged total assets. As above, we include this variable because we expect firm investments in R&D will be associated with their financing choices. The regression also includes controls for year and industry fixed effects. Industries are Fama-French 48 industries. Table 4 Panel A presents estimated coefficients for regression model (1). Consistent with our hypotheses, perceived competition is positively associated with cash holdings and negatively associated with leverage and dividend payout ratio after controlling for other traditional explanatory variables. The coefficients on Comp_Rank are statistically significant at the 1-percent level in all three regressions. The economic magnitude of these relationships is quite large. Firms in the highest decile of perceived competition hold 6.8 percent more of their total assets as cash relative to firms in the lowest decile. Firms in the top decile of perceived competition also have on average 6.3 percent lower leverage than firms in the lowest decile. Finally, dividend payout ratio is 4 percent lower for firms in the highest decile of perceived competition compared to firms in the lowest decile. These results provide evidence consistent with Hypotheses 1-3 and suggest that firms with higher perceived competition tend to implement more conservative financing policies. The coefficient on Herfindahl Index is negative and statistically significant when cash holding is the dependent variable and positive and statistically significant when leverage and dividend payout ratio are dependent variables. The signs of the coefficient on Herfindahl Index are consistent with prior studies. The fact that both Herfindahl Index and our measure of competition predict financing policies suggest 18 that our measure of perceived competition captures a unique aspect of competition distinct from industry concentration. Table 4 Panel B presents regression results for model (1) after adding lagged dependent variables to the model (i.e., prior year cash holdings, leverage and dividend payout ratio). There is both an advantage and disadvantage of including lagged dependent variables as a control when the dependent variables are highly persistent, as would be expected for cash holdings, leverage and dividend payout ratio. On the one hand, including lagged dependent variables helps mitigate concerns about omitted variable biases because the coefficients on lagged dependent variables capture unobservable characteristics associated with the dependent variable. On the other hand, including highly persistent lagged dependent variables may cause a downward bias in the coefficients of other independent variables, especially those independent variables that are also persistent (Achen 2001). This is particularly a concern in the setting we are investigating because our explanatory variable of interest is perceived competition, which is expected to be highly persistent. The regression results appear to confirm this concern. When we include lagged dependent variables in the regressions, the coefficients on Comp_Rank decrease sharply, but still remain highly statistically significant, which provides confirming evidence of our hypotheses using this alternative specification.7 In addition, the R2 significantly increases when the lagged dependent variables are included. Given the advantage and disadvantage of 7 In terms of economic significance using this specification, the results suggest that firms in the highest decile of perceived competition increase their holdings of cash by 1 percent of total assets relative to firms in the lowest decile. Although this may seem small, given the average firm holds 19 percent of their assets in cash, this represents a 5.3-percent increase in their cash holdings for the average firm. For leverage, the coefficient in Panel B suggests 0.5 percent lower leverage for firms in the highest versus those in the lowest decile, equivalent to a 2.1-percent decrease in leverage for the average firm. For the dividend payout ratio, the coefficient in Panel B suggests a decrease in the payout ratio of 2.3 percent, equivalent to a 19.2-percent decrease for the average firm. 19 including lagged dependent variables in the regression, we present both results and leave it for readers to decide which results to take away from the paper. Turning to control variables, large firms tend to hold more cash, have lower leverage and higher dividend payout ratio. Firms with more cash flow volatility tend to hold more cash and have lower leverage. Firms with more growth opportunities (as measured by market-to-book ratio) hold more cash and have lower leverage. More profitable firms have more cash, lower leverage and higher dividend payout ratio. Firms with higher PP&E tend to have lower cash, higher leverage and pay more dividends. As expected, firms with more net working capital tend to hold less cash as net working capital can be a substitute for cash. Those firms also have lower leverage and higher dividend payout ratio. Firms with more investments in either R&D, acquisition or capital expenditure tend to have lower dividend payout ratio. However, the effects of these investments on cash holdings and leverage are not consistent. 3.3. Relations between Perceived Competition and Future Investments 3.3.1. Univariate Test Results Table 5 presents the mean and median R&D expenditure (RD_Exp[t+1, t+3]), number of acquisitions undertaken (MA_Deals[t+1, t+3]) and total value of acquisitions undertaken (MA_Value[t+1, t+3]) over the subsequent three years for the sample firmyears sorted into deciles based on the level of perceived competition (Comp_Pct). Mean future R&D expenditure increases monotonically with the level of perceived competition. Future number of acquisitions (MA_Deals[t+1,t+3]) and value of acquisitions (MA_Value[t+1, t+3]) undertaken also increase with the level of perceived competition, although only the latter increases monotonically. In terms of economic significance, the variation is most significant for R&D expenditure. Specifically, the 20 difference in mean (median) RD_Exp[t+1, t+3] between firms in Decile 10 and those in Decile 1 is 23.4 (19.0) percent of total assets, which is economically significant given the sample mean (median) RD_Exp[t+1, t+3] is 19 percent (0 percent). While univariate tests provide evidence consistent with our hypotheses that firms with high perceived competition tend to invest more in R&D and acquisitions, these tests do not control for other differences across firms that can potentially explain their different investing activities. In the multivariate tests that follow, we attempt to rule out alternative explanations by controlling for variables that prior studies have found related to firms’ investments. 