Information, competition, and investment sensitivity to peer stock prices

Transcription

1 Information, competition, and investment sensitivity to peer stock prices Arzu Ozoguz Michael Rebello* First draft: March 15, 2012 This draft: July 2, 2013 Abstract We show empirically that firms investment responds to innovations in stock prices of peer firms. This response is stronger and more positive when peer firms have greater informed trading and more informative prices. We also find higher competition, faster growth, greater correlation in fundamentals, and higher capital intensity within the peer group all increase this sensitivity. Our results suggest that managers rely on information in peer firms prices in making capital allocation decisions, especially when, because of a challenging or rapidly changing operating environment, they face higher costs of inaction and higher rewards from a strong and prompt response. *We thank Samuel Knupfer, Rui Silva, and the participants at the 2012 Finance Workshop at Koc University, LBS Summer Symposium, and the 8th Annual Early Career Women in Finance Conference for helpful comments. Arzu Ozoguz and Michael Rebello are both at the Naveen Jindal School of Management, University of Texas at Dallas. Ozoguz can be reached at [email protected] and Rebello can be reached at [email protected]. 1

2 1 Introduction Whether financial markets have real effects on the economy has long been an important question in financial economics. 1 Recently, there has been growing interest in the informational role of stock prices in guiding firm investment (see Edmans, Goldstein, and Jiang (2012a, 2012b) and the recent survey by Bond, Edmans, and Goldstein (2012)). The main channel behind this role is that stock prices, through the trading process by informed traders, aggregate diverse pieces of private information on factors such as the state of the economy, the strategic position of the competitors, and consumer demand, and managers can, in turn, learn about the prospects of their firms from this information aggregated into their stock prices. 2 The idea that managers can obtain from their firms stock prices information that guides their capital allocation decisions has considerable empirical support. For example, Durnev, Morck, and Yeung (2004) and Chen, Goldstein, and Jiang (2007) both find a positive relation between the sensitivity of investment to Tobin s q and measures of private information embedded in prices. Using a different approach, Bakke and Whited (2010) also show that the information in stock prices guides investment. In a study of merger completions, Luo (2005) finds that merger announcement returns have predictive power, even after controlling for deal quality, suggesting that merging firms use the information in prices. Foucault and Fresard (2012) document that the sensitivity of investment to prices is higher for firms crosslisted in the U.S., and argue that cross-listing enhances managers reliance on stock prices 1 Starting with Barro (1990), Morck, Schleifer, and Vishny (1990), and Blanchard, Rhee, and Summers (1993), several studies have sought to understand the nature and the significance of the relation between corporate investment and stock prices. See, for example, Baker, Wurgler, and Stein (2003), Gilchrist, Himmelberg, and Huberman (2005), Chirinko and Schaller (1996, 2001), Polk and Sapienza (2009), and Graham and Campello (2007). Fazzari, Hubbard, and Petersen (1988, 2000) argue that investment is more sensitive to cash flow for financially constrained firms. Erickson and Whited (2000) point out the measurement error problems associated with firm Q and show that the estimated coefficients of firm Q on investment can therefore be biased. 2 While this idea can be traced back to Hayek (1945), Dow and Gorton (1997) and Subrahmanyam and Titman (1999) have, more recently, formalized this notion that firm managers can learn from the information in stock prices about the prospects of their firms. 1

3 due to more informative prices. We contribute to this growing body of evidence on the informational role of stock prices in guiding firm investment by examining whether investment is also influenced by the stock prices of peer firms. To the extent that stock prices aggregate private information, such a relation should be natural. Firms are surrounded by and related to other firms. These peer firms stock prices should aggregate private information about factors that affect them. Since firms are exposed to many of the same factors as their peers, the information aggregated in peers stock prices should be informative for the firms manager. For example, stock prices of firms in a customer industry can convey important information about the robustness of demand for a firm s output; competitors stock prices can indicate the health of the industry; a peer s stock price may also signal information about the viability of its strategies, allowing a manager to craft a better response to the peer s strategies. We assess the implications of this learning from peer prices hypothesis using a large sample of firms over the period of by testing whether a firm s investment expenditure responds to the information in peer firms stock prices. We start our analysis by examining whether there is any link between firms investment and innovations in their peer firms stock prices. Our initial tests indicate that a firm s investment is positively related to the average innovation in peer firms stock prices. We show that higher peer firms prices are associated with a statistically significant increase in firms investment. We confirm that this relation is independent of the information in the firm s own stock price; we also verify that this relation is not simply an artifact of similar firms in an industry mimicking each other s investment policies. The positive relation between firm investment and peer firms stock prices appears to be robust to various estimation methodologies as well as to alternative definitions of peer groups and alternative measures of corporate investment. Finally, the effect of peer firms prices on firms investment is also economically meaningful; one standard deviation innovation in peer firms stock prices results in an increase of two to five 2

4 percent in investment spending for the median firm. Given this finding that peer stock prices have a significant influence on firm investment, we next examine whether this relation varies in ways that is consistent with our learning from peer prices hypothesis. Specifically, we investigate two forces that we believe shape the relation between firm investment and peer firm stock prices: the informativeness of peer firms stock prices and the strategic value of the information in peers prices to a manager. Consistent with the informational role of stock prices in guiding investment, we predict investment sensitivity to peers prices to be higher when these prices are more informative. We also expect investment sensitivity to peers prices to be higher when the information in these prices is strategically more valuable to managers. Since we can identify neither the exact nature of the information that managers gather from peer firms stock prices, nor the value of this information to managers directly, we test these predictions by exploiting the cross-sectional variation in investment sensitivity to peers stock prices across firms and across peers. Specifically, we test whether the investment sensitivity to peer prices correlates with measures of the information contained in peers prices and with characteristics of the firm s operating environment that should determine the strategic value of this information in ways that are consistent with our main hypothesis. How informative peers stock prices are to a manager is likely to vary both across peer groups as well as within the firm s peer group. For example, the information embedded in competitors stock prices is likely to be more relevant, and therefore, more informative to a manager if the other firms in its peer group share, on average, greater commonality in their exposure to economic factors. We, therefore, expect a greater investment sensitivity to peer firms stock prices among firms with more homogenous peers. The amount of information aggregated in stock prices can also vary across peer firms because of informed investors trading decisions. 3 These investors will choose to place their information-based trades in 3 Tookes (2008) presents a model of informed trading where informed traders may have incentives to make 3

5 those stocks where they can achieve the greatest returns. Their choices will be determined by a combination of the ease with which they can disguise their trades and the relevance of the information for the stock. Therefore, given a private information signal about the aggregate demand for an industry, for example, informed investors may have incentives to trade in only some stocks in an industry; these trades would then help impound more of this information into the prices of these stocks relative to other stocks in the same industry. 4 Consequently, we expect that, amongst a firm s peers, its investment will be most sensitive to the prices of those peers that are most influenced by the trades of privately informed investors. Collectively, these arguments support a testable prediction of the learning from peer prices hypothesis: investment will be more sensitive to prices of those peer firms with characteristics that are associated with greater informed trading and more informative prices. 5 We test this prediction in two ways. First, we use three different measures of the average information content of the stock prices of a firm s peer group, and we find significantly higher investment sensitivity to peer firms stock prices when these prices are more informative. Using a measure of the firm-specific price variation, for example, we find this relation to be positive and significant when peer firms stock prices contain significant amount of private information. We also find increased investment sensitivity to peers stock prices when the information aggregated in peers stock prices is likely to be more relevant, as is the case, for example, when the fundamentals of the peer group is, on average, more correlated. Finally, we find that investment sensitivity to peers prices is higher when peer firms, on average, information-based trades in the stocks of competitors. Studying earnings announcements, she finds support for the hypothesis that net order flow and returns in the stocks of nonannouncing competitors have information content for announcing firms. 4 Consistent with this idea, Hou (2007) shows evidence of slow diffusion of industry information from large to small stocks within an industry. 5 For ease for exposition, we sometimes refer to this collection of arguments tying the relation between firm investment and peer stock prices to the informativeness of peer firm s stock prices as managers information motive to learn from peers stock prices. 4

6 have greater analyst coverage. This is also consistent with the information motive to learn from peer firms prices, if greater analyst coverage of peer firms helps incorporate more information from peer firms managers into their prices. Second, we test the prediction by examining whether, within each peer group, investment is more sensitive to the stock prices of those peers that have more informative prices. Using three different proxies for the information content of individual peers prices, we find that a firm s investment is significantly more sensitive to the prices of those peers with more informative stock prices. We find this to be the case, for example, for those peers that have greater firm-specific price return variation and those with less liquid shares. Taken all together, these findings provide strong support for the information motive to learn from peer prices. As we argued previously, the investment sensitivity to peers stock prices should also be higher when managers place a greater strategic value on the information in these prices. This strategic value will depend on how costly or rewarding it is for the firm that it responds to the information promptly and appropriately. Accordingly, we expect such costs or rewards, that determine the value of the information in peers prices, to vary with firms operating environment. Both the cost of inaction and the reward from responding promptly and appropriately to the information in peers prices are likely to be higher in more competitive industries since greater competition will make firms more exposed and vulnerable to changes in their peers fortunes and strategies. Therefore, in a competitive industry, a manager will place a higher strategic value on the information signaled by peer firms stock prices. Similarly, we expect that a manager may find the information in peers stock prices to be of greater strategic value in the face of a greater need to cope with a rapidly changing operating environment, as in a fast-growing industry. The information in peers stock prices is also likely to be more valuable when firms operate in more capital-intensive industries, where capital investment 5

7 plays a more important role for firms. Collectively, these arguments support another testable prediction of the learning from peer prices hypothesis: the investment sensitivity to peers stock prices should increase with the strategic value of the information in these prices that is, in more competitive, fast-growing, and capital intensive industries. 6 Since price informativeness can also vary with the operating environment, we test this prediction, while controlling for the effect of the informativeness of peer prices. 7 We find strong empirical support for the prediction in our data regardless of the controls we employ for peer price informativeness. We observe that, consistent with our hypothesis, investment responds more positively to peer firms stock prices precisely when internalizing the information in peer prices and the investment decision become more important, either because of more intense competition, a rapidly changing operating environment, or greater dependence on capital in the production process. Our results also indicate that, in contrast, in industry environments characterized by relatively weak competition, slow growth, and low capital intensity, the relation between firm investment and peer stock prices sometimes turns significantly negative. We also uncover a complementarity between the effect of the informativeness of prices and that of the strategic value of that information in competitive, fast-growing, and highly capital intensive industries. We find that in these industries, investment sensitivity to peer stock prices increases significantly more with measures of the information content in peer prices. That is, the operating environment in competitive, fast-growing, and highly capital intensive industries intensifies the positive relation between investment sensitivity to peer prices and the informativeness of these prices. We obtain similar results, when we examine the investment sensitivity to peer prices 6 For ease for exposition, we sometimes refer to this collection of arguments tying the relation between firm investment and peer stock prices to industry operating conditions and peers firms relative standing within an industry as managers strategic motive to learn from peers stock prices. 7 For example, Tookes (2008) demonstrates how industry and firm characteristics can influence how investors trade to take advantage of their private information. 6

8 within peer groups. Consistent with both the strategic and the information motives associated with learning from peer prices, we show that, among a firm s peer group, not only is investment sensitivity highest to prices of those peers with characteristics correlated with more informative prices, but this sensitivity is also significantly larger in environments where the strategic value of this information is likely to be greater more competitive, fastgrowing, and highly-capital intensive industries. This evidence provides further support and additional insight for the learning from peer prices hypothesis. Finally, we exploit the fact that each peer s relative market position will determine how a manager might value the information in its price. On one hand, a manager may place greater value on the information embedded in prices of more dominant peers since they pose the greatest threat to his firm. On the other hand, the information in the weakest peers prices might be the most valuable since these firms offer the greatest opportunity to prey on and are also likely to be the most sensitive to changes in the operating environment. These arguments indicate that the investment sensitivity to peers stock prices will also vary with the relative positions of the peers within the industry. We test these hypotheses using four different measures of peers relative market position; specifically, we exploit the variation in peers market capitalization, market share, profitability, and sales growth. We expect the investment response to the information in peer stock prices to vary with these characteristics if managers respond to either take advantage of weak competitors or preempt on the gains of strong competitors. Our evidence indicates that peers relative market position is an important determinant of the investment-peer stock price sensitivity. Consistent with the notion that higher prices for weaker peers signal improved investment opportunities, firms appear to increase their investment more strongly in response to positive price innovations for smaller and slow-growing peers. Again, in support of the learning hypothesis, we find that this relation is stronger when prices contain more private information. This evidence, once again, underscores the complementarity between 7

