I. Introduction

One of the basic principles of economics in general, and macroeconomics in particular, is that expectations influence decisions. In line with this principle, the use of survey-based expectations data has been the mainstay of macroeconomic analysis since the 1940s, analyzing variables such as railroad shippers’ forecasts. The NBER published several volumes on data of this kind, such as The Quality and Economic Significance of Anticipations Data (1960), showing that forecasts help to explain real decisions by firms, including investment and production.

The use of expectations data took a nosedive following the Rational Expectations Revolution. Under rational expectations, the model itself dictates what expectations rational agents should hold to be consistent with the model (Muth 1961), so anticipations data are redundant. Economists also became skeptical about the quality of expectations data; in fact, this skepticism predates rational expectations (Manski 2004). According to Prescott (1977, 30), “Like utility, expectations are not observed, and surveys cannot be used to test the rational expectations hypothesis” (emphasis his). In finance, as in macroeconomics, the Efficient Markets Hypothesis implies that expectations of asset returns are predicted by the model (Campbell and Cochrane 1999; Lettau and Ludvigson 2001), so expectations data are not commonly used.

In our view, the marginalization of research on survey expectations deprives economists of extremely valuable information. Whether or not survey expectations predict behavior is an empirical question. The rational expectations assumption should not be taken for granted, but rather confronted with actual expectations data, imperfect as they are. Today, we have theoretical models that do not rely on the rational expectations assumption and make testable predictions, as well as expectations data to compare alternative models. Indeed, Manski (2004) argues forcefully and convincingly that expectations data are necessary to distinguish alternative models in economics.

As an illustration, take the case of finance, where data on expectations of asset returns have been rejected as uninformative (Cochrane 2011). Yet there is mounting evidence that expectations are highly consistent across different surveys of different types of investors, that they have a fairly clear extrapolative structure, that they predict investor behavior, and that they are useful in predicting returns (Greenwood and Shleifer 2014). Most important, expectations of returns obtained from surveys are negatively correlated with measures of expected returns obtained from rational expectations models. The trouble seems to be with conventional rational expectations models of asset prices, not with expectations data.

The message we take from this discussion is that expectations data can be used to address two questions: (a) Do expectations affect behavior? and (b) Are expectations rational? The questions are related. If expectations do not affect behavior, it matters little whether they are rational or not. If, however, expectations do affect behavior, the question of their rationality becomes quite relevant, since it allows us to consider alternative models of belief formation underlying economic decisions.

In this paper, we try to answer these questions for the case of corporate investment. We use new data assembled by John Graham and Campbell Harvey at Duke University to examine expectations formed by Chief Financial Officers (CFOs) of large US corporations and their relationship to investment plans and actual investment of these firms. The Duke data are based on quarterly surveys of CFOs which, among other things, collect information on earnings growth expectations and investment plans. We match these data with Compustat to get information on actual investment and other accounting variables. We also consider earnings forecasts made by Wall Street financial analysts regarding individual firms, which happen to be highly correlated with CFO forecasts.

To organize our discussion, we present a simple q-theory-based model of investment, but one relying on actual expectations rather than stock market data. We then conduct a number of empirical tests suggested by the model of the relationship between earnings growth expectations and investment growth, both in the aggregate and firm-level data. The results suggest that expectations are statistically and substantively important predictors of both planned and actual investment, and have explanatory power beyond traditional variables such as market-based proxies of Tobin’s q, discount rates, measures of financial constraints, or uncertainty. We then conduct a number of empirical tests on the rationality of expectations. In our data, expectations do not appear to be rational in the sense that—both in the aggregate and at the level of individual firms—expectational errors are consistently predictable from highly relevant publicly available information, such as past profitability. Some evidence points to the extrapolative structure of earnings expectations, similar to the evidence from finance.

Our paper is related to several very large strands of research. Most clearly, it is related to a large literature on determinants of investment, such as Barro (1990), Hayashi (1982), Fazzari, Hubbard, and Petersen (1988), Morck et al. (1990), Lamont (2000), and many others. Eisner (1978) is the classic study of the effects of sales anticipations on investment, with results broadly similar to ours. Four further papers are closely related to our work. Cummins, Hassett, and Oliner (2006) replace the traditional market-based Tobin’s q used in investment equations by q computed using analyst expectations data, and find that the fit of the equation is much better. Guiso, Pistaferri, and Suryanarayanan (2006) use direct expectations data on Italian firms to study the relationship between expectations, investment plans, and actual investment. Arif and Lee (2014) use accounting data to show that high aggregate investment precedes earnings disappointments, and argue that fluctuations in investor sentiment account for the evidence. Greenwood and Hanson (2015) specifically study the shipping industry, and find evidence of boom-bust cycles driven by volatile (and incorrect) expectations and investment that follows them.