3.3.2. Multivariate Test Results In our multivariate tests, we examine the relation between perceived competition and future investments by estimating the following regression model. Fut_Investit = β0 + β1Comp_Rankit + ∑φControls + ∑δYear + ∑λIndustry + ζ (2) The dependent variable (Fut_Investit) is alternatively RD_Exp[t+1, t+3], MA_Active[t+1,t+3], MA_Deals[t+1,t+3] and MA_Value[t+1,t+3], each is defined as follows: RD_Exp[t+1, t+3]: sum of R&D expenditure (XRD) scaled by lagged total assets over the three years t+1 through t+3. MA_Active[t+1,t+3]: a dummy variable equal to 1 if the firm engages in at least one acquisition over the three years t+1 through t+3, 0 otherwise. MA_Deals[t+1,t+3]: natural logarithm of (1+NM&A), with NM&A being the number of acquisitions that the firm undertakes over the three years t+1 through t+3. We add one to the number of acquisitions so that the variable is defined for firms that do not undertake any acquisition. 21 MA_Value[t+1,t+3]: total value of all acquisitions scaled by lagged total assets that the firm undertakes over the three years t+1 through t+3. The independent variable of interest is Comp_Rankit, defined as in regression model (1). Following prior studies, we include the following control variables. Herfindahl_Index: sum of squared market shares of all firms in each industry. Industries are defined in terms of the two-digit SIC assigned by Compustat. We include this variable to mitigate the concern that our measure of competition merely captures the effects of industry concentration, a traditional measure of market competition. Size: natural logarithm of market value of equity (PRCC_F x CSHO) at the end of the current year. MB_Ratio: market value divided by book value of equity (CEQ) at the end of the current year. This controls for growth opportunities. We expect this variable to be positively correlated with future investments. Sale_Growth: current year sales (SALE) minus prior year sales, divided by prior year sales. CFO_Vol: standard deviation of operating cash flows (OANCF) scaled by total assets, calculated over 10 years up to and including the current year. We require that firms have available operating cash flows for at least three years. This variable is included to control for cash flow risk. ROA: net income (NI) divided by average total assets. This controls for the firm’s profitability. Cash_Holding: cash and cash equivalents (CHE) scaled by total assets. We expect that firms with more cash have more resources to invest and, 22 therefore, this variable should be positively associated with future investments. Leverage: the ratio of long-term debt (DLTT) plus debt in current liabilities (DLC), divided by total assets. Leverage might constrain firms’ investments. Therefore, we expect a negative association between leverage and future investments. The regression also includes controls for year and industry fixed effects. Industries are Fama-French 48 industries. Table 6 Panel A presents the estimated coefficients for regression model (2). Consistent with the results in Table 5 and with our hypotheses, perceived competition is positively associated with both future R&D expenditure and acquisition activities after controlling for other traditional explanatory variables. The coefficients on Comp_Rank are statistically significant at the 1-percent level in all regressions. The economic magnitudes of these results are also quite significant. Firms in the highest decile of perceived competition spend 5.9 percent more of their total assets on R&D over the subsequent three years relative to firms in the lowest decile. With regard to future spending on acquisitions, firms in the highest competition decile spend 2.9 percent more of total assets on acquisitions in the subsequent three years relative to firms in the lowest competition decile. This represents a 45 percent increase in spending on acquisitions for the average firm over the subsequent three years. In summary, these results provide evidence consistent with Hypotheses 4 and 5 that firms with higher managerial perceived competition tend to invest more in R&D and acquisitions. The coefficient on the Herfindahl Index is negative but statistically insignificant in all but one regression. A negative coefficient is expected if the 23 Herfindahl Index captures to some extent the degree of market competition, and competitive threats motivate firms to invest in R&D and acquisitions. However, the lack of statistical significance for the coefficient suggests that the Herfindahl Index may not be a very reliable measure of competition at least with respect to its ability to predict future investments. Similar to the tests of financing policies, in Table 6 Panel B we present the regression results for model (2) after adding lagged dependent variables to the model (i.e., current year R&D expenditure and acquisition activities). When predicting future R&D expenditure, including current R&D as a control substantially decreases the coefficient on Comp_Rank, although the coefficient is still statistically significant at the 1-percent level.8 This is expected as both R&D expenditure and competition are all highly persistent (Achen, 2001). When predicting future acquisition activities, including current year acquisition activities as a control does not significantly affect the coefficient on Comp_Rank. Turning to other control variables, large firms are more likely to undertake acquisitions. However, firms size has no consistent effect on R&D expenditure, the coefficient is positive when the regression does not include lagged R&D as a control variable, but turns negative when lagged R&D is included. There is some evidence that growth firms (those with high market-to-book ratio and sales growth) are more likely to invest in acquisitions. In contrast, the effects of market-to-book ratio and sales growth on R&D expenditure is not robust, depending on whether lagged R&D is included in the model. Interestingly, more profitable firms are less likely to invest in R&D as well as acquisitions. Firms with more cash holdings are more likely to invest 8 In this specification, firms in the highest decile of perceived competition increase their spending on R&D by 1.6 percent of total assets relative to firms in the lowest decile, which represents an 8.4percent increase in R&D for the average firm. 24 in future R&D, but less likely to engage in acquisitions, although the dollar amount that those firms spend on acquisitions is higher. 