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Macroeconomic Activity Under Uncertainty: How Firm-Level and Aggregate-Level Uncertainties Interact Alon Kalay, Suresh Nallareddy, and Gil Sadkay January 26, 2016 Abstract Prior studies suggest that both aggregate and …rm-level uncertainty adversely a¤ect economic growth, because they reduce investment and impede the reallocation of resources in the economy. In this paper, we hypothesize and …nd that the e¤ects of …rm-level and aggregate-level uncertainty interact. Therefore, the e¤ects of each type of uncertainty (…rm-level or aggregate-level) are exacerbated in the presence of the other, and their combined e¤ect is larger than the sum of the individual e¤ects. We further show that the interaction term explains the puzzling relation between the equity premium and earnings dispersion, and predicts unemployment and industrial production forecast errors. Our …ndings with respect to the interaction of di¤erent forms of uncertainty furthers our understating of how uncertainty a¤ects macroeconomic activity and has implications for policy setters. JEL classi…cation: E32, G12, G14, M41 Keywords: stock prices; aggregate earnings; illiquidity; expected returns; expected earnings We would like to thank an anonymous reviewer and Associate Editor, Dan Amiram, Mary E. Barth, Tuhin Biswas, Samuel Bonsall (discussant), John Donaldson, Trevor Harris, Bjorn Jorgensen, Urooj Khan, Mauricio Larrain, Emi Nakamura, Maria Ogneva, Stephen Penman, Shyam Sunder (discussant), and the participants at AAA annual meeting, the Burton Conference at Columbia University, The Indian School of Business Conference, Tel Aviv University Conference, and research seminar at University of Miami and SUNY Bu¤alo for their helpful comments and suggestions. We are grateful to Ethan Rouen for the excellent research assistance. Any remaining errors are our own. y Alon and Suresh are from Columbia University. Gil is from University of Texas at Dallas. email: [email protected], [email protected], [email protected]. 1 Introduction A recent stream of literature in economics (e.g., Bloom, 2009; Bloom, Floetotto, Jaimovich, Saporta-Eksten, and Terry, 2012; Baker and Bloom, 2013) suggests that uncertainty is an important driver of economic activity. Analytical and empirical results suggest that uncertainty lowers investment and impedes the reallocation of labor and capital. Consequently, higher uncertainty damps economic activity. This paper extends this line of research and shows that the e¤ects of …rm-level uncertainty are exacerbated when macroeconomic uncertainty is high and vice versa. We hypothesize and show that the combined e¤ects of …rm-level and aggregate-level uncertainty are larger than the sum of the individual a¤ects. In a recent review, Bloom (2014) describes the varieties of uncertainty that investors and …rms face. One form is aggregate uncertainty, regarding macroeconomic conditions. A second is …rm-level uncertainty, that is, uncertainty about which …rm, industry, or sector is more likely to grow. While the literature acknowledges that these di¤erent types exist, it does not consider their interaction. Thus existing studies suggest that uncertainty, regardless of its form, impedes economic growth because it hinders investment and the reallocation of labor and capital. In this study, we focus on uncertainty with respect to the inability to forecast investment outcomes. Our analysis shows that the correlations between cross-sectional dispersion and policy uncertainty— common measures of …rm- and macro-level uncertainty, respectively— are approximately 20%. This suggests that these two types of uncertainty di¤er and that there are multiple periods when only one type of uncertainty is high. Thus investors and …rms can face both types of uncertainty independently or simultaneously. We further hypothesize that the e¤ects of uncertainty are most pronounced when aggregate and …rm-level uncertainty are high simultaneously, exacerbating one another. Our hypothesis is based on two economic arguments. First, portfolio theory suggests that aggregate uncertainty limits investors’ options to mitigate the e¤ects of …rm-level uncertainty and vice versa. When …rms and investors face only one type of uncertainty, diversi…cation and resource reallocation allow them to mitigate the e¤ects of a speci…c form of uncertainty. For example, if …rms/investors are uncertain about which sector of the economy will grow faster but they have more clarity regarding overall growth, they can invest in a large portfolio of projects/…rms to mitigate the e¤ect of …rm-level uncertainty. By investing in this way, they gain the average growth of the economy. However, if they are also uncertain about the aggregate growth in the economy, diversi…cation simply substitutes one form of uncertainty with another. A related argument, based on resource reallocation, can be made during periods with minimal …rm-level uncertainty and signi…cant aggregate uncertainty (see Section 2 below). Therefore, while uncertainty hurts investment, as shown by Bloom et al. (2012), the e¤ect of …rm-level uncertainty is exacerbated when economic agents also face aggregate uncertainty and vice versa. Second, uncertainty a¤ects the reallocation of labor and impedes sectoral shifts (Lucas and Prescott, 1974; Lilien, 1982; Abraham and Katz, 1986; Hosios, 1994; Lazear and Spletzer, 2012).1 The sectoral shift theory suggests that cross-sectional dispersion in performance increases unemployment. This occurs because employees migrate from poorly performing …rms or sectors to better performing ones, and this migration takes time due to frictions in the labor market (such as search costs). Thus unemployment increases with layo¤s. All else equal, higher dispersion implies a greater proportion of poorly performing …rms, which inevitably lay o¤ employees. Consequently, unemployment increases during periods of high dispersion. We extend this logic and argue that the relation between unemployment and sectoral shifts also depends on the overall state of the economy. Speci…cally, periods when both dispersion is high and macroeconomic conditions are poor, are the periods with the highest proportion of poor-performing …rms. More struggling …rms increases layo¤s and unemployment. In other words, the e¤ects of dispersion are higher when the overall economy is struggling. Thus, according to the sectoral shift hypothesis, the interaction between …rm-level and aggregate-level performance a¤ects unemployment, even if it is unrelated 1 Loungani, Rush, and Tave (1990) …nd that dispersion in stock prices is associated with higher unemployment. 2 to uncertainty. The sectoral shift theory also assumes a constant hiring rate. In this paper, we further hypothesize that uncertainty slows the migration process because it lowers hiring rates, which exacerbates the frictions described in the sectoral shift hypothesis and results in even greater unemployment. Following our discussion above, we further expect the implications of …rm-level uncertainty on hiring rates and unemployment to increase when faced with aggregate uncertainty, and vice versa. Our empirical analysis examines the contemporaneous relation between three macroeconomic indicators, measures of aggregate uncertainty, and measures of …rm-level uncertainty. Our measures of macroeconomic activity are investment, unemployment, and industrial production. To proxy for aggregate-level uncertainty, we employ aggregate pro…tability, economic policy uncertainty, and the CBOE Volatility Index (VIX). For …rm-level uncertainty, we employ cross-sectional earnings dispersion as our main measure, but we also use idiosyncratic return volatility. As noted above, the state of the economy and earnings dispersion relate to unemployment even if they do not capture uncertainty. Consistent with prior studies, we …nd that our measures of aggregate uncertainty and the cross-sectional dispersion in …rm-level performance are associated with lower levels of investment and industrial production and higher levels of unemployment. To test the existence of an interactive relation between …rm-level and aggregate-level uncertainty, we add the interaction term for aggregate uncertainty and earnings dispersion. Consistent with our predictions, we …nd that the e¤ects of dispersion are exacerbated in periods with high aggregate uncertainty, and that the combined e¤ects of dispersion and aggregate uncertainty are larger than the sum of the individual e¤ects. The explanatory power of the model increases signi…cantly when we add the interaction term. While one cannot determine causality in a model that relies on an interaction term, we attempt to provide more causal evidence on the relation between …rm-level and aggregate-level uncertainty and macroeconomic activity. We use several shocks employed by Bloom et al. (2012) when examining the relationship. We attempt to identify shocks that likely a¤ect both aggregate and …rm-level uncertainty and compare their e¤ects to those of shocks that a¤ect only one type of uncertainty. If shocks that increase both have 3 stronger e¤ects than those that increase aggregate uncertainty alone, we can infer a stronger causal link between the interaction e¤ect and macroeconomic activity. Consistent with our hypotheses, we …nd that the election of Bill Clinton in 1992, which only a¤ected policy uncertainty, had a relatively mild e¤ect on macroeconomic conditions. In contrast, the terrorist attacks of Sept. 11, 2001, and the collapse of Lehman Brothers in 2008, which increased both …rm-level and aggregate uncertainty, led to more signi…cant declines in macroeconomic activity. While we cannot eliminate the causality concerns related to our study, this analysis helps mitigate them to some degree. We conduct two additional analyses. First, we build on the above …ndings to shed light on the relation between cross-sectional earnings dispersion and aggregate stock returns. We examine whether the relation between earnings dispersion, the interaction of micro and macro uncertainty, and the macroeconomy help explain the robust relation between earnings dispersion and aggregate stock returns (Jorgensen, Li, and Sadka, 2012). Our results show that the relation between aggregate stock returns and future earnings dispersion is conditional on the amount of aggregate uncertainty in the economy. Thus our …ndings suggest that economic theories on the e¤ects of uncertainty can help explain, at least in part, the somewhat surprising relation between earnings dispersion and aggregate stock returns. Second, we …nd that our interaction term partly predicts macroeconomists’forecast errors.2 Our …ndings have several important policy implications. First, if the economy is in a period of high …rm-level uncertainty, adopting more stable and predictable policies, which lower aggregate uncertainty, should improve macroeconomic conditions. In as similar vein, all else equal, initiating aggregate (macro) policies during periods of low …rm-level uncertainty mitigates the potential negative implications of heightened policy (aggregate) 2 Our paper also extends the literature that shows accounting information is useful for understanding and predicting macroeconomic activity. Much of the prior evidence suggests that aggregate earnings contain macroeconomic information (e.g., Anilowski et al., 2007; Shivakumar, 2007; Kothari et al., 2013; Hann et al., 2012; Bonsall et al., 2013; Ogneva, 2013) and that aggregate earnings predict the U.S. Federal Reserve’s monetary policy (Gallo et al., 2013). While prior literature uses aggregate earnings to understand and predict macroeconomic indicators, we employ dispersion in earnings and conditional dispersion to further highlight the usefulness of accounting information in understanding macroeconomic activity. In a contemporaneous paper, Nallareddy and Ogneva (2015) …nd that earnings dispersion predicts errors in initial macroeconomic estimates released by government statistical agencies. 