Copyright Brookings Institution 2000In the Old Economy, the value of a company was mostly in its hard assets-its buildings, machines, and physical equipment. In the New Economy, the value of a company derives more from its intangibles-its human capital, intellectual property, brainpower, and heart. In a market economy, it's no surprise that markets themselves have begun to recognize the potent power of intangibles. It's one reason that net asset values of companies are so often well below their market capitalization.
-Vice President Al Gore, speech at the Microsoft CEO Summit, May 8, 1997
I think there is such an overvaluation of technology stocks that it is absurd ... and I'd put our company's stock in that category.
-Steve Ballmer, president of 
Microsoft Corporation, quoted in the Wall Street Journal, p. C1, September 24, 1999
BROADLY SPEAKING, there are two opposing views about the relationship between the stock market and the new economy. In one view, expressed in the quotation from Vice President Gore, intangible investment helps explain why companies' market values are so much greater than the values of their tangible assets. In the other view, expressed, ironically, by the president of one of the leading firms in the new economy, stock market valuations have become unhinged from company fundamentals.1 Whatever the motivations of Gore and Ballmer in making these comments, their perspectives frame the debate about the relationship between the stock market and the new economy.
One way to start thinking about this relationship is in terms of the theory of stock market efficiency. When the stock market is strongly efficient, the market value of a company is, at every instant, equal to its fundamental value, defined as the expected present discounted value of future payments to shareholders. If we abstract from adjustment costs and market power, we can highlight the central role that strong stock market efficiency plays: it equates the company's market value to its enterprise value-that is, the replacement cost of its assets.
However, the most readily available measure of enterprise value in a company's accounts, the book value of tangible assets, is typically just a fraction of the company's market value. For companies in the new economy, book value is an even smaller fraction of market value, because these companies rely more on intangible assets than old economy companies do. Hence, the rest of this enterprise value must come from adjusting for the replacement cost of tangible assets and including intangible assets. When price inflation, economic depreciation, and technical progress are modest, the difference between the replacement cost and the book value of tangible assets is relatively small.2 This means that intangibles account for the remaining difference.
Unfortunately, it is difficult to gauge whether intangibles do in fact make up the difference, because they are, by their very nature, difficult to measure. For this reason, the Financial Accounting Standards Board (FASB) calls for a conservative treatment of intangibles: companies must select methods of measurement that yield lower net income, lower assets, and lower shareholders' equity in earlier years than other measures would. Thus expenditures for research and development (R&D), advertising, and the like are expensed rather than treated as assets, even though they are expected to yield future profits.3 The stock market forms an expected value of these future profits, but the assets generating them will never show up on the balance sheet.4 Consequently, many researchers argue that the fundamental accounting measurement process of periodically matching costs with revenues is seriously distorted, and that this reduces the informativeness of financial information.5
The practical appeal of thinking in terms of strong efficiency is that the purported growth of intangible capital that characterizes the new economy provides a ready explanation for the recent sharp rise in stock prices. Some researchers have even argued that the value of intangible assets can be inferred from the gap between market capitalization and the measured value of tangible assets.6 The practical drawback, however, is that this makes the inferred valuation of intangible capital the critical determinant of market efficiency. At a basic level, then, the logic of this approach is circular: accounting principles for intangible assets are unsatisfactory, making it difficult for market participants to value companies; but strong stock market efficiency is assumed in order to assign a value to intangibles.7 In essence, intangibles are the new economy version of dark matter in cosmology. The fundamental question in the two fields is the same: can an elegantly simple model be justified based on what we cannot easily measure?
When the stock market is not strongly efficient, a firm's market value can differ from its fundamental value. This formulation sidesteps the question of whether intangibles account for the missing value of companies, only to point up another question just as thorny. If the stock market fails to properly value intangibles, what do market prices represent? One perspective is that the stock market is efficient in the sense that prices reflect all information contained in past prices, or that they reflect not only past prices but all other publicly available information. The first of these is called weak efficiency and the second semistrong efficiency. These weaker concepts of market efficiency are not necessarily inconsistent with deviations of market prices from fundamental prices that are caused, for example, by bubbles. Another perspective eschews efficiency in favor of behavioral or psychological models of price determination. For our purposes we focus only on whether market prices deviate from fundamentals, not why, so we use the term "noisy" share prices as synecdoche for any of the potential reasons for mispricing.