3.4. Effects of Perceived Competition on the Rate of Diminishing Marginal Returns Conditional on Financing and Investing Activities. In this section, we test whether firms’ conservative financing policies and aggressive investments in R&D and acquisitions mitigate the effect of competition on the rate of diminishing marginal returns. Li et al (2013) use the following model to examine how perceived competition affects the rate of diminishing marginal returns on both existing and new investments. ∆RNOAit+1 = β0 + β1RNOAit + β2∆NOAit + β3Comp_Rankit*RNOAit + β4Comp_Rankit*∆NOAit + ∑δYear + ∑λIndustry + ζ (3) RNOAit is the return on net operating assets for firm i in year t, calculated as operating income after depreciation (OIADP) for year t divided by average NOA for year t, where NOA is defined as net accounts receivable (RECT) + inventories (INVT) + other current assets (ACO) + net property, plant and equipment (PPENT) + intangibles (INTAN) + other non-current assets (AO) – accounts payable (AP) – other current liabilities (LCO) – other non-current liabilities (LO). ∆RNOAit+1 is the change in RNOA from year t to year t+1. ∆NOAit is the change in NOA over year t. In model (3), the coefficients β1 and β2 capture the rate of diminishing marginal returns on existing and new investments made in year t, respectively, while the coefficients β3 and β4 capture the effects of perceived competition on the rate of diminishing marginal 25 returns.9 Using a battery of tests, Li et al (2013) find that the coefficients β3 and β4 are negative and statistically significant, consistent with firms with high perceived competition experiencing higher rates of diminishing marginal returns. We first replicate the results in Li et al (2013). We then partition our sample into several subsamples, based on their financing policies and levels of investment in R&D and acquisitions. We define firms with conservative financing policies as those with cash holdings above the median, leverage below the median and dividend payout ratio below the median (measured for year t). High R&D firms are those with the average R&D expenditure over the three years t-2 through t above the 75th percentile. M&A active firms are those that undertake at least one acquisition over the three years t-2 through t. We then estimate model (3) separately for each subsample. We examine whether the coefficients β3 and β4 differ for firms with more conservative financing policies versus firms with less conservative financing policies, firms with high versus low R&D, and firms that are M&A active versus those that are not. The results are shown in Table 7. The first column of Table 7 confirms the results in Li et al (2013). The coefficients on the interaction terms Comp_Rank*RNOA and Comp_Rank*∆NOA are both negative and statistically significant. The next two columns present coefficients for subsamples of firms with more conservative financing policies versus other firms. While the coefficient on the interaction Comp_Rank*RNOA is similar across the two subsamples, the coefficient on the interaction Comp_Rank*∆NOA differs significantly. In fact, the coefficient is only statistically significant for firms with conservative financing policies. The last four columns present coefficients for firms 9 Following Li et al (2013), we exclude the following observations from the regression: (1) firm-years with negative sales, NOA or total assets; (2) firm-years with market value of equity lower than $1 million; (3) firm-years with the absolute value of RNOA greater than 1; (4) firm-years with sales growth greater than 1,000% or less than -100%. 26 with high R&D versus firms with low R&D, and firms that actively engaged in acquisitions versus firms that did not. The coefficients on both interaction terms (Comp_Rank*RNOA and Comp_Rank*∆NOA) are more negative for firms with higher R&D and firms that actively engaged in acquisitions. These results suggest that firms that invest more in R&D or actively engage in acquisitions experience higher rates of diminishing marginal returns. Thus, we do not find evidence that conservative financing policies or aggressive investments in R&D and acquisitions help firms mitigate the negative impact of competition on the rate of diminishing marginal returns. Rather, the results appear to suggest that firms that extensively discuss competition in their 10-K filings and simultaneously take costly actions (i.e., conservative financing policies and aggressive investments in R&D and acquisitions) are those that are genuinely faced with strong competition. In other words, the extensive competition disclosures of those firms are more serious and credible. 4. ADDITIONAL ANALYSIS Our main empirical findings suggest a positive association between managerial perceived competition and the firm’s future investments in R&D and acquisitions, presumably to combat competitive threats. In additional analysis, we examine whether the investment vehicle firms use (R&D or acquisitions) reflects the investment strategies that are common to their industries. The availability of particular investment options may be limited by the particular type of firm. For example, we generally would not expect retail firms to engage in additional R&D expenditures as a strategy to combat competition. We perform this analysis by partitioning firms into intensive and non-intensive industries for R&D and acquisition activities. In each year, industries that have average R&D expenditures over the prior 27 five years that are above the 75th percentile of all industries are classified as R&D intensive. We partition sample firm-years similarly based on the average number of acquisitions undertaken by firms in each industry over the