4 uncertainty. Finally, our study implies that policies that potentially increase both …rm-level and aggregate-level uncertainty have more signi…cant negative implications for economic growth. Therefore such policies have higher economic costs. The rest of the paper is organized as follows. Section 2 describes our hypotheses in more detail. Section 3 discusses our research design and variable measurement. Section 4 presents our main empirical results. Section 5 presents our additional analysis related to stock returns and macroeconomist forecast errors. Section 6 concludes. 2 Uncertainty and Macroeconomic Activity Recent analytical and empirical analyses suggest uncertainty is a signi…cant determinant of macroeconomic activity (e.g., Bloom, 2009; Bloom, Floetotto, Jaimovich, Saporta-Eksten, and Terry, 2012; Baker and Bloom, 2013). These studies show that macroeconomic activity slows during periods of high uncertainty, because investors and …rms are more uncertain about the prospects of their investments, which increases the option value of postponing investments. These postponements lead to signi…cant shortfalls in hiring, investment, and output. Consequently, the reallocation of capital and labor is then hindered, resulting in lower growth in investments, production, productivity, and employment. Consistent with this theory, empirical analysis shows that shocks to uncertainty result in lower growth and may even cause recessions. In a recent review of this literature, Bloom (2014) highlights that uncertainty may take di¤erent forms. Agents in the economy can be uncertain about overall macroeconomic conditions and future growth, the prospects of di¤erent individual …rms, or both. Consistently, prior studies employ both aggregate uncertainty measures, such as VIX and policy uncertainty, and …rm-level measures of uncertainty, such as the cross-sectional dispersion in earnings and stock returns, when examining the e¤ect of uncertainty on macroeconomic activity. Prior evidence suggests that both types of uncertainty, while di¤erent, can lower economic growth because they hinder investments and limit the reallocation of resources (from poorly performing to better performing projects). The two 5 types of uncertainty not only di¤er but may also occur independently, as shown in Table 2. Economic agents can face uncertainty about economic policy and overall growth, while having a clearer understanding of which sector/industry/…rm in the economy is more likely to succeed and capture a larger portion of overall growth. In contrast, investors can be con…dent about the overall state of the economy but uncertain about which sectors/industries/…rms are more likely to succeed. Take, for example, the development of the high-de…nition video market. Several companies and technologies competed for the same market, with only one potential winner. While there was relative certainty regarding the demand for the product and the overall growth in the market, it was not clear which …rm and technology would win. Prior studies explore both types of uncertainty but fail to consider how they overlap and interact. Bloom, Floetotto, Jaimovich, Saporta-Eksten, and Terry (2012) show that both microeconomic and macroeconomic uncertainty ebb and ‡ow through the business cycle, by examining uncertainty at the plant, …rm, and industry level (see also Kozeniauskas, Orlik, and Veldkamp, 2014). They build a dynamic stochastic general equilibrium (DSGE) model to study how the economy reacts to uncertainty shocks. In their model, a single process determines the economy’s uncertainty regime, leading to two possible scenarios. Speci…cally, both micro- and macroeconomic uncertainty can be either high or low. In this study, we extend their analysis and empirically examine the e¤ects of high macro and microeconomic uncertainty relative to scenarios where only one type of uncertainty is high, in addition to scenarios where both types of uncertainty are low (four possible cases). We argue that the e¤ects of uncertainty are most pronounced when economic agents are uncertain about both macroeconomic growth and …rm-level prospects because the combined/interactive e¤ects are larger than the sum of the individual a¤ects. We aim to show that the e¤ects of uncertainty are not driven solely by high levels of uncertainty but also by the interaction of …rm-level and macroeconomic uncertainty. Our hypothesis is based on how investors and …rms react to di¤erent types of uncertainty. Consider an economy with two …rms, A and B. In the …rst scenario, investors have some certainty about overall economic growth but are uncertain about which …rm will succeed. Investors will …nd it di¢ cult to reallocate resources between the …rms, as they cannot identify 6 where capital and labor will be most productive.3 However, because investors know that the overall economy— that is, …rms A and B together— will grow, they can invest in both …rms. On average, their investment will succeed as long as the economy grows. While productivity and growth will su¤er from over- (under-) investment in the less (more) productive …rm, investment will still occur as long as investors can diversify and invest in both …rms. In sum, as long as the overall economy grows, investment will occur, though at somewhat slower pace than when there is no …rm-level uncertainty. Alternatively, consider a second scenario where investors know that …rm A will be more productive than …rm B but are uncertain about the growth of the economy. In this scenario, investors do not know how much to invest overall, because of the di¢ culty forecasting growth. However, they do know that, all else equal, diverting labor and capital from …rm B to …rm A will increase e¢ ciency and thus productivity and growth. Thus overall aggregate investment will be lower due to aggregate uncertainty, but some reallocation of resources and investment will still occur, increasing productivity and growth. Similar to the …rst scenario, diversi…cation and resource allocation allow investors and …rms to react to, and mitigate, the e¤ects of a single and speci…c form of uncertainty. Finally, consider the scenario where investors are uncertain about both overall economic growth as well as which …rm in the economy will be more productive and pro…table. In this scenario, not only will overall investment decline due to uncertainty about aggregate performance, but investment will also be a¤ected by the di¢ cultly of reallocating resources. In such a scenario, diversi…cation helps mitigate the e¤ects of …rm-level uncertainty but does not mitigate the e¤ects of aggregate-level uncertainty. Hence it simply replaces one form of uncertainty with another and does not help investors/…rms protect themselves against the e¤ects of uncertainty. Thus aggregate uncertainty exacerbates the e¤ects of …rm-level uncertainty. Also, cross-sectional resource allocation, which helps mitigate the e¤ects of aggregate uncertainty on investments, is di¢ cult in this scenario because it is not clear which …rm, if any, will better employ the resources. Thus …rms and investors have less ability to react to, and mitigate, the e¤ects of aggregate uncertainty during such 3 Temporary resource misallocation can also be an outcome of ambiguity aversion (Caskey 2009). 7 periods. Thus …rm-level uncertainty exacerbates the e¤ects of macroeconomic uncertainty. Therefore we expect the negative e¤ects of uncertainty on macroeconomic activity to be most pronounced during periods when aggregate as well as …rm-level uncertainty are high, due to their interactive e¤ect. 2.1 Sectoral Shifts and Unemployment The relation between earnings dispersion and macroeconomic conditions is also driven by the reallocation of labor in the economy. In addition to business cycles, economists have long recognized that sectoral shifts are one of the main drivers of unemployment. The underlying causes for unemployment under this theory are frictions in the labor market and layo¤s.4 Lucas and Prescott (1974) and Lilien (1982) develop and empirically test the sectoral shift theory.5 They argue that unemployment depends not only on the state of the economy and the overall hiring rate but also on the amount of layo¤s. Since it takes time for employees to …nd new jobs, layo¤s increase unemployment, irrespective of the overall hiring rate. Consider, for example, an economy that is growing at 2%, where all …rms have a growth rate of 2%. In such an economy, layo¤s are minimal because all …rms are using employees e¢ ciently, and no gains are achieved by reallocating employees across …rms. In contrast, consider a di¤erent economy that is also growing at 2%. However, in this economy, …rms grow at di¤erent rates. Some …rms grow at rates signi…cantly above 2%, while others have negative growth rates. In such an economy, the poor performers lay o¤ employees. Since these employees need time to move to the more productive …rms, unemployment rises. Thus while both economies grow at the same rate, dispersion in performance is associated with higher levels of unemployment. According to the sectoral shift hypothesis, macroeconomic conditions as well as dispersion a¤ect unemployment through layo¤s. We extend the sectoral shift hypothesis and argue 4 These frictions include job training, education, geographical distance, and search costs, among others. They are the main reason why economists consider a four to six percent unemployment rate as full employment as opposed to a zero unemployment rate. 5 Since the work of Lilien (1982), the economics literature has debated whether sectoral shifts or business cycles are the main driver of unemployment (e.g., Abraham and Katz, 1986; Hosios, 1994; Lazear and Spletzer, 2012). This literature does not examine how these two e¤ects interact. 8 that, since layo¤s occur when …rm performance is low, overall layo¤s are highest during periods of low aggregate growth and high dispersion. In other words, there is an interactive e¤ect. This is because the proportion of …rms performing poorly, which are likely to layo¤ employees, is highest when the economy is performing poorly and cross-sectional dispersion is high. Thus Lilien’s (1982) model suggests that unemployment is jointly determined by aggregate economic growth and dispersion. Prior studies include aggregate economic growth and dispersion independently. However, the correct speci…cation with which to examine Lilien’s (1982) model is to employ an interaction term. The use of an interaction term captures the idea that aggregate performance and dispersion jointly determine layo¤s and unemployment. Lilien’s (1982) model also assumes a constant hiring rate, which implies that the reallocation of employees is time invariant (an assumption questioned by Lilien as well). In other words, in this model, dispersion a¤ects unemployment only because it increases layo¤s. We extend Lilien’s (1982) model to include the uncertainty framework introduced by Bloom (2009). As discussed above, empirical and analytical studies on uncertainty suggest that employers are generally more reluctant to hire during periods when uncertainty is high. Therefore, according to these studies, hiring rates are not constant. Hence employee migration during periods of poor economic growth and high dispersion will take longer, due to increased uncertainty, and lengthier migration results in greater unemployment. In other words, when the uncertainty framework introduced by Bloom (2009) is superimposed onto Lilien’s (1982) sectoral shift model, the hiring rate becomes a decreasing function of uncertainty. Since we hypothesize that the implications of uncertainty are most pronounced during periods of both high aggregate and …rm-level uncertainty, we expect unemployment