Another way to begin thinking about the relationship between the stock market and the new economy is purely empirical. Tobin's average q-- which is defined, in its simplest form, as the ratio of the stock market value of the firm to the replacement cost of its assets-provides the empirical link. Under conditions familiar from the q theory of investment, average q equals unity when the stock market is strongly efficient and taxes, debt, and adjustment costs are ignored. This means that the market value of the firm is just equal to the replacement cost of its tangible and intangible assets. Since intangible capital is difficult to measure, in practice average q is computed using tangible capital. This is why average q can exceed unity and why it must increase as intangible assets become a larger fraction of total assets.
To take specific examples, consider two companies that are intangibles-- intensive: Coca-Cola and Microsoft. Most of the market value of the CocaCola Company consists of the value of its secret formula and marketing know-how, neither of which is recorded on its balance sheet.8 Similarly, according to its chairman Bill Gates, Microsoft's "primary assets, which are our software and our software development skills, do not show up on the balance sheet at all."9 Hence average q, constructed using only the replacement cost of tangible capital, should exceed unity for these companies.
The upper panel of figure 1 plots Coca-Cola's average q, denoted as qE, where the superscript indicates that we construct the variable using equity price data. In 1982, at the start of the time period we use in our empirical work, Coca-Cola's q^supE^ is equal to one. 10 If we assume for the sake of argument that we constructed the replacement cost value of tangible assets without error, this indicates that the market undervalued CocaCola's intangible assets-indeed, it gave them no value at all. In contrast, in 1998, at the end of our sample period, Coca-Cola's qE exceeds 34. If we assume strong efficiency, this means that the value of Coca-Cola's intangible assets increased from zero to thirty-three times the value of the company's tangible assets over those sixteen years. In other words, according to the market, Coca-Cola's intangibles are now worth thirty-three times what its tangible assets are worth, whereas they used to be worth nothing.
We can benchmark Coca-Cola's q^supE^ by comparing it with a measure of the company's fundamental value based on the profits that the company is expected to generate. We do so using earnings forecasts made by professional securities analysts, supplied by I/B/E/S International and also contained in our data set. The upper panel of figure 1 also plots CocaCola's q, which estimates q using the present discounted value of stock market analysts' consensus earnings forecasts for the firm rather than the firm's market value.
The construction uses analysts' one- and two-year-ahead forecasts and their five-year growth forecast.11 We discount expected earnings over the next five years using the current interest rate on thirty-year U.S. Treasury bonds plus an 8 percent risk premium, and we include a terminal value correction to account for the value of the company beyond our forecast horizon. We choose the timing of the forecasts so that 4 is based on the same information set as qE. Through the choice of this timing, the market-based measure already incorporates the information contained in the forecasts. In all other respects 4 is identical to q^supE^. The time-series comparison between Coca-Cola's 4 and its q^supE^ suggests that professional analysts do not expect the company's intangible asset growth (as inferred using the assumption of strong efficiency) to generate similar profit growth.
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The lower panel of figure 1 plots Microsoft's q^supE^ and 4. When Microsoft enters our sample in 1987, having first issued public equity in 1986, its qE is equal to 24. By the end of the sample period it has risen to 74. The volatility of this measure in Microsoft's case is perhaps even more notable than the threefold increase. Consider these two facts: that in 1990 Microsoft's qE dropped by more than half, only to more than double in the following year; and that around half the total increase over the sample period occurred after 1997, when the value of q^supE^ was 39. We can benchmark these changes by comparing them with changes in Microsoft's 4. When the 50 percent drop in qE occurred, 4 also dropped, but only by about 30 percent. And when qE recovered dramatically in the following year, 4 increased by less than 15 percent. Finally, when q^supE^ doubled from 1997 to 1998, 4 grew by about one-third. This comparison suggests that the change in the value of Microsoft's intangibles (as inferred using the assumption of strong efficiency) is not closely associated with changes in what the analysts expect Microsoft to earn in the future.