Our paper is also related to research on expectations in macroeconomics. A large literature studies inflation expectations and their rationality (e.g., Figlewski and Wachtel 1981; Zarnowitz 1985; Keane and Runkle 1990; Ang, Bakaert, and Wei 2007; Monti 2010; Del Negro and Eusepi 2011; Coibion and Gorodnichenko 2012, 2015; Smets, Warne, and Wouters 2014). Souleles (2004) finds that consumer expectations are biased and inefficient, yet are strong predictors of household spending. Burnside, Eichenbaum, and Rebelo (2015) present a model of “social dynamics” in beliefs about home prices and match the model to survey expectations data. Fuhrer (2015) shows that survey expectations improve the performance of dynamic stochastic general equilibrium (DSGE) models. Some research suggests that analyst expectations of corporate profits are rational at very short horizons (Keane and Runkle 1998), although the overwhelming majority of studies reject rationality of analyst forecasts (De Bondt and Thaler 1990; Abarbanell 1991; La Porta 1996; Liu and Su 2005; Hribar and McInnis 2012). There is also a literature on expectations shocks in macroeconomics, which generally maintains the assumption of rational expectations (Lorenzoni 2009; Angeletos and La’O 2010; Levchenko and Pandalai-Nayar 2015).

Perhaps most closely related to our work is research in behavioral finance, where biases in expectations have been examined for many years (e.g., Cutler, Poterba, and Summers 1990; De Long et al. 1990). Some of the recent papers include Amromin and Sharpe (2014), Bacchetta, Mertens, and Wincoop (2009), Hirshleifer, Li, and Yu (2015), and Greenwood and Shleifer (2014), to which we return later. Several of these papers find that investor expectations are extrapolative. In the bond market, Piazzesi, Salomao, and Schneider (2015) use data on interest-rate forecasts and also find substantial deviations from rationality. Vissing-Jorgensen (2004) and Fuster, Hebert, and Laibson (2012) are two recent Macroeconomics Annual papers that also address expectations formation and rationality.

In the next section, we briefly summarize some of the evidence on the relationship between investor expectations and asset prices, and address some of the criticisms of expectations data. Section III describes our data. Section IV presents a simple q-theory model of expectations and investment that organizes our empirical work. Section V follows with the basic empirical results on expectations and investment. Section VI examines the structure of expectations. Section VII concludes with a brief discussion of implications of the evidence for macroeconomics.

II. Recent Research on Expectations and Asset Prices in Finance

Before turning to our main results on investment, we briefly summarize recent research on expectations and stock market returns, which illustrates the usefulness of expectations data. In recent models with time-varying expected returns (e.g., Campbell and Cochrane 1999; Lettau and Ludvigson 2001), expected returns (ER) are given by required returns, which in turn depend on consumption: investors require higher returns when consumption is low (relative to some benchmark), and lower returns when consumption is high. This research does not generally use data on expectations. Rather, it adopts a rational expectations approach in which ERs are determined by the model itself, so the ER is inferred from the joint distribution of consumption and realized returns.

As discussed in the introduction, recent work has started to use actual expectations data. For our purposes, the most relevant paper is Greenwood and Shleifer (2014). They use data on expectations of returns from six different surveys of investors, including a Gallup survey, investor newsletters, and the survey of CFOs of large corporations that we use in the current paper. The paper reports four main findings relevant to our analysis, which we summarize in tables 1 and 2.

First, expectations of aggregate stock returns are highly correlated across investor surveys, despite the fact that different data sets survey different investors and ask somewhat different questions (see table 1). These measured expectations are also highly positively correlated with equity mutual fund inflows. Survey expectations are thus hardly misleading or uninformative: Why would they otherwise be strongly correlated across groups, across questions, and with fund flows?

Second, return expectations appear to be extrapolative: they are high after a period of high market returns, and low after a period of low market returns (see table 1 and figure 1).

Third, and critically, expectations of returns are strongly negatively correlated with model-based measures of the ER (see again table 1). Put simply, when investors expect returns to be high, models predict that the ER is low. A plausible interpretation of this finding is that model-based ER does not actually capture expectations.

Fourth, when expectations of returns are high and the ER is low, actual returns going forward are low (see table 2). To us, this piece of evidence points to the interpretation, dating back to Campbell and Shiller (1987, 1988), that high market valuations and consumption reflect overvaluation and excessive investor optimism (as directly measured by expectations), and portend reversion going forward. Model-based ER, in other words, does not measure expectations, but rather proxies for overvaluation.

We draw two lessons from this analysis. At the most basic level, direct survey estimates of expectations are useful: they have a well-defined structure across different surveys, and they predict fund flows as well as future returns. Second, to the extent that survey estimates actually measure expectations is accepted, the evidence points against rational expectations models of stock market valuation. Actual expectations are strongly negatively related to the measures of expected returns that these models generate. In the remainder of this paper, we consider some related findings for corporate investment.

III. Data for Studying Expectations and Investment

Our empirical analysis of corporate investment draws on two main categories of data: (a) data on expectations, primarily of future profitability; and (b) data on firm financials and investment activities. We focus on nonfinancial firms in the United States. We collect data both at the aggregate and at the firm level, and all data are available at quarterly frequencies. Appendix B provides a list of the main variables, including their construction and the time range for which each variable is available.¹

A. Expectations Data

We have data on the expectations of two groups of people: CFOs and equity analysts. We first describe these data and then show that expectations of CFOs and equity analysts are highly correlated.

CFO Expectations

Our data on CFO expectations come from the Duke/CFO Magazine Business Outlook Survey led by John Graham and Campbell Harvey, which was launched in July 1996 and takes place on a quarterly basis. Each quarter, the survey asks CFOs their views about the US economy and corporate policies, as well as their expectations of future firm performance and operational plans.² Starting in 1998, the CFO survey consistently asks respondents their expectations of the future twelve-month growth of key corporate variables, including earnings, capital spending, and employment, among others. The original question is presented to the CFOs as follows:

Relative to the previous 12 months, what will be your company’s PERCENTAGE CHANGE during the next 12 months? (e.g., +3%, −2%, etc.) [Leave blank if not applicable]

Earnings: ________; Cash on balance sheet: ________; Capital spending: ________;

Prices of your product: ________; Number of domestic full-time employees: ________;

Wage: ________; Dividends: ________.