9 the effects of price informativeness and the strategic value of that information in determining the relation between investment and peer stock prices. Our study contributes to two main strands of literature. First, we extend the literature that analyzes the relation between stock prices and corporate investment by documenting the cross-effect of peer firms stock prices on firms investment. Within this literature, we also contribute to the debate over what drives the relation between investment and stock prices as our approach has an important advantage over the previous studies. While many studies have documented a positive correlation between firm investment and firm s own stock prices, it has proved difficult to disentangle the information effect of prices from that of mispricing. Indeed, Stein (1996) and Baker, Stein, and Wurgler (2003) argue that investment may correlate with stock prices simply because financially constrained firms take advantage of high stock prices to raise equity capital and use the funds to finance investment. Our approach is immune to this problem as we examine the incremental effect of peer firms stock prices that do not directly impact the financial constraints faced by a given firm. Our findings relate closely to those of Chen, Goldstein, and Jiang (2007) and support the view that prices in financial markets convey information to managers that guide corporate investment decisions. Our analysis is also close to that of Foucault and Fresard (2012) who also examine the link between firm investment, peer firm stock prices, and the informativeness of peer stock prices. However, unlike our analysis, theirs does not examine how the dependence of investment on peer stock prices is affected by the firm s operating environment. They also do not examine how the operating environment and peer price informativeness interact to influence investment. Because we consider the role of a firm s operating environment including industry characteristics such as the level of competition, growth, and capital intensity, our study also contributes to a new strand of literature that attempts to understand how linkages between firms and the nature of the industrial organization affect corporate financial policies. Grullon 8

10 and Michaely (2007), for example, study the relation between product market competition and managers payout decisions. Leary and Roberts (2010) examine how peer firms can influence firms capital structure decisions. Hertzel, Li, Officer, and Rodgers (2008) show significant effects of financial distress among firms that are connected along the supply chain. Fresard and Valta (2012) investigate the impact of increased product market competition on corporate investment and financing. The rest of the paper is organized as follows: In the next section, we describe the sample, the construction of our relative Q measures and the empirical methodology. In Section 3, we present our baseline empirical results on the relation between firm investment and peers stock prices. In Section 4, we examine the role of the information content in peers stock prices in the investment-peer stock price relation. In Section 5, we focus on the industry environment and examine how the firm s operating environment interacts with peer price informativeness to influence investment. Section 6 concludes. 2 Data and Methodology 2.1 Sample construction We study a large, unbalanced panel of firms that are in the intersection of Standard & Poor s Annual COMPUSTAT database and CRSP over the period from 1970 to For each firm, we obtain accounting data from the COMPUSTAT annual files and merge them with return and pricing data from CRSP stock return files using the CRSP-COMPUSTAT link file generated by CRSP. We exclude financial firms (SIC codes between 6000 and 6999), utilities (SIC codes between 4900 and 4999) as well as government entities (SIC codes greater than or equal to 9000). We also exclude firms with less than $10 million in total assets, or with less than 30 days of trading activity in a year, as well as firms with negative sales. Finally, we drop firm-year observations with missing information on firm investment in the 9

11 current year (t) and on firm Q in the prior year (t 1). To mitigate the influence of extreme observations and eliminate potential data coding errors, we winsorize all ratios at the first and ninety-ninth percentile; that is, we set all observations beyond these levels to the first and ninety-ninth percentile values. An important choice for our research design is the identification of a firm s peer group. Throughout our study, we focus on three-digit SIC classifications to define industry peer groups. This choice reflects a balance between, on one hand, minimizing concern about the possibility of grouping together firms in unrelated businesses, and on the other hand, ensuring we have a meaningful number of peers for each firm in the sample. We also restrict the sample to include only firm-years with a minimum of five other peer firms. Although we focus our analysis mostly on three-digit SIC industry peer groups, we also employ, for robustness, alternative peer definitions that do not rely on traditional industry classifications and obtain qualitatively similar results. Our selection procedure leaves us with a final sample of 85,309 firm-year observations corresponding to 9,497 unique firms in 193 industries. We collect data on analyst coverage for this final sample of firms from I/B/E/S. Analyses using the analyst coverage data have fewer observations because I/B/E/S coverage starts from Table 1 presents the summary statistics for our sample. 8 In Panel A we report the summary statistics for the firm-level variables used in our study, and in Panel B, we report summary statistics for the peer-firm averages for these variables. We construct each peerfirm average using all firms within an industry peer group excluding the firm itself. A comparison across Panels A and B indicates that the two groups have very similar mean values for most variables. However, the median values of several variables such as capital expenditure (CAPX), book value of assets (ASSETS), equity market capitalization (SIZE), and sales (SALES), are perceptibly larger for peer firms. These differences are a natural 8 Appendix A provides details regarding the construction of our key variables. 10

12 result of the fact that large firms, since they are included in most peer groups, have a greater influence on the peer averages. Panel C reports the number of industries and the distribution of the number of peer firms per industry across years. There are fewer firms per industry in the earlier part of our sample; this number nearly doubles, however, in the second half of our sample. Over the entire sample, each firm has an average (median) of 19 (12) peer firms in its industry peer group. 2.2 Measuring the investment to peer stock price sensitivity In an efficient market, stock prices will reflect the marginal product of capital, and a value-maximizing firm will invest as long as the shadow value of an additional unit of capital the marginal Q exceeds unity (Tobin (1969)). This Q-theory of investment implies a positive relationship between investment and firm Q, or alternatively, a positive investment-to-price sensitivity. 9,10 Several past studies have tested this theory and found that investment is related to standard proxies for firm market valuation, such as firm Q (e.g. Fazzari, Hubbard, and Petersen (1988), Baker, Stein, and Wurgler (2003), and Chen, Goldstein, and Jiang (2007)). Chen, Goldstein, and Jiang (2007) extend this chain of thought and examine whether stock prices in fact guide managers investment decisions. Consistent with the idea that prices aggregate and convey new information to managers, they find that the investment-to-price sensitivity is higher when stock prices are more informative. We depart from this literature by focusing on the sensitivity of firm investment to stock prices of other peer firms in its industry. The Q-theory does not allow for a role for peer prices in informing firm investment. However, when prices are not fully revealing, a firm s own Q alone may not be sufficient to guide its investment. The firm s manager can also 9 More precisely, Hayashi (1982) shows the equality of marginal Q and average Q under constant returns to scale and perfect competition. If financial markets are efficient, the average Q will equal the ratio of the value of the capital stock to its replacement cost, that is, Tobin s Q. 10 Erickson and Whited (2000) point out the potential bias in estimating coefficients of firm investment on Q due to the measurement error problems in firm Q, 11

13 inform his investment decisions by paying attention to the stock prices of peer firms. These prices will also reflect the firm s prospects since the firm s prospects are tied to those of its peers. Moreover, peer prices will contain information about the firm s prospects that is not already reflected in the firm s own stock since stock price informativeness varies within an industry along with the attractiveness of the stocks as venues for informed investors to exploit their information advantage (e.g., Tookes (2008)). For example, informed investors may find it easiest to profit from their information by concentrating their trades in the stocks that are most sensitive to industry performance. Alternatively, they may trade in the most liquid stocks to take advantage of information about less liquid peers. To test our hypothesis, we need to capture the components of peers stock prices that are distinct from the firm s own stock price. The average value of Q across peer firms for this purpose is unlikely to satisfy this requirement since a firm s own Q and the average Q across its peers are likely to move together and contain a significant amount of common industry information. 11 Instead, to minimize the overlap between a firm s Q and that of its peers, we construct a measure of relative peer Q based on the innovation in stock prices. We follow a simple two-step procedure. First, for each firm i in industry j, we construct its relative RELQ ijt in year t as its actual Q, Q ijt, minus the expected industry Q, Q jt : RELQ ijt = Q ijt Q jt. (1) Following Baker et. al (2003), and Chen et. al (2007), we define Q ijt as the the market value of equity (price times shares outstanding from CRSP) plus book value of assets minus the book value of equity, scaled by book assets. We proxy for Q jt using the lagged value of the industry average, Q j,t 1. That is, RELQ measures for each firm the innovation in its own Q 11 The data in Tables 1 and 2 show that the average peer Q has a very similar distribution to the firm s own Q and these two variables are highly correlated. 12

14 conditional on the industry and year. 12 Next, we construct a relative peer Q measure for each firm, denoted as RELP EERQ jt, i, as the equally weighted average of RELQ ijt across all its peers in the three-digit SIC group that the firm belongs. In other words, RELP EERQ measures for each firm the average innovation in Q among its peer firms. Figure 1 presents the empirical histogram for RELP EERQ. 13 Table 2 describes the properties of the relative peer Q measure, RELP EERQ. First, in Panel A, we report its summary statistics. It has an unconditional mean that is close to zero. It also has a large standard deviation of 0.308, indicating that RELP EERQ varies substantially over our sample. In panel B, we present the correlation structure between firm Q, average (raw) peer Q, and RELP EERQ. Firm Q and average peer Q have a correlation coefficient of This strong positive correlation is not surprising and underscores the need for constructing a different measure that captures the information in peers stock prices that is not reflected in the firm s own stock price. Second, the correlation between firm Q and RELP EERQ is very low; it is only This low correlation supports our belief that RELP EERQ does well at capturing the information in peer stock prices that is distinct from the information in firm s own Q. It also suggests that, on average, any industry-wide price innovation that is reflected in both firm Q and RELP EERQ is relatively small. 2.3 Baseline model To test whether a firm s investment responds to changes in the stock prices of its industry peer group, throughout our study, we estimate variations of the following baseline empirical 12 In a previous draft, we obtained the expected Q jt by first estimating a regression equation using a rolling 10-year window of lagged data in each industry separately and by using the estimated industry-coefficients to compute the expected Q jt for firm i in year t. We obtained similar results. 13 It is important to note that RELQ reflects both an industry-wide price innovation and a firm-specific price innovation. Therefore, RELP EERQ reflects the average of the industry-wide and firm-specific price innovations. As the industry definition gets coarser, and the number of firms approach infinity, RELP EERQ should converge to the industry-wide price innovation. Therefore, if the industry-wide price innovation is zero, RELP EERQ will converge to zero as the number of firms in an industry increases. As in Leary and Roberts (2010), our definition of the peer group restricts the size and the composition of the peer group. 13

15 model: I ijt = λ t + α j + β 1 Q ijt 1 + β 2 RELP EERQ jt 1, i + γ 1 CONT ROL F irm it + γ 2 CONT ROL P eer jt, i + ε ijt, (2) where the dependent variable I ijt is a measure of investment for firm i in industry j in year t, and λ t and α j represent year and industry fixed effects, respectively. Q ijt 1 is the normalized stock price for firm i in year t 1, and it is measured by the firm s Q as described above. This regression model is distinct from those employed in earlier studies of the relation between Q and firm investment because it also includes RELP EERQ jt 1, i, the average innovation in Q across peer firms in industry j in year t 1. Accordingly, our focus is on the coefficient β 2 which captures the investment sensitivity to the incremental information in peer stock prices. A priori, it is not clear what the sign of the coefficient β 2 on RELP EERQ should be. First, it is unclear whether good news for a firm s peers is good or bad news for the firm. For example, if the innovations in peer prices primarily reflect the health of the industry, positive (negative) innovations in peer stock prices will signal better (worse) investment returns for the firm. Alternatively, if the innovations in peer prices primarily signal a strengthening (weakening) of the peers relative position, positive (negative) innovations in peers stock prices would be bad (good) news for the firm. Second, it is not clear how firms should react to good or bad news for their peers. For example, it may be optimal for firms to try and preempt and internalize the expected profitable expansion of their peers signaled by the peers stock prices by, for example, increasing investment. Alternatively, it may be optimal for firms to scale back their investment in response to the expansion of their peers. These choices are likely to be sensitive to both the fidelity of the information signaled by peer prices and the operating environment of the industry in which the firms operate. 14

16 Our first task, therefore, is to test whether firm investment changes at all in response to unexpected changes in peers stock prices. We expect that if managers choose the level of investment to maximize firm value using all the information available to them, in addition to any information in the firm s own stock price, they should utilize incremental information that is aggregated into peer firms prices. Therefore, to the extent innovations in peer firms stock prices are informative beyond the firm s own stock price, we should expect firm investment to respond to RELP EERQ jt, i ; that is, we expect β 2 0. The baseline regression also includes a set of firm-level control variables, CONT ROL F irm it, known to affect investment decisions. First, to account for the well-known effect of cash flow on investment documented by Fazzari, Hubbard, and Petersen (1988), we include a measure of the contemporaneous cash flow, CF it. We measure CF it as the sum of net income before extraordinary items (data item 18), depreciation and amortization expenses (data item 14), and R&D expenses (data item 46) scaled by beginning-of-year book assets. Second, we control for the possibility that investment is influenced by managers attempts to time the market. Loughran and Ritter (1995), Baker and Wurgler (2002), and Baker, Stein, and Wurgler (2003) argue that firms may invest more when their stocks are overvalued, or put differently, when expected future stock returns are lower. Therefore, we follow Baker, Stein, and Wurgler (2003) and include firms future returns RET 3 it+3 to control for managers market timing of investment. These future realized returns are a noisy measure of the future returns expected by managers, and hence, their views about over- and undervaluation. We measure RET 3 it as the cumulative excess stock return for firm i in years t + 1, t + 2, and t + 3. Finally, to mitigate any potential spurious correlation between I it and Q it 1 that may be induced by the common scaling variable, we also include as a control variable the inverse of lagged assets, INV ASSET S it 1. An important concern in testing our hypothesis is the possibility that firms may mimic or simply react to their peers actions rather than respond to the information in their prices. 15