past five years. We then reestimate regression (2) separately on subsamples of firms in R&D intensive industries, firms in acquisition intensive industries versus the remaining firms. Table 8 presents the results of these tests. Control variables are included in the model but not presented. We note the positive coefficients on Comp_Rank are much stronger for the firms in R&D intensive industries when predicting future R&D expenditures and acquisition intensive industries when predicting future acquisition activities. The results of these tests suggest that firms generally use the investment strategies that are common in the industry when responding to perceived competitive threats. This test also rules out an alternative explanation that our main results are driven by differences across industries (i.e., firms in certain industries are less likely to invest in R&D regardless of the level of competition, and those same firms also have less extensive discussion of competition in their 10-K filings). We also examine whether our observed relations between perceived competition and financing policies and investments in R&D and acquisitions apply to both market leaders and market followers. Market leaders that extensively discuss competition in their 10-K filings may be more likely to take costly actions to protect market share if their disclosure represents real competitive pressure. However, firms with a large market share may strategically use competition disclosure as a deterrent to shareholder litigation and regulatory intervention, but do not change their financing policies and strategic investments because they view their dominant positions as mostly safe. We define market leaders as firms that have at least 5 percent market share in their industry and followers as firms with less than 5 percent market share. 28 Industries are defined based on the Fama-French 48 industry classifications. Table 9 Panel A presents the results of these tests when the dependent variables are financing choices, and Panel B presents the regression results when the dependent variables are R&D expenditure or acquisition activities. Control variables are included in the model but not presented. In the financing policies regressions, the positive association between perceived competition and cash holdings, and the negative coefficients between perceived competition and both leverage and dividend payout ratio only hold for market followers but not market leaders. Similarly, in the regressions that predict future R&D and acquisition activities, the positive and significant coefficient on Comp_Rank is only observed for market followers but not market leaders. These results are expected if market followers feel and react to competitive pressure more strongly than market leaders, since their survival requires immediate actions. It also suggests that market leaders may be using their competition disclosures for strategic purposes to perhaps ward off potential entrants. Finally, we revisit the findings in Li et al. (2013) that high perceived competition leads to higher rate of diminishing marginal returns. Given our findings that firms with high perceived competition have higher future R&D investments, a potential explanation for their findings could be that increased R&D spending in the future, which must be expensed under U.S. accounting standards, mechanically leads to lower future operating returns. To test if this is the case, we re-estimate model (3) in the previous section after adjusting both RNOA and ∆NOA by adding back R&D expenditure to net operating assets and operating income. If the results in Li et al (2013) were driven by the expensing of R&D expenditure, the coefficients on the interactions between Comp_Rankt and RNOAt and ∆NOAt would be diminished or even become zero after this adjustment. Table 10 presents the results for this test, for 29 comparison the first column shows the results without adjustment for R&D expenditure.10 We find that, after adjusting for the effects of R&D expenditure, the coefficient on the interaction between Comp_Rank and RNOAt decreases in magnitude but is still highly significant, and the coefficient on the interaction between Comp_Rank and ∆NOAt is still negative and even increases in magnitude. These results suggest that additional future R&D expenses do not fully explain the results in Li et al (2013), and competitive pressures remain as the fundamental reason for the higher rates of diminishing marginal returns for firms with more extensive discussion of competition in their 10-K filings. 5. CONCLUSION We examine how firms’ disclosure of competition in the 10-K can be useful for financial statement users in interpreting financial statements and predicting future investments. We show that the frequency of competition references in the 10-K is associated predictably with important financing choices such as cash holdings, financial leverage and dividend payout ratio. Thus, firms’ disclosure of competition in the 10-K can be useful for the interpretation of financial statements. We also find that firms with greater frequency of competition references have significantly higher future investments in R&D and acquisitions. This result suggests that incorporating firms’ discussion of competition can enhance investors’ ability to forecast the firm’s future investments and cash flows. Consistent with Li et al (2013), our results suggest that firms’ disclosure of competition in the 10-K is not merely boilerplate, but contains important information about the firm’s competitive environment as well as 10 The sample used in this test is slightly smaller than the sample presented in the first column in Table 7 due to an additional data requirement. Specifically, we exclude observations with adjusted RNOA lower than -1 or higher than 1 30 firms’ strategies in response to competitive threats. Our findings provide additional support for using firms’ own disclosure as a measure of perceived competition to understand how firms respond to competitive threats. 