(Selected items are listed as examples. For a complete listing, please refer to original questionnaires posted on the CFO survey’s website.)

We use CFOs’ answers on earnings growth over the next 12 months as the main proxy for CFO expectations of future profitability. As the survey does not ask for expectations beyond the next 12 months, we will explain in Section IV how we interpret and extract information from earnings expectations over the next 12 months.

We then use CFOs’ answers on capital spending growth in the next 12 months as a proxy for firms’ current investment plans. In the empirical analysis, we investigate how investment plans relate to expectations of future profitability. We adopt this approach in light of well-documented lags between decisions to invest and actual investment spending (Lamont 2000). With lags in investment implementation, current expectations about future profitability may not translate into capital expenditures instantly. Instead, they will affect current investment plans, and show up in actual investment spending with some delay. As a result, it can be more straightforward to detect the impact of earnings expectations by looking at investment plans. We discuss this issue in more detail in sections IV and V.

Our analyses use both aggregate time-series and firm-level panel data. Aggregate variables are revenue-weighted averages of firm-level responses, and they are published on the CFO survey’s website. While the survey does not require CFOs to identify themselves, some respondents voluntarily disclose this information. It is then possible to match a fraction of the firm-level responses with data from the Center for Research in Security Prices (CRSP) and Compustat to perform firm-level tests. For example, Ben-David, Graham, and Harvey (2013) use matched firm-level data to study how managerial miscalibration affects corporate financial policies. Because there are privacy restrictions associated with these data, Graham and Harvey helped us implement firm-level analysis using a subsample of their matched data set. The firm-level data we use has 1,133 firm-year observations, spanning from 2005Q1 to 2012Q4.³ We exclude firms that have negative earnings in the past 12 months because, in that case, earnings growth is not well defined. We also winsorize outliers at the 1% level.

Analyst Expectations

We obtain data on equity analysts’ expectations of future firm performance from the Institutional Brokers’ Estimate System (IBES) dataset. Beginning in the 1980s, IBES collects analyst forecasts of quarterly earnings per share (EPS) for the next 1 to 12 quarters. We take consensus EPS forecasts (i.e., average forecast for a given firm-quarter in the future) and compute forecasts of total earnings by multiplying by the number of shares outstanding. To compare the results with those using CFO expectations, we compute analyst expectations of future 12-month earnings growth. We calculate aggregate analyst expectations of future 12-month earnings growth by summing up expected future earnings of all firms in the next four quarters, and then divide by the sum of earnings of all firms in the past four quarters. We calculate firm-level analyst expectations of future earnings growth by taking the forecast of total firm earnings in the next four quarters, and then divide by total earnings in the past four quarters. We exclude firms that have negative earnings in the past 12 months when calculating expected future earnings growth.

The sample with analyst expectations covers both a longer time span and a larger set of firms. We set the start date of the aggregate time series and firm panel to be 1985Q1 because some of the quarterly Compustat data items we use only become systematically available around 1985, and because aggregate analyst forecasts have some outliers before 1985. We set the end of the sample to be 2012Q4 so we can match expectations to realized next 12-month earnings growth with accounting data ending in 2013Q4. In total, we have 145,281 firm-level observations of expected earnings growth over the next 12 months, and we winsorize outliers at the 5% level.

Correlation between CFO and Analyst Expectations

The expectations of CFOs and analysts with respect to next 12-month earnings growth are highly correlated. Figure 2 shows aggregate time series of expected next 12-month earnings growth from the CFO survey and from analyst forecasts. The raw correlation between these two series is 0.65. At the firm level, the raw correlation between CFO and analyst expectations of next 12-month earnings growth is 0.4 if we demean by firm, and 0.3 if we demean by both firm and time. The high correlation between expectations of CFOs and analysts indicate that expectations data are consistent and meaningful, and expectations of both groups incorporate information about general business outlook shared by managers and the market.

IV. Expectations and Firm Investment: Empirical Specifications

We motivate our empirical specification with a basic q-theory model. A firm is run by a risk-neutral owner who discounts the future by factor β < 1,⁵ and the firm’s horizon is infinite. In the model, we interpret each period t to be 12 months. The firm’s output in period t is obtained by combining capital and labor using a constant returns to scale production function $A_t{K}_t^{\mathrm{\alpha }}{L}_t^{1-\mathrm{\alpha }}$. At the beginning of period t, the owner hires labor L_t at wage w and makes decisions about investment during this year I_t. Investment takes one year to implement, so K_t+1 = (1 − δ) K_t + I_t, where δ is capital depreciation rate. The firm’s optimal policy in year t maximizes the expected present value of earnings:

In the optimization problem above, the operator ${\mathbb{E}}_t$ (.) denotes the owner’s expectations conditional on his information at the beginning of year t, computed according to his possibly distorted beliefs. We allow for departures from rational expectations, but restrict to beliefs that preserve the law of iterated expectations. By standard arguments, appendix A⁶ shows that the firm’s optimal investment chosen at the beginning of year t is described by:

To estimate equation (1), ideally we would like to know expectations of earnings in all future periods. Unfortunately, this is not feasible in practice. For instance, CFOs only report expectations of earnings growth in the next 12 months. Formally, in the CFO survey we only have information about ${\mathbb{E}}_t$ (∏_t), namely expectations at the beginning of year t about earnings ∏_t in the following 12 months (which are not yet known, so expectations are well defined). With respect to investment, we have information on: (a) planned investment over the next 12 months, and (b) actual capital spending in each quarter. We denote investment plans for the next 12 months as $I_t^p$, which captures the plan made at the beginning of the year about investment in the rest of the year.