to increase more signi…cantly during periods when both macro- and microeconomic uncertainty are high. In sum, one reason why the impact of uncertainty is most pronounced when both …rm-level and aggregate-level uncertainty interact, is because unemployment increases more signi…cantly due to their interactive e¤ect. 9 3 Variable Measurement and Research Design 3.1 Estimating Aggregate Uncertainty Shocks, Firm-Level Uncertainty Shocks, and the Interaction of Aggregate and Firm-Level Uncertainty Our empirical analysis focuses on the relation between the interaction of aggregate and …rm-level uncertainty, and macroeconomic conditions. A large literature in economics and …nance suggests that persistent variables can provide misleading predictive evidence (e.g., Yule 1926; Granger and Newbold, 1974; Ferson et al., 2003). Speci…cally, if two variables are highly persistent over time, a regression including one as a dependent variable and one as an independent variable is likely to …nd evidence of predictability, even if the two variables are unrelated. Persistent variables are ones that have large autocorrelations. Macroeconomic variables are highly persistent, as are aggregate earnings and dispersion measures. To overcome the spurious regression bias, Campbell (1991) and Ferson et al. (2003) suggest stochastic de-trending of persistent variables. That is, removing the persistent component in both the independent and dependent variables. In our analysis, we use an AR (2) model to isolate the persistent component of all the key variables. We then employ the residuals from the AR(2) models in our analysis. We refer to the residuals as shocks. This should alleviate concerns related to the spurious regression bias. In addition, since our variables have no serial correlation, there is no serial correlation in the error term. Therefore we do not need to correct for any time-series autocorrelation in the residuals when employing the shocks in our regression analysis. In untabulated robustness tests we re-estimate our main regression models using Newey West adjusted standard errors and …nd identical results. To measure aggregate uncertainty, we utilize a performance based measure and the economic policy uncertainty index employed in Baker et al., 2015. We view periods of low aggregate performance and periods of high policy uncertainty, as periods with high aggregate uncertainty (e.g., Barry and Brown, 1985, and Bloom et al., 2012). We also employ a performance based measure because the sectoral shift hypothesis described in Section 2 10 predicts that the interaction of dispersion and aggregate performance a¤ects unemployment, even if aggregate performance does not capture uncertainty. Our performance-based measure is an aggregate earnings measure, computed using forward-looking analyst forecasts. We estimate aggregate earnings shocks in three steps. First, we measure the revision in earnings expectations for …rm i during quarter t for the next …scal year (one-year-ahead): Et (earni;s+1 ) Et Pi;t 1 t revi;s+1 = 1 (earni;s+1 ) (1) t where revi;s+1 is the revision in quarter t. s represents the current year, while s + 1 represents the next full year. Et (earni;s+1 ) is the median analyst earnings forecast at the end of quarter t for …rm i, for the year s + 1. Pi;t end of quarter t 1 is the price per share for …rm i at the 1. Second, we estimate aggregate revisions for each quarter as the equally weighted average of all …rm-level revisions: Nt 1 X AggREVt = revit N i=1 (2) where AggREVt equals the aggregate revision for quarter t, and N is the number of eligible …rms (common stocks) during that quarter. Third, because aggregate revisions in earnings are more persistent than at the …rm-level, we use the following AR (2) model to estimate the aggregate earnings shock: AggREVt = 0 + 1 AggREVt 1 + 2 AggREVt 2 + et (3) where et is the aggregate earnings shock for quarter t. Finally, we convert aggregate earnings shocks into a binary variable (REVt ). Speci…cally, aggregate earnings (revisions) shocks in the lowest quartile are assigned a value of one, and the rest of the observations are assigned a value of zero. (We explain the rationale for converting aggregate earnings 11 shocks into a binary variable below.) Put di¤erently, the most negative quartile of aggregate earnings shock quarters is assigned a value of one, and the remaining quarters receive a value of zero.6 REVt is the variable we use in our analysis (see model 5a below). As an alternative measure of aggregate uncertainty, we employ the economic policy uncertainty index (Baker et al., 2015). We follow similar steps to the ones used when computing REVt . First, we use an AR(2) model similar to equation (3) to compute the aggregate uncertainty shock.7 Next, we de…ne the variable U nc, which is an indicator variable equal to one for the highest quartile of political uncertainty shocks. In other words, the variable U nc receives the value of one for the most positive quartile of political uncertainty shock quarters and zero otherwise. We view periods of low performance and high policy uncertainty as periods of high aggregate, or macroeconomic, uncertainty. Therefore we employ the variable U nc in our alternative speci…cation (see model 5b below). To measure …rm-level (microeconomic) uncertainty, we employ a dispersion based measure (e.g., Bloom et al., 2012). To estimate earnings dispersion, we estimate earnings revisions as described in step 1 above. Next, we estimate earnings dispersion as the standard deviation of revisions in a quarter as follows: v u Nt u1 X t Dist = (revit N i=1 AggRevt )2 (4) where Dist is the dispersion for quarter t, AggRevt is the aggregate earnings revision for quarter t, and n is the number of …rms (eligible common stocks) during that quarter. Finally, to isolate the nonpersistent component in earnings dispersion, we employ an AR (2) model, similar to equation (3) above. The AR (2) residual is the proxy we use to measure earning dispersion shocks (Rev_Disp).8 Rev_Disp is the variable we employ in our analysis (see models 5a and 5b). 6 The AR (1) coe¢ cient of aggregate revision shocks is 0.02 and is statistically insigni…cant (t-value 0.21), suggesting that the AR (2) process does a good job of isolating aggregate revision shocks. 7 The AR (1) coe¢ cient of aggregate uncertainty shocks is -0.03 and is statistically insigni…cant (t-value -0.28), suggesting that the AR (2) process does a good job of isolating aggregate uncertainty shocks. 8 The AR (1) coe¢ cient for earnings dispersion shocks is -0.02 and statistically insigni…cant (t-value -0.23), suggesting that the AR (2) process does a good job of isolating earnings dispersion shocks. 12 Finally, we de…ne the interaction term between aggregate uncertainty shocks and …rm-level uncertainty shocks (earnings dispersion shocks). We convert the aggregate uncertainty variables into binary variables (as described above). We do this because both …rm-level uncertainty shocks and aggregate uncertainty shocks can obtain positive and negative values, which in turn a¤ect the sign of the interaction term. For example, a positive shock to both aggregate uncertainty and earnings dispersion, and a negative shock to both aggregate uncertainty and earnings dispersion, both result in a positive sign for the interaction term. However, the two scenarios are economically di¤erent. To address this issue, we convert the aggregate uncertainty variables into binary variables. Thus our aggregate uncertainty measures are always nonnegative, and the interaction term will not have the same sign in the two scenarios described above. In our empirical models, we include aggregate GDP as an additional continuous control variable. This variable is added to ensure that our results are not driven by the use of a binary variable to measure aggregate uncertainty. 3.2 Research Design We test whether the interaction of aggregate and …rm-level uncertainty is associated with investment, unemployment, and industrial production. To test our hypothesis, we estimate the following time-series regression model for each of our macroeconomic indicators. M acro_indt = 0 + 1 Revt + Rev_Dispt + 2 [Revt Rev_Dispt ] 3 + 4 [D_GDPt ] + 5 [D_Const ] + + 7 [D_Y ieldt ] + 8 [D_Inft ] + 6 9 (5a) [D_T ermt ] [D_Deft ] + "t where M acro_indt is one of the three macroeconomic indicators examined, D_Invt , D_U nempt , and D_Indt , which equal the residuals from an AR (2) model of quarterly investment, unemployment rates, and industrial production growth, respectively. Rev is an aggregate earnings dummy equal to one for the lowest quartile of aggregate earnings shocks. 13 Rev_Disp measures earnings dispersion shocks using the residuals from an AR(2) model of earnings dispersion. As we note above, since the variables employed in the regression are residuals from AR(2) models, they are not serially correlated. Therefore, we do not need to correct for any time series correlation in the error terms. Our predictions for investment and industrial production are as follows. First, we expect a negative coe¢ cient on aggregate earnings shocks, 1 < 0. A negative coe¢ cient implies that investment and industrial production are lower when aggregate pro…tability is lower. In other words, investment and production are lower during contractions. Second, if higher levels of …rm-level uncertainty (dispersion in earnings) impede investment, we expect the coe¢ cient for earnings dispersion to be negative, that is, 2 < 0. Finally, we hypothesize that the e¤ects of dispersion are conditional on the amount of aggregate uncertainty in the economy because the two types of uncertainty have an interactive e¤ect. Speci…cally, we expect the impact of dispersion on investment and industrial production to increase (become more negative) when aggregate pro…tability is lower. Therefore we expect the coe¢ cient on the interaction term to be negative, that is, 3 < 0. In other words, the adverse e¤ects of …rm-level uncertainty on investment and production are exacerbated during periods of low or negative economic growth, when aggregate uncertainty is high. Our predictions for unemployment have the opposite sign. First, we expect a positive coe¢ cient on aggregate earnings shocks, 1 > 0. A positive coe¢ cient implies that unemployment is higher when aggregate pro…tability is lower. In other words, unemployment is higher during contractions. Second, if higher levels of dispersion in earnings increase unemployment, we expect the coe¢ cients on the dispersion measures to be positive, that is, 2 > 0. Finally, we hypothesize that the e¤ects of dispersion are conditional on the state of the economy. Speci…cally, we expect the impact of dispersion on unemployment to increase (become more positive) when aggregate pro…tability is lower. Therefore we expect the coe¢ cient on the interaction term to be positive, that is, 3 > 0. In other words, the adverse e¤ects of dispersion on employment are exacerbated during periods of low or negative economic growth, when aggregate uncertainty is high. In an alternative speci…cation, we replace our performance-based measure with the policy 14 uncertainty index, and estimate the following model: M acro_indt = + 0 1 U nct + Rev_Dispt + 2 [U nct Rev_Dispt ] 3 + 4 [D_GDPt ] + 5 [D_Const ] + + 7 [D_Y ieldt ] + 8 [D_Inft ] + [D_T ermt ] 6 9 (5b) [D_Deft ] + "t where U nc is an indicator variable equal to one for the highest quartile of policy uncertainty shocks. 1; 2; 3, Our predictions for 1; 2; 3 are the same as our predictions for because we expect aggregate uncertainty to be higher during periods of low performance and high policy uncertainty. 