31 APPENDIX VARIABLE DESCRIPTIONS Variable Name Description Calculation RD_Exp[t+1, t+3] R&D expenditure three years ahead Sum of R&D expenditure (XRD) scaled by lagged total assets over three years t+1 through t+3 MA_Active[t+1,t+3] Indicator if a firm is active in M&A over three years ahead An indicator equal to 1 if the firm undertakes at least one acquisition over three years t+1 through t+3, 0 otherwise MA_Deals[t+1,t+3] Number of acquisitions undertaken three years ahead Natural logarithm of (1+NM&A), with NM&A being the number of acquisitions that the firm undertakes over the three years t+1 through t+3 MA_Value[t+1,t+3] Dollar value of acquisitions undertaken three years ahead Total dollar value of acquisitions undertaken by the firm scaled by lagged total assets over three years t+1 through t+3 Cash_Holding Cash holdings at the end of current year Cash and cash equivalents (CHE) scaled by total assets (AT) Leverage Financial leverage at the end of current year Long-term debts (DLTT) plus debt in current liabilities (DLC), divided by total assets (AT) Div_Payout_Ratio Dividend payout ratio Common dividend (DVC) divided by net income (NI). We only include firm-years with non-negative DVC and positive NI. RD_Exp R&D expenditure year t XRD for year t scaled by lagged total assets. Acquisition Cash paid for acquisitions in year t AQC for year t scaled by lagged total assets. Note that this variable does not include the value of stock-for-stock acquisitions. Cap_Exp Capital expenditure year t CAPX for year t scaled by lagged total assets. MA_Active Indicator if a firm is active in M&A in year t An indicator equal to 1 if the firm undertakes at least one acquisition in year t, 0 otherwise MA_Deals Number of acquisitions undertaken in year t Natural logarithm of (1+NM&A), with NM&A being the number of acquisitions that the firm undertakes in year t MA_Value Dollar value of acquisitions undertaken in year t Total dollar value of acquisitions undertaken by the firm in year t scaled by lagged total assets Comp_Rank Perceived competition rank Decile rank of the number of references to competition scaled by total number of words in the firm’s 10-K, scaled to be in [0, 1] Herfindahl_Index Herfindahl index Sum of squared market shares of all firms in each industry. Industries are Fama-French 48 industries. Size Firm size Natural logarithm of market value of equity at the end of year t 32 MB_Ratio Market-to-book ratio Market value divided by book value of equity at the end of year t Sale_Growth Sales growth Sales (SALE) for year t minus sales for year t-1, divided by sales for year t-1. Net_WC Non-cash working capital Working capital (WCAP) minus cash and cash equivalents (CHE), scaled by total assets. CFO_Vol Operating cash flow volatility Standard deviation of operating cash flows scaled by total assets over 10 most recent years (firms must have operating cash flows data for at least 3 years). ROA Return on assets Net income (NI) divided by the average total assets. PPE Net property, plant and equipment Net property, plant and equipment (PPENT) scaled by total assets at the end of year t RNOA Return on net operating assets Operating income after depreciation (OIADP) divided by average NOA, where NOA is defined as net accounts receivable (RECT) + inventories (INVT) + other current assets (ACO) + net property, plant and equipment (PPENT) + intangibles (INTAN) + other noncurrent assets (AO) – accounts payable (AP) – other current liabilities (LCO) – other noncurrent liabilities (LO). ∆RNOA[t+1] Change in return on net operating assets from year t to year t+1 RNOA for year t+1 minus RNOA for year t ∆NOA Change in net operating assets in year t NOA at the end of year t minus NOA at the end of year t-1 33 References Achen, C.H., 2001. Why Lagged Dependent Variables Can Suppress the Explanatory Power of Other Independent Variables. Working Paper. Ahuja, G. and Katila, R. 2001. Technological acquisitions and the innovation performance of acquiring firms: a longitudinal study. Strategic Management Journal 22 (3), 197-220. Arrow, K. 1962. Economic welfare and the allocation of resources for invention. The Rate and Direction of Inventive Activity: Economic and Social Factors. Barclay, M.J., Smith, C.W., Watts, R.L., 1997. The determinants of corporate leverage and dividend policies. Journal of Financial Education 23: 1-15. Bates, T.W., Kahle, K.M., Stulz, R.M., 2009. Why do US firms hold so much more cash than they used to? The Journal of Finance 64 (5): 1985-2021 Benoit, J. P., 1984. Financially Constrained Entry in a Game with Incomplete Information. The RAND Journal of Economics 15: 490–499. Booth, L., Zhou, J., 2009. Market Power and Dividend Policy: a Risk-Based Perspective. Working Paper. Brav, A., Graham, J.R., Harvey, C.R., Michaely, R., 2005. Payout policy in the 21st century. Journal of Financial Economics 77: 483–527. Brav, A., Graham, J.R., Harvey, C.R., Michaely, R., 2008. Managerial response to the May 2003 dividend tax cut. Financial Management 37: 611–624. Coad, A., 2009. The Growth of Firms: A Survey of Theories and Empirical Evidence. Edward Elgar Publishing Ltd, Cheltenham, UK. Dedman, E., and C. Lennox, 2009. Perceived Competition, Profitability and the Withholding of Information about Sales and the Cost of Sales. Journal of Accounting and Economics 48: 210–30. Feinberg, R. 1984. Conglomerate Mergers and Subsequent Industry Effects. Review of Industrial Organization 1: 128-137. Fudenberg, D., Gilbert, R., Stiglitz, J., and Tirole, J. 1983. Preemption, leapfrogging and competition in patent races. European Economic Review 22 (1): 3-31. Gallagher, D. 2014. Remarks to the Forum for Corporate Directors, Orange County, California. January 24. Available at: http://www.sec.gov/News/Speech/Detail/Speech/1370540680363#.VBLvyxCa8Q A Gallo, C. 2014. The 7 Innovation Secrets of Steve Jobs. Forbes. http://www.forbes.com/sites/carminegallo/2014/05/02/the-7-innovation-secretsof-steve-jobs/. Gaughan, P.A., 2005. Mergers: What Can Go Wrong and How to Prevent It. John Wiley & Son, Inc. Gilbert, R., 2006. Looking for Mr. Schumpeter: Where Are We in the CompetitionInnovation Debate? Innovation Policy and the Economy 6: 159–215. Goldberg, L. 1973. The Effects of Conglomerate Mergers on Competition. Journal of Law and Economics 16: 137-158. 34 Goldberg, L. 1974. Conglomerate Mergers and Concentration Ratios. Review of Economics and Statistics 56: 303-309. Harris, C. and Vickers, J. 1985. Patent Races and the Persistence of Monopoly. The Journal of Industrial Economics 33: 461-481. Haushalter, D., Klasa, S., Maxwell, W., 2007. The influence of product market dynamics on a firm's cash holdings and hedging behavior. Journal of Financial Economics 84: 797–825. Healy, P., and K. Palepu. 