Given implementation lags in the investment process, it may be most straightforward to test how expectations at a given point in time affect firms’ investment plans.⁷ Accordingly, we approximate equation (1) by

Given this corroborating evidence, it appears that within the limitations of our data, equation (2) is a reasonable approximation of equation (1). For the purpose of our empirical analysis, it is convenient to log-linearize equation (2) and express it in growth rates, since all variables in the CFO survey are in terms of percentage change in the next 12 months relative to the past 12 months. By expressing equation (2) in growth rates, we can directly employ these variables without using them to reconstruct levels. If we denote logs by lowercase variables, then derivations in appendix A⁸ show that our equation for investment plans can be approximated as:

The intuition of equation (3) is as follows: When firms think that earnings will increase by a lot in the next 12 months, they also tend to believe that future earnings will be higher for a sustained period of time. As a result, they want to invest more, which leads to an immediate increase in planned investment. In equation (3) we need to control for the change in capital stock because both investment and profitability are affected by the size of capital stock. We can also arrive at a specification very similar to equation (3) in a simpler setting with time to build but without adjustment costs.⁹ Empirically we use equation (3) to map a basic investment model to testable predictions in our data set. We refrain from testing the parameter restrictions implied by a strict adherence to the approximated q equation.

While investment plans are a convenient starting point to detect the impact of expectations, for equation (2) to be informative about how expectations influence investment, it must also be the case that plans are closely related to realizations. In Section V.C, we show that investment plans are highly correlated with actual capital spending over the planned period. In other words, a significant fraction of capital spending over the next few quarters appears to be determined by ex ante investment plans, consistent with previous findings by Lamont (2000). To the extent that there is a close correspondence between investment plans and realized investment over the planned period, it would also be of interest to test how current expectations translate into actual capital spending in the next 12 months. This additional test allows us to further assess whether expectations have a substantial impact on actual investment activities. We present results from these tests in Section V.C.

V. Expectations and Investment

In this section, we test the relationship between investment decisions and earnings expectations. We focus on CFO expectations, and provide supplementary results using expectations of equity analysts. We begin by studying investment plans. In Section V.A we consider the role of expectations at the aggregate level, and in Section V.B we consider the role of expectations at the firm level. Then, in Section V.C we evaluate the relationship between plans and realized investment, and document the link between expectations and actual capital spending.

A. Expectations and Investment Plans: Aggregate Evidence

Figure 3 visually represents the association between aggregate CFO expectations and aggregate investment. Panel (A) plots CFOs’ expectations of next 12-month earnings growth, along with planned investment growth in the next 12 months. Panel (B) adds to panel (A) actual aggregate investment growth in the next 12 months. We see that there is a strong comovement between earnings expectations and investment plans, and between investment plans and actual capital spending. At the very least, expectations data do not appear to be uninformative noise.

We then estimate versions of equation (3) using quarterly regressions:

$$\Delta {\widehat{CAPX}}_{{\mathrm{q}}_t}=\mathrm{\alpha }+\mathrm{\beta }{E}_{{\mathrm{q}}_t}^{*}\left[\mathrm{\Delta Earnings}\right]+\mathrm{\lambda }{X}_{{\mathrm{q}}_t}+{\epsilon }_{{\mathrm{q}}_t}$$

where $\Delta {\widehat{CAPX}}_{{\mathrm{q}}_t}$ is planned investment growth in the next 12 months reported in quarter q_t, and $E_{{\mathrm{q}}_t}^{*}\left[\mathrm{\Delta Earnings}\right]$ is CFO expectations of next 12-month earnings growth reported in quarter q_t; $X_{{\mathrm{q}}_t}$ includes past change in capital stock as shown in equation (3), as well as a set of additional controls we discuss below. We use Newey-West standard errors with twelve lags.¹⁰

Table 4, columns (1) and (2), report our baseline results. We find that CFOs’ earnings expectations have significant explanatory power for firms’ investment plans, both statistically and economically. A one standard deviation increase in earnings growth expectations is associated with a 0.8 standard deviation increase in planned investment growth.¹¹ Put differently, a 1 percentage point increase in CFO expectations is accompanied by a 0.6 percentage point increase in planned investment growth.¹² Quantitatively, CFO expectations have major explanatory power for aggregate investment.