We have chosen these companies because they are widely familiar and because their experience has been remarkable, but they are by no means unusual examples. Rather, the sharp increase in the level of qE (illustrated by Coca-Cola) and the high volatility of q^supE^ (illustrated by Microsoft) make these companies microcosms of the broader experience of the more than 1,100 companies in our sample. Figure 2 plots the unweighted average of q^supE^ in each year for the entire sample of companies we observe in that year. In 1982 there are about 300 companies in the sample, and the average of q^supE^ is about 0.7. By the end of the sample there are more than 1,000 firms, and qE is about 3.0-a 330 percent increase. Our sample is an unbalanced panel of firms, and so the increase could reflect entry and exit, but it does not: the average value of qE increases by about 300 percent for those firms that are in the sample from 1982 to 1998.
Figure 2 also plots the average annual values of 4 for the entire sample. This variable is about 0.5 in 1982 and about 1.5 in 1998, a 200 percent increase.12 In every year the standard deviation of q^supE^ across firms is greater than that of 4. We can further measure the difference between qE and 4 by defining a new variable QDIF = (qE - q)/q. The median value of QDIF is 0.15 in 1982 and 0.75 in 1998, indicating that a wide gap has opened over time for the median firm in the sample.
Figure 3 plots the average annual growth rates of q^supE^ and 4 for the whole sample. In a number of years the two move together. Notably, the two measures rise and fall dramatically at the start of the sample and track each other through the one recession in the sample, that of 1990-91. But what is striking overall is that the series are only loosely correlated, with a correlation coefficient of only 0.14. Hence there seems to be limited agreement between the market valuation and the analysts' valuation of companies. One way for those who believe that we have entered a new economy to rationalize this finding is to argue that the market is more farsighted than the analysts who cover the firms. If intangibles are like dark matter, this is akin to saying that the average person who looks up into the sky is better able to measure the missing mass of the universe than the professional astronomer.
To put the issue simply, q^supE^ can increase in either of two ways: its denominator may increasingly omit assets that generate value, or its numerator may increasingly overvalue assets in general. Although the comparison between qE and q seems to support the latter interpretation, we cannot conclusively distinguish between these explanations by examining just these two variables. But we can distinguish between them by focusing on the relationship between our measures of q and investment behavior. Under certain assumptions, detailed below where we formally derive our model, average q is a sufficient statistic for total investment. This means that it embodies all the relevant information about investment opportunities.
To understand why studying investment behavior is helpful, consider the first of the two reasons why qE can increase. If a firm's assets increasingly consist of intangibles, it would be unsurprising to find that qE is only loosely related, or perhaps even unrelated, to tangible investment behavior. Turning to figures 4 and 5, we find that this possibility is not inconsistent with the data. Figure 4 plots qE and the tangible investment rate, denoted I/K, where I is tangible investment and K is the stock of tangible capital. Figure 5 compares the growth rates of RK and qE. The correlation coefficient for the two series is positive, but RK does not closely track qE: the growth rate of RK follows the growth rate of qE during the 1990-91 recession, but the correlation is actually negative since 1994.13
This is the basic puzzle about investment behavior that has been confirmed time and again in empirical studies.14 The disconnect between I/K and qE results in econometric estimates of the coefficient on qE that are small in magnitude or imprecise, or both, which implies that investment is subject to enormous adjustment costs.15 This has sparked a number of active research inquiries. The most prominent of these focus on whether capital market imperfections or nonconvex adjustment costs help rationalize this finding.16
We believe, in contrast, that the previous results may be spurious for either or both of two reasons: that the underlying model ignores intangibles that are an important part of total investment, or that share prices are noisy signals of the fundamentals. These possibilities have not been extensively considered because intangibles and fundamentals are difficult to measure." Our strategy uses a two-step procedure to deal with these measurement problems. The first step is to develop a model that requires data on the flow of intangible capital only, not its stock. There is no practical way to calculate the stock of intangible assets for the companies in our sample-indeed, we have already alluded to the active debate about whether such an endeavor would be feasible even with new accounting regulations. But no one disputes that intangible investments in the form of advertising, R&D, and the like are observable-these items are expensed on the income statement. We show how we use this information in the following section where we introduce our model.