3.3 Unemployment and Industrial Production Forecast Error Data Unemployment and industrial production forecast error data are obtained from the Survey of Professional Forecasters (SPF). We are interested in the precision of the predictions as well as the forecast errors. Therefore in addition to the signed forecast errors, we examine absolute forecast errors. The forecast error is estimated as follows: F E = Actualq+1 F orecastq+1 q (6) where F E equals the forecast error, Actualq+1 is the realized macroeconomic value released in quarter t+1, and F orecastq+1 is the consensus SPF forecast of the macroeconomic q variable, based on the median forecast for quarter t+1, as of quarter t. The absolute forecast errors are de…ned as follows: AF E = Actualq+1 15 F orecastq+1 q (7) where AFE equals the absolute forecast error (Baghestani, 2009). 3.4 Sample Our sample is constructed from the intersection of I/B/E/S, CRSP, and Compustat during 1985 to 2011. We restrict the sample to ordinary common shares (share codes 10, 11) that are traded on the NYSE, AMEX, or NASDAQ exchanges. Further, to align data across …rms, we include only …rms with …scal year-ends in March, June, September, or December. Finally, every quarter, we winsorize the extreme two percent of observations when calculating the aggregate earnings revision measures.9 Macroeconomic data are collected from FRED, the Federal Reserve Bank of St. Louis. Investment equals the quarter-over-quarter growth in seasonally adjusted U.S. aggregate gross private domestic investment (measured and reported by the U.S. Department of Commerce’s Bureau of Economic Analysis). The quarterly average unemployment data (averaged over 3 months) is the seasonally adjusted civilian unemployment rate, which is de…ned as the number of unemployed people as a percent of the labor force (measured and reported by the U.S. Department of Labor’s Bureau of Labor Statistics). The quarterly average industrial production data (averaged over three months) is the seasonally adjusted real output in production, expressed as a percentage growth term (measured and reported by Board of Governors of the Federal Reserve System). Economic policy uncertainty is the average quarterly (three month average) economic policy uncertainty index from Baker et al., 2015. The index is constructed from three components: (1) newspaper coverage of policy-related economic uncertainty, (2) the number of federal tax code provisions set to expire in future years, and (3) disagreement among economic forecasters.10 Unemployment and industrial production forecast error data is obtained from the Survey of Professional Forecasters (SPF).11 9 Our results are robust to winsorizing the extreme one percent of observations. The economic policy uncertainty data is taken from the webpage: http://www.policyuncertainty.com/us_monthly.html. 11 http://www.phil.frb.org/research-and-data/real-time-center/survey-of-professional-forecasters/ 10 16 3.5 Descriptive Statistics Table 1 presents descriptive statistics for the macro and uncertainty variables. Our sample includes 105 quarters from the fourth quarter of 1985 up to and including the fourth quarter of 2011. The mean values of the macro and dispersion variables are zero by construction, as these estimates are residuals from various AR (2) speci…cations. More speci…cally, the mean values of investment shocks (D_Invt ), unemployment shocks (D_U nempt ), industrial production shocks (D_Iprodt ), and earnings dispersion shocks (Rev_Dispt ), hereafter earnings dispersion, are zero. The median dispersion measure has a negative value, while the median unemployment and industrial production shocks have a value of zero. The median investment shock is slightly positive. One-quarter-ahead mean and median unemployment forecast errors are negative. That is, unemployment forecasts are higher than their realizations. This evidence suggests that unemployment forecasts are optimistic. Industrial production forecast errors are on average negative, but the median forecast error is positive. Therefore unlike unemployment forecasts, we do not observe any optimism in industrial production forecasts. This evidence is consistent with …ndings in prior literature that show macro forecasts are not systematically optimistic (Hann et al. 2012). The univariate results are presented in Table 2. Consistent with our expectations, aggregate earnings shocks are negatively related to investment and industrial production shocks and positively related to unemployment shocks. For example, aggregate earnings shocks have a Pearson (Spearman) correlation of -0.36 (-0.31) with investment shocks. That is, investment shocks are lower during periods of lower aggregate pro…tability. The correlations between political uncertainty shocks, our alternative measure of aggregate uncertainty, and the macroeconomic indicators yield similar inferences. Additionally, investment and industrial production shocks are negatively correlated with earnings dispersion, while unemployment shocks are positively related to earnings dispersion. Finally, investment and industrial production shocks are negatively correlated with the interaction term, and unemployment shocks are positively related to the interaction term. For example, the Pearson correlation between investment shocks and the interaction terms ranges from 17 -0.45 to -0.50, depending on the measure of aggregate uncertainty employed. 4 4.1 Empirical Analysis The Interaction of Firm-level and aggregate-Level Uncertainty, and Macroeconomic Shocks The multivariate results are presented in Tables 3–5. Panel A in each table presents the results using our earnings-based measure of aggregate uncertainty. Panel B presents the results using the policy uncertainty index as our measure of aggregate uncertainty. The investment-related results are presented in Table 3. Consistent with our predictions, the coe¢ cients for the aggregate uncertainty indicator variables are negative. The coe¢ cient estimates range from -1.79 to -2.50. These results suggest that periods with negative aggregate earnings shocks (in the bottom quartile) and positive policy uncertainty shocks, have lower investment shocks. These results are statistically signi…cant at the 1 percent level. Aggregate uncertainty explains 6–13 percent of the variation in investment shocks. Firm-level uncertainty shocks are also negatively related to investment shocks and explain more of the variation, with an R-squared of 18 percent. When both aggregate and …rm-level uncertainty shocks are included simultaneously in the regression model, the R-squared increases to 20 percent. However, when we interact the measures of aggregate uncertainty and …rm-level uncertainty, the interaction term is signi…cantly negative, and the R-squared increases to 24 and 25 percent across the speci…cations. Furthermore, the joint e¤ect of …rm-level and aggregate-level uncertainty on investment is statistically signi…cant in both panels (F -values of 16.64 and 22.17). These results highlight that the combined e¤ects of aggregate and …rm-level uncertainty are larger than the sum of the individual e¤ects. In other words, uncertainty impedes investment most when investors and …rms are uncertain about the overall growth in the economy, and also about which …rms/industries/sectors are likely to succeed. Figure 1 present the results graphically, further highlighting the strong relation between our interaction term and investment shocks. 18 The unemployment-related results are presented in Table 4. Consistent with our predictions, the coe¢ cient on our aggregate uncertainty measures are positive. coe¢ cient estimates range from 0.08 to 0.15 across the panels. The These results suggest that periods with high aggregate uncertainty shocks have higher unemployment shocks and are consistent with the considerable amount of prior evidence which demonstrates that unemployment rises during periods of low economic growth (e.g., Abraham and Katz, 1986). Aggregate uncertainty shocks explain 2-10 percent of the variation in unemployment shocks. The results in column (2), in Panels A and B, are also consistent with the arguments presented in Lilien (1982). On its own, dispersion explains 16 percent of the shocks to unemployment. Including aggregate uncertainty and dispersion together increases the explanatory power of the model. Speci…cally, the R-squared in column (3) increases weakly to 16–22 percent, depending on the measure of aggregate uncertainty employed. This evidence suggests that aggregate earnings and earnings dispersion together do a better job of explaining unemployment shocks, relative to each measure independently. More interestingly, the results in column (4) are consistent with our hypothesis that the e¤ects of dispersion on unemployment are conditional on the state of the economy. The coe¢ cient for the interaction term in Panel A is positive and statistically signi…cant. Moreover, the R-squared increases by over 50% when we include the interaction term. These results support our hypothesis that the correct speci…cation with which to test Lilien (1982)’s model is with an interaction term. We …nd similar results when we employ the policy uncertainty index as a measure of aggregate uncertainty, in Panel B. The joint e¤ect of the interaction terms on unemployment is also statistically signi…cant across the panels. (F -values of 29.53 and 20.81). Figure 2 further highlights the strong relation between the interaction term and unemployment shocks. Taken together, this evidence highlights the importance of the relation between dispersion, and aggregate performance and uncertainty, when explaining unemployment shocks. The multivariate regression results related to industrial production are presented in Table 5. These results provide further evidence consistent with our predictions. The coe¢ cient on aggregate uncertainty shocks in column (1) is negative across both panels. Aggregate 19 uncertainty explains 5–7 percent of the variation in industrial production shocks. Next, we examine the relation between dispersion and industrial production. In column (2), we show that dispersion explains 14 percent of the shocks to industrial production. Furthermore, including aggregate earnings or policy uncertainty and dispersion together (column (3)) increases the explanatory power of the model. Speci…cally, the R-squared increases weakly to 14–17 percent depending on the measure of aggregate uncertainty used. The results in Table 5 are also consistent with our hypothesis that the e¤ects of dispersion on industrial production are conditional on the state of the economy. The coe¢ cient on the interaction term in column (4) is negative and statistically signi…cant in both models. Furthermore, the interaction term explains an additional 5-6 percent of the variation in industrial production shocks. Moreover, the joint e¤ect of dispersion and the interaction on industrial production is statistically signi…cant (F -values of 17.09 and 21.71). The evidence in these analyses suggests that aggregate uncertainty and …rm-level uncertainty shocks jointly explain industrial production shocks better than each measure individually. The relation between the interaction term and industrial production shocks presented in Figure 3 also highlights the importance of the relation between aggregate uncertainty shocks and …rm-level uncertainty, or dispersion, in understanding industrial production shocks. Additionally, in Tables 3-5, the speci…cation in column (5) includes the various macroeconomic control variables. Including these control variables adds to the explanatory power of the model. For example, in Table 3 Panel A, the adjusted R-squared increases by 2 percent when we include the control variables. Not surprisingly, real GDP growth has a signi…cant association with investment, unemployment, and industrial production. While the control variables enhance the explanatory power of the model, they do not signi…cantly alter the relation between the interaction term and the macroeconomy. These …ndings suggest that the observed relation between the interaction term and the macroeconomy is not attributable to prior known factors. In sum, both aggregate uncertainty and cross-sectional dispersion in …rm-level performance, or …rm-level uncertainty, are associated with lower levels of macroeconomic activity (investment, unemployment, and industrial production shocks). 