	Planned Investment Growth in the Next 12 Months
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
CFO expectations of next 12m earnings growth	0.6313	0.5959	0.5869	0.4235	0.4853	0.5997	0.5435
	(9.39)	(11.65)	(11.40)	(7.21)	(12.83)	(11.79)	(9.78)
Q			0.0532
			(1.68)
Past 12m agg. stock returns				0.1082
				(3.64)
Past 12m credit spread change					−0.0352
					(−2.26)
Log(D/P)						0.0271
						(0.62)
Cay							−0.9700
							(−1.86)
Past 12m asset growth		0.2181	0.1461	0.0784	0.2643	0.2481	0.2536
		(3.97)	(2.39)	(1.89)	(5.88)	(2.97)	(3.92)
Observations	56	56	56	56	56	56	56
R-squared	0.616	0.660	0.672	0.741	0.685	0.663	0.674
	Planned Investment Growth in the Next 12 Months
	(8)	(9)	(10)	(11)	(12)	(13)
CFO expectations of next 12m earnings growth	0.5969	0.5429	0.5301	0.5882	0.5573	0.4315
	(11.37)	(8.20)	(14.03)	(8.55)	(16.72)	(8.21)
Past 12m credit spread change						−0.0447
						(−1.49)
Surplus consumption	0.0154
	(0.30)
Past 12m change of net income/asset		0.0433				−0.0433
		(1.70)				(−1.03)
Past 12m agg. stock vol. change			−0.0044			0.0421
			(−0.37)			(2.51)
Bloom policy uncertainty index (past 12m change)			−0.0328			−0.0303
			(−2.11)			(−2.23)
Past 12m GDP growth				0.6118		2.3154
				(0.77)		(1.89)
Past 12m investment growth					−0.1300	−0.5391
					(−2.26)	(−2.69)
Past 12m asset growth	0.2114	0.1716	0.2376	0.0547	0.3859	0.4349
	(3.40)	(3.25)	(4.22)	(0.29)	(6.21)	(3.59)
Observations	56	56	56	56	56	56
R-squared	0.660	0.672	0.694	0.666	0.667	0.739

Note. This table presents aggregate quarterly regression $\Delta {\widehat{\mathit{CAPX}}}_t=\alpha +\beta E_t^{*}\left[\mathrm{\Delta Earnings}\right]+\lambda X_t+{\epsilon }_t$. $E_t^{*}\left[\mathrm{\Delta Earnings}\right]$ is aggregate CFO expectations of earnings growth in the next 12 months, and $\Delta {\widehat{\mathit{CAPX}}}_t$ is aggregate planned investment growth in the next 12 months. All control variables are measured at the end of quarter t − 1. Past 12-month stock returns is index returns from the end of quarter t − 5 to the end of quarter t − 1. Past 12-month aggregate credit spread change is log change in credit spread from the end of quarter t − 5 to the end of quarter t − 1. Past 12-month changes in stock volatility and Bloom policy uncertainty index, as well as past 12-month asset growth, are calculated in the same way (i.e., as the log difference between values at the end of quarters t − 5 and t − 1). Past 12-month change of net income/asset is net income from t − 4 to t − 1 normalized by asset at the end of quarter t − 5 minus normalized net income in the previous four quarters. Past 12-month GDP (investment) growth is the log difference between GDP (investment) in quarters t − 4 through t − 1 and GDP (investment) in the previous four quarters. A constant is included but not reported, and a linear time trend is included. t-statistics in parentheses. Standard errors are Newey-West with 12 lags.

View Table Image: 1 | 2

In interpreting these results, three issues arise. First, how do CFO expectations relate to traditional proxies of Tobin’s q? Do data on managers’ expectations contain information beyond market-price-based measures of q? Second, is the role of expectations robust to controlling for alternative theories of corporate investment? Third, could the correlation between expectations and investment reflect a reverse causality problem, whereby investment affects expectations of future earnings rather than the other way around? In the following, we address these issues by augmenting our baseline regressions.

Some variables may affect investment but are likely to do so only through their influence on expectations, such as information relevant for predicting future product demand. In principle, a large part of expectations are formed, perfectly or imperfectly, based on observable information, instead of being exogenous innovations. Thus a flexible enough function of observable information should be able to approximate expectations. The focus of our present analysis is to test the extent to which expectations as a whole, as measured in our data, affect firms’ investment decisions. It is not specifically about the impact of variations in expectations that are not explained by observables (so-called “expectational shocks”). Accordingly, we do not explore whether our expectations variables can or cannot be driven out by a full set of factors that are primarily used to explain expectations. Instead, we emphasize controls that represent alternative determinants of investment (such as discount rates, financial constraints, etc.).¹³

CFO Expectations and Market-Based Proxies for Tobin’s q

We begin with a comparison of CFO expectations and traditional proxies of Tobin’s q. This exercise helps us assess whether expectations data contain additional information relative to standard market-based q measures. In table 4, column (3), we include the empirical proxy of q. In line with previous research, the explanatory power of equity q is very weak. It is well known that equity q is highly persistent and does not line up well with fluctuations in investment activities. In our context, to explain investment growth, the direct theoretical counterpart is not q in levels, but the log change in q. Barro (1990) shows changes in q are almost equivalent to stock returns. He finds that changes in q from the beginning of year t − 1 to the beginning of year t is highly correlated with investment growth in year t, and stock returns from the beginning of year t − 1 to the beginning of year t perform incrementally better. In column (4), we include past 12-month stock returns. The coefficient on this variable is positive and statistically significant, as predicted by theory. The coefficient on CFO expectations remains large and highly significant.¹⁴ The views of CFOs appear to contain a substantial amount of additional information for investment plans not captured by equity q.