17 Since peer firms investment choices will themselves be correlated with their stock prices, we may mistake the mimicking action of a firm as choosing its investment in response to RELP EERQ. To address this concern, we include in Equation (2) a second set of peer-level control variables, CONT ROL P eer jt 1, i. First, to control for any direct effect of peer firms investment choices, we include the lagged value of the industry-wide average investment CAP X P eer jt 1, i, measured as the equally-weighted average value of capital expenditures across all firms in industry j, excluding firm i in year t 1. In addition, we control for peer-firm counterparts of known determinants of investment by including lagged value of CF P eer jt, i, the equally-weighted average cash flow in industry j, excluding firm i, and lagged value of RET 3 P eer jt, i, the equally-weighted average 3-year cumulative excess return for industry j, excluding firm i. We estimate the regression equation (2) using an unbalanced panel and allow the error term to be serially correlated for the same firm. We correct standard errors for heteroskedasticity and within-firm clustering, as in Petersen (2009). It is possible that clustering at the firm-level may still underestimate standard errors since our sample includes multiple firms from the same industry. Our findings are robust, however, to clustering standard errors at the industry level as well. Moulton (1986) has shown that fixed effect specifications at the firm level can produce negatively biased standard errors in the presence of additional industry-level variables. We therefore leave out firm fixed effects from specifications that include peer-level control variables. We do, however, present estimation results from various specifications with industry and year fixed effects. 16

18 3 Investment sensitivity to peer stock prices 3.1 Baseline results Table 3 presents estimates of regression equation (2) using our baseline measure of firm investment, CAP X it, which is defined as the ratio of capital expenditures (Compustat data item 128) in year t to beginning-of-year book assets (data item 6). We check whether our findings are robust to the way investment is measured by also considering two alternative measures of investment: CAP XRND it, which is the ratio of the sum of capital expenditures and research and development expenses (data item 46) to beginning-of-year book assets, and CHGASSET it, which measures the percentage change in book assets over the year. To simplify the interpretation of the results, we normalized both firm Q and RELP EERQ by subtracting from each measure its sample mean and dividing the difference by its sample standard deviation. Column (1) presents the estimates from the simplest version of our baseline model with just the relative peer Q (RELP EERQ), the firm s own Q (F IRM Q), and year fixed effects included in the regression. The coefficient estimate on RELP EERQ, β 2, is positive and equal to It is statistically significant at conventional levels. In columns (2) and (3), we present estimates where we add other firm-level control variables that are known to influence firms investment choices to the regression. The estimates reported in column (2) also include firm fixed effects to control for unobserved, time-invariant firm characteristics, while the estimates in column (3) control for industry fixed effects. The coefficient estimate for β 2 remains essentially unchanged in column (2), and it weakens slightly in column (3) when we include industry fixed effects. In column (4), we add peer-level control variables to the model. The inclusion of these controls perceptibly strengthens the coefficient estimate for β 2. In column (5), we also include in the regression model the average peer (raw) Q. This 17

19 change allows us to assess the explanatory power of average peer Q relative to RELP EERQ and identify whether it is the average peer Q or the average value of the innovation component in peer Q that is tied to firm investment. As we argued in the previous section, there is a high degree of correlation between a firm s own Q and the average peer Q, which suggests that these two may contain largely similar information. The coefficient estimate on P EER Q indicates firm investment responds negatively to the average peer Q, but the relation is statistically insignificant. In contrast, the coefficient estimate on RELP EERQ remains positive and is equal to 0.111; it also remains statistically significant. These findings imply that firm investment is sensitive to the innovation in peer stock prices and not to the peer firms Q itself. This suggests that, indeed, the innovation in peer Q conveys information that is not captured by firms own stock prices. In columns (6) and (7), we check the robustness of our findings to changes in our baseline definition of capital expenditure. Our findings remain essentially unchanged when we switch to either of the two alternative measures, CAP XRND it or CHGASSET it. The coefficient estimates on RELP EERQ remains positive and statistically significant, irrespective of the definition of investment we use. It is worth noting that the average innovation in peer firms stock prices has an especially pronounced effect on firm investment when investment is measured using CHGASSET it, which also accounts for firms acquisitions and divestitures. A one standard deviation increase in the average level of peers stock prices results in an increase of almost ten percent in total assets for the average firm. 14 These estimates indicate that firms investment policies respond to changes in their peer firms stock prices. Specifically, higher stock price levels for peer firms have, on average, a positive and significant effect on firms investment levels. Even after controlling for the firm s own stock price and peer firms investment levels, one standard deviation increase in 14 This is consistent with the fact that merger waves are typically correlated with high stock market valuations. See Jovanovic and Rousseau (2002), Shleifer and Vishny (2003), Rhodes-Kropf and Viswanathan (2004), Rhodes-Kropf, Robinson, and Viswanathan (2005). 18

20 the average relative level of peers stock prices increases investment by about two percent for the median firm. The coefficient estimates in Table 3 on the firm-level control variables are all consistent with evidence in previous studies. First, we confirm that firm investment is positively and significantly related to its stock price. The coefficient estimates on Q it 1 are all positive. They range from to in columns (1) through (5), when we use CAP X it to measure investment. In columns (6) and (7), these coefficients are and , respectively. All these estimates are statistically significant at the one percent level. Therefore, consistent with the Q-theory of investment, we find that firms raise their capital expenditures when their stock prices signal greater investment opportunities. Second, as in Fazzari, Hubbard, and Petersen (1988), investment depends positively on cash. Finally, consistent with mispricing arguments in Loughran and Ritter (1995), Baker and Wurgler (2002), and Baker, Stein, and Wurgler (2003), the coefficient estimates on RET 3 are all negative, suggesting that firms invest more when their stock prices are overpriced. We note that peer investment and cash flows also appear to explain firm investment. The coefficient estimate on P EER CAP X is positive, and it is statistically significant at the one percent level in all but column (7). This indicates that it is important to control for the average level of investment by peer firms in order to isolate the role of incremental information conveyed by peer firms stock prices in our tests. The positive coefficient on the average level of peer cash flow in columns (4) and (5), where we use CAP X it to measure investment is consistent with the idea that firms, on average, invest more in more profitable industries with more investable resources. 19

21 3.2 Alternative definitions of peer groups In choosing to use three-digit SIC codes to define industry peer groups, we tried to minimize the possibility of grouping firms in unrelated businesses while ensuring a reasonable number of peers for each firm. There may still be concerns, however, about whether we achieve either goal and, therefore, whether our findings are sensitive to the way we have defined the set of peer firms. To address this potential mismeasurement concern, we take two different approaches. First, in order to address the potential concern about having an inadequate number of peer firms, we redo our analysis using two-digit SIC codes to define industry peer groups. Column (1) in Table 4 shows that our findings are unaffected by the switch to this coarser classification of industry peers; the coefficient estimate on RELP EERQ is, in fact, higher than in columns (1) through (5) in Table 3, where we also use CAP X it to measure investment. This indicates a more economically significant response in firm investment to higher levels of stock prices for peer firms. While defining industries based on SIC classifications has been the standard approach in much of the finance literature, recently, there have been efforts to improve on these static classifications by using data from SEC filings on firms business and product descriptions. Using a text-based analysis of these descriptions to construct product similarity scores, Hoberg and Phillips (2011) propose a more flexible set of text-based network industry classifications (TNIC) and show that these text-based classifications improve significantly in their ability to identify firms likely competitors over the standard static classifications such as the SIC codes. 15 We therefore check whether our findings are robust to these alternative industry definitions and repeat our analysis with peer groups identified from TNIC3 indus- 15 An alternative approach is taken by Rauh and Sufi (2010). In order to identify a firm s direct competitors, they use information from Capital IQ on firms self-reported competitors and show that firm capital structure better reflects that of these competitors than that of firms in the same SIC code. Capital IQ, however, provides such information only for the current year and therefore is not suitable for the purpose of our study. 20

22 tries provided by Hoberg and Phillips (2011). 16 We report the results from estimating the baseline specification using these peer groups in column (2) of Table 4. Even though the sample period now spans only from 1997 to 2006, the coefficient estimates are very similar to those obtained earlier; our main result that firm investment is positively related to the peer firms stock prices remains unchanged. 17 Column (3) in Table 4 is a falsification test. The idea that firm investment may respond to peer firms stock prices is based on the premise that these peer firms prices contain some relevant incremental information for the firm. We therefore do a sanity-check on this premise by testing whether firm investment shows sensitivity to stock prices when we should expect none. Specifically, each year, for each firm in our sample, we first construct a set of pseudo-peers by drawing a random sample of firms from outside of the firm s industry. We then reestimate our baseline regression model from equation (2). To the extent that stock prices of these pseudo-peer firms contain no relevant incremental information for firm investment, we should expect to find no significant relationship between firm investment and the relative level of stock prices of these pseudo-peers. The results of this falsification test confirm this expectation. First, as we expect, the coefficient estimate on RELP EERQ is insignificant. Moreover, unlike all other estimates of β 2 in Tables 3 and 4, this estimate is negative. Therefore, we conclude that firm investment shows no relationship to stock prices of other firms, when prices contain no relevant incremental information. Second, note that we also find no relationship between firm investment and investment levels of these pseudo-peers. Since there is a large common component in investment for related firms (e.g., Hoberg and Phillips (2011)), this gives us additional comfort 16 We thank Gerard Hoberg and Gordon Phillips for making these industry classifications on their website. Hoberg and Phillips (2011) report that TNIC3 industries are designed to have the same granularity as the three-digit SIC codes. 17 Given that peer groups are defined in a time-varying fashion, it is not possible to control for time-invariant industry-fixed effects using TNIC industries. However, for this estimate, we include firm-fixed effects to control for unobserved, time-invariant firm characteristics. 21

23 that our design for the falsification strategy is successful in selecting unrelated firms. 3.3 Instrumental variable estimation The investment sensitivity to peer stock prices discussed in the previous section is estimated based on the assumption that our proxy for the average innovation in peer firms stock prices, RELP EERQ, captures information about future growth opportunities that is distinct from what is in a firm s own stock price. Given our inability to directly isolate or perfectly measure these constructs, however, it is possible that our findings merely reflect either a measurement error or an omitted-variables problem. One possibility, for example, is that RELP EERQ may be correlated with the error with which a firm s Q captures its investment opportunities. To address these concerns, we use instrumental variable regressions. Our instrument for the distinct information in peer prices about future growth opportunities is the idiosyncratic component of peer firms stock returns. 18 This instrument choice is a natural fit for our analysis. First, stock returns should reflect value-relevant information. Therefore, they should be significantly related to future growth opportunities. Second, idiosyncratic returns are unique to a firm by construction; they do not contain information about other firms conditional on observable determinants. Therefore, peer firms idiosyncratic returns should be orthogonal to a firm s own investment opportunities, making them a perfect candidate instrument for the distinct information contained in peers stock prices. We follow Leary and Roberts (2010) in constructing the instrument. We specify a similar model for returns that is general enough to capture as much common variation in returns as possible by nesting together the market model, the Fama-French (1993) three-factor model as well as industry-adjusted returns. Specifically, we estimate the following five-factor model 18 Leary and Roberts (2010) also use the idiosyncratic component of stock returns as an instrument. However, their strategy is to use it as an instrument for peer firm financial policy, namely, capital structure choice. 22

24 of returns for each firm: r ijt = α ijt + βijt m (r mt r ft ) + βijt SMB SMB t + β HML ijt HML t + β MOM ijt MOM t + β IND ijt (r ijt r ft ) + η ijt (3) where r ijt is the total return for firm i industry j over month t, and the first four factors are the excess market return (r mt r ft ), the small minus big portfolio return (SMB t ), the high minus low portfolio return (HML t ), and the momentum portfolio return (MOM t ) as in Fama and French (1993) and Carhart (1997). In addition, we also include the excess return on an equally-weighted industry portfolio, excluding firm i s return, in order to remove any common variation in returns across firms within the same peer groups. We require at least twenty-four months of historical data for each firm and estimate equation (3) for each firm that meets this criterion using a rolling window of five years of historical monthly return data. We then use the betas obtained from the estimation to compute each firm s expected return over the next twelve months as follows: 19 r ijt = α ijt + β m ijt (r mt r ft ) + SMB β ijt SMB t + HML β ijt HML t + MOM IND β ijt MOM t + β ijt (r ijt r ft ) (4) The idiosyncratic return for each firm is computed as the realized minus the expected return: η ijt = r ijt r ijt Finally, we construct our instrument for each firm by averaging the annual idiosyncratic 19 Note that we allow the beta estimates to be firm-specific and to vary over time, but they remain constant over the calendar year immediately following the estimation period. 23