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The second ingredient is analysts' earnings expectations, which we have already introduced. Jason Cummins, Kevin Hassett, and Steven Oliner first showed that there is a close time-series link between investment and analysts' forecasts.18 Although we use the earnings forecasts in a different way, we confirm this finding. Figures 6 and 7 plot, respectively, annual averages and growth rates of I/K from figures 4 and 5 along with those of 4. Figure 7 shows the close correlation between the two series. What is particularly striking is that the growth of q predicts the turning points in the growth of I/K.19 Of course, this finding is meant only to be suggestive. Tobin's average q, whether constructed with equity price data or with analysts' earnings expectations, is an endogenous variable. News, for example about a new product invention, affects investment as well as the stock market price and analysts' forecasts. The econometric approach we discuss in detail later in this paper can correct for this endogeneity. In addition, in constructing our measures of fundamentals we have almost surely introduced measurement error. This is likely to be particularly acute in the case of q because a number of assumptions are needed to calculate the present discounted value of expected future profits. However, under certain conditions our econometric approach can also control for this type of measurement error. In our empirical work, we show that the close association between tangible investment and 4 is robust to controlling for these econometric issues.
Figures 1 through 7 have set the stage for our investigation. Figures 1, 2, and 3 showed, using specific company examples and our entire sample of firms, that much is happening in the level and variance of the stock market-based measure of company fundamentals that has nothing to do with the measure based on analysts' expected earnings. Figures 4 and 5 illustrated the weak relationship between tangible investment and the stock market-based measure of average q. Although this could reflect the growing importance of intangible capital, if this were the main reason, we should also find a weak relationship between tangible investment and our measure of average q based on analysts' earnings forecasts. In fact, we find a close relationship between tangible investment and this measure of q, as shown in figures 6 and 7. Thus, although it is conceivable that more and more capital has gone missing from the balance sheet, a compelling alternative explanation of the divergence of qE from q is that share prices are noisy.
Our formal empirical work confirms these findings. Although we find a limited role for intangibles in our model of tangible investment, we nevertheless find a strong relationship between tangible investment and q that is not mirrored in the relationship between tangible investment and q^supE^. The puzzle in the relationship between stock prices and investment can be explained by the importance of noisy share prices, and the story of the new economy as it relates to the stock market rise appears to be largely a fiction.
The Model
We use the neoclassical model of investment as the basis for our investigation. First we describe the model and present the empirical investment equation that relates Tobin's q and the demand for fixed capital when there is a single capital good. Next we show how this empirical model can be modified to incorporate the key feature of the new economy, namely, that we should distinguish between two different types of capital, only one of which can be measured, Finally, we modify the model to incorporate the key feature of noisy share prices, namely, that we should allow for the value of the firm being mismeasured because asset prices deviate from their fundamental value.
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To understand the different measures of intangible investment we use, it is helpful to review some basic accounting. The income statement contains information about expenditures internal to the firm that generate intangible assets. Accountants highlight two types of information about intangible investment that are available on the income statement: advertising (data item 45) and R&D (data item 46).27 (Some intangible expenditures are also included in selling, general, and administrative expenses, but that category of expenses is so broad that it is unlikely to be useful as a measure of intangible investment.) Both of these measures of intangible investment are deflated using the sectoral IPD for total investment and divided by the current-period replacement cost of the firm's tangible capital stock. Using alternative deflators did not affect the empirical results.