Philippon (2009) finds that a proxy of q obtained from bond yields is also highly correlated with investment activities. Philippon’s bond q series end in 2007, which is five years before the end of our sample. However, bond q is highly correlated with credit spread. For example, the correlation between changes in bond q and changes in credit spread over four quarters is 0.84. In column (5), we include changes in credit spread in the past four quarters in lieu of bond q. In addition, credit spread can be relevant as a control also because it may reflect credit availability and financial constraints. The coefficient on this variable is negative and significant— consistent with theory—but CFO expectations retain significant explanatory power.

Overall, CFO expectations explain investment plans beyond market-based q proxies, statistically and economically. Indeed, CFOs may possess information that markets participants either do not possess or process imperfectly. To the extent that managers’ and markets’ views differ, it is natural that managers’ beliefs have a major impact on investment decisions. As we show in Section V.C, this result also extends to actual capital spending.

CFO Expectations and Alternative Theories of Investment

We now test the role of expectations against alternative theories of investment. We introduce a set of variables motivated by these theories, which are the key controls in our analysis.

Time-Varying Discount Rates

A prominent idea in traditional finance holds that variations in required returns, or discount rates, are central to explaining investment in both financial and real assets (e.g., Cochrane 1991, 2011). Lamont (2000) postulates that firm investments rise and fall in response to changes in discount rates so that high investment growth is associated with low future stock returns. Lettau and Ludvigson (2002) argue that time-varying risk premia, as proxied for by the consumption-wealth ratio (known as cay), can forecast future investment growth. In table 4, columns (6) to (8), we control for three common measures of discount rates: log dividend yield, cay, and the surplus consumption ratio as constructed by Campbell and Cochrane (1999). Cay is somewhat significant, surplus consumption is not, and dividend yield tends to enter with the wrong sign. The explanatory power of CFO expectations is unaffected. We get similar results if we include these variables in past 12-month changes instead of in levels.

We can also control for risk premia implied by long-run risks models, as constructed by Bansal et al. (2014). Unfortunately their series is annual, which leaves us with few observations. We interpolate the series to quarterly frequencies in multiple ways and find it tends to enter with the wrong sign. Taken together, none of these variables compare in their explanatory power to CFO expectations, and their inclusion does not have much of an influence on the coefficient on expectations.¹⁵

Because proxies for discount rates are generally quite persistent, their coefficients can suffer from Stambaugh (1999) biases. In our case, Stambaugh bias will tend to attenuate the coefficients on discount rates toward zero or make them have the wrong sign.¹⁶ In appendix C, table C6,¹⁷ we report Stambaugh bias-adjusted results, using a multivariate version of the bootstrap method in Baker, Taliaferro, and Wurgler (2006). The bias adjusted results are very similar.

Financing Constraints

A well-known empirical result, dating back to Fazzari, Hubbard, and Petersen (1988), is that investment is positively correlated with recent firm cash flows. The leading interpretation is that financially constrained firms invest more when high cash flows increase internal resources. In column (9), we control for cash flows in the past 12 months.¹⁸ We can include cash flow variables either in levels or in changes, and results are similar: the coefficient on expectations barely changes, and the coefficient on past cash flows tends to be insignificant. This result confirms earlier findings by Cummins et al. (2006) that unveil the fragility of financial constraint variables once earnings expectations are taken into account.

Economic Uncertainty

A blooming literature studies the impact of uncertainty on economic activities when investment is irreversible or has fixed adjustment costs (Leahy and Whited 1996; Guiso and Parigi 1999; Bloom, Bond, and Van Reenen 2007; Bloom 2009, among others). During periods of high uncertainty, the theory goes, managers do not want to exercise the option of investing: they prefer to wait for better times and information. It is legitimate to ask whether our measure of CFO expectations still matters when we include proxies for uncertainty in our regression.

In table 4, column (10), we include stock price volatility as a standard uncertainty proxy following Leahy and Whited (1996) and Bloom, Bond, and Van Reenen (2007), together with economic policy uncertainty as measured by Baker, Bloom, and Davis (2015). We can use these variables in levels or in past 12-month changes. In either case, these uncertainty proxies have only weak explanatory power, and the coefficient on CFO earnings expectations remains highly significant.

In table 4, columns (11) and (12), we additionally control for past GDP growth and past investment growth. In the last column, we include multiple controls together. The statistical and economic significance of CFO expectations remains largely intact.

Overall, these tests illustrate that CFO earnings expectations have significant explanatory power that is not accounted for by variables capturing alternative theories, such as time-varying discount rates, financial constraints, and uncertainty. As we show below, similar results hold when we connect expectations to actual capital spending. Our results suggest that expectations data provide substantive information about fluctuations in aggregate investment, and changes in expectations can be central to understanding investment activities.

Reverse Causality

One possible concern is our baseline results could be affected by reverse causality. Specifically, if a firm plans to invest a lot in the next 12 months, managers might also expect earnings to increase as investment leads to more output and sales. This mechanism seems unlikely to be driving our results. First, investment in the next 12 months generally does not translate into output and sales immediately. Second, even if it does, investment is an incremental addition to the capital stock. It is unlikely that a 1% increase in investment (which increases the firm’s capital stock by much less than 1%) can instantly lead to a 1% or more increase in firm earnings, as would be required to match the magnitude of coefficients in the data.

We further address the reverse causality concern in supplementary tests, drawing on another question in the CFO survey, which asks respondents to rate their optimism about the US economy on a scale from 0 to 100 (with 0 being the least optimistic and 100 the most optimistic). In appendix C, table C1, we show that CFOs’ optimism about the US economy is significantly positively correlated with investment. It is hard to argue that firms’ investment plans will mechanically cause CFOs to be more optimistic about the US economy. Instead, this result is very much in line with previous findings that firms’ expectations and sentiments appear to be a key driver of investment activities.