2007. Business Analysis and Valuation: Using Financial Statements, Text and Cases, Fourth edition. Boston, MA: South-Western College Publishers. Hitt, M., Hoskisson, R., Johnson, R., and Moesel, D. 1996. The Market for Corporate Control and Firm Innovation. Academy of Management Journal 39 (5): 10841119. Hoberg, G., Phillips, G., Prabhala, N., 2014. Product Market Threats, Payouts, and Financial Flexibility. The Journal of Finance 69: 293–324. J S Ramalho, J., da Silva, J.V., 2009. A two-part fractional regression model for the financial leverage decisions of micro, small, medium and large firms. Quantitative Finance 9: 621–636. Kovenock, D., Phillips, G., 1995. Capital Structure and Product-Market Rivalry: How Do We Reconcile Theory and Evidence? The American Economic Review 85: 403–408. Li, F., Lundholm, R., Minnis, M., 2013. A Measure of Competition Based on 10-K Filings. Journal of Accounting Research 51: 399–436. Lintner, J., 1956. Distribution of incomes of corporations among dividends, retained earnings, and taxes. The American Economic Review 46 (2): 97-113. Lundholm, R., and R. Sloan. 2007. Equity Valuation and Analysis. New York: McGraw-Hill MacKay, P., Phillips, G., 2005. How Does Industry Affect Firm Financial Structure? Review of Financial Studies 18: 1433–1466. Michaely, R., Thaler, R.H., Womack, K.L., 1995. Price Reactions to Dividend Initiations and Omissions: Overreaction or Drift? The Journal of Finance 50 (2): 573–608. Morellec, E., Nikolov, B., Zucchi, F., 2013. Competition, Cash Holdings, and Financing Decisions. Working Paper. Penman, S., 2009. Financial Statement Analysis and Security Valuation. Fourth edition. New York: McGraw-Hill. Ramalho, J. J. S., & Silva, J. V. D., 2009. A two-part fractional regression model for the financial leverage decisions of micro, small, medium and large firms. Quantitative Finance 9 (5): 621–636. Schmidt, K.M., 1997. Managerial Incentives and Product Market Competition. The Review of Economic Studies 64 (2): 191-213. Schumpeter, J.A., 1942. Capitalism, Socialism, and Democracy. Harper and Brothers, N.Y. 35 Seidman, L.F. 2012. Remarks at Compliance Week Annual Conference. June 4. Available at: http://www.fasb.org/resources/ccurl/263/963/Seidman%20Remarks%20%20Compliance%20Week%20Conference%20-%2006-04-12%20%20FINAL.pdf 36 Table 1 Sample Distribution by Industry Fama-French Industry Obs Communication Computers Electronic Equipment Business Services Measuring and Control Equipment Medical Equipment Recreation Pharmaceutical Products Wholesale Machinery Candy & Soda Electrical Equipment Retail Fabricated Products Printing and Publishing Consumer Goods Steel Works Etc Beer & Liquor Textiles Rubber and Plastic Products Automobiles and Trucks Entertainment Business Supplies Shipbuilding, Railroad Equipment Healthcare Apparel Construction Materials Defense Food Products Shipping Containers Transportation Aircraft Miscellaneous Chemicals Restaurants, Hotels, Motels Construction Personal Services Tobacco Products Utilities Agriculture Petroleum and Natural Gas Coal Non-Metallic and Industrial Metal Mining Precious Metals Total 2,336 3,684 5,177 10,221 1,998 2,951 725 4,809 3,147 2,730 173 1,314 4,135 293 621 1,243 1,074 262 377 752 1,150 1,385 911 167 1,579 1,153 1,504 151 1,346 222 1,976 338 982 1,562 1,578 989 1,030 90 2,110 273 3,275 158 252 213 72,416 37 Mean Comp 1.411 1.318 1.312 1.173 1.171 1.156 1.104 1.062 0.997 0.983 0.951 0.951 0.923 0.917 0.897 0.897 0.893 0.892 0.888 0.878 0.877 0.876 0.875 0.872 0.868 0.868 0.866 0.852 0.842 0.833 0.824 0.815 0.815 0.813 0.810 0.808 0.799 0.716 0.680 0.651 0.557 0.548 0.479 0.214 1.004 Median Comp 1.311 1.256 1.259 1.123 1.111 1.096 1.006 1.031 0.935 0.897 0.930 0.926 0.845 0.889 0.862 0.830 0.854 0.765 0.851 0.811 0.821 0.813 0.798 0.868 0.796 0.815 0.826 0.835 0.793 0.756 0.759 0.741 0.747 0.759 0.776 0.757 0.748 0.719 0.583 0.586 0.499 0.539 0.391 0.187 0.927 Sd 0.676 0.583 0.573 0.550 0.533 0.492 0.525 0.402 0.482 0.503 0.412 0.447 0.485 0.432 0.455 0.455 0.456 0.537 0.402 0.443 0.413 0.464 0.482 0.347 0.408 0.382 0.464 0.348 0.448 0.425 0.404 0.396 0.412 0.414 0.364 0.413 0.393 0.260 0.477 0.407 0.324 0.204 0.348 0.127 0.532 Table 2 Descriptive Statistics Comp_Pct is the number of references to competition per thousand words in the 10-K. MVE is market value of equity at the end of the current fiscal year. Total_Asset is total assets at the end of the current fiscal year. See the Appendix for a description of other variables. All continuous variables are winsorized at the 1st and 99th percentiles. Variable N Mean Median Sd Min Max Comp_Pct (%) 72,416 1.00 0.93 0.53 0.07 2.73 Cash_Holding 72,416 0.19 0.10 0.22 0.00 0.90 Leverage 72,416 0.24 0.19 0.26 0.00 1.44 Div_Payout_Ratio 70,171 0.12 0.00 0.31 0.00 2.01 RD_Exp[t+1, t+3] 49,894 0.19 0.00 0.38 0.00 2.24 MA_Deals[t+1,t+3] 50,092 0.26 0.00 0.62 0.00 3.00 MA_Value[t+1,t+3] 47,141 0.06 0.00 0.25 0.00 1.78 RD_Exp 72,413 0.07 0.00 0.13 0.00 0.81 MA_Deals 72,416 0.10 0.00 0.34 0.00 2.00 MA_Value 70,943 0.02 0.00 0.11 0.00 0.89 Herfindahl_Index 72,416 0.06 0.04 0.05 0.01 0.31 MVE 72,179 1,826.57 171.03 5,783.49 1.61 43,453.13 Total_Asset 72,416 1,774.78 176.67 5,209.25 1.71 35,841.13 MB_ratio 72,166 2.79 1.87 5.70 -19.99 35.19 ROA 72,413 -0.09 0.02 0.35 -2.02 0.35 Sale_Growth 71,731 0.24 0.08 0.85 -0.76 6.41 38 Table 3 Perceived Competition and Financing Policies (Univariate Analysis) This table presents mean and median cash holdings (Cash_Holding), leverage (Leverage) and dividend payout ratio (Div_Payout_Ratio) for sample firm-years sorted into deciles based on perceived competition (Comp_Pct). See the Appendix for a description of variables. All continuous variables are winsorized at the 1st and 99th percentiles. To calculate dividend payout ratio, we only include firm-years with nonnegative common dividend (DVC) and positive net income (NI). Cash_Holding Mean Median 0.122 0.052 Obs 7,233 Leverage Mean 0.293 0.054 7,246 0.304 0.269 6,903 0.171 0.000 0.137 0.065 7,238 0.296 0.254 6,941 0.153 0.000 7,243 0.159 0.075 7,243 0.279 0.227 6,977 0.123 0.000 5 7,242 0.174 0.083 7,242 0.262 0.211 7,040 0.119 0.000 6 7,243 0.194 0.099 7,243 0.244 