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We employ data on expected earnings from IB/E/S International Inc., a private company that has been collecting earnings forecasts from securities analysts since 1971.28 To be included in the IB/E/S database, a company must be actively followed by at least one securities analyst who agrees to provide IB/E/S with timely earnings estimates. According to I/B/E/S, an analyst "actively follows" a company if he or she produces research reports on the company, speaks to company management, and issues regular earnings forecasts. These criteria ensure that I/B/E/S data come from well-informed sources. The I/B/E/S earnings forecasts refer to net income from continuing operations as defined by the consensus of securities analysts following the firm. Typically, this consensus measure removes from earnings a wider range of nonrecurring charges than the "extraordinary items" reported on firms' financial statements.
For each company in the database, I/B/E/S asks analysts to provide forecasts of earnings per share over the next four quarters and each of the next five years. We focus on the annual forecasts to match the frequency of our Compustat data. In practice, few analysts provide annual forecasts beyond two years ahead. IB/E/S also obtains a separate forecast of the average annual growth of the firm's net income over the next three to five years-the "long-term growth forecast." To conform with the timing of the stock market valuation we use to construct QE, we construct Q using analysts' forecasts issued at the beginning of the accounting year.
We abstract from any heterogeneity in analysts' expectations for a given firm-year by using the mean across analysts for each earnings measure (which I/B/ElS terms the "consensus" estimate). We multiply the oneyear-ahead and two-year-ahead forecasts of earnings per share by the number of shares outstanding to yield forecasts of future earnings. Forecasts of earnings for subsequent periods are obtained by increasing the average of these two levels in line with the forecast long-term growth rate. We discount expected earnings over the next five years using the current interest rate on thirty-year U.S. Treasury bonds plus an 8 percent risk premium, and we use a terminal value correction to account for earnings in later years. Appendix B provides further details.
The sample we use for estimation includes all firms with at least four consecutive years of complete Compustat and I/B/E/S data. We require four years of data to allow for first-differencing and the use of lagged variables as instruments. We determine whether the firm satisfies the fouryear requirement after deleting observations that fail to meet a standard set of criteria for data quality.
We deleted observations in cases where qE or 4 is less than zero, the theoretical minimum, or greater than 50. These types of rules are common in the literature, and we employ them because extreme outliers can affect the empirical results.
Time-Series and Cross-Sectional Evidence on the New Economy
Finally, we separate the companies in our sample by industry. We classify firms as being representative of the new economy if they are in the North American Industrial Classification System under computer and electronic product manufacturing, computer software, or telecommunications. These firms account for just over 10 percent of all firms and firm-- year observations in the overall sample. Broader categorizations are certainly possible; for example, one might also include pharmaceutical and biotechnology companies. But when we tried broader categorizations, we found no effect on the central result that we highlight here. New economy and old economy firms are surprisingly similar: share prices are noisy for both groups, and when we consider Q we can better understand the time-series behavior of tangible investment.
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Figures 12 and 13 plot, for the old economy and the new economy companies, respectively, the mean, median, and interquartile ranges of the difference between the share price-based and analyst-based measures of the value of the company, as a percentage of the latter.34 The two figures also define the old economy and new economy companies by the more familiar SIC codes and industry names.
Within industries, regardless of whether one looks at old economy or new economy companies, the interquartile ranges are all broad. Hence there is considerable disagreement between the two measures, regardless of the type of company. Across industries, there do appear to be important variations in the mean and the median difference in the valuations. In some industries, such as paper and allied products in figure 12 and computer and office equipment in figure 13, the mean and the median are similar. But in others, such as petroleum and coal products in figure 12 and telephone communications in figure 13, the differences are rather dramatic. Hence in some sectors there is a bigger gulf between the marketbased and the analyst-based valuations. The interesting thing to notice is that these large differences occur in both types of companies. Noisy share prices, then, seem to be a much broader feature of the data; they are not confined to certain easily categorized sectors.