In appendix C, table C2, we present the same set of tests using analyst expectations. We find analyst expectations are also significantly correlated with investment plans, although not surprisingly, the magnitude of the relationship is smaller; the coefficients on analyst expectations are generally about one-half of the size of the coefficients on CFO expectations. The evidence suggests that expectations elicited from different sources are consistent, and there are general views shared by managers and the market that play an important role in shaping aggregate investment dynamics.

VI. Are Expectations Rational?

Since expectations shape investment, it is critical to understand their determinants. We now take a first step in analyzing the structure of CFO and analyst expectations about future earnings growth. In particular, we check whether expectations of managers and market participants are consistent with rational benchmarks, or are systematically biased in predictable ways.

The simplest test of rational expectations is to run a regression with realized future earnings growth on the left-hand side and ex ante expectations on the right-hand side. In our context, such tests take the form:

Rational expectations postulate that α = 0 and β = 1 (and a zero coefficient on any other variable in time t information set). Using both CFO and analyst expectations, we find β to be significantly lower than 1, and a list of variables known at time t enter significantly. This finding, however, does not necessarily mean that expectations are excessively volatile relative to outcomes. It could be that expectations are measured with error, which would cause a downward bias in β.

An alternative approach is to study the predictability of ex post expectational errors. If expectations are rational, forecast errors should be orthogonal to all information available at the time when the forecast is made, and forecast errors should be unpredictable. If, on the other hand, expectations are systematically biased, then ex post errors would be predictable using information available ex ante. In this case, the structure of error predictability could help us understand potential sources of excessive optimism and pessimism.

To take a first look, consider figure 4. Panel (A) shows errors in aggregate CFO expectations about next 12-month earnings growth against past year corporate profitability. Panel (B) shows the same series using analysts’ expectations data.²¹ The figures show a striking pattern: expectational errors appear to be systematic and recurring. In particular, they are consistent with the presence of excessive optimism in good times and excessive pessimism in bad times: future realized earnings growth systematically falls short of expectations when past earnings are high, and exceeds expectations when past earnings are low.²²

To statistically corroborate the patterns in figure 4, we present regressions of expectational errors on past profitability. Column (1) of table 8, panel (A), reports the results using CFO data. Column (1) of table 9, panel (A), reports the results using analyst data. In both cases, high past-year profitability is correlated with over-optimism, while low past-year profitability is correlated with over-pessimism.²³ The magnitude of the bias is large. A one standard deviation increase in past profitability is associated with a 0.6 standard deviation increase in the magnitude of CFOs’ expectational errors.²⁴Figure 5 further illustrates these results with scatter plots of expectational errors against past profitability. It shows that the bias is present throughout the sample period, and not driven by a single outlier event.

Column (2) in panel (A) of tables 8 and 9 correlate expectational errors with past GDP growth to check whether over-optimism and over-pessimism are predictable by aggregate economic performance. We also find higher past GDP growth is associated with over-optimism, and vice versa (although the statistical significance on past GDP growth is a bit lower).²⁵

One way to interpret these results is that expectations depart from rationality in the direction of being extrapolative: when CFOs or analysts observe good or bad earnings realization, they think that similar realization persists into the future and fail to correct for mean reversion. To illustrate, we separately examine, and then compare, how actual future earnings growth and expected future earnings growth correlate with past year profitability. When past year earnings over assets increase by 1 percentage point, actual earnings growth in the next 12 months on average slows down by 0.12. However, CFOs only expect next 12-month earnings growth to slow down by about 0.03. The difference between the true and perceived reversion corresponds to the coefficient of −0.09 in panel (A), column (1).

We find very similar results at the firm level. In table 8, panel (B), we examine expectational errors of individual CFOs. We use past-year firm profitability and GDP growth as main predictors of expectational errors. Consistent with previous evidence at the aggregate level, expectational errors of individual CFOs are also strongly correlated with firm profitability and general economic conditions, in a way that appears to be extrapolative. At the firm level, as past-year earnings over assets increase by 1 percentage point, next 12-month earnings growth tends to slow down by 0.06, whereas CFOs only expect it to slow down by 0.01 on average. This results in a difference of −0.05, as shown by panel (B), column (1).

Note that profitability is likely to be heterogeneous across firms, and the normal level of earnings over assets can be quite different for established firms than for young firms, and for firms in different industries (this can be of particular concern when we turn to the analyst sample, which is much more heterogeneous than the CFO survey sample). To account for firm-specific average profitability, we include firm fixed effects. In the setting of error predictability regressions, strict exogeneity required by traditional fixed effect estimators may not be satisfied. Under the null of rational expectations, only sequential exogeneity will be satisfied. We perform robustness checks using dynamic panel estimators as in Arellano and Bover (1995),²⁶ and results are very similar. For standard errors in firm-level tests, we always cluster by firm and we double cluster by both firm and time whenever the length of the panel makes it appropriate to do so.