25 return measures across all other firms in the firm s industry peer group. 20 Using this instrument, we then re-estimate our baseline specification in Tables 3 and 4 using two-stage least square (2SLS) estimation, where in the first stage we instrument for RELP EERQ by modeling it as a function of the average peer idiosyncratic return (instrument) and all of the regressors in the second stage. 21 Table 5 presents our findings for peer groups defined by three-digit SIC code, two-digit SIC code, and Hoberg and Phillips s TNIC3 industry groups, respectively. First, the average peer idiosyncratic return is positively and significantly associated with RELP EERQ in all three columns, satisfying the relevance condition of instrument validity; the Cragg-Donald Wald F-statistics also confirm that our instrument passes the weak instrument tests in each column (Stock and Yogo (2005)). The second-stage results confirm our main result that firm investment is strongly and positively related to RELP EERQ. Moreover, the positive and significant coefficient estimates for the instrumented RELP EERQ suggest that firm investment is sensitive to innovations in peer firms prices, even when the innovations do not reflect industry-wide information. Table 5 also indicates that the IV coefficient estimates are significantly larger than the OLS estimates; one standard deviation increase in the innovation in peer stock prices leads to more than ten percent increase in firm investment within the three-digit SIC peer groups. The conclusions from the instrumental variables regression analysis are similar to those obtained in Table 3 and the remaining results in Table 4: Firms, on average, raise their capital expenditures in response to positive shocks to their peers stock prices. Moreover, given that our instrument is constructed using peer firm price innovations that are orthogonal to innovations in firms own prices, the results also suggest that firm investment is sensitive to idiosyncratic innovations in the prices of peer firms. 20 We annualize the monthly idiosyncratic returns for each firm by compounding over the calendar year. 21 We have dropped P EER Q from the 2SLS estimation. Its inclusion does not change the results in any significant way. 24

26 4 The role of peer firm information In the previous section, we documented that firm investment responds positively and significantly to peer firms stock prices. This relationship is consistent with the view that peer firms stock prices convey information that helps guide managers investment decisions. In this section, we adopt a different approach to testing the learning from peer prices hypothesis: we focus on the relation between the sensitivity of firm investment and measures of the information contained in peer firms stock prices. Managers can learn from peer firms stock prices if the prices contain information that is not already known to them or reflected in their own firms stock prices. Their reliance on peers stock prices will rise as these prices become more informative, and as they signal more relevant information. Therefore, to assess whether peer firms stock prices convey information that help guide managers investment decisions, we now examine whether the sensitivity of investment to peer firms stock prices rises with the informativeness of these prices and the relevance of the information they signal. We first consider this variation across our entire sample; later, we examine how this relation varies across peer firms within the firm s industry. 4.1 Measuring peer firm information First, we consider the effect of stock price informativeness. Managers are more likely to pay attention to peer prices if they contain information that is new to the manager. Investors with private information who trade in the shares of peer firms are the likely source of this information. Therefore, we measure stock price informativeness using the variable Info, which is a direct proxy for the private information contained in stock prices; it is, as in Chen, Goldstein, and Jiang (2007), the firm-specific return variation. The intuition is that informed trades based on firm-specific information will make the firm s stock less synchronous with the rest of the market and increase the firm s idiosyncratic return variation (Roll (1988), 25

27 Durnev et al. (2003), Morck, Yeung and Yu (2000), and Durnev, Morck and Yeung (2004)). We first compute Info for each firm as one minus the R-squared obtained from regressing firm s daily stock returns in year t 1 on a constant, the CRSP value-weighted market index, and the return of the three-digit SIC industry portfolio. We then construct a relative Info measure for each firm by subtracting out the industry average value. Finally, our measure of peer firm stock price informativeness, Peer Info, is the equally-weighted average of the relative Info measures across all other firms in the firm s peer group. Since higher values of Peer Info should indicate greater informed trading and greater amount of private information impounded into prices, we expect firms with higher Peer Info to show greater investment sensitivity to peers stock prices. Our second measure focuses on the relevance of the information signaled by peer prices to a firm s manager. We use the degree of homogeneity in an industry, which reflects the extent of the commonality in economic shocks experienced by firms in the industry, to capture information relevance. Firms in more homogeneous industries are likely to face more highly correlated economic shocks. Therefore, an innovation in one firm s price is more likely to be informative about the prospects of another firm in the same industry: managers are more likely to interpret positive (negative) innovations in their peer firms stock prices as positive (negative) signals of their own future prospects. Our homogeneity measure is based on the historical correlation between a firm s quarterly earnings and the industry average earnings, excluding the firm itself. Our measure of the degree of homogeneity among peer firms, Peer Fundamental Correlation, is the equally-weighted average of absolute value of the correlations across all pairs of other firms in the firm s peer group. Our final measure is the average amount of analyst coverage among a firm s peer firms (Peer Coverage). Analysts may play a role in disseminating greater amount of industrywide information and helping this information get impounded into prices. Moreover, as Chen, Goldstein, and Jiang (2007) argue, analysts may make prices more informative by 26

28 disseminating information from the management. 22 This suggests that greater coverage of peer firms is likely to lead to, on average, greater peer firm managerial information and industry-wide information being impounded into peer stock prices. We expect therefore a stronger investment sensitivity to peer prices among peer groups with greater average analyst coverage. 4.2 Evidence on the role of information across all firms To test these predictions, each year, we first allocate all firms into one of two groups, High and Low, depending on whether the average value of an informativeness measure among a firm s peers is above or below on the sample median in that year. We repeat this exercise with each of the peer information measures. We then estimate our baseline regression model in equation (2) for each group. Our primary interest is how the coefficient β 2 on RELP EERQ compares across the two groups; that is, we focus on comparing the coefficients for RELP EERQ across firms whose peers score Low vs. High on the information variables considered above. Accordingly, we also present tests of the differences between the coefficients across these two groups. Table 6 presents the estimation results. The first two columns present estimates when we sort firms based peer stock price informativeness, Peer Info. In column (1), the coefficient estimate for RELP EERQ is 0.055, and it is statistically insignificant. This indicates that firm investment is not related to peer firm stock prices when peers prices contain little private information. In contrast, in column (2), the coefficient estimate for RELP EERQ is 0.134, and it is statistically significant at the one percent level; this indicates that firm investment responds strongly to peers stock prices when they contain a relatively large 22 Chen, Goldstein, and Jiang (2007) report a negative relation between a firm s own analyst coverage and its investment-price sensitivity. If analysts mainly disseminate information from the management and help this information get impounded into prices, this information would not necessarily affect managers investment decisions since it would not be new to them. The implications for greater coverage of peer firms, however, are obviously different. 27

29 amount of private information. In columns (3) and (4), we compare how firm investment responds to peer stock prices when the peers have highly correlated fundamentals (Peer Fundamental Correlation) to when their peers have less correlated fundamentals. The coefficient estimate for RELP EERQ for firms in the Low-group in column (3) is statistically insignificant while the estimate in column (4) is positive and significant at the one percent level. The coefficient estimate in column (4) is also statistically significantly larger than the estimate in column (3). These two estimates indicate that the relation between firm investment and peer firms stock prices is significantly stronger among firms in more homogenous peer groups where firms are more likely to face correlated economic shocks to their fundamentals. Moreover, the magnitude of this effect appears to be economically important as well: one standard deviation increase in the average correlation among firms fundamentals, as measured by their earnings, more than doubles firm investment sensitivity to peer stock prices. Finally, in the last two columns, we group firms by Peer Coverage and obtain similar results. Investment is significantly more sensitive to peer stock prices among firms in peer groups with greater analyst coverage: In column (5), the coefficient estimate on RELP EERQ is among firms with peers with relatively poor analyst coverage, and it is statistically insignificant; in contrast, in column (6), it is 0.158, and highly significant, among firms in the High-group. This latter coefficient is also statistically larger than that for firms with peers with low coverage. These results indicate that investment responds more strongly to peer firms stock prices when they are likely to contain more information from peer firms managers or more industry-level information. Overall, the evidence in Table 6 supports our argument that the link between firm investment and peer stock prices is strengthened when prices are more likely to signal information. Specifically, we find that firms investment does not respond in a statistically significant manner to peer firms stock prices when the prices are relatively uninformative, but investment 28

30 responds strongly when peers prices are relatively informative. Moreover, the investment response is typically significantly stronger when peers have, on average, more informative stock prices. We also find that firms increase investment in response to potential positive news for their peers when peers stock price movements signal industry prospects or reflect more private information from investors or peer firm management. 4.3 Evidence on the role of variation in information within peer groups The amount of information contained in peer firms stock prices is likely to vary across the peers within a firm s peer group. Therefore, if managers seek to learn from peer firms stock prices, they should pay more attention to the prices of firms in their peer group that are more informative; in other words, within a peer group, we should observe greater sensitivity of investment to the prices of those peers that are more informative. We next test this prediction by exploiting the cross-sectional variation in firms investment sensitivity to peers prices across peer firms within an industry peer group. We use three measures of stock price informativeness at the peer firm level. The first two measures are similar to those we used in the previous section: Peer Info, which, as we had described previously, measures the amount of private information contained in stock prices, and Peer Coverage, which proxies for the amount of information from peer firms managers and industry-level information that should be captured in their prices. 23 Our third measure is based on stock liquidity. Stock price informativeness is a function of liquidity, as is informed trading. On one hand, as is common in the microstructure literature, informed traders will tend to prefer to trade in more liquid stocks; greater liquidity would enable them to hide their trades better and help them improve their profits. On the other hand, however, the 23 It is possible that greater analyst coverage could discourage the incorporation of private information into firms stock prices. To the extent analysts help incorporate public and industry information into prices, informed traders expected returns will fall and, therefore, reduce their incentives to trade on private information. Indeed, in support of this view, Easley, O Hara and Paperman (1998) find an inverse relation between the likelihood of informed trading, as measured by their PIN measure, and analyst coverage. 29

31 equilibrium price impact of informed trades is likely to be greater in illiquid stocks if it is possible to have information-based trades in these stocks at all. In this case, peer firms with more illiquid stocks will have more informative price innovations. We measure peer firm stock liquidity, Peer Liquidity, using Amihud s measure of illiquidity. To assess how a firm s investment sensitivity to peers stock prices varies across its peers, in each year, we allocate all firms in a firm s peer group into one of two groups, High or Low, depending on whether the value of an informativeness measure for a given peer firm is above or below the industry median in that year. We repeat this exercise for each of the peer information variables. We then estimate the following version of our baseline specification: I ijt = λ t + α j + β 1 Q ijt 1 + β 2 RELP EERQ Low jt 1, i + β 3 RELP EERQ High jt 1, i + γ 1 CONT ROL F irm it + γ 2 CONT ROL P eer jt, i + ε ijt, (5) where RELP EERQ Low ijt 1 and RELP EERQ High ijt 1 are the average innovation in stock prices of peer firms in the Low and High groups, respectively. CONT ROL F irm it and CONT ROL P eer ijt are as defined previously in Section 3. We focus on identifying whether the firm s investment is most sensitive to the Low-information or the High-information peers; that is, we are interested in testing for any significant difference between the coefficient estimates β 2 and β 3. Table 7 presents the estimation results. In each column, we report the results using a different information measure Peer Info, Peer Liquidity, and Peer Coverage to sort peer firms into Low- and High-information groups. In the first column, where we use Peer Info to group peers, the coefficient estimate β 3 on RELP EERQ High is 0.161; this estimate is significant at the one percent level. In contrast, the coefficient estimate for the Lowinformation peers is , and it is not statistically different from zero. The difference in firms investment sensitivity to peers prices across the two groups is economically large 30

32 the investment sensitivity to the High group is almost three times as that to the Low group and it is statistically significant at the one percent level, as indicated by p-value reported in the bottom line of Table 7. This result is consistent with our earlier findings from Table 6; investment responds more significantly to stock prices of those peer firms that are likely to convey more private information. We find a similar significant difference in the second column, where we use Peer Liquidity to sort peer firms. Consistent with innovations in prices of less liquid stocks being more informative, investment responds substantially more to changes in prices of those peer firms that have, on average, lower stock liquidity. The difference in investment sensitivity to prices of peers with less liquid stocks and to prices of those with more liquid stocks is also statistically significant at the one percent level. Finally, in the third column, we do not find any significant difference in firms investment sensitivity to peers stock prices between Low-coverage and High-coverage peer firms. We, therefore, conclude that the amount of analyst coverage does not determine which firms it is within a firm s peer group that its investment is most sensitive to. One possibility could be that spillovers from analyst research across stocks in an industry limit the variation in information across firms in an industry. The results in Table 7 suggests that a firm s investment sensitivity to peers stock prices also varies systematically within its peer group. Investment is not statistically significantly related to peer prices when they are relatively uninformative but tends to be positively and significantly related when the prices are informative. This evidence mirrors that in Table 6, where we document how the sensitivity of investment to peers stock prices varies with the average informativeness of the prices. Overall, the results in Table 7 suggest that, when presented with a set of peers, managers are more likely to pay attention to the stock prices of those peer firms that are most likely to signal information. They are more likely to increase investment more strongly in response to potential positive news for peers whose prices are 31