Perhaps counterintuitively, there does not appear to be strong evidence that the market mismeasures the value of new economy companies more than it does the value of old economy companies. The correlation coefficient between the growth rate of qE and the growth rate of q is only 0. 15 for new economy companies, about the same as for the entire sample (0.14; see figure 3). Indeed, as can be seen by comparing figure 14 with figure 5, tangible investment tracks qE more closely for new economy companies than for the entire sample of companies (which were overwhelmingly old economy companies at least until very recently). But as can been seen by comparing these two figures with figure 15 and figure 7, our measure of q based on analysts' earnings forecasts predicts tangible investment better than qE does, for both old economy and new economy companies.
By itself, the difference in VI and V could indicate measurement error in the latter (as a measure of the firm's fundamental valuation) rather than error in VE. But we have now shown that our 1V/measure provides much more information about firms' tangible investment behavior than does the VI measure. So at least within the structure of the Q model, we infer that the more serious measurement error pertains to the equity valuations. This finding holds whether or not we allow for the presence of intangible assets, and whether or not we focus on intangibles-intensive or new economy firms.
Conclusion
The fundamental issue we have addressed is whether the increase in stock market prices relative to the measured stock of tangible capital reflects a growing role of intangible capital in generating profits (that is, the birth of the new economy) or a persistent and broadly based increase in the market valuation of companies relative to their fundamental value (that is, noisy share prices). We introduced a new approach based on the Q model of investment that is rich enough to encompass both these possibilities. We then studied investment behavior, in both tangible and intangible capital, and assessed whether it is consistent with one or both explanations. Although we could identify a limited role for intangible investment, we found no evidence that this factor alone can account for the spectacular rise in the stock market valuation of firms. Our evidence points to serious anomalies in the behavior of share prices.
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Our findings suggest that even when we account for the role of intangible investment, there is a wide, and growing, gap between the market valuation of firms and a valuation based on expected future profits. The latter is demonstrably more informative about these firms' tangible investment behavior. Perhaps most surprising, we found that stock prices contain no information about investment behavior once we control for fundamentals using expected future profits. Hence fluctuations in share prices that are unrelated to earnings forecasts appear to be both pervasive and a sideshow for investment. And although this is found to be true for intangibles-intensive or new economy companies, our results are not limited to these firms. Our findings suggest that persistent deviations of equity values from firms' fundamental valuations are an important feature of U.S. stock markets over the past two decades, and that this can account for the weak observed relationship between share prices and investment. Our findings further suggest that managers make investment decisions to maximize the present value of expected future profits and are not influenced by the seemingly anomalous behavior of share prices. One implication is that monetary policymakers need not be unduly concerned about the impact of "irrational exuberance" on business investment, although they may nevertheless be interested in the behavior of the stock market for other reasons, such as wealth effects on consumption.
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| [Footnote] |
| 1. In his public comments, Ballmer consistently emphasizes this point, saying, for example, that market participants' expectations about Microsoft's growth are "outlandish and crazy," because Microsoft has "more competition than we ever have had before" (www.microsoft.com/msft/speech/analystmtg99/ballmerfam99.htm). |
| 2. Economic depreciation and technical progress affect the relationship between book value and replacement cost in the opposite way from price inflation. Rapid inflation makes the book value of assets less than their value at current prices, whereas rapid economic depreciation and technical progress cause the book value of assets to exceed their value in quality-adjusted prices. In this sense, book value may actually exceed replacement cost for certain types of capital goods that have experienced rapid depreciation and technical progress, such as computers. |