20 Furthermore, we …nd that the e¤ects of dispersion are exacerbated during periods of high aggregate uncertainty. Therefore the addition of an interaction between aggregate uncertainty and earnings dispersion substantially improves our understanding of how uncertainty relates to the macroeconomy. 4.2 Alternative Cuto¤s As we explain in Section 3, the measures of aggregate uncertainty employed in our analyses are indicator variables that measure aggregate earnings shocks in the lowest quartile (Rev) and the most positive quartile of political uncertainty shock quarters (U nc). We further explain the requirement to use indicator variables in Section 3.2. To ensure that our results are not sensitive to the cuto¤ used to de…ne the indicator variables, we reestimate the models presented in Tables 3–5, column (5), using alternative cuto¤s to de…ne the indicator variables. More speci…cally, we rede…ne Rev to equal 1 for observations in the lowest quintile, tercile, median, and for all negative shocks. Similarly, we rede…ne U nc to equal 1 for observations in the highest quintile, tercile, median, and for all positive shocks. The results from this analysis are presented in Table 6. The results for investment, unemployment, and industrial production shocks are presented in Panels A, B, and C, respectively. Our main results are robust to the alternative cuto¤s. We continue to …nd a robust relation between our interaction term and macroeconomic indicators. The main di¤erence relative to Tables 3–5, is that the relation between industrial production and the interaction term using the policy uncertainty index (Panel C) is more sensitive to alternative cuto¤s. However, the coe¢ cient remains negative in all the speci…cations, with t-statistics ranging form -1.49 to -2.77. Furthermore, the R-squared values presented across the panels are similar in magnitude to those presented in Tables 3–5. In untabulated analysis, we also examine how the relation between the interaction between …rm-level and aggregate-level uncertainty and macroeconomic indicators di¤ers across groups of high and low aggregate uncertainty. We …nd that the relation between the interaction term and the macroeconomic shocks is signi…cantly stronger for the high group 21 (quarters with the highest quartile or tercile of aggregate uncertainty shocks) compared to that of the low group. 4.3 Alternative Measures of Firm-Level and Aggregate-Level Uncertainty As an additional robustness test, we employ additional measures of uncertainty which are commonly employed in the …nance literature. These measures are based on stock prices. As an alternative measure of aggregate uncertainty, we employ the Chicago Board Options Exchange (CBOE) market volatility index (the VIX index). As an alternative measure of …rm-level uncertainty, we employ the average idiosyncratic-return volatility. For our aggregate uncertainty based measure we …rst estimate the residuals from an AR (2) model of the VIX index. We then de…ne the variable VIX, which is an indicator variable equal to one for the highest quartile of residuals (VIX shocks). We estimate the …rm-level uncertainty measure using the following steps. First, for each …rm and quarter we estimate the daily idiosyncratic returns as the residuals from the regression of daily stock returns on daily market, size, book-to-market, and momentum factors (Fama and French (1993); Carhart, 1997). Second, for each …rm-quarter, we compute the standard deviations of daily idiosyncratic returns (from step 1) and de…ne them as …rm-level idiosyncratic volatility. Third, using all …rms in a quarter, we compute the average …rm-level idiosyncratic volatility (from step 2) and de…ne it as aggregate idiosyncratic volatility. Finally, shocks to aggregate idiosyncratic volatility, Idio_vol, are de…ned as the residuals from an AR (2) model of aggregate idiosyncratic volatility (from step 3). The results using these alternative measures are reported in Table 7. The results are consistent with our main …ndings. The addition of the interaction term between …rm-level and aggregate-level uncertainty, signi…cantly increases the explanatory power of the model. Also, the coe¢ cient on our interaction term is statistically signi…cant as well. Finally, adding the interaction term signi…cantly reduces the independent e¤ect of idiosyncratic volatility, and marginally reduces the e¤ect of VIX. These …nding further support our hypothesis that 22 the interaction of aggregate and …rm-level uncertainty helps explain macroeconomic activity. 4.4 Uncertainty Shocks and the Macroeconomy One caveat of our empirical analysis so far is that the results only report associations and do not imply a causal link. Bloom (2014) discusses the di¢ culty in determining causality in this literature, as uncertainty can arise endogenously during recessions. Thus to identify causality, empirical studies must identify exogenous shocks or instrumental variables to identify the causal link between uncertainty and macroeconomic activity. Dealing with causality is particularly challenging in our study because we focus on an interaction term. This makes identifying exogenous variation di¢ cult, as we need exogenous variation that is related to our interaction term but not to the individual components. In other words, we would require a shock to the interaction term that does not impact dispersion and aggregate uncertainty. Since such exogenous variation does not exist, determining causality in our study is di¢ cult. Nevertheless, we employ several exogenous shocks employed in Bloom et al. (2012) to highlight the importance of the interaction between …rm-level and aggregate-level uncertainty. We attempt to identify shocks that likely a¤ect aggregate uncertainty as well as …rm-level uncertainty and compare them to shocks that a¤ect only one type of uncertainty. In particular, we focus on three shocks. First, we focus on Bill Clinton’s presidential election in 1992, which represents the …rst Democratic president after 12 consecutive years of Republican presidents. We expect such a shock to a¤ect policy uncertainty but not …rm-level uncertainty (or dispersion). Second, we examine the Sept. 11, 2001, terrorist attacks. We expect the attacks to a¤ect policy and aggregate uncertainty, as the U.S. was considering how to deal with the implications of the event and future threats. In addition, since the attacks a¤ected some industries more than others (e.g., airline and transportation), we expect dispersion and …rm-level uncertainty to rise as well. Finally, we focus on the collapse of Lehman Brothers in 2008. We expect both aggregate uncertainty as well as …rm-level uncertainty to rise following this event. The prospects of new …nancial regulations following the collapse increased policy 23 uncertainty in 2008. Also, since …rms vary in their sensitivity to shocks in the …nancial industry (e.g., real estate and housing, banks), we expect dispersion to rise following this event as well. Consistent with our expectations, we …nd that only aggregate uncertainty rises following the Clinton election in 1992, while both aggregate uncertainty and …rm-level uncertainty rise following the other two other shocks. In other words, Sept. 11 and the collapse of Lehman Brothers resulted in higher …rm-level and aggregate-level uncertainty. By comparing the macroeconomic implications of these di¤erent shocks, we can examine how higher levels of both …rm-level and aggregate-level uncertainty a¤ect the economy, compared to how aggregate uncertainty alone a¤ects macroeconomic activity. If shocks that increase both micro and macro uncertainty have more pronounced e¤ects compared to shocks to aggregate uncertainty alone, then we can infer a more causal link between uncertainty and macroeconomic activity. We conduct our analysis at the annual level.12 For each event, we plot investment, unemployment, and industrial production shocks surrounding the event. Our results are presented in Figures 4–6. Consistent with our regression results presented earlier, in Figure 4, we …nd that the Clinton election, which only a¤ected policy uncertainty, had a relatively mild e¤ect on macroeconomic conditions. In contrast, Figure 5 shows how the Sept. 11 terrorist attacks, which resulted in higher …rm-level and aggregate-level uncertainty, led to noticeable negative declines in macroeconomic activity. Investment and industrial production shocks are lowest in 2001, and employment shocks reach their peak during 2001 as well. A similar picture emerges following the collapse of Lehman (Figure 6). For example, investment, which experienced negative shocks throughout 2006 - 2009, reaches it lowest point in 2008. While the e¤ects of Sept. 11 on macroeconomic activities were relatively short lived, the e¤ects of the Lehman Brother’s collapse were longer lasting. One reason for this di¤erence is that, in the post Sept. 11 period, the interaction term decreased (see Figures 1–3). In contrast, the collapse of Lehman Brothers was followed by more periods of high uncertainty, both 12 For this analysis, we use the average of the quarterly residuals employed in Tables 3–5 to compute annual levels of investment, unemployment, and industrial production shocks. 24 …rm-level and aggregate-level uncertainty. Therefore the collapse of Lehman Brothers had a more severe and prolonged negative impact on macroeconomic activity. In addition to these shocks, in unreported results, we also examine China’s accession to the World Trade Organization (WTO) in 2005. Consistent with Bloom et al. (2012), we …nd that dispersion increased following this event.13 However, as depicted in Figures 1–3, the interaction term is not high during the same period. In other words, while dispersion increased following the event, aggregate uncertainty did not. Therefore consistent with our hypothesis, China’s accession to the WTO did not have signi…cant adverse e¤ects for U.S. macroeconomic activity. These …ndings, which are similar to those presented in Bloom et al. (2012), further show that the e¤ects of uncertainty on macroeconomic activity are most pronounced when both …rm-level and aggregate-level uncertainty are high simultaneously, because of their interactive e¤ect. While the causality concerns related to our study remain, this analysis helps mitigate the concern to some degree. The analysis (weakly) implies a more causal link between uncertainty and macroeconomic activity. We further note that, in our analysis, we focus on only three main events. We focus on these events because they are not surrounded by other confounding e¤ects. For example, another identi…ed event, Black Monday (1987), follows a signi…cant tax reform implemented in 1986, which makes it di¢ cult to draw inferences related to the e¤ects of the Black Monday shock. 4.5 Employing Normalized Variables In our primary analysis we use an indicator variable to measure aggregate uncertainty. As an alternative approach, we normalize the earnings dispersion and aggregate-level uncertainty shocks, and then add a constant to ensure that both variables are positive. Thus each variable as well as the interaction term is always positive. We then use the normalized variables when estimating models 5a and 5b, capturing the full variation in each variable. Our …ndings using this approach are largely consistent with our primary results, using the indicator variable. 13 We …nd that the increase in dispersion occurred largely in the third quarter of 2006. 25 However, the standardized model is not intuitive to interpret. For brevity, we do not report these results. 