0.189 7,039 0.097 0.000 7 7,237 0.217 0.123 7,237 0.221 0.155 7,047 0.082 0.000 8 7,244 0.241 0.157 7,244 0.198 0.118 7,094 0.075 0.000 9 7,240 0.258 0.187 7,240 0.179 0.085 7,113 0.061 0.000 10 7,250 0.285 0.237 7,250 0.163 0.050 7,101 0.083 0.000 0.164 0.185 -0.130 -0.211 -0.111 0.000 Comp Decile 1 Obs 7,233 2 7,246 0.118 3 7,238 4 (10) - (1) 39 Median 0.261 Div_Payout_Ratio Obs Mean Median 6,916 0.194 0.000 Table 4 Perceived Competition and Financing Policies (Multivariate Analysis) This table presents estimated coefficients for regression model (1). The dependent variable is alternatively Cash_Holding, Leverage or Div_Payout_Ratio. See the Appendix for a description of variables. All continuous variables are winsorized at the 1st and 99th percentiles. Panel A: Regression Results Without Controlling for Lagged Dependent Variables Comp_Rank Herfindahl_Index Size MB_ratio Sale_Growth CFO_Vol ROA PPE Net_WC Cap_Exp Acquisition RD_Exp Constant Cash_Holding Leverage Div_Payout_Ratio 0.068*** (30.20) -0.104*** (8.25) 0.001*** (4.41) 0.002*** (15.34) 0.003*** (2.73) 0.023*** (6.39) 0.063*** (17.38) -0.344*** (81.34) -0.107*** (26.40) 0.097*** (11.42) -0.268*** (50.05) 0.462*** (46.81) 0.198*** (18.62) Yes Yes 69136 0.44 -0.063*** (21.69) 0.088*** (4.11) -0.005*** (11.20) -0.004*** (16.52) -0.002 (1.34) -0.028*** (5.55) -0.116*** (20.30) 0.247*** (36.39) -0.332*** (47.74) -0.165*** (11.92) 0.261*** (35.85) -0.243*** (20.28) 0.264*** (17.09) Yes Yes 69136 0.3 -0.040*** (10.55) 0.155*** (4.32) 0.026*** (48.35) 0 (0.10) -0.005*** (5.81) 0 (0.23) 0.021*** (9.07) 0.173*** (20.79) 0.017*** (4.48) -0.328*** (22.19) -0.027*** (2.66) -0.047*** (8.61) 0.089*** (2.76) Yes Yes 67011 0.2 Year fixed effects Industry fixed effects Observations R-squared Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 40 Table 4 (continued) Panel B: Regression Results Controlling for Lagged Dependent Variables Cash_Holding Leverage Div_Payout_Ratio Comp_Rank 0.010*** (6.81) -0.005*** (2.83) -0.023*** (7.41) Cash_Holding[t-1] 0.733*** (223.54) Leverage[t-1] 0.760*** (135.12) Div_Payout_Ratio[t-1] Herfindahl_Index Size MB_ratio Sale_Growth CFO_Vol ROA PPE Net_WC Cap_Exp Acquisition RD_Exp Constant -0.021*** (2.58) 0.001*** (5.59) 0.001*** (7.31) -0.009*** (9.52) 0.004 (1.28) 0.043*** (14.24) -0.130*** (42.82) -0.052*** (19.11) -0.016** (2.03) -0.212*** (40.02) 0.144*** (18.71) 0.063*** (11.49) Yes Yes 69133 0.78 0.035*** (2.73) -0.002*** (5.93) -0.002*** (10.82) -0.004*** (3.40) -0.048*** (11.08) -0.092*** (18.62) 0.072*** (15.28) -0.142*** (26.11) 0.004 (0.31) 0.232*** (31.70) -0.143*** (13.53) 0.065*** (8.50) Yes Yes 69051 0.73 Year fixed effects Industry fixed effects Observations R-squared Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 41 0.524*** (52.78) 0.073*** (2.58) 0.012*** (24.96) 0 (0.08) -0.004*** (5.89) -0.001 (0.30) 0.005*** (2.97) 0.081*** (13.15) 0.011*** (3.59) -0.156*** (14.07) -0.024*** (2.95) -0.021*** (4.75) -0.003 (0.11) Yes Yes 65487 0.45 Table 5 Perceived Competition and Future Investments in R&D and Acquisitions (Univariate Analysis) This table presents mean and median research and development expenditure (RD_Exp[t+1, t+3]), number of acquisitions undertaken (MA_Deals[t+1,t+3]) and total value of acquisitions undertaken (MA_Value[t+1,t+3]) over the subsequent three years for sample firm-years sorted into deciles based on perceived competition (Comp_Pct). See the Appendix for a description of variables. All continuous variables are winsorized at the 1st and 99th percentiles. RD_Exp[t+1, t+3] Obs Mean Median 5120 0.062 0.000 MA_Deals[t+1, t+3] Obs Mean Median 5151 0.237 0.000 MA_Value [t+1, t+3] Obs Mean Median 4763 0.038 0.000 2 5058 0.088 0.000 5079 0.257 0.000 4730 0.044 0.000 3 4989 0.129 0.000 5002 0.258 0.000 4684 0.047 0.000 4 5014 0.145 0.000 5031 0.260 0.000 4710 0.051 0.000 5 4963 0.177 0.000 4976 0.251 0.000 4683 0.058 0.000 6 5048 0.200 0.014 5068 0.256 0.000 4776 0.062 0.000 7 4995 0.236 0.036 5016 0.254 0.000 4764 0.067 0.000 8 4948 0.268 0.068 4969 0.278 0.000 4718 0.083 0.000 9 4927 0.281 0.128 4944 0.286 0.000 4713 0.083 0.000 10 4832 0.296 0.190 4856 0.316 0.000 4600 0.091 0.000 0.234 0.190 0.078 0.000 0.053 0.000 Comp Decile 1 (10) - (1) 42 Table 6 Perceived Competition and Future Investments in R&D and Acquisitions (Multivariate Analysis) This table present estimated coefficients for regression model (2). The dependent variable is alternatively RD_Exp[t+1, t+3], MA_Active[t+1, t+3], MA_Deals[t+1, t+3] or MA_Value[t+1, t+3]. See the Appendix for a description of variables. All continuous variables are winsorized at the 1st and 99th percentiles. Panel A: Regression Results Without Controlling for Lagged Dependent Variables Comp_Rank Herfindahl_Index Size MB_ratio Sale_Growth CFO_Vol ROA Cash_Holding Leverage Constant RD_Exp[t+1,t+3] 0.059*** (14.48) -0.006 (0.41) 0.006*** (9.53) 0.002*** (5.10) -0.003 (1.00) 0.112*** (10.14) -0.377*** (36.30) 0.432*** (41.48) -0.011 (1.30) -0.042*** (3.69) Yes MA_Active[t+1,t+3] 0.126*** (5.30) -0.104 (0.69) 0.157*** (44.31) 0.001 (0.54) 0.047*** (5.29) 0.002 (0.06) -0.024 (0.85) -0.079** (1.97) 0.04 (1.18) -1.543*** (11.40) Yes Year fixed effects Industry fixed effects Yes Yes Observations 48808 48992 R-squared 0.52 Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% MA_Deals[t+1,t+3] 0.024*** (4.53) -0.027 (0.76) 0.040*** (47.40) 0.000* (1.79) 0.010*** (5.37) 0.011* (1.90) -0.002 (0.43) -0.033*** (3.85) 0.011* (1.70) 0.01 (0.35) Yes MA_Value[t+1,t+3] 0.029*** (7.38) -0.028* (1.67) 0.002*** (3.12) 0.001*** (3.25) 0.010*** (4.75) 0.006 (0.97) -0.023*** (3.74) 0.038*** (5.03) -0.012** (2.13) 0.043*** (3.31) Yes Yes 48992 0.08 Yes 46081 0.03 Table 6 (continued) Panel B: Regression Results Controlling for Lagged Dependent Variables Comp_Rank RD_Exp RD_Exp[t+1,t+3] 0.016*** (4.99) 1.908*** (68.26) MA_Active[t] MA_Active[t+1,t+3] 0.112*** (4.69) MA_Deals[t+1,t+3] 0.019*** (3.77) 0.594*** (27.95) MA_Deals[t] 0.290*** (29.27) MA_Value[t] Herfindahl_Index Size MB_ratio Sale_Growth CFO_Vol ROA Cash_Holding Leverage Constant MA_Value[t+1,t+3] 0.027*** (6.92) -0.014 (1.34) -0.003*** (6.72) 0 (1.19) -0.020*** (8.05) 0.030*** (3.50) -0.116*** (13.87) 0.173*** (19.43) 0.001 (0.17) 0.020*** (2.96) Yes -0.099 (0.65) 0.141*** (38.88) 0.001 (0.54) 0.025*** (2.82) 0.004 (0.11) 0.001 (0.04) -0.028 (0.70) 