In columns (5) and (6) of table 8, panel (B), we report results adding time fixed effects, which helps us assess the extent to which expectational errors load on the idiosyncratic component of firm profitability. By teasing out aggregate shocks, time fixed effects may also attenuate issues in rational expectations tests when the panel is relatively short (this can be a concern with the CFO panel, which spans seven years and 28 quarters, and is less of a concern with the analyst panel, which covers 28 years and 112 quarters).²⁷ We find that CFOs seem to significantly extrapolate the idiosyncratic component of past profitability. Together, results in table 8 show that the extrapolative structure of CFO expectations appears pervasive. Past year economic conditions, and both the aggregate and the idiosyncratic component of firm profitability, are all correlated with CFOs’ expectational errors.

In table 9, we perform the same set of tests as in table 8 using analyst expectations. The results are similar: analysts also tend to overestimate next 12-month earnings growth when past-year firm profitability is high and when past-year economic conditions are favorable, and underestimate future earnings growth when the past year is rough.

At first glance, the patterns in CFO and analyst expectations are quite consistent with extrapolative biases observed in financial markets. As shown by Greenwood and Shleifer (2014), many market participants— including both individual and institutional investors, as well as CFOs surveyed by Graham and Harvey—tend to extrapolate stock price trends formed in the past year. They generally think that the past year’s trend would continue, whereas in reality it tends to revert. As explained in Section II and shown in figure 1, investors tend to become significantly more optimistic about stock market performance in the next 12 months when market returns in the past year are high, and vice versa (figure 1, panel [A]). Corporate CFOs are equally extrapolative in their expectations about next 12-month market returns (figure 1, panel [B]). Piazzesi, Salomao, and Schneider (2015) also find that bond market investors tend to perceive interest rate trends to be more persistent than they are. The extrapolative tendency in expectations formation resonates with the well-known representativeness bias in human judgment (Kahneman and Tversky 1972; Tversky and Kahneman 1974), which can lead people to view events similar to recent experiences as typical and likely, and to discount scenarios that are different from the prevailing situation (Gennaioli, Shleifer, and Vishny 2015).

While our evidence is consistent with extrapolation in earnings expectations, other factors may contribute to apparent deviations from rationality. In the following, we address a set of possible concerns.

VII. Summary and Implications

Our analysis of data on earnings expectations and investment yields two basic conclusions. First, expectations data appear to be extremely helpful in understanding corporate investment plans and actual investment, more so than some traditional measures of determinants of investment. Second, several empirical tests appear to reject the rational expectations benchmark, pointing to the extrapolative nature of expectations.

At a minimum, the evidence endorses Manski’s (2004) call for collecting and using expectations data to evaluate economic theories. Our project would not have been possible without John Graham and Campbell Harvey’s CFO data. But as is often the case with a preliminary analysis and with limited data, there are many questions that we cannot address with the data we have.

Yet even our preliminary results suggest some directions for future work. The finding that expectations do so well in accounting for investment plans and actual investment suggests a possible strategy for reviving the q-theory of investment. It is well known that the standard q-theory investment equation does not work well in levels, and requires first differencing to have some explanatory power (Barro 1990). Our findings, along with earlier research by Cummins et al. (2006), suggest that the problem may be not with the q-theory itself, but with the stock market-based measures of Tobin’s q. This is not entirely surprising in light of the growing evidence of noisiness and nonfundamental fluctuations of stock prices (Shiller 1981; Morck et al. 1990). A measure of investment opportunities based on actual expectations of corporate managers does much better in explaining investment, regardless of whether the equation is estimated in levels or in changes. A constructive way to revive q-theory is to start with data on expectations, and not on stock market valuations.

The second direction suggested by our evidence, as well as by the previous work, is the need to construct plausible models of expectations. In macroeconomics, in particular, such models have to come to grips with some version of the Lucas critique, which was applied initially precisely to Cagan’s and other models of adaptive expectations. In our view, market participants might be backward looking and extrapolative to some extent, but surely they also have some ideas of how the economy evolves that are forward looking. Developing models along these lines is an open problem. Our own approach has been to use psychological models of expectations that incorporate both forward- and backward-looking elements (e.g., Bordalo, Gennaioli, and Shleifer 2013; Gennaioli et al. 2015), but this research is still some way from being ready for a macroeconomic model.

The larger question, of course, is whether expectations have a significant role to play in macroeconomic modeling. A recent literature in macroeconomics considers expectations “shocks,” and discusses the extent to which they can contribute to economic fluctuations (Beaudry and Portier 2004; Angeletos and La’O 2010; Lorenzoni 2009; Barsky and Sims 2012; Angeletos and La’O 2013; Levchenko and Pandalai-Nayar 2015). Our perspective is quite different; the evidence suggests that expectations have a fairly precise extrapolative structure, and do not just vibrate randomly. They are not noise; there is a systematic pattern of errors. Moreover, we believe—and our evidence seems supportive of this belief—that market participants make common expectational errors. Here we show that these errors are common to analysts and CFOs; we suspect the beliefs of policymakers are highly correlated with those of market participants. It seems to us that, as a consequence, plausible models would consider common errors among many economic agents, which therefore would have potential aggregate effects.

The final question is whether such errors can account for some part of macroeconomic fluctuations, such as aggregate overbuilding in important sectors like the housing market, or prolonged recessions with a lack of corporate investment and hiring. Our data have limitations for quantifying how the erroneous component of expectations affects investment, since the rational level of investment is not observable, but has to be inferred from making additional assumptions about the decision makers’ environment and information, which requires extra discretion. It is a further step still that a common error translates into aggregate investment distortions. We leave this to future work.