5 Additional Analysis 5.1 Firm-Level and Aggregate-Level Uncertainty and Market Returns As we note above, Jorgensen, Li, and Sadka (2012) show that earnings dispersion has a strong association with aggregate stock returns. Speci…cally, they …nd a negative association between aggregate stock returns and the cross-sectional standard deviation of earnings changes (scaled by stock prices). The negative association between aggregate stock returns and earnings dispersion is most pronounced between aggregate stock returns and future earnings dispersion. Their …ndings are consistent with the vast amount of …rm-level evidence showing that information in prices leads earnings (e.g., Collins, Kothari and Rayburn, 1987, and Collins and Kothari, 1989). Our …ndings in Tables 3–5 suggest that the relation between earnings dispersion and the macroeconomy is conditional on the state of the economy. Therefore we extend the analysis in Jorgensen, Li, and Sadka (2012) to examine whether the relation between aggregate stock returns and future earnings dispersion is also conditional on the state of the economy. Since the relation between earnings dispersion and the macroeconomy is conditional on the state of the economy, we expect the relation between aggregate stock returns and earnings dispersion to depend on the state of the economy. The results from this analysis are presented in Table 8. To be consistent with Jorgensen, Li, and Sadka (2012) we begin the analysis by computing aggregate earnings and earnings dispersion using changes in quarterly earnings as opposed to revisions of earnings forecasts. The results using these measures are reported in Panel A. Consistent with the results reported in Jorgensen, Li, and Sadka (2012), we …nd a negative coe¢ cient on EARt+1 , which implies that higher contemporaneous stock returns are associated with higher future earnings. As for dispersion, our …ndings are also consistent with Jorgensen, Li, and Sadka (2012). We 26 show that lower aggregate stock returns are associated with higher future dispersion in earnings (column (2)), and that the relation between aggregate stock returns and future dispersion dominates the relation between aggregate stock returns and aggregate earnings growth (column (3)). More importantly, the results in Table 8 are consistent with the conclusions drawn from Tables 3–5. The relation between aggregate stock returns and future earnings dispersion is conditional on the state of the economy. The coe¢ cient on the interaction term is negative and statistically signi…cant. In addition, the explanatory power of the model increases when the interaction term is included. For example, in Panel A, the explanatory power increases from 12 to 16 percent. We …nd similar results when we employ our performance based measure of aggregate uncertainty (Panel B) and the policy uncertainty index (Panel C). In both panels, the interaction term is negative and signi…cant, and the explanatory power of the model increases when the interaction terms is included. Taken together, these …ndings suggest that the surprising relation between aggregate stock returns and earnings dispersion is driven (at least in part) by the relation between earnings dispersion, the interaction of earnings dispersion and aggregate performance, and the macroeconomy. 5.2 Uncertainty and Macroeconomic Forecast Errors In this section, we examine whether unemployment and industrial production forecast errors are predicted by the interaction of …rm-level and aggregate-level uncertainty.14 Unemployment and industrial production forecast error data are obtained from the Survey of Professional Forecasters (SPF).15 We are interested in the precision of the predictions as well as the level of errors. Therefore we examine both absolute forecast errors and signed forecast errors. We examine both signed forecast errors and absolute forecast errors because each 14 In a similar vein, Konchitchki and Patatoukas (2013a) and Konchitchki and Patatoukas (2013b) show that aggregate earnings predict GDP forecast errors. 15 The SPF does not forecast investment. Hence we restrict this analysis to unemployment and industrial production. 27 one re‡ects a di¤erent aspect of forecasting. Our tests using signed forecast errors examine whether forecasters incorporate the implications of …rm-level and aggregate-level uncertainty when making forecasts. More speci…cally, our analysis with signed forecast errors examines whether forecasters understand that periods of high …rm-level and aggregate-level uncertainty are followed by slower economic growth. In contrast, absolute forecast errors measure the accuracy of the forecast. Since our interaction term is a measure of uncertainty, we expect absolute forecast errors to be positively associated with the interaction term because forecasting is more di¢ cult in periods of high uncertainty. This tests also helps us validate that our interaction term is indeed a measure of uncertainty. For this analysis, we only employ our performance-based measure of aggregate uncertainty. Table 9, Panel A, presents the relation between unemployment forecast errors and the interaction term. The results in columns (1) and (2) show that the interaction of …rm-level and aggregate-level uncertainty predicts macroeconomists’absolute forecast errors. The results in columns (3) and (4) show that the interaction term predicts macroeconomists’ signed forecast errors but not when controlling for other macroeconomic indicators. However, while the coe¢ cient for the interaction term in column (4) is no longer signi…cant, it remains positive and has a similar economic magnitude to the coe¢ cient in column (3). Furthermore, the reduction in signi…cance likely arises from the number of variables included in the regression, none of which have a signi…cant relation with the forecast error. Taken together, this evidence implies that macroeconomic forecasters do not incorporate the relation between …rm-level and aggregate-level uncertainty and unemployment in their forecasts. We note that in the case of unemployment, the interaction term may explain macroeconomic activity even in the absence of uncertainty, due to sectoral shifts. In Panel B, we test whether our interaction term predicts absolute forecast errors related to industrial production. We …nd that it predicts macroeconomists’absolute forecast errors, as well as their signed forecast errors. Taken together, the results in Table 9 highlight the importance of the interaction term in understanding and predicting macroeconomic activity. In addition, the positive relation between …rm-level and aggregate-level uncertainty and absolute forecast errors supports the hypothesis that the interaction term is related to 28 uncertainty about macroeconomic growth. 6 Conclusion Economic theory suggests that both dispersion in performance, a measure of …rm-level uncertainty, and aggregate uncertainty result in lower levels of macroeconomic activity (e.g., Bloom, 2009). We hypothesize and …nd that the e¤ects of uncertainty on macroeconomic activity are most pronounced when both aggregate uncertainty and …rm-level uncertainty are high simultaneously, because each type of uncertainty exacerbates the e¤ect of the other. 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Capitalism, socialism, and democracy. New York: Harper and Brothers. Shivakumar, L., 2007. Aggregate earnings, stock market returns and macroeconomic activity: a discussion of ‘does earnings guidance a¤ect market returns? The nature 31 and information content of aggregate earnings guidance.’ Journal of Accounting and Economics 44, 64–73. Yule, G.U., 1926. Why do we sometimes get nonsense –correlations between time-series?-A study in sampling and the nature of time-series. Journal of the Royal Statistical Society 89, 1–63. 32 Figure 1. The Interaction of Aggregate Uncertainty and Firm-Level Uncertainty, and Investment Shocks 15 0.035 0.03 10 0.025 0.02 5 0.015 0 0.01 0.005 ‐5 ‐0.005 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 0 ‐10 ‐0.01 ‐0.015 ‐15 Interaction of Aggregate and Firm‐Level Uncertainty Investment Shocks Figure 1 presents shocks to aggregate investment for 105 quarters from Q4:1985 to Q4:2011. Shocks to investment equal the residuals from an AR (2) model of investment. The interaction of Aggregate and FirmLevel Uncertainty equals the interaction between aggregate uncertainty shocks and firm-level uncertainty shocks (earnings dispersion shocks). Aggregate uncertainty shocks equal 1 for the highest tercile of shocks to the economic policy uncertainty index and zero otherwise. The magnitudes of investment shocks are displayed on the right vertical axis, and the magnitudes of the interaction term are displayed on the left vertical axis. 33 Figure 2. The Interaction of Aggregate Uncertainty and Firm-Level Uncertainty, and Unemployment Shocks 1 0.035 0.03 0.8 0.025 0.6 0.02 0.4 0.015 0.2 0.01 0.005 0 ‐0.005 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 0 ‐0.2 ‐0.4 ‐0.01 ‐0.015 ‐0.6 Interaction of Aggregate and Firm‐Level Uncertainty Unemployment Shocks Figure 2 presents shocks to aggregate unemployment rates for 105 quarters from Q4:1985 to Q4:2011. Shocks to unemployment rates equal the residuals from an AR (2) model of unemployment rates. The interaction of Aggregate and Firm-Level Uncertainty equals the interaction between aggregate uncertainty shocks and firm-level uncertainty shocks (earnings dispersion shocks). Aggregate uncertainty shocks equal 1 for the highest tercile of shocks to the economic policy uncertainty index and zero otherwise. The magnitudes of unemployment rate shocks are displayed on the right vertical axis, and the magnitudes of the interaction term are displayed on the left vertical axis. 34 Figure 3. The Interaction of Aggregate Uncertainty and Firm-Level Uncertainty, and Industrial production Shocks 3 0.035 0.03 2 0.025 0.02 1 0.015 0 0.01 0.005 ‐1 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 ‐0.005 1985 0 ‐2 ‐0.01 ‐0.015 ‐3 Interaction of Aggregate and Firm‐Level Uncertainty Industrial Production Shocks Figure 3 presents shocks to aggregate industrial production for 105 quarters from Q4:1985 to Q4:2011. Shocks to industrial production equal the residuals from an AR (2) model of industrial production. The interaction of Aggregate and Firm-Level Uncertainty equals the interaction between aggregate uncertainty shocks and firm-level uncertainty shocks (earnings dispersion shocks). Aggregate uncertainty shocks equal 1 for the highest tercile of shocks to the economic policy uncertainty index and zero otherwise. The magnitudes of industrial production shocks are displayed on the right vertical axis, and the magnitudes of the interaction term are displayed on the left vertical axis. 35 Figure 4. Clinton Election (1992) 1.5 1 0.5 0 1990 1991 1992 1993 ‐0.5 ‐1 ‐1.5 ‐2 ‐2.5 Unemployment Shocks Industrial Production Shocks Investment Shocks Figure 4 presents shocks to macroeconomic indicators (investment, unemployment, and industrial production) for the period before and after the Clinton election, event years 1990–1993. The shocks to the macroeconomic indicators equal the residuals from an AR (2) model of the respective indicator. The magnitudes of the macroeconomic indicator shocks are displayed on the left vertical axis. Years are displayed on the horizontal axis. 36 Figure 5. Sept. 11 Terrorist Attacks (2001) 1 0.5 0 1999 2000 2001 2002 ‐0.5 ‐1 ‐1.5 ‐2 ‐2.5 ‐3 Unemployment Shocks Industrial Production Shocks Investment Shocks Figure 5 presents shocks to macroeconomic indicators (investment, unemployment, and industrial production) for the period before and after the Sept. 11 terrorist attacks, event years 1999–2002. The shocks to the macroeconomic indicators equal the residuals from an AR (2) model of the respective indicator. The magnitudes of the macroeconomic indicator