0.034 (1.01) -1.527*** (11.14) Yes Year fixed effects Industry fixed effects Yes Yes Observations 48808 48992 R-squared 0.73 Absolute value of z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 44 -0.025 (0.69) 0.034*** (41.38) 0.000* (1.72) 0.003* (1.86) 0.011** (2.03) 0.005 (0.93) -0.017** (2.07) 0.008 (1.31) 0.02 (0.69) Yes 0.173*** (9.34) -0.026 (1.50) 0.001*** (2.62) 0.001*** (3.16) 0.007*** (3.25) 0.006 (1.10) -0.022*** (3.48) 0.039*** (5.09) -0.011** (2.05) 0.043*** (3.23) Yes Yes 48992 0.12 Yes 45249 0.03 Table 7 Perceived Competition and the Rate of Diminishing Marginal Returns Conditional on Financing Policies and Investments This table presents estimated coefficients for regression model (3) for the overall sample as well as various subsamples formed based on firms’ financing policies and investments in R&D and acquisitions. Firms with conservative financing policies are those with cash holdings above the median, leverage below the median and dividend payout ratio below the median (measured for year t). High R&D firms are those with average R&D expenditure over 3 years t-2 through t above the 75th percentile. M&A active firms are those that undertake at least one acquisition over 3 years t-2 through t. The dependent variable is ∆RNOA[t+1]. See the Appendix for a description of variables. RNOA ∆NOA Comp_Rank Baseline Model Conservative Financing Others High R&D Low R&D M&A Active M&A Inactive -0.179*** -0.252*** -0.156*** -0.200*** -0.178*** -0.159*** -0.183*** (12.90) (7.69) (10.54) (6.19) (11.39) (5.20) (11.78) -0.104*** -0.129*** -0.110*** -0.123*** -0.116*** -0.064*** -0.128*** (12.15) (3.56) (12.97) (4.30) (12.64) (5.32) (10.84) 0.014*** 0.018 0.005 0.006 0.004 0.034*** 0.009* (3.02) (1.37) (0.99) (0.48) (0.86) (3.22) (1.71) -0.155*** -0.098** -0.105*** -0.149*** -0.070** -0.277*** -0.121*** (6.57) (2.11) (3.60) (3.27) (2.34) (5.02) (4.63) -0.042*** -0.096* -0.002 -0.089** 0.028* -0.059*** -0.035* (2.76) (1.89) (0.15) (2.07) (1.79) (2.65) (1.70) 0.030*** -0.164* 0.029*** 0.134*** 0.017 0.063** 0.020* (2.92) (1.67) (3.12) (5.82) (1.59) (2.57) (1.82) Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 41442 9847 30250 9657 28973 9262 32083 R-squared 0.14 0.15 Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 0.11 0.16 0.1 0.18 0.13 Comp_Rank*RNOA Comp_Rank*∆NOA Constant Year fixed effects Industry fixed effects Observations Table 8 Perceived Competition and Future Investments Conditional on Industry Investment Intensity The table presents selected coefficients for regression model (2) conditional on whether the firm is in an R&D intensive or M&A intensive industry. For each industry-year, we calculate the average R&D expenditure and number of acquisitions undertaken over the past 5 years. R&D-intensive industries are those with the average R&D expenditure above the 75th percentile. M&A-intensive industries are those with the average number of acquisitions above the 75th percentile. The dependent variable is alternatively RD_Exp[t+1, t+3], MA_Deals[t+1, t+3] or MA_Value[t+1, t+3]. See the Appendix for a description of variables. All continuous variables are winsorized at the 1 st and 99th percentiles. RD_Exp[t+1, t+3] Comp_Rank RD_Exp R&D Intensive 0.024*** (4.10) 1.854*** (62.37) MA_Deals[t+1, t+3] Others 0.004** (2.20) 2.145*** (18.35) MA_Deals M&A Intensive 0.032*** (3.33) Others 0.011* (1.90) 0.300*** (19.49) 0.274*** (21.36) MA_Value Controls Year and Industry FE Observations R-squared MA_Value[t+1, t+3] M&A Intensive 0.048*** (6.14) Others 0.016*** (3.69) 0.135*** (5.77) Yes Yes 30,380 0.03 Yes Yes Yes Yes 0.205*** (7.16) Yes Yes 24,935 0.69 Yes 23,873 0.61 Yes 16,266 0.13 Yes 32,726 0.10 Yes 14,869 0.04 Table 9 Perceived Competition, Financing Policies and Future Investments Conditional on Market Power This table presents selected coefficients for regression models (1) and (2) conditional on firms’ market power. We define market leaders as firms with at least a 5 percent market share (for each Fama-French industry year) and market followers as those with less than 5 percent market share. In Panel A, the dependent variable is alternatively Cash_Holding, Leverage or Div_Payout_Ratio. In Panel B, the dependent variable is alternatively RD_Exp[t+1, t+3], MA_Deals[t+1, t+3] or MA_Value[t+1, t+3]. See the Appendix for a description of variables. All continuous variables are winsorized at the 1 st and 99th percentiles. Panel A: Perceived Competition and Financing Policies Cash_Holding Leaders Comp_Rank Cash_Holding[t-1] Leverage Div_Payout_Ratio Followers Leaders Followers Leaders Followers 0.002 0.010*** (0.57) (6.45) 0.761*** 0.731*** (32.15) (220.83) 0.013** (2.08) -0.006*** (3.10) -0.024 (1.03) -0.022*** (7.12) Leverage[t-1] 0.883*** 0.756*** (50.25) (131.57) Div_Payout_Ratio[t-1] 0.487*** 0.522*** (12.91) (50.69) Controls Yes Yes Yes Yes Yes Yes Year and Industry FE Observations R-squared Yes Yes Yes Yes Yes Yes 2822 0.8 66311 0.78 2820 0.87 66231 0.73 2549 0.42 62938 0.45 Panel B: Perceived Competition and Future Investments RD_Exp[t+1, t+3] Comp_Rank RD_Exp Leaders 0.003 (1.09) 1.422*** (4.46) MA_Deals[t+1, t+3] MA_Value[t+1, t+3] Leaders -0.012 (0.39) Followers 0.021*** (4.01) Leaders -0.009* (1.91) Followers 0.028*** (6.79) 0.257*** (7.09) 0.284*** (27.64) 0.172*** (9.21) Yes Yes 43,434 0.03 Followers 0.017*** (5.20) 1.907*** (68.07) MA_Deals MA_Value Controls Year and Industry FE Observations R-squared Yes Yes Yes Yes 0.104* (1.76) Yes Yes 2,318 0.74 Yes 46,490 0.73 Yes 2,322 0.23 Yes 46,670 0.11 Yes 1,815 0.12 47 Table 10 Perceived Competition and Rate of Diminishing Marginal Returns after Adjusting for R&D Expenditure The dependent variable is ∆RNOA[t+1]. In column (2), RNOA and ∆NOA are adjusted by adding back R&D expenditure to operating income and net operating assets. See the Appendix for a description of variables. (1) (2) Results Based on Reported Data Results Based on Adjusted Data -0.181*** -0.184*** (13.17) (15.43) -0.105*** -0.107*** (12.28) (12.83) 0.011** 0.013*** (2.45) (3.20) -0.140*** -0.076*** (6.08) (4.25) -0.038** -0.073*** (2.52) (5.21) 0.030*** 0.028*** (2.93) Yes (2.72) Yes Yes Yes 41,324 41,324 0.13 0.15 RNOA ∆NOA Comp_Rank Comp_Rank*RNOA Comp_Rank*∆NOA Constant Year fixed effects Industry fixed effects Observations R-squared Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 48