33 especially more likely to contain private information. 5 The effect of industry environment So far, we have documented that firm investment responds positively and significantly to peer firms stock prices, and this relation is stronger when peer firms stock prices signal more relevant information. What remains unclear is the reason why firm investment is, on average, positively correlated with the innovation in peers stock prices. Managers have to decide how to respond to the information signaled by peers stock price movements. They will respond more strongly to the information signaled by peers prices when the information is strategically more valuable. This strategic value is a function of the cost of inaction and the reward from a strong and prompt response. Both the cost of inaction and the reward from an appropriate response to the information signaled by peers prices will vary systematically with the competitive landscape and industry characteristics. How managers make investment decisions in response to peer firms stock prices will be based both on the managers interpretation of the information in peer prices and on their operating environments. The managers have to decide whether the peer firms stock prices are signaling a potential threat from their peers, improved prospects, or potential opportunities of their peers. Based on their assessment, the managers then have to formulate their optimal responses. For example, they may decide it is optimal to raise investment to preempt a perceived threat from their peers. They may also decide it is optimal to raise investment to capitalize on their peers opportunities. The optimal investment in response to peer firms stock prices is also likely to vary systematically with the competitive landscape and industry characteristics. Therefore, to obtain deeper insights into the manner in which firm investment responds to peer firms stock prices, we now continue our examination of the cross-sectional variation of investment sensitivity to peer stock prices at the industry 32

34 peer group level by considering additional industry characteristics. 5.1 Industry characteristics and investment sensitivity to peers prices We focus on three industry characteristics that are likely to be important determinants of managers optimal responses to innovations in peer firms stock prices: the degree of competition in an industry, the growth rate of the industry, and its capital intensity. First, consider the effect of greater competition. When competition is fierce, firms are more exposed to changes in their peers fortunes, and thus, the cost of inaction and the reward from taking appropriate actions in response to changes in peers fortunes is higher. It follows that managers in more competitive industries will place greater strategic value on the information signaled by peers prices, and will face greater pressure to act when prices signal improved prospects for their peers. Therefore, we expect firms in a highly competitive industry will respond more strongly to the information in peer prices. Second, firms in fast-growing industries are likely to experience a rapidly changing operating environment. To cope with this rapid change, managers will demand greater information, and thus, will likely place a greater strategic value on useful information. Faced with a rapidly changing environment, managers are also likely to encounter increased pressure to act when prices signal improved prospects for their peers. Therefore, we expect that firms in a fast-growing industry will respond more strongly to the information in peer firms stock prices. Industry growth is also likely to determine the manner in which investment responds to peer firms prices. In high growth industries, it is less likely that an improvement in a competitor s prospects will come at the expense of a firm s prospects. Therefore, firms in fast growing industries are more likely to raise investment when their peer firms stock prices signal improved prospects for the peers. Finally, it is natural to expect managers to respond to the information signaled by peer 33

35 firms prices through their investment policy in industries in which capital expenditures are especially important. Therefore, firms in more capital intensive industries will have a greater investment-sensitivity to peer prices. 24 Moreover, if greater capital intensity is associated with a lower cost of capital misallocation possibly due to lower capital adjustments costs firms in more capital intensive industries will be more likely to respond more strongly to good news for their peers by raising investment. 25 We characterize each firm s industry environment along these dimensions using three measures. First, we measure the degree of competition among the firm s industry peer group using a sales-based Herfindahl-Hirschmann Index (HHI) that we compute for each three-digit SIC industry from the COMPUSTAT universe; our competition measure is equal to one minus this index. 26 We use the average level of investment, which we construct as the equally-weighted average across all other firms in a firm s peer group, to proxy for industry growth. Finally, we capture capital intensity using the ratio of fixed-capital (net property, plant and equipment) stock to number of employees for each firm; we construct the capital intensity of a firm s industry peer group as the equally-weighted average across all other firms in the firm s peer group. We identify the effect of the industry environment on firms investment sensitivity to peer firm prices as follows: First, as we did previously in Table 6, in each year, we allocate all firms in our sample into one of two groups (High or Low), depending on whether the average value of a measure of price informativeness of a firm s peers is above or below the sample median in that year. We then also sort firms into one of two groups (IC High or IC Low ), depending on whether the value of an operating environment measure for the firm s industry is above 24 Foucault and Fresard (2012) find greater investment-price sensitivity for cross-listed firms for which investment is relatively more important. They conclude that their findings support the learning hypothesis. 25 In neo-classical structural models such as in Riddick and Whited (2009), lower capital adjustment costs result in higher equilibrium investment. 26 Our HHI is based on COMPUSTAT public data. For robustness, we also used HHI from Hoberg and Phillips (2010) which also accounts for privately held firms by combining data from COMPUSTAT with data from the Commerce Department and the Bureau of Labor Statistics to compute fitted HHIs. Our results remain unchanged. 34

36 or below the sample median in that year. Finally, for each partition we obtained using the informativeness of peer prices, we re-estimate our baseline model in equation (2) by interacting RELP EERQ with IC High and IC Low. We replace IC, alternately, with each of the three industry-characteristic measures we described above. Table 8 reports the estimation results. 27 In Panel A, we present our findings when we use Peer Info as a measure of the information content in peer firms stock prices to allocate firms into High and Low groups. First, in columns (1) and (2), we examine the effect of the intensity of competition. The coefficient estimates on RELP EERQ for firms in less competitive industries are negative but statistically insignificant in both columns. That is, investment does not appear to be significantly sensitive to peer stock prices in relatively uncompetitive industries. In contrast, the coefficient estimates on RELP EERQ for firms in highly-competitive industries are positive in both columns. In column (1), even among firms in the low Peer Info group where peer prices contain relatively little information content, the coefficient estimate on RELP EERQ is positive and significant at the ten percent level for firms in highly competitive industries. We also find evidence that the sensitivity of investment to peer prices rises with industry competitiveness; in column (2), the investment sensitivity is significantly larger for those firms that are in more competitive industries than it is for similar firms in less competitive industries. This difference is statistically significant at the five percent level, as indicated by the p-value reported in the last row of column (2) in Panel A. The remaining results in Panel A provide additional evidence in support of the hypothesis that the industry environment has a significant influence on the extent to which managers will react to peer firms price movements. In each of the columns (3) through (6), the coefficient estimates on RELP EERQ are all positive and highly statistically significant for firms in fast- 27 For brevity, in Table 8 and all remaining tables, we suppress the reporting of the coefficient estimates on the control variables. These estimates are qualitatively similar to the estimates reported in earlier tables. They are naturally available from the authors upon request. 35

37 growing and capital intensive industries; investment sensitivity to peer firms stock prices is always significantly higher for firms in these industries, regardless of the information content of peer stock prices, as proxied by Peer Info. In contrast, we find that the coefficient estimates on RELP EERQ are uniformly negative for firms in slow-growing and less capital intensive industries. Moreover, in slow-growing industry these coefficient estimates are statistically significant at conventional levels. These results are consistent with our conjectures: (1) managers are more likely to change investment in response to innovations in peer firms stock prices in fast-growing industries that have a rapidly changing environment and capitalintensive industries where capital investment is especially important; (2) managers are more likely to respond to positive innovation in peer firm s stock prices by increasing investment in fast-growing industries and decreasing investment in slow-growing industries. Next, in Panels B and C, we check whether these findings are robust when we use other measures of the information content in peers stock prices. In Panel B, we use Peer Fundamental Correlation to measure the relevance of the information in peers stock prices, as described before. We find, with one exception in column (1), the coefficient estimates on RELP EERQ are positive and statistically significant for firms in highly competitive, fastgrowing and capital intensive industries. In less competitive, slow-growing and less capital intensive industries, with the exception of an estimate in column (4), the coefficient estimates on RELP EERQ are typically negative and statistically insignificant. This pattern is consistent with a stronger response of firm investment to peer stock prices in more competitive, faster-growing and more capital intensive industries. However, the coefficients on RELP EERQ do not vary significantly with industry characteristics in all columns in Panel B. In fact, the pattern we observe points to the presence of a significant and complementary effect of the industry environment on the relation between firm investment and peer stock prices: firm investment is uniformly and significantly more sensitive to peer prices 36

38 in highly competitive, fast-growing, and capital intensive industries when firms have more related peers, as measured by Peer Fundamental Correlation. The coefficient estimates (in columns (2), (4), and (6)) range between and 0.508; they are all statistically significant at the one percent level, and they are all statistically significantly different from their counterparts in remaining industries, as indicated by the p-values in the last line of Panel B. With the exception of industry capital intensity, we find no such similar effect of the industry environment on the investment sensitivity to peer stock prices among firms with less correlated peers. This complementarity between the effects of price informativeness and industry characteristics is also apparent when we compare the coefficient estimates on RELP EERQ across columns. In highly competitive, fast-growing and highly capital-intensive industries, the coefficient estimates on RELP EERQ are statistically significantly higher for firms with high Peer Fundamental Correlation than firms with low Peer Fundamental Correlation. The magnitude of this effect is significantly large. For example, the column (6) estimate RELP EERQ is more than three times greater then the column (5) estimate. These results indicate that greater competition, higher industry growth rate, and higher capital intensity all amplify the importance of the information contained in peer stock prices. Finally, in Panel C, we use Peer Coverage as a measure of the amount of industry and peer firm managerial information contained in peer stock prices, and we sort firms into High and Low groups based on the average value of this measure among a firm s peers in each year. We find that whenever peer stock prices are likely to be more informative that is, for those firms in the high Peer Coverage group (in columns 2, 4, and 6) greater competition, faster industry growth, and greater capital intensity all increase firms investment sensitivity to peer stock prices significantly. The coefficient estimates are all positive, significant at the one percent level, and significantly larger than those for similar firms in less competitive, slow-growing, and less capital intensive industries. Moreover, in highly competitive, fastgrowing and highly capital-intensive industries, the coefficient estimates on RELP EERQ 37

39 are statistically significantly higher for firm with high Peer Coverage and firms with low Peer Coverage. Taken together, the estimates in Table 8 highlight one of our key findings: the sensitivity of firm investment to peer firms stock prices varies systematically with the intensity of the competition in the industry, even after controlling for the information content in peer firms stock prices. In competitive industries, managers respond to the potential good news in peers stock prices by raising investment. In contrast, we find that they do not do so in less competitive industries. This pattern of behavior is consistent with managers need to respond and counter peers potential gains through preemptive investment in competitive industries, while investment in response to good news for peer firms may be counterproductive in less competitive industries. The results in Table 8 also highlight when firm investment responds negatively to innovations in peers stock prices. In all three panels, we find that investment is negatively and significantly related to peers stock prices in slow-growing industries, especially when peers stock prices are likely to have greater information content. We find a similar negative response in firm investment to innovations in peers stock prices in less capital intensive industries, but the effect is significant only in Panel C among firms in the high Peer Coverage group. The negative relation between firms investment and peer stock prices that consistently appears in slow-growing industries indicates that managers are less likely to respond to positive innovations in peer firms stock prices by increasing investment in slow-growing industries. Overall, the results in Table 8 provide additional insight into how the relation between firm investment and peer firms stock prices varies with industry characteristics. The investment sensitivity to peer stock prices is significantly stronger in competitive, fast-growing, and capital intensive industries where the information content in peer prices is likely to be more valuable. That is, firm investment responds more positively to peer firms stock prices pre- 38

40 cisely when internalizing the information in peers prices and the investment decision become more important either because of more intense competition, greater dependence on capital in the production process, or faster growth. The results in Table 8 also confirm our previous finding that investment is more sensitive to peer prices when prices are more informative, or contain more relevant information. We find that the effect of the information in peer prices is complementary to the effect of the industry environment. In competitive, fast growing, and capital intensive industries, we find that more informative peer stock prices are associated with a greater positive change in firm investment in response to peer price increases. On the other hand, more informative peer stock prices are associated with a greater negative change in firm investment in slow-growing and less capital-intensive industries. We conclude that managers interpret good news for their peers to come at the expense of their own firms investment opportunities in slow-growing industries and less capital-intensive industries. 5.2 Industry characteristics and variation in information across peer firms In the last section, we show that managers responses to peer prices vary systematically with their industry environment. In particular, greater competition, higher industry growth, and higher capital intensity all magnify the importance of the information contained in peers stock prices and, accordingly, intensify firms investment sensitivity to these prices. We now examine how the industry environment affects the variation in a firm s investment sensitivity to peers prices across peer firms within the industry peer group. Consistent with our earlier findings, among a firm s peer group, we expect investment to be more sensitive to prices of peer firms that are more likely to contain information that is new to the managers, and this effect to intensify in competitive, fast-growing, and capital intensive industries. To tests these predictions, as we did previously in Table 7, we first allocate all other firms in a firm s peer group into one of two groups, High or Low, depending on whether the value 39