| [Footnote] |
| 3. The difficulty of measuring these future benefits is the reason usually advanced for the requirement to expense these items. Generally Accepted Accounting Principles require that internal R&D, advertising, and other such costs be written off to expense when incurred. In contrast, purchases of intangibles from outside the firm-such as patents, trademarks, formulas, and brands-are recorded as assets, because market prices are available for these. The only exception to this asymmetric treatment is the capitalization of some software development costs (FASB, 1985). |
| 4. This overview of the accounting treatment of intangibles is standard fare in introductory accounting textbooks. We base our discussion on Horngren, Sundem, and Elliot (1996). 5. The seminal research on intangibles by Baruch Lev and his collaborators forms much |
| of the empirical basis for those who advocate fundamental reform of accountancy. For an overview of this research see Lev and Zarowin (1999). |
| 6. Hall (1999) makes this case, for example. |
| [Footnote] |
| 7. The perspective of Blair and Wallman (www.stern.nyu.edu/ross/Projectlnt/about. html), who head up the Brookings Institution's Intangible Assets research project (which is spearheading an effort to reform the accounting for intangibles), is so remarkable in this regard that it is worth quoting at length: "Currently, less than half (and possibly as little as one-third or less) of the market value of corporate securities can be accounted for by 'hard' assets-property, plant and equipment.... The rest of the value must, necessarily, be coming from organizational and human capital, ideas and information, patents, copyrights, brand names, reputational capital, and possibly a whole host of other assets, for which we do not have good rules or techniques for determining and reporting value" (italics added). Yet only under a number of strong assumptions, of which strong efficiency is just one, must intangibles make up the rest of a company's market value. Blair and Wallman believe that accountancy fails to convey crucial information about intangibles, so the assumption of strong efficiency would seem to be questionable. Of course, one need not take such an extreme position to justify efforts to collect better data. |
| [Footnote] |
| 8. Coca-Cola divested itself of most of its physical assets when it spun off Coca-Cola Enterprises in 1986. In the calculations that follow we use consistent time-series data from Compustat that relate only to what is now the Coca-Cola Company. |
| 9. www.microsoft.com/BillGates/Speeches/03-26london.htm. |
| 10. Each annual observation here refers to the start of the firm's financial year. We discuss in greater detail the composition of our broader sample and the construction of the variables in it, including the ones we introduce in this section, below, and in appendix B. In particular, the two measures of fundamentals that we introduce here contain all the usual adjustments for debt, taxes, and so forth. |
| [Footnote] |
| 11. A large literature examines the properties of earnings forecasts. The consensus in the finance and accounting literature is that analysts are too optimistic about the near-term prospects of companies: see, for example, Brown (1996) and Fried and Givoly (1982). Keane and Runkle (1998) show, however, that the studies in this literature suffer from material econometric deficiencies. When these are corrected, Keane and Runkle find that analysts' quarterly forecasts are rational expectations forecasts. |
| [Footnote] |
| 12. The comparable increase for the firms that are continuously in the sample from 1982 to 1998 is 150 percent, indicating that new entrants do have an appreciable effect on growth in q for the sample as a whole. This is perhaps not surprising, since part of the entry in our sample comes from firms that analysts have chosen to track precisely because of their high potential growth opportunities. |
| [Footnote] |
| 13. Results of an ordinary least-squares (OLS) regression of the growth of I/K, GIK, on the growth of qE, Gq^supE^, are as follows: |
| GIK, = -0.002/NT + 0. IOOGq^supE^subi^ t = 1983-98 |
| (0.019) (0.102) Adjusted R2 = 4.003; Durbin-Watson = 2.08 |
| [Footnote] |
| 14. See, for example, Chirinko (1993a). |
| 15. The consensus view seems to be that this result remains even when the underlying firm data are used in conjunction with an estimator that attempts to address the endogeneity of qE. A number of papers by Cummins and collaborators argue that this consensus is premature. Cummins, Hassett, and Oliner (1999) and Cummins, Hassett, and Hubbard (1994, 1996) all obtain more economically significant estimates of the effect of fundamentals when they control for endogeneity, measurement error, or both. |
