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
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 oper- ating environment. To cope with this rapid change, managers will demand greater informa- tion, 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.
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 orLow), 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 (ICHigh or ICLow), 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.
25In neo-classical structural models such as in Riddick and Whited (2009), lower capital adjustment costs result in
higher equilibrium investment.
26Our 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.
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
interactingRELP EERQ withICHigh andICLow. 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 onRELP EERQfor 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 onRELP 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 onRELP EERQare all positive and highly statistically significant for firms in fast-
27For 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.
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 byPeer Info. In contrast, we find that the coefficient estimates
onRELP EERQare 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 capital- intensive 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 Fun-
damental 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 EERQare positive and statistically significant for firms in highly competitive, fast-
growing 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 esti-
mates 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 com- petitive, faster-growing and more capital intensive industries. However, the coefficients on
RELP EERQdo 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 complemen- tary 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
in highly competitive, fast-growing, and capital intensive industries when firms have more
related peers, as measured byPeer Fundamental Correlation. The coefficient estimates (in
columns (2), (4), and (6)) range between 0.291 and 0.508; they are all statistically signifi- cant 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 corre- lated peers. This complementarity between the effects of price informativeness and industry
characteristics is also apparent when we compare the coefficient estimates onRELP EERQ
across columns. In highly competitive, fast-growing and highly capital-intensive industries,
the coefficient estimates onRELP EERQare 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 EERQis 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 intoHigh
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 highPeer 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, fast-
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 in- novations 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 in-
dustries, but the effect is significant only in Panel C among firms in the highPeer Coverage
group. The negative relation between firms’ investment and peer stock prices that consis- tently 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 invest- ment 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-
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 firmswithin 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
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 (ICHigh orICLow), 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 EERQLow and RELP EERQHigh with ICLow and ICHigh. As
before, we replaceIC, 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 inten- sity of competition. In each column, we use a different measure of the information content in
peers’ prices to sort peer firms into Low andHigh 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 usingPeer 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