41 of an informativeness measure for a given peer firm is above or below the industry median in that year. We then also sort firms into one of two groups ( IC High or IC Low ), depending on whether the value of an industry characteristic measure for its peer firms is above or below the sample median in that year. Finally, we re-estimate the regression model in equation (5) by interacting both RELP EERQ Low and RELP EERQ High with IC Low and IC High. As before, we replace IC, alternately, with each of the three industry-characteristic measures. Table 9 reports the estimation results. In Panel A, we first consider the effect of the intensity of competition. In each column, we use a different measure of the information content in peers prices to sort peer firms into Low and High groups. In line with the findings in Table 8, the investment-to-peer price sensitivity is uniformly higher in more competitive industries. Although we fail to find a statistically significant difference among the coefficient estimates in the first column, where we measure the information content in stock prices using Peer Info, when we measure informativeness using Peer Liquidity and Peer Coverage in columns (2) and (3), respectively, we observe a significant positive effect of greater competition on firms investment sensitivity. We also find evidence of the complementarities between effects of the information in peer prices and the industry environment. In line with our findings in Table 8, greater competition intensifies firms investment sensitivity to prices of peers with more illiquid stocks and with greater analyst coverage. The difference in the coefficient estimates between the high-competition and low-competition groups is economically large, and statistically significant at the five percent level, as indicated by the F-tests at the bottom of Table 9. In Panel B, we examine the influence of the industry growth rate. As before, we proxy for industry growth rate by the average industry investment among the firm s peer group. In line with the results in Table 8, investment sensitivity tends to be significantly higher to the stock prices of peers in fast-growing industries and this effect is concentrated on peers with relatively more informative prices. In column (1) where we proxy for price informativeness 40

42 using Peer Info, the coefficient estimates for investment sensitivity to high-info peers are and in slow- and fast-growing industries, respectively. Both estimates are statistically significant, and the difference between them is large and statistically significant at the one percent level, as indicated by the F-tests at the bottom of Table 9. In column (2), where we consider Peer Liquidity, the coefficient estimates for investment sensitivity to low-liquidity peers are and in slow- and fast-growing industries, respectively. Once again, both coefficients are statistically significant, and the difference between the two coefficients is statistically significant at the one percent level. We also find that the sensitivity of investment to peers prices is significantly higher in fast growing industries when we measure price informativeness using Peer Coverage in column (3). However, this effect is significant only for peers with relatively little coverage. While this result appears inconsistent with our findings of a complementary relation between effects of the information in peer prices and the industry environment, this may not, in fact, be the case. As we pointed out earlier, greater analyst coverage could discourage private information production. As a result, peers with less analyst coverage may have stock prices that contain more private information. Moreover, the combination of the positive coefficient on RELP EERQ for fastgrowing industries and negative coefficient in slow-growing industries, is consistent with our result in Table 7 where we find that the sensitivity of investment to peers stock prices does not vary across peers with different levels of coverage. In Panel C, we consider the effect of capital intensity and obtain very similar results to those in Panels A and B. Consistent with the notion that managers might rely more on stock prices where capital investment is more important, the coefficient estimates on RELP EERQ are uniformly higher for firms in more capital intensive peer groups. However, consistent with our earlier evidence on the complementary nature of the effects of industry characteristics and peer price informativeness, the effect of capital intensity on the sensitivity of investment to peer prices only tends to be significant for peers with relatively more informative prices. 41

43 We find the difference in investment sensitivity between low-capital intensive and highcapital intensive group to be statistically significant only among peers with relatively more informative stock prices (in column 1), and with less liquid shares (in column 2). We find a similar significant effect of greater capital intensity in column (3); however, as in Panel B, investment sensitivity is greater to those peers with low analyst coverage. In line with our previous findings, the results in Table 9 also underline once again that investment-to-peer price sensitivity is higher when peers stock prices are more likely to contain information that is new to managers. All three panels indicate, however, this relation is more pronounced for firms in more competitive, fast-growing, and capital intensive industries; in eight of the nine columns across the three panels, the difference in the coefficient estimates on RELP EERQ among the low-info and high-info peer groups is statistically significant only for firms in competitive, fast-growing, and capital intensive industries. Taken together, the results in Table 9 suggest that there is a strong complementarity between the effect of the information content in prices and that of the industry environment. The information in peers stock prices becomes more valuable when there is greater competition, faster industry growth, and greater emphasis on capital investment. 5.3 Relative market position and investment sensitivity to peer prices In the previous sections, we have documented how the industry environment changes managers response to the information in peer stock prices. We believe that the industry environment influences managerial responses since it determines both the cost of inaction and the reward from a strong and prompt response to peer prices. Similarly, we expect that the cost of inaction and the reward from a strong and prompt response to peer prices to also vary with a firm s relative competitive position in its industry. For example, a firm s profitability may be threatened if the fortunes of dominant peers improve. On the other hand, 42

44 an improvement in the fortunes of marginal peers may signal better profit opportunities. Therefore, we next attempt to capture the effect of competitive concerns within a peer group on the sensitivity of investment to peer prices. We use four measures of peers relative market position. First, we use each peer s market capitalization (Size) and its share in total industry sales (Market Share) to proxy for the strength of its competitive position within its industry. We also consider two operating performance measures: profitability, measured as the return on assets (ROA), and sales growth ( Sales), measured as the annual percentage change in sales revenues. If managers responses to the information in peer stock prices reflect an attempt either to capitalize on the opportunities of weak competitors or to preempt the gains of strong competitors, investment sensitivity to peer prices should vary with peers competitive position. To tests these predictions, we first allocate all firms in a firm s peer group into one of two groups, High or Low, depending on whether the amount of private information contained in their prices as measured by Peer Info is above or below the industry median in that year, as we did previously in Table 7. We then also sort them in each year into one of two groups, High or Low, depending on whether the value of a competitive position measure is above or below the industry median in that year. This leaves us with four groups of peers sorted based on both the amount of private information and a measure of relative competitive position. We repeat this procedure for each one of the four competitive position measures. Using this classification of peer firms, we test our predictions by estimating the investment sensitivity to each group of peers using a variation of our baseline model in equation (2). Specifically, we replace RELP EERQ with the average innovation in stock prices for each of the four group of peers. Table 10 indicates that peer relative industry position has a statistically significant effect on the sensitivity of investment to peer prices. In column (1), where we focus on peer firms market capitalization (Size), firm investment responds positively to innovations in 43

45 prices of smaller peers in both the Low-Peer Info and the High-Peer Info groups. The two coefficient estimates are and 0.263, they are both statistically significant, and they are statistically significantly larger than their counterparts for larger peers with similar Peer Info. Similarly, in column (4) when we measure peers relative operating performance based on their sales growth, Sales, investment is positively and significantly related to stock prices of slower growing peers. For peers in the Low-Peer Info group, the coefficient estimate on RELP EERQ is significantly larger when peers have relative low growth than that when they have relatively high sales growth. These results indicate that, controlling for the information content in peer firms stock prices, firm investment responds more strongly to stock prices of relative small peers and slower-growing peers. This is consistent with the idea that investment is influenced by the desire to take advantage of peer firms with weaker market positions. It is also consistent with the idea that stock prices of smaller peer firms may be more sensitive signals of changes in the opportunities available to firms. The results in columns (2) and (3), where we use Market Share or ROA to measure peer firms relative market position, do not indicate a significant difference between the dependence of investment on the prices of weaker and stronger peers. This suggest that the relation between peer industry position and the sensitivity of investment to peer prices may be sensitive to how one measures relative market position. Table 10 also confirms our earlier finding that the sensitivity of investment to peer prices rises with the amount of private information contained in peers stock prices. In all four columns, we find that investment sensitivity increases with more informative peer prices when comparing peers with similar market position or performance. For example, in column (1), the coefficient estimate on RELP EERQ for small-size peers in the High-Peer Info group is significantly larger than that for similarly sized peer firms in the Low-Info group. We obtain similar results in column (2), when we use Market Share; the coefficient estimate on RELP EERQ for peers with low Market Share in the High-Peer Info group is significantly 44

46 larger than that for peers in the Low-Info group with similar Market Share. We find a similar relation between price informativeness and investment sensitivity when we focus on the operating performance measures ROA or Sales in columns (3) and (4), respectively. In column (3), investment is significantly more sensitive to prices of peers with high ROA and more informative prices than to peers with similar performance, but less informative prices. In column (4) investment is significantly more sensitive to prices of peers with high Sales and more informative prices than to peers with similar performance, but less informative prices. Overall, the results in Table 10 suggest important insights into what drives firms investment sensitivity to peers stock prices. First, the relation between firm investment and peers stock prices varies significantly across peer firms. Second, there appear to be two forces driving this variation. The first is consistent with learning from peers prices, as we documented in previous sections: investment sensitivity to peer firms stock prices is higher when peer prices are more informative. The second, on the other hand, relates to peers relative market positions: we provide evidence, albeit mixed, that firms appear to increase their investment in response to positive price innovations for weaker peers. These findings imply that firms investment sensitivity to peers stock prices is driven both by information and competitive considerations. 6 Conclusion There is a growing body of evidence on the informational role of stock prices in guiding firm investment. This research has tended to focus on the effect of a firm s own stock price on its investment and provided considerable empirical support for the view that managers can learn from their own stock prices. We contribute to the growing literature by examining whether investment is also influenced by the stock prices of peer firms. Firms exist together 45

47 with other firms in the market place; to the extent that stock prices aggregate private information, it is natural to expect that the information aggregated in peer firms stock prices should be informative for managers. We test the implications of this learning from peer prices hypothesis using a large sample of firms over the period of by examining whether a firm s investment expenditure responds to the information in peer firms stock prices. Using a measure of the innovation in the average relative level of peers stock prices that we construct to limit the overlap with the information contained in the firms own stock price, we show that firms investment responds positively to peer firms stock prices. We show that higher relative levels of peer firms prices are associated with a statistically significant increase in firms investment. We confirm that this relation we document between a firm s investment and the stock prices of its peer firms is independent of the information in the firm s own stock price; we also verify that this relation is not simply an artifact of similar firms in an industry simply mimicking each other s investment policies. The positive relation between firm investment and its peer firms stock prices appears to be robust to various estimation methodologies as well as to alternative definitions of peer groups and alternative measures of corporate investment. Finally, the effect of peer firms prices on firms investment is also economically meaningful; one standard deviation innovation in peer firms stock prices results in an increase of two to five percent in investment spending for the median firm. We investigate two forces that we believe shape the relation between firm investment and peer firm stock prices: the informativeness of peer firms stock prices and the value of the information in peers price to a manager. In support of the idea that managers learn from their peers stock prices, we find a positive and statistically significant relation between firm investment and peer firms stock prices when the stock prices are more likely to signal information. Investment sensitivity to peer stock prices is higher, when these prices contain 46

48 more private information and reflect more relevant information. We also find support for the idea that the firm s response to its peer stock prices varies with its manager s valuation of the information. We find that a firm s industry environment, which is likely a key determinant of the manager s valuation of the information in peer stock prices, has a significant influence on the relation between firm investment and peer prices. We find that greater competition, faster industry growth, and greater capital intensity all amplify the value of the information in peers stock prices and increase firms investment sensitivity to these prices. We also find that the peer firms relative position in their industry influences how managers respond to innovations in the peers prices. Investment is more sensitive to the prices of weaker peers as is to be expected if managers place a higher value on the information signaled by the prices of these peers. Finally, we find that the effect of price informativeness and managerial valuation of peer price information complement each other. Thus, overall, our results provide additional empirical support for the learning hypothesis that managers can learn from prices, even peer firms stock prices, when making investment decisions. 47

49 Appendix A: Variable Definitions CAPX Capital expenditures scaled by beginning-of-year book assets CAPXRND Capital expenditures plus research and development expenses scaled by beginningof-year book assets CHGASSETS Change in assets scaled by beginning-of-year assets Q Market value of equity (price times shares outstanding from CRSP) plus book value of assets minus the book value of equity scaled by book value of assets CF Sum of net income before extraordinary items, depreciation and amortization expenses and RandD expenses, scaled by lagged assets RET3 Equally-weighted market adjusted firm return over the next three years ASSETS Book value of total assets (in billions) SIZE Market capitalization (in millions) SALES Total sales revenues (in millions) CHG SALES Annual percentage change in sales revenues ROA Operating income (i.e. earnings before interest, taxes, depreciation, and amortization) as a percentage of assets PROFIT MARGIN Operating income (i.e. earnings before interest, taxes, depreciation, and amortization) as a percentage of sales revenues MARKET SHARE Share of firm sales in total industry sales, defined by three-digit SIC code 48