| 16. For surveys of these literatures see Hubbard (1998) and Caballero (1999), respectively. |
| [Footnote] |
| 17. The techniques used by Blundell and others (1992) and Hayashi and Inoue (1991) correct for measurement error in average q when it is serially uncorrelated by using lagged values of average q as instrumental variables. We argue below that the measurement error in qE is serially correlated, and that this explains why using lagged values of average q does not successfully control for measurement error. |
| [Footnote] |
| 18. Cummins, Hassett, and Oliner (1999). |
| 19. The results of an OLS regression of the growth of I/K, GIK, on the growth of 4, GO, are as follows: |
| [Footnote] |
| The measure of 4 is constructed using earnings forecasts that are available at the start of the period over which this investment expenditure occurs. |
| [Footnote] |
| 20. The firm index i is suppressed to economize on notation except when we present the empirical investment equations, where it clarifies the variables that vary by firm. |
| 21. Hayashi (1982). We use lowercase qi, to denote the valuation ratio V,,/[p,(1 - 8)Ki.,- j] and capital Q, to denote the function of this ratio that enters the investment equation. |
| [Footnote] |
| 22. Lach and Schankerman (1989) and Nickell and Nicolitsas (1996) have considered the relationship between R&D expenditures and subsequent investment. |
| 23. For example, Cummins and Dey (2000) estimate the dynamic demand for equipment and structures using firm-level panel data. |
| [Footnote] |
| 24. For previous treatments of the Q model with multiple capital inputs see Chirinko (1993b) and Hayashi and Inoue (1991). |
| [Footnote] |
| 25. Shiller (1981), among others, has suggested that equity valuations are excessively volatile compared with fundamental values. Blanchard and Watson (1982) and Froot and Obstfeld (1991) have developed models of rational bubbles that do not violate weaker concepts of market efficiency. Campbell and Kyle (1993) have analyzed models with noise traders that have similar empirical implications. |
| [Footnote] |
| 26. This depreciation rate is constructed as in Hulten and Wykoff (1981). |
| [Footnote] |
| 27. The FASB has acted to ensure that special items (data item 17) on the income statement, which typically represent restructuring charges, do not include costs that will benefit future periods. In effect, the FASB has ruled that special items do not represent investment (Horngren, Sundem, and Elliott, 1996). |
| 28. This discussion draws on joint work with Steven Oliner and Kevin Hassett. |
| [Footnote] |
| 29. Blundell and others (1992). 30. Arellano and Bond (1998). 31. Arellano and Bond (1991). |
| [Footnote] |
| 32. For further details see, for example, Arellano and Bond (1991) and Blundell and others (1992). Formally, the Sargan statistic is a test that the overidentifying restrictions are asymptotically distributed XI, -, where n is the number of instruments and p is the number of parameters. |
| [Footnote] |
| 33. A third possibility is that the discount rate that should be used to value expected future profits has fallen faster than our construction of cj allows. We allow the discount rate to fall in line with nominal interest rates, but we assume a time-invariant risk premium. |
| [Footnote] |
| 34. We plot only manufacturing firms among old economy companies, because there are so few firms in some industries outside of manufacturing that their inclusion would make the figure difficult to read. |
| [Footnote] |
| 35. Abel and Blanchard (1986). |
| [Footnote] |
| 36. Salinger and Summers (1983). 37. Hulten and Wykoff (1981). |
| [Footnote] |
| 38. Cummins, Hassett, and Hubbard (1994). 39. http: //www.nber.org/nberprod. |
| 40. We grow out the average of the one- and two-year-ahead forecasts rather than the two-year-ahead forecast because I/B/ElS defines GR as the expected trend growth of the company's earnings, not the growth rate from the two-year-ahead forecast of earnings. |
| [Footnote] |
| 41. As noted by Brealey and Myers (1996). 42. Brealey and Myers (1996, p. 78). |
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| [Author Affiliation] |
| STEPHEN R. BOND Institute for Fiscal Studies, London |
| JASON G. CUMMINS New York University |
| [Author Affiliation] |
| We thank participants at the Brookings Panel on Economic Activity and Tor Jakob Klette for helpful comments and suggestions. We also thank Haibin Jiu for his superb research assistance. Stephen Bond gratefully acknowledges financial support from the ESRC Centre for Fiscal Policy at the Institute for Fiscal Studies. Jason Cummins gratefully acknowledges financial support from the C. V. Starr Center for Applied Economics. The data on earnings expectations are provided by I/B/E/S International Inc. |
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