50 INFO One minus R2 from regressing daily return on market and industry index over year t. We construct a relative INFO measure for each firm by subtracting its industry average value. FUNDAMENTAL CORRELATION Historical correlation in firmâăźs quarterly earnings and the industry average, excluding the firm itself CAPITAL INTENSITY Ratio of fixed capital (net property, plant and equipment) stock to number of employees AMIHUD ILLIQUIDITY Log of one plus the absolute value of the return divided by dollar return volume COVERAGE One plus the average number of analysts with outstanding valid forecasts for the firm in year t, in logarithm COMPETITION One minus the Herfindhal index of sales in industry, defined by threedigit SIC code 49

51 References [1] Alti, A., 2003, How sensitive is cash flow when financing is frictionless?, Journal of Finance 58, [2] Baker, M. and J. Wurgler, 2002, Market timing and capital structure, Journal of Finance 57, [3] Baker, M., J. Stein, and J. Wurgler, 2003, When does the market matter? Stock prices and the investment of equity-dependent firms, Quarterly Journal of Economics 118, [4] Bakke, T.-E., and T. Whited, 2010, Which firms follow the market? An analysis of corporate investment decisions, Review of Financial Studies 23, [5] Barro, R. J., 1990, The stock market and investment, Review of Financial Studies 3, [6] Blanchard, O., C. Rhee, and L. Summers, 1993, The stock market, profit, and investment, Quarterly Journal of Economics 113, [7] Bond, P., A. Edmans, and I. Goldstein, 2012, Real effects of financial markets, Annual Review of Financial Economics 4, [8] Bushman, R., A. Smith, X. Zhang, 2009, Investment-cash flow sensitivities are really investment-investment sensitivities, working paper, University of Chicago. [9] Chen, Q., I. Goldstein, and W. Jiang, 2007, Price informativeness and investment sensitivity to stock price, Review of Financial Studies 20, [10] Chirinko, R. and H. Schaller, 1996, Bubbles, fundamentals, and investment: A Multiple equation testing strategy, Journal of Monetary Economics 38,

52 [11] Chirinko, R. and H. Schaller, 2001, Business fixed investment and bubbles : The Japanese case, American Economic Review 91, [12] Dow, J. and G. Gorton, 1997, Stock market efficiency and economic efficiency: Is there a connection? Journal of Finance 52, [13] Dow, J. and R. Rahi, 2003, Informed trading, investment and economic welfare, Journal of Business 76, [14] Durnev, A., R. Morck, and B. Yeung, 2004, Value enhancing capital budgeting and firm-specific stock return variation, Journal of Finance 59, [15] Durnev, A., R. Morck, B. Yeung, and P. Zarowin, 2003, Does greater firm-specific return variation mean more or less informed stock pricing?, Journal of Accounting Research 41, [16] Easley, D., M. O Hara, and J. Paperman, 1998, Financial analysts and informationbased trade, Journal of Financial Markets 1, [17] Edmans, A., I. Goldstein, and W. Jiang, 2012a, Feedback effects and limits to arbitrage, Working paper. [18] Edmans, A., I. Goldstein, and W. Jiang, 2012b, The real effects of financial markets: The impact of prices on takeovers, Journal of Finance 67, [19] Erickson, T. and T. M. Whited, 2000, Measurement error and the relationship between investment and q, Journal of Political Economy 108, [20] Fazzari, S., R. G. Hubbard, and B. C. Petersen, 1988, Financing constraints and corporate investment, Brookings Papers on Economic Activity 1,

53 [21] Fazzari, S., R. G. Hubbard, and B. C. Petersen, 2000, Investment-cash flow sensitivities are not useful: A comment on Kaplan and Zingales, Quarterly Journal of Economics 15, [22] Foucault, T. and L. Fresard, 2012, Cross-listing, investment sensitivity to stock price and the learning hypothesis, Review of Financial Studies 25, [23] Foucault, T. and L. Fresard, 2012, Learning from peers stock prices and corporate investment, Working paper. [24] Fresard, L., and P. Valta, 2012, Competitive pressure and corporate investment: Evidence from trade liberalization, Working paper [25] Gilchrist, S., C. P. Himmelberg, and G. Huberman, 2005, Do stock price bubbles influence corporate investment? Journal of Monetary Economics 4, [26] Gomes, J., 2001, Financing investment, American Economic Review 91, [27] Graham, J. R., and M. Campello, 2007, Do stock prices influence corporate decisions? Evidence from the technology bubble, Working paper, Duke University. [28] Grullon, G., and R. Michaely, 2007, Corporate payout decisions and product market competition, Working paper. [29] Hayashi, F., 1982, Tobin s Marginal Q and Average Q: A neoclassical interpretation, Econometrica 50, [30] Hayek, F.A., 1945, The use of knowledge in society, American Economic Review 35,

54 [31] Hertzel, M., Z. Li, M.Officer, and K. Rodgers, 2008, Inter-firm linkages and wealth effects of financial distress along the supply chain, Journal of Financial Economics 87, [32] Hoberg, G. and G. Phillips, 2010, Real and financial industry booms and busts, Journal of Finance 65, [33] Hou, K., 2007, Industry information diffusion and the lead-lag effect in stock returns, Review of Financial Studies 20, [34] Jovanovic, B., and P. Rousseau, 2002, The q-theory of mergers, American Economic Review Papers and Proceedings 92, [35] Kaplan, S. and L. Zingales, 1997, Do investment-cash flow sensitivities provide useful measures of financing constraints?, Quarterly Journal of Economics 112, [36] Kaplan, S. and L. Zingales, 2000, Investment-cash flow sensitivities are not valid measures of financing constraints?, Quarterly Journal of Economics 115, [37] Leary, M. and M. Roberts, 2010, Do peer firms affect corporate capital structure?, working paper [38] Loughran, T. and J. Ritter, 1995, The new issues puzzle, Journal of Finance 50, [39] Luo, Y., 2005, Do insiders learn from outsiders? Evidence from mergers and acquisitions, Journal of Finance 60, [40] Morck, R., A. Shleifer, and R. Vishny, 1990, The stock market and investment: Is the market a sideshow?, Brookings Papers on Economic Activity 2,

55 [41] Morck, R., B. Yeung, and W. Yu, 2000, The information content of stock markets: Why do emerging markets have synchronous stock price movements?, Journal of Financial Economics 59, [42] Moulton, B., 1986, Random group effects and the precision of regression estimates, Journal of Econometrics 32, [43] Peress, J., 2010, Product market competition, insider trading and stock market efficiency, Journal of Finance 65, [44] Petersen, M., 2009, Estimating standard errors in finance panel data sets, Review of Financial Studies 22, [45] Polk, C. and P. Sapienza, 2009, The stock market and corporate investment: A test of catering theory, Review of Financial Studies 22, [46] Rhodes-Korpf, M. and S. Vishwanathan, 2004, Market valuation and merger waves, Journal of Finance 59, [47] Rhodes-Korpf, M., D. T. Robinson, and S. Vishwanathan, 2005, Valuation waves and merger activity: The empirical evidence, Journal of Financial Economics 77, [48] Riddick, L. A., and T. M. Whited, 2009, The corporate propensity to save, Journal of Finance 64, [49] Roll, R., 1988, R2, Journal of Finance 43, [50] Shleifer, A., and R. Vishny, 2003, Stock market driven acquisitions, Journal of Financial Economics 70, [51] Stein, J., 1996, Rational capital budgeting in an irrational world, Journal of Business 69,

56 [52] Subrahmanyam, A. and S. Titman, 1999, The going-public decision and the development of financial markets, Journal of Finance 54, [53] Tobin, J., 1969, A general equilibrium approach to monetary policy, Journal of Money, Credit and Banking 1, [54] Tookes, H. E., 2008, Information, trading and product market interactions: Crosssectional implications of informed trading, Journal of Finance 63,

57 Table 1: Summary statistics. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. This table presents the summary statistics for the main variables used in this study. Appendix A provides details on the construction of all variables. We winsorize all ratios at the first and ninety-ninth percentiles. Panels A and B report the summary statistics for the firm-level variables and their peer average counterparts, respectively. Firm-level variables correspond to firm i in year t; peer firm averages refer to the average of all firms within an industry peer group, excluding firm i itself. We define a firm s industry peer group based on its 3-digit SIC code. Panel C reports the number of distinct industry peer group and the distribution of the number of peers within each group across years. Panel A: Firm variables Panel B: Peer-firm variables Mean SD 25% 50% 75% Mean SD 25% 50% 75% CAPX CAPXRND CHGASSETS Q CF RET ASSETS SIZE SALES SALES GROWTH ROA PROFIT MARGIN MARKET SHARE KL RATIO INFO AMIHUD ILLIQUIDITY COVERAGE

58 Panel C: Number of peer firms Year No. of Industries Mean SD 25% 50% 75%

59 Table 2: Relative Peer Q measure: RELPEERQ. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. Table 2 presents the summary statistics for the relative peer Q (RELPEERQ) measure. RELPEERQ is constructed using a two-step procedure. First, we compute the innovation in firm Q conditional on industry and year by subtracting the lagged value of the industry average Q from firm Q. RELPEERQ is then constructed for each firm as the equally-weighted average of the firm-level innovations across all peer firms within the same three-digit SIC group that the firm belongs. Panel A presents the descriptive statistics for RELPEERQ. Panel B reports the correlation matrix between firm Q, the average (raw) peer Q, and RELPEERQ. Panel A: Descriptive statistics Mean SD 25% 50% 75% RELPEERQ Panel B: Correlation matrix Q PEER Q RELPEERQ Q PEER Q RELPEERQ

60 Table 3: Investment sensitivity to peer stock prices: Main results. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. Table 3 reports the regression coefficients and standard errors obtained from the estimation of Equation (2). In columns (1) through (5), the dependent variable is CAPX, defined as the ratio of capital expenditures in year t to beginning-of-year book assets; in columns (6) and (7), we replace the dependent variable with CAPXRND and CHGASSET, respectively. Firm Q and RELPEERQ are as defined in Table 2. All other variables are defined as in Appendix A. Peer firm average variables are constructed as the average across all other firms in a given firm s peer industry group. A firm s industry peer group is defined based on its three-digit SIC code classification. In all specifications, we estimate equation (2) with pooled OLS using an unbalanced panel with year fixed effects, and we correct for arbitrary heteroskedasticity and for correlation within firms. In column (2), we include firm and year fixed effects; in columns (3) through (7) we include industry and year fixed effects. Symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent variable: CAPX CAPXRND CHGASSETS (1) (2) (3) (4) (5) (6) (7) FIRM Q RELPEER Q CF RET INVASSETS Industry fixed effects No No Yes Yes Yes Yes Yes Firm fixed effects No Yes No No No No No Year fixed effects Yes Yes Yes Yes Yes Yes Yes Peer Firm Averages PEER Q PEER CAPX PEER CF PEER RET N 85,309 83,673 83,673 83,673 83,673 83,673 83,673 Adj. R-square

61 Table 4: Investment sensitivity to peer stock prices: Robustness. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. Table 4 presents various robustness tests of the main results reported in Table 3. We re-estimate the specification in column (5) in Table 3. First, in column (1), we repeat our analysis using the two-digit SIC code to define industry peer groups. In column (2), we define peer groups based on the Text-based Network Industry Classifications (TNIC) from Hoberg and Phillips (2011) provided on their website and repeat our analysis for the sample of firms for which we have TNIC3 classification. In column (3), we construct for each firm in our sample a set of pseudo-peers by drawing every year a random sample of firms outside the firm s industry and repeat our analysis. Firm Q and RELPEERQ are as defined in Table 2. All other variables are defined as in Appendix A. Peer firm average variables are constructed as the average across all other firms in a given firm s peer industry group. We estimate all specifications with pooled OLS using an unbalanced panel with year and industry fixed effects and we correct for arbitrary heteroskedasticity and for correlation within firms. The exception is column (2) where we include firm and year fixed effects. The sample period is 1970 to 2010 in columns (1) and (3), and it is from 1997 to 2006 in column (2). Symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. SIC2 TNIC3 RANDOM Dependent variable: CAPX (1) (2) (3) FIRM Q RELPEER Q CF RET INVASSETS Industry fixed effects Yes No Yes Firm fixed effects No Yes No Year fixed effects Yes Yes Yes Peer Firm Averages PEER Q PEER CAPX PEER CF PEER RET N 96,239 41,272 85,931 Adj. R-square

62 Table 5: Investment sensitivity to peer stock prices: Instrumentals variables (IV) regression. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1Q. Table 5 presents the regression coefficients and standard errors obtained from the estimation of Equation (2). The dependent variable is variable is CAPX, defined as the ratio of capital expenditures in year t to beginning-of-year book assets. The method of estimation is two-stage least squares estimation. The endogenous variable is RELPEERQ, and the instrument is the average peer firm idiosyncratic component of stock returns. Peer firm average variables are constructed as the average across all other firms in a given firm s peer industry group. A firm s industry peer group is defined based on, alternately, its three-digit SIC code classification in column (1), its two-digit SIC code classification in column (2), and, finally, its TNIC3 classification from Hoberg and Phillips (2011) in column (3). The table also reports the heteroskedasticity-corrected Cragg-Donald statistic testing for weak instruments (First-stage Multivariate F-test). Symbols ***, **, and * indicate significant difference from zero at the 1%, 5%, and 10% levels, respectively. SIC3 SIC2 TNIC3 Dependent variable: CAPX (1) (2) (3) First-stage Instrument PEER IDIOSYNCRATIC RET FIRM Q RELPEER Q CF RET INVASSETS Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes Peer Firm Averages PEER CAPX PEER CF PEER RET First-stage Multivariate F-test N 83,673 96,239 41,271 61

63 Table 6: Investment sensitivity to peer stock prices: Role of information across peer groups. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. We first sort all firms in our sample in each year in two groups, Low and High, using three different peer firm information variables: Peer Info, Peer Fundamental Correlation, and Peer Coverage. Table 6 presents the coefficients and standard errors obtained from estimating regression Equation (2) for each group. The dependent variable is variable is CAPX, defined as the ratio of capital expenditures in year t to beginning-of-year book assets. Firm Q and RELPEERQ are as defined in Table 2. All other variables are defined as in Appendix A. Peer firm average variables are constructed as the average across all other firms in a given firm s peer industry group. A firm s industry peer group is defined based on its three-digit SIC code classification. In all specifications, we estimate equation (2) with pooled OLS using an unbalanced panel with year and industry fixed effects, and we correct for arbitrary heteroskedasticity and for correlation within firms. Symbols ***, **, and * indicate significant difference from zero at the 1%, 5%, and 10% levels, respectively. a, b, and c denote significant differences between the Low and High groups at 1%, 5%, and 10% levels, respectively. Dependent variable: CAPX PEER INFO PEER FUNDAMENTAL PEER COVERAGE CORRELATION Low High Low High Low High (1) (2) (3) (4) (5) (6) FIRM Q c c b b RELPEER Q b b c c CF c c b b RET b b INVASSETS b b Industry fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Peer Firm Averages PEER CAPX c c c c c c PEER CF b b b b PEER RET a a N ,001 41,122 41,497 37,420 37,588 Adj. R-square

64 Table 7: Investment sensitivity to peer stock prices: Role of information within peer groups. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. Table 7 reports the regression coefficients and standard errors obtained from the estimation of equation (5); we first sort all other firms in each firm s industry peer group into two groups (Low or High) based on three different peer firm information variables: Peer Info, Peer Liquidity, and Peer Coverage; RELPEERQ Low ijt 1 and RELPEERQ High ijt 1 are constructed as the average innovation in stock prices of peer firms in the Low and High group, respectively. The dependent variable is CAPX, defined as the ratio of capital expenditures in year t to beginning-of-year book assets. Firm Q and RELPEERQ are as defined in Table 2. All other variables are defined as in Appendix A. Peer firm average variables are constructed as the average across all other firms in a given firm s peer industry group. A firm s industry peer group is defined based on its three-digit SIC code classification. In all specifications, we estimate equation (5) with pooled OLS using an unbalanced panel with year and industry fixed effects, and we correct for arbitrary heteroskedasticity and for correlation within firms. We also report in the last line of the table the p-value of the F-test that evaluates whether the coefficient estimates on RELPEERQ Low ijt 1 and RELPEERQ High ijt 1 are equal. Symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively Dependent variable: CAPX PEER INFO PEER LIQUIDITY PEER COVERAGE (1) (2) (3) FIRM Q RELPEERQ LOW P EER (a) RELPEERQ HIGHP EER (b) CF RET INVASSETS Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes Peer Firm Averages PEER CAPX PEER CF PEER RET N Adj. R-square (a) = (b) p-value

65 Panel B: PEER FUNDAMENTAL CORRELATION Dependent Variable: CAPX Industry Characteristic (IC) Competition Industry Growth Capital Intensity Low Peer Corr High Peer Corr Low Peer Corr High Peer Corr Low Peer Corr High Peer Corr (1) (2) (3) (4) (5) (6) FIRM Q RELPEER Q x IC Low (a) b b RELPEER Q x IC High (b) b b a a a a Industry fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes N Adj. R-square (a) = (b) p-value Panel C: PEER COVERAGE Dependent Variable: CAPX Industry Characteristic (IC) Competition Industry Growth Capital Intensity Low Peer Cov High Peer Cov Low Peer Cov High Peer Cov Low Peer Cov High Peer Cov (1) (2) (3) (4) (5) (6) FIRM Q RELPEER Q x IC Low (a) c c b b RELPEER Q x IC High (b) a a a a a a Industry fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes N Adj. R-square (a) = (b) p-value

66 Table 8: Investment sensitivity to peer stock prices: Peer information and industry competition, growth, and capital intensity across peer groups. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. The dependent variable is CAPX, defined as the ratio of capital expenditures in year t to beginning-of-year book assets. Firm Q and RELPEERQ are as defined in Table 2. Firms are allocated into Low and High groups based on whether the average value of information content measure among a firm s peers is below or above the sample median in each year. We use three different peer information measures: Peer Info, Peer Fundamental Correlation, and Peer Coverage. Table 8 reports the regression coefficients and standard errors obtained from estimating the baseline model in equation (2) by interacting RELPEERQ with IC Low and IC High for each partition. IC Low (IC High ) is a dummy variable that takes a value of one in year t for a firm, if the firm s industry-characteristic variable is below (above) the sample median in that year. We use three different industry-characteristic variables: Competition is measured as one minus a sales-based HHI, computed for each of the three-digit SIC code from the COMPUSTAT universe; industry growth is measured as equally-weighted average level of investment across all other firms in a firm s peer group; capital intensity is measured as the ratio of fixed-capital (net property, plant and equipment) stock to number of employees for each firm; we construct the capital intensity of a firm s industry peer group as the equally-weighted average across all other firms in the firm s peer group. All other variables are defined as in Appendix A. Peer firm average variables are constructed as the average across all other firms in a given firm s peer industry group. A firm s industry peer group is defined based on its three-digit SIC code classification. In all estimations, we use pooled OLS using an unbalanced panel with year and industry fixed effects, and we correct for arbitrary heteroskedasticity and for correlation within firms. We also report in the last line of the table the p-value of the F -test that evaluates whether the coefficient estimates on RELPEERQ ijt 1 IC Low and RELPEERQ ijt 1 IC High are equal. Symbols ***, **, and * indicate significant difference from zero at the 1%, 5%, and 10% levels, respectively. a, b, and c denote significant differences between the Low and High groups at 1%, 5%, and 10% levels, respectively. Panel A: PEER INFO Dependent Variable: CAPX Industry Characteristic (IC) Competition Industry Growth Capital Intensity Low Peer Info High Peer Info Low Peer Info High Peer Info Low Peer Info High Peer Info (1) (2) (3) (4) (5) (6) FIRM Q RELPEER Q x IC Low (a) RELPEER Q x IC High (b) Industry fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes N Adj. R-square (a) = (b) p-value

67 Panel B: Industry growth rate Dependent variable: CAPX PEER INFO PEER LIQUIDITY PEER COVERAGE (1) (2) (3) FIRM Q RELPEERQ LOW PEER GROUP x IC Low (a) RELPEERQ LOW PEER GROUP x IC High (b) RELPEERQ HIGH PEER GROUP x IC Low (c) RELPEERQ HIGH PEER GROUP x IC High (d) Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes N Adj. R-square (a) = (b) p-value (c) = (d) p-value (a) = (c) p-value (b) = (d) p-value Panel C: Industry capital intensity Dependent variable: CAPX PEER INFO PEER LIQUIDITY PEER COVERAGE (1) (2) (3) FIRM Q RELPEERQ LOW PEER GROUP P x IC Low (a) RELPEERQ LOW PEER GROUP x IC High (b) RELPEERQ HIGH PEER GROUP x IC Low (c) RELPEERQ HIGH PEER GROUP x IC High (d) Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes N Adj. R-square (a) = (b) p-value (c) = (d) p-value (a) = (c) p-value (b) = (d) p-value

68 Table 9: Investment sensitivity to peer stock prices: Peer information and industry competition, growth, and capital intensity within peer groups. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to We exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. The dependent variable is CAPX, defined as the ratio of capital expenditures in year t to beginning-of-year book assets. Firm Q and RELPEERQ are as defined in Table 2. All peer firms in a firm s peer group are allocated into one of two groups, High or Low, based on three different peer firm information variables: Peer Info, Peer Liquidity, and Peer Coverage. Table 9 reports the regression coefficients and standard errors obtained from estimating the baseline model in equation (5) by interacting both RELPEERQ Low and RELPEERQ High with IC Low and IC High. IC Low (IC High ) is a dummy variable that takes a value of one in year t for a firm, if the firm s industrycharacteristic variable is below (above) the sample median in that year. We use three different industry-characteristic variables: Competition is measured as one minus a sales-based HHI, computed for each of the three-digit SIC code from the COMPUSTAT universe; industry growth is measured as equally-weighted average level of investment across all other firms in a firm s peer group; capital intensity is measured as the ratio of fixed-capital (net property, plant and equipment) stock to number of employees for each firm; we construct the capital intensity of a firm s industry peer group as the equally-weighted average across all other firms in the firm s peer group. All other variables are defined as in Appendix A. Peer firm average variables are constructed as the average across all other firms in a given firm s peer industry group. A firm s industry peer group is defined based on its three-digit SIC code classification. In all estimations, we use pooled OLS using an unbalanced panel with year and industry fixed effects, and we correct for arbitrary heteroskedasticity and for correlation within firms. We also report in the last line of the table the p-value of the F -test that evaluates whether the coefficient estimates on RELPEERQ Low ijt 1 IC Low, RELPEERQ High ijt 1 IC Low, RELPEERQ Low ijt 1 IC High and RELPEERQ High ijt 1 IC High are pairwise equal. Symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Industry competitiveness Dependent variable: CAPX PEER INFO PEER LIQUIDITY PEER COVERAGE (1) (2) (3) FIRM Q RELPEERQ LOW PEER GROUP x IC Low (a) RELPEERQ LOW PEER GROUP x IC High (b) RELPEERQ HIGH PEER GROUP x IC Low (c) RELPEERQ HIGH PEER GROUP x IC High (d) Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes N Adj. R-square (a) = (b) p-value (c) = (d) p-value (a) = (c) p-value (b) = (d) p-value

69 Table 10: Investment sensitivity to peer stock prices: Relative market position and information. The sample includes all nonfinancial, nonutility and non-governmental firms that are in the intersection of Standard and Poor s COMPUSTAT database and CRSP over the period from 1970 to exclude firms with less than $10 million in total assets or with less than thirty days of trading activity in year. We also drop firm-year observations with missing information on year-t investment and year t 1 Q. The dependent variable is CAPX, defined as the ratio of capital expenditures in year t to beginning-of-year book assets. Firm Q and RELPEERQ are as defined in Table 2. We first partition each firm s peers in each year into one of two groups, Low and High, depending on the amount of private information contained in each peer s stock price, as measured by Peer Info. We then also allocate peers into two groups, Low and High, based on a measure of relative competitive position. We use four measures of peers relative market position: peer s market capitalization (Size), its share in total industry sales (Market Share), its profitability, measured as return on assets (ROA), and sales growth (Sales), measured as the annual percentage change in sales revenues. Table 10 reports the regression coefficients and standard errors obtained from estimating the baseline model in equation (2) where we replace RELPEERQ ijt 1 with the RELPEERQ ijt 1 constructed for each of the four peer groups as the average innovation in stock prices of peer firms. All other variables are defined as in Appendix A. Peer firm average variables are constructed as the average across all other firms in a given firm s peer industry group. A firm s industry peer group is defined based on its three-digit SIC code classification. In all estimations, we use pooled OLS using an unbalanced panel with year and industry fixed effects, and we correct for arbitrary heteroskedasticity and for correlation within firms. We also report in the last four lines of the table the p-values of the F-test that evaluates whether the coefficient estimates are pairwise equal. Symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent variable: CAPX SIZE MARKET SHARE ROA SALES (1) (2) (3) (4) FIRM Q RELPEERQ LOW GROUP + LOW PEER INFO (a) RELPEERQ HIGH GROUP + LOW PEER INFO (b) RELPEERQ LOW GROUP + HIGH PEER INFO (c) RELPEERQ HIGH GROUP + HGH PEER INFO (d) Industry fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes N Adj. R-square (a) = (b) p-value (c) = (d) p-value (a) = (c) p-value (b) = (d) p-value We 68

70 Figure 1