shocks are displayed on the left vertical axis. Years are displayed on the horizontal axis. 37 Figure 6. Lehman Brothers Collapse (2008) 0.5 0 2006 2007 2008 2009 ‐0.5 ‐1 ‐1.5 ‐2 ‐2.5 ‐3 ‐3.5 ‐4 Unemployment Shocks Industrial Production Shocks Investment Figure 6 presents shocks to macroeconomic indicators (investment, unemployment, and industrial production) for the period before and after the collapse of Lehman Brothers, event years 2006–2009. The shocks to the macroeconomic indicators equal the residuals from an AR (2) model of the respective indicator. The magnitudes of the macroeconomic indicator shocks are displayed on the left vertical axis. Years are displayed on the horizontal axis. 38 Table 1: Descriptive statistics D_Invt Mean 0.00 Std Dev 3.01 5 Perc -5.11 Median 0.02 95 Perc 4.69 D_Unempt 0.00 0.20 -0.27 0.00 0.32 D_Iprodt 0.00 0.85 -1.39 0.00 1.18 Mktrett-1 (%) 1.16 9.21 -17.21 2.64 14.53 Unemp_FEt+1 -0.03 0.13 -0.23 -0.05 0.22 Unemp_AFEt+1 0.11 0.08 0.00 0.09 0.27 Iprod_FEt+1 -0.22 2.61 -4.58 0.06 3.27 Iprod_AFEt+1 2.02 1.65 0.16 1.56 5.27 Revt 0.25 0.43 0.00 0.00 1.00 Rev_Dispt (*100) 0.00 0.65 -0.65 -0.08 0.88 Revt (x) Rev_Dispt (*100) 0.14 0.38 0.00 0.00 0.76 UNCt 0.26 0.44 0.00 0.00 1.00 Unct (x) Rev_Dispt (*100) 0.06 0.38 -0.25 0.00 0.56 D_GDPt 0.00 0.54 -0.86 0.01 0.80 D_Const 0.00 0.49 -0.80 -0.05 0.89 D_Termt 0.00 0.42 -0.68 -0.05 0.76 D_Yieldt 0.00 0.17 -0.26 0.01 0.32 D_CPIt 0.00 0.49 -0.61 0.02 0.68 D_Deft 0.00 0.21 -0.21 -0.01 0.25 This table presents descriptive statistics for the macro, aggregate earnings, and uncertainty variables. The sample includes 105 quarters from Q4:1985–Q4:2011. D_Invt is the residual from an AR (2) model of quarterly private domestic investments, in quarter t. D_Unempt is the residual from an AR (2) model of quarterly unemployment rates, in quarter t. D_Iprodt is the residual from an AR (2) model of quarterly industrial production growth measures in percentages. Mktret is the quarterly value-weighted market return. 39 FE (AFE) is the forecast error (absolute forecast error) related to the macroeconomic indicator forecasted. All forecast error data is obtained from the Survey of Professional Forecasters. Rev is an indicator variable equal to one for the lowest quartile of aggregate earnings shocks. Aggregate earnings shocks equal the residual from an AR (2) model of aggregate earnings revisions. Aggregate earnings revisions are defined as the equally weighted average of firm-level earnings forecast revisions, for one-year-ahead earnings, that occur during the current quarter, deflated by the beginning of the quarter stock price. t i , s 1 rev 1 E ( earni ,s 1 ) Et 1 ( earni , s 1 ) , t Agg Re vt Nt Pi ,t 1 Nt (rev i 1 t i ,s 1 ) Earnings dispersion (Rev_Disp) is the residual from an AR (2) model of aggregate earnings dispersion (AggDis). AggDis is the standard deviation of firm-level earnings forecast revisions, deflated by beginning of the quarter stock price. AggDist 1 Nt Nt ( rev i 1 i ,t Agg Re vt ) 2 UNC is an indicator variable equal to one for the highest quartile of economic policy uncertainty shocks. Economic policy uncertainty shocks equal the residuals from an AR (2) model of the economic policy uncertainty index. For the calculation of aggregate earnings revisions and dispersion, to align data across firms in a quarter, we only include firms with fiscal year-ends in March, June, September, and December. Further, every quarter, we winsorize the top and bottom 2 percent of observations when calculating aggregate earnings and revision measures. D_GDP is the AR (2) residual of seasonally adjusted quarterly real gross domestic product. D_CON is the AR (2) residual of seasonally adjusted quarterly real personal consumption expenditures. D_Term is the AR (2) residual of change in term spread (10-Year Treasury constant maturity rate minus three-Month Treasury bill secondary market rate). D_Yield is the AR(2) residual of change in yield spread (effective Federal Funds Rate minus three-Month treasury bill secondary market rate). D_Def is the AR (2) residual of change in default spread (Moody’s seasoned Baa corporate bond yield minus Moody’s seasoned Aaa corporate bond yield). D_CPI is the AR (2) residual of seasonally adjusted quarterly consumer price index. 40 Table 2: Correlation matrix Inv Rest Unemp Rest Iprod Rest Mktrett1 Unemp FEt+1 Unemp AFEt+1 Iprod FEt+1 Iprod AFEt+1 Revt Rev Dispt Revt (x) Rev_Dispt Unct Unct (x) Rev_Dispt 1 -0.39 0.51 0.32 -0.17 -0.08 0.26 0.06 -0.36 -0.43 -0.50 -0.26 -0.45 Unemp_Rest -0.35 1 -0.55 -0.24 -0.04 0.23 -0.07 0.06 0.16 0.41 0.46 0.34 0.46 Iprod_Rest 0.47 -0.50 1 0.41 -0.02 0.06 -0.01 0.18 -0.25 -0.38 -0.44 -0.28 -0.46 Mktrett-1 0.13 -0.10 0.27 1 -0.10 0.03 0.08 0.07 -0.31 -0.56 -0.49 -0.31 -0.51 Unemp_FEt+1 -0.14 -0.09 -0.03 -0.11 1 -0.16 -0.55 0.47 0.24 0.12 0.16 0.06 0.13 Unemp_AFEt+1 -0.08 0.22 0.14 0.02 -0.28 1 -0.12 0.21 0.01 0.12 -0.05 0.01 -0.02 Iprod_FEt+1 0.21 -0.01 -0.01 0.05 -0.48 0.00 1 -0.41 -0.18 -0.25 -0.15 0.05 -0.16 Iprod_AFEt+1 0.18 0.01 0.20 0.15 0.36 0.13 -0.10 1 -0.05 0.16 -0.08 -0.06 -0.08 Revt -0.31 0.12 -0.22 -0.21 0.26 -0.01 -0.21 -0.09 1 0.49 0.63 0.22 0.39 Rev_Dispt -0.30 0.25 -0.25 -0.41 0.08 0.08 -0.11 -0.07 0.65 1 0.66 0.20 0.59 Revt (x) Rev_Dispt -0.34 0.18 -0.25 -0.23 0.25 0.00 -0.22 -0.10 0.99 0.68 1 0.25 0.81 Unct -0.23 0.33 -0.25 -0.27 0.04 0.00 0.03 -0.07 0.22 0.21 0.23 1 0.25 Unct (x) Rev_Dispt -0.25 0.18 -0.34 -0.30 0.16 -0.01 -0.16 -0.16 0.44 0.49 0.45 0.18 1 Inv_Rest This table presents Pearson (above diagonal) and Spearman (below diagonal) correlations among the key variables of interest for 105 quarters from Q4:1985–Q4:2011. The sample and variable definitions are described in Table 1. Correlations significant at the 10% level or better are highlighted in bold. 41 Table 3: Firm-level and aggregate-level uncertainty, and contemporaneous investment shocks Panel A: Measures of aggregate earnings and contemporaneous investment shocks Dependent Variable : D_INVt Intercept Revt (1) (2) (3) (4) (5) 0.62 (1.95)* 0.00 (0.00) 0.34 (1.07) 0.47 (1.53) 0.45 (1.47) -1.36 (-1.95)* -0.41 (-0.54) 0.15 (0.19) -80.76 (-1.53) -72.02 (-1.36) -2.50 (-3.93)*** Rev_Dispt -199.14 -154.81 (-4.86)*** (-3.34)*** Revt (x) Rev_Dispt -269.70 -355.08 (-2.67)*** (-3.35)*** D_Const -1.62 (-2.78)*** D_Termt -0.05 (-0.08) D_Yieldt -1.56 (-1.00) D_CPIt -0.06 (-0.10) D_Deft -2.26 (-1.51) Adj. R2 0.13 0.18 F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0 42 0.20 0.25 0.27 16.64*** 22.42*** Table 3: Continued Panel B: Measures of economic policy uncertainty and contemporaneous investment shocks Dependent Variable : D_INVt Intercept Unct (1) (2) (3) (4) (5) 0.46 (1.39) 0.00 (0.00) 0.32 (1.05) 0.38 (1.26) 0.36 (1.22) -1.25 (-2.05)** -0.98 (-1.62) -0.86 (-1.37) -182.55 (-4.44)*** -111.09 (-2.28)** -108.99 (-2.24)** -219.96 (-2.57)** -256.03 (-2.93)*** -1.79 (-2.74)*** Rev_Dispt -199.14 (-4.86)*** Unct (x) Rev_Dispt D_Const -1.53 (-2.68)*** D_Termt 0.10 (0.17) D_Yieldt 0.01 (0.01) D_CPIt -0.26 (-0.43) D_Deft -1.71 (-1.13) Adj. R2 0.06 0.18 0.20 F-test: Rev_Dispt+ Unct (x) Rev_Dispt =0 0.24 0.26 22.17*** 25.27*** This table reports the contemporaneous relation between investment shocks and measures of uncertainty for 105 quarters from Q4:1985 ̶ Q4:2011. All the variables are defined in Table 1. Panel A reports the contemporaneous relation between investment shocks and uncertainty, where aggregate uncertainty is derived from aggregate earnings. Panel B presents the contemporaneous relation between investment shocks and uncertainty, where aggregate uncertainty is derived from the economic policy uncertainty index. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 43 Table 4: Firm-level and aggregate-level uncertainty, and contemporaneous unemployment shocks Panel A: Measures of aggregate earnings and contemporaneous unemployment shocks Dependent Variable : D_UNEMPt (1) (2) (3) (4) (5) Intercept -0.02 (-0.87) 0.00 (0.06) 0.01 (0.25) -0.01 (-0.37) 0.00 (-0.16) Revt 0.08 (1.69)* -0.02 (-0.48) -0.11 (-2.14)** -0.10 (-1.99)** 13.44 (4.22)*** 6.72 (1.90)* 7.00 (2.00)** 24.46 (3.63)*** 20.32 (2.83)*** Rev_Dispt 12.69 (4.59)*** Revt (x) Rev_Dispt D_GDPt -0.10 (-2.57)** D_Const -0.01 (-0.32) D_Termt -0.03 (-0.80) D_Yieldt 0.10 (0.94) D_CPIt 0.02 (0.47) D_Deft 0.03 (0.31) Adj. R2 0.02 0.16 0.16 F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0 44 0.25 0.30 29.53*** 20.12*** Table 4: Continued Panel B: Measures of economic policy uncertainty and contemporaneous unemployment shocks Dependent Variable : D_UNEMPt Intercept Unct (1) (2) (3) (4) (5) -0.04 (-1.89)* 0.00 (0.06) -0.03 (-1.61) -0.04 (-1.83)* -0.03 (-1.66)* 0.12 (3.02)*** 0.10 (2.60)** 0.10 (2.41)** 11.07 (4.08)*** 6.24 (1.94)* 7.02 (2.23)** 14.88 (2.64)*** 10.44 (1.78)* 0.15 (3.63)*** Rev_Dispt 12.69 (4.59)*** Unct (x) Rev_Dispt D_GDPt -0.08 (-2.27)** D_Const -0.04 (-0.99) D_Termt -0.05 (-1.19) D_Yieldt 0.02 (0.17) D_CPIt 0.02 (0.47) D_Deft -0.07 (-0.67) Adj. R2 0.10 0.16 0.22 F-test: Rev_Dispt+ Unct (x) Rev_Dispt =0 0.27 0.31 20.81*** 12.69*** This table reports the contemporaneous relation between unemployment shocks and measures of uncertainty for 105 quarters from Q4:1985–Q4:2011. All the variables are defined in Table 1. Panel A reports the contemporaneous relation between unemployment shocks and uncertainty, where aggregate uncertainty is derived from aggregate earnings. Panel B presents the contemporaneous relation between unemployment shocks and uncertainty, where aggregate uncertainty is derived from the economic policy uncertainty index. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 45 Table 5: Firm-level and aggregate-level uncertainty, and industrial production shocks Panel A: Measures of aggregate earnings and contemporaneous industrial production shocks Dependent Variable : D_IPRODt Intercept Revt (1) (2) (3) (4) (5) 0.13 (1.37) 0.01 (0.08) 0.05 (0.49) 0.09 (0.95) 0.05 (0.57) -0.16 (-0.77) 0.13 (0.57) 0.15 (0.71) -44.79 (-3.29)*** -22.41 (-1.45) -23.44 (-1.67)* -81.52 (-2.76)*** -56.67 (-1.97)* -0.49 (-2.61)** Rev_Dispt -49.94 (-4.22)*** Revt (x) Rev_Dispt D_GDPt 0.57 (3.83)*** D_Const 0.05 (0.28) D_Termt 0.24 (1.48) D_Yieldt -0.34 (-0.82) D_CPIt 0.02 (0.11) D_Deft -0.58 (-1.44) Adj. R2 0.05 0.14 0.14 F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0 46 0.19 0.36 17.09*** 10.73*** Table 5: Continued Panel B: Measures of economic policy uncertainty and contemporaneous industrial production shocks Dependent Variable : D_IPRODt Intercept Unct Rev_Dispt (1) (2) (3) (4) (5) 0.14 (1.54) 0.01 (0.08) 0.11 (1.25) 0.13 (1.50) 0.07 (0.94) -0.40 (-2.29)** -0.31 (-1.82)* -0.15 (-0.95) -44.61 (-3.77)*** -21.22 (-1.53) -23.14 (-1.83)* -71.99 (-2.96)*** -48.11 (-2.04)** -0.53 (-2.91)*** -49.94 (-4.22)*** Unct (x) Rev_Dispt D_GDPt 0.54 (3.66)*** D_Const 0.08 (0.50) D_Termt 0.28 (1.72)* D_Yieldt -0.12 (-0.31) D_CPIt -0.01 (-0.09) D_Deft -0.47 (-1.18) Adj. R2 0.07 0.14 F-test: Rev_Dispt+ Unct (x) Rev_Dispt =0 0.17 0.23 21.71*** 0.38 13.10*** This table reports the contemporaneous relation between industrial production shocks and measures of uncertainty for 105 quarters from Q4:1985–Q4:2011. All the variables are defined in Table 1. Panel A reports the contemporaneous relation between unemployment shocks and uncertainty, where aggregate uncertainty is derived from aggregate earnings. Panel B presents the contemporaneous relation between industrial production shocks and uncertainty, where aggregate uncertainty is derived from the economic policy uncertainty index. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 47 Table 6: Firm-level and aggregate-level uncertainty, and contemporaneous macro shocks: alternative cut-offs Panel A: Contemporaneous investment shocks Quintile Intercept Revt (1) 0.34 (1.14) 0.45 (0.50) Unct Rev_Dispt Revt (x) Rev_Dispt Unct (x) Rev_Dispt -91.32 (-1.75)* -346.11 (-3.10)*** (2) 0.38 (1.35) -1.11 (-1.67)* -104.00 (-2.20)** -275.46 (-3.15)*** D_Const -1.80 -1.63 (-3.05)*** (-2.89)*** D_Termt -0.01 0.16 (-0.02) (0.27) D_Yieldt -1.49 -0.16 (-0.93) (-0.10) D_CPIt -0.13 -0.17 (-0.22) (-0.28) D_Deft -2.50 -1.63 (-1.63) (-1.10) Adj. R2 0.26 0.29 F-test: Rev_Dispt+ Interaction =0 20.76*** 26.72*** Tercile -269.62 (-3.11)*** -1.66 -1.45 (-2.85)*** (-2.54)** -0.09 -0.11 (-0.14) (-0.18) -1.38 -0.27 (-0.87) (-0.18) -0.09 -0.37 (-0.15) (-0.60) -2.44 -2.16 (-1.64) (-1.44) 0.25 0.26 Median (5) (6) 0.43 0.46 (1.12) (1.30) 0.02 (0.03) -0.69 (-1.34) -70.04 -74.15 (-1.23) (-1.41) -321.55 (-3.43)*** -281.87 (-3.50)*** -1.59 -1.36 (-2.72)*** (-2.42)** -0.14 -0.02 (-0.23) (-0.04) -1.49 0.09 (-0.94) (0.06) -0.15 -0.36 (-0.25) (-0.60) -2.35 -2.15 (-1.57) (-1.47) 0.26 0.27 Negative/Positive (7) (8) 0.38 0.34 (1.04) (1.00) 0.16 (0.26) -0.43 (-0.83) -84.75 -74.50 (-1.49) (-1.42) -304.65 (-3.14)*** -291.10 (-3.58)*** -1.57 -1.38 (-2.69)*** (-2.45)** -0.09 0.02 (-0.14) (0.03) -1.26 0.03 (-0.80) (0.02) -0.15 -0.38 (-0.24) (-0.62) -2.33 -2.28 (-1.55) (-1.55) 0.25 0.27 25.65*** 29.19*** 25.60*** (3) 0.31 (0.92) 0.63 (0.91) -88.63 (-1.59) -339.03 (-3.29)*** (4) 0.42 (1.30) -0.64 (-1.13) -92.97 (-1.83)* 27.08*** 48 34.46*** 35.04*** Table 6: Continued Panel B: Contemporaneous unemployment shocks Quintile Intercept Revt (1) -0.01 (-0.44) -0.10 (-1.73)* Unct Rev_Dispt Revt (x) Rev_Dispt Unct (x) Rev_Dispt D_GDPt 6.25 (1.84)* 22.26 (3.02)*** (2) -0.02 (-1.27) 0.09 (1.99)** 8.02 (2.53)** 8.11 (1.33) -0.09 (-2.26)** -0.04 (-0.87) -0.05 (-1.28) 0.03 (0.27) 0.01 (0.24) -0.06 (-0.64) 0.28 -0.09 (-2.58)** D_Const -0.01 (-0.25) D_Termt -0.03 (-0.84) D_Yieldt 0.10 (0.96) D_CPIt 0.02 (0.51) D_Deft 0.04 (0.38) Adj. R2 0.30 F-test: Rev_Dispt+ Interaction=0 20.10*** 9.77*** Tercile (3) -0.02 (-0.75) -0.05 (-1.15) 4.89 (1.35) 20.98 (3.06)*** (4) -0.04 (-1.91)* 0.08 (2.29)** 5.38 (1.64) -0.09 (-2.43)** -0.02 (-0.51) -0.04 (-0.90) 0.12 (1.18) 0.01 (0.36) 0.02 (0.17) 0.30 13.25 (2.28)** -0.07 (-2.03)** -0.05 (-1.18) -0.03 (-0.80) 0.03 (0.35) 0.03 (0.75) -0.02 (-0.24) 0.32 21.08*** 15.82*** 49 Median -0.09 (-2.37)** -0.03 (-0.74) -0.04 (-0.88) 0.11 (1.01) 0.01 (0.36) 0.00 (0.03) 0.27 10.73 (1.90)* -0.08 (-1.96)* -0.05 (-1.23) -0.04 (-1.09) 0.01 (0.13) 0.02 (0.55) -0.03 (-0.27) 0.30 Negative/Positive (7) (8) -0.01 -0.04 (-0.39) (-1.93)* -0.04 (-1.14) 0.08 (2.43)** 5.53 6.01 (1.50) (1.74)* 18.42 (2.86)*** 11.18 (1.98)** -0.09 -0.08 (-2.57)** (-2.02)** -0.02 -0.05 (-0.54) (-1.17) -0.03 -0.05 (-0.86) (-1.15) 0.11 0.03 (1.07) (0.32) 0.02 0.02 (0.47) (0.60) 0.01 -0.02 (0.13) (-0.24) 0.30 0.30 16.65*** 15.96*** 22.02*** (5) -0.02 (-0.82) 0.00 (0.04) 5.28 (1.40) 15.14 (2.34)** (6) -0.04 (-1.81)* 0.08 (2.30)** 6.28 (1.79)* 16.04*** Table 6: Continued Panel C: Contemporaneous industrial production shocks Quintile (1) (2) Intercept 0.03 0.04 (0.36) (0.48) Revt 0.25 (1.09) Unct -0.05 (-0.27) Rev_Dispt -25.96 -28.22 (-1.90)* (-2.22)** Revt (x) Rev_Dispt -59.10 (-1.99)** Unct (x) Rev_Dispt -37.00 (-1.51) D_GDPt 0.59 0.56 (4.02)*** (3.73)*** D_Const 0.02 0.07 (0.10) (0.44) D_Termt 0.24 0.27 (1.44) (1.68)* D_Yieldt -0.30 -0.17 (-0.73) (-0.41) D_CPIt 0.00 0.02 (0.03) (0.11) D_Deft -0.63 -0.48 (-1.55) (-1.19) Adj. R2 0.36 0.36 F-test: Rev_Dispt+Interaction=0 11.05*** 9.93*** Tercile 0.55 (3.82)*** 0.05 (0.28) 0.24 (1.48) -0.41 (-1.02) 0.01 (0.05) -0.61 (-1.57) 0.38 -63.73 (-2.77)*** 0.49 (3.39)*** 0.11 (0.68) 0.23 (1.46) -0.17 (-0.45) -0.06 (-0.37) -0.59 (-1.53) 0.40 Median (5) (6) 0.11 0.15 (1.13) (1.62) -0.06 (-0.37) -0.27 (-1.98)* -14.50 -23.88 (-0.98) (-1.71)* -58.69 (-2.32)** -33.44 (-1.49) 0.54 0.52 (3.68)*** (3.40)*** 0.08 0.13 (0.50) (0.82) 0.23 0.26 (1.44) (1.64) -0.39 -0.05 (-0.96) (-0.13) 0.01 -0.01 (0.06) (-0.06) -0.56 -0.46 (-1.41) (-1.19) 0.38 0.38 Negative/Positive (7) (8) 0.11 0.13 (1.13) (1.49) -0.02 (-0.15) -0.25 (-1.80)* -14.53 -22.50 (-1.00) (-1.63) -62.27 (-2.44)** -36.92 (-1.64)* 0.55 0.52 (3.81)*** (3.47)*** 0.07 0.12 (0.42) (0.75) 0.24 0.27 (1.49) (1.67)* -0.39 -0.11 (-0.96) (-0.29) 0.00 -0.02 (0.01) (-0.11) -0.58 -0.49 (-1.48) (-1.25) 0.38 0.38 14.94*** 18.01*** 13.93*** 14.40*** (3) 0.08 (0.97) 0.09 (0.53) -15.27 (-1.06) -71.12 (-2.62)** (4) 0.11 (1.34) -0.17 (-1.15) -14.98 (-1.15) 50 11.48*** 12.00*** This table reports the contemporaneous relation between macroeconomic shocks and measures of uncertainty for 105 quarters from Q4:1985– Q4:2011 using alternative cutoffs to measure aggregate uncertainty. Rev is an indicator variable equal to one for the lowest quintile of aggregate earnings shocks for the “Quintile” partition, the lowest tercile for the “Tercile” partition, below the median for the “Median” partition, and below zero for the “Negative/Positive” partition. UNC is an indicator variable equal to one for the highest quintile of economic policy uncertainty shocks for the “Quintile” partition, the highest tercile for the “Tercile” partition, above the median for the “Median” partition, and above zero for the “Negative/Positive” partition. All the remaining variables are defined in Table 1. Panel A reports the contemporaneous relation between investment shocks and uncertainty. Panel B presents the contemporaneous relation between unemployment rate shocks and uncertainty. Panel C presents the contemporaneous relation between industrial production shocks and uncertainty. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 51 Table 7: VIX, aggregate idiosyncratic volatility, and contemporaneous macroeconomic shocks Panel A: Contemporaneous investment shocks Dependent Variable : D_INVt Intercept VIXt (1) (2) (3) (4) (5) 0.60* (1.68) -0.01 (-0.02) 0.52 (1.52) 0.58* (1.78) 0.60* (1.78) -2.05*** (-3.01) -1.89*** (-2.89) -1.89*** (-2.73) -33.53 (-0.39) -15.36 (-0.16) -2.36*** (-3.36) Idio_Volt -232.83*** -202.49*** (-3.40) (-3.06) VIXt (x) Idio_Volt -361.96*** -400.29*** (-2.84) (-2.81) D_Const -0.71 (-1.00) D_Termt -0.61 (-0.84) D_Yieldt -0.63 (-0.32) D_CPIt -0.18 (-0.25) D_Deft -1.24 (-0.72) Adj. R2 0.11 0.11 F-test: Idio_Volt+ VIXt (x) Idio_Volt =0 52 0.19 0.25 0.22 18.05*** 17.29*** Table 7: Continued Panel B: Contemporaneous unemployment shocks Dependent Variable : D_UNEMPt (1) (2) (3) (4) (5) Intercept -0.04 (-1.62) 0.00 (0.17) -0.04 (-1.48) -0.04* (-1.76) -0.03 (-1.30) VIXt 0.17*** (3.50) 0.16*** (3.20) 0.15*** (3.09) 0.11** (2.22) 10.28** (2.14) -2.33 (-0.37) -4.69 (-0.69) 27.00*** (2.93) 27.76*** (2.73) Idio_Volt 12.62** (2.52) VIXt (x) Idio_Volt D_GDPt -0.06 (-1.40) D_Const -0.02 (-0.43) D_Termt -0.03 (-0.64) D_Yieldt -0.06 (-0.42) D_CPIt 0.06 (1.20) D_Deft 0.06 (0.49) Adj. R2 0.12 0.06 0.15 F-test: Idio_Volt+ VIXt (x) Idio_Volt =0 53 0.22 0.22 13.42*** 9.91*** Table 7: Continued Panel C: Contemporaneous industrial production shocks Dependent Variable : D_IPRODt Intercept VIXt Idio_Volt (1) (2) (3) (4) (5) 0.12 (1.09) -0.01 (-0.12) 0.10 (0.91) 0.11 (1.08) 0.02 (0.26) -0.42** (-2.00) -0.38* (-1.85) -0.03 (-0.16) -55.37*** (-2.71) -15.49 (-0.57) -3.51 (-0.13) -85.43** (-2.13) -75.87* (-1.91) -0.50** (-2.35) -61.56*** (-3.00) VIXt (x) Idio_Volt D_GDPt 0.49*** (2.73) D_Const 0.28 (1.28) D_Termt 0.21 (1.05) D_Yieldt -0.22 (-0.41) D_CPIt -0.04 (-0.21) D_Deft -0.65 (-1.36) Adj. R2 0.05 0.09 F-test: Idio_Volt+ VIXt (x) Idio_Volt =0 0.12 0.15 11.91*** 0.32 7.75*** This table reports the contemporaneous relation between macroeconomic shocks and measures of uncertainty for 86 quarters from Q3:1990–Q4:2011 using alternative measures of aggregate and firm-level uncertainty. VIX is an indicator variable equal to one for the highest quartile of VIX shocks. VIX shocks equal the residuals from an AR (2) model of the VIX index, which is the Chicago Board Options Exchange (CBOE) market volatility index. 54 Idio_Vol is defined as the shocks to aggregate idiosyncratic volatility. Idio_Vol is estimated as follows: First, for each firm and quarter daily idiosyncratic returns are estimated as the residuals from the regression of daily stock returns on daily market, size, book-to-market, and momentum factors (Fama and French (1993); Carhart, 1997). Second, for each firm-quarter the standard deviations of daily idiosyncratic returns (from step 1) are computed and defined as firm-level idiosyncratic volatility. Third, using all firms in a quarter, the average firm-level idiosyncratic volatility (from step 2) is computed and defined as aggregate idiosyncratic volatility. Finally, shocks to aggregate idiosyncratic volatility are defined as the residuals from an AR (2) model of aggregate idiosyncratic volatility (from step 3). All the remaining variables are defined in Table 1. Panel A reports the contemporaneous relation between investment shocks and uncertainty. Panel B presents the contemporaneous relation between unemployment rate shocks and uncertainty. Panel C presents the contemporaneous relation between industrial production shocks and uncertainty. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 55 Table 8: Firm-level and aggregate-level uncertainty, and market returns Panel A: Aggregate earnings and market returns Dependent Variable : Market Rett (1) (2) (3) (4) (5) Intercept 0.02 (1.98)* 0.01 (1.39) 0.02 (1.76)* 0.02 (2.03)** 0.02 (1.71)* Eart+1 -0.04 (-1.69)* -0.02 (-1.11) -0.02 (-0.94) 0.00 (0.23) -1.46 (-3.52)*** -0.40 (-0.67) -0.68 (-1.13) -1.99 (-2.46)** -2.04 (-2.57)** Ear_Dispt+1 -1.54 (-3.78)*** Eart+1 (x) Ear_Dispt+1 D_GDPt+1 0.00 (0.28) D_Const+1 0.01 (0.46) D_Termt+1 0.02 (0.94) D_Yieldt+1 0.01 (0.18) D_CPIt+1 -0.01 (-0.62) D_Deft+1 -0.15 (-2.94)*** Adj. R2 0.02 0.11 F-test: Ear_Dispt+1+ Eart+1 (x) Ear_Dispt+1=0 0.16 18.61*** 56 0.12 0.21 21.63*** Table 8: Continued Panel B: Aggregate earnings revisions and market returns Dependent Variable : Market Rett (1) (2) (3) (4) (5) Intercept 0.03 (2.80)*** 0.01 (1.47) 0.01 (1.55) 0.02 (1.84)* 0.02 (1.79)* Revt+1 -0.07 (-3.33)*** -0.01 (-0.56) 0.01 (0.32) 0.01 (0.37) -7.61 (-5.76)*** -6.16 (-4.02)*** -6.10 (-3.97)*** -5.27 (-1.80)* -5.44 (-1.73)* Rev_Dispt+1 -7.98 (-6.90)*** Revt+1 (x) Rev_Dispt+1 D_GDPt+1 0.00 (0.18) D_Const+1 0.02 (0.88) D_Termt+1 0.03 (1.68)* D_Yieldt+1 0.02 (0.44) D_CPIt+1 -0.01 (-0.61) D_Deft+1 -0.10 (-2.18)** Adj. R2 0.10 0.31 F-test: Rev_Dispt+1+ Revt+1 (x) Rev_Dispt+1 =0 57 0.31 0.32 21.02*** 0.35 18.63*** Table 8: Continued Panel C: Economic policy uncertainty and market returns Dependent Variable : Market Rett (1) (2) (3) (4) (5) Intercept 0.03 (2.87)*** 0.01 (1.47) 0.02 (2.63)*** 0.02 (2.86)*** 0.02 (2.71)*** Unct+1 -0.07 (-3.35)*** -0.04 (-2.57)** -0.04 (-2.20)** -0.03 (-1.79)* -7.37 (-6.43)*** -5.55 (-4.05)*** -5.53 (-4.08)*** -5.53 (-2.31)** -6.20 (-2.47)** Rev_Dispt+1 -7.98 (-6.90)*** Unct+1 (x) Rev_Dispt+1 D_GDPt+1 -0.01 (-0.63) D_Const+1 0.02 (1.05) D_Termt+1 0.03 (1.90)* D_Yieldt+1 0.05 (1.11) D_CPIt+1 -0.02 (-0.96) D_Deft+1 -0.08 (-2.00)** Adj. R2 0.09 0.31 F-test: Rev_Dispt+1+ Unct+1 (x) Rev_Dispt+1=0 0.35 0.38 32.02*** 0.40 31.47*** This table reports the relation between market returns and measures of uncertainty for 105 quarters from Q4:1985–Q4:2011. Aggregate realized earnings (EARt+1) and realized earnings dispersion (Ear_Dispt+1) are computed using realized seasonal random walk earnings, following the methodology used to compute Rev and Rev_Dsip. All the remaining variables are defined in Table 1. Panel A reports the relation between market returns and an interaction term based on aggregate realized earnings. Panel B presents the relation between market returns and an interaction term based on aggregate earnings revisions. Panel C reports the relation between market returns and interaction term based on the economic policy uncertainty index. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 58 Table 9: Firm-level and aggregate-level macroeconomist forecast errors uncertainty, and one-quarter-ahead Panel A: Measures of aggregate earnings and unemployment forecast errors UNEMP_AFEt+1 (1) (2) 0.09 (9.59)*** 0.10 (9.51)*** -0.04 (-2.74)*** -0.04 (-2.46)** 0.01 (0.60) 0.01 (0.32) -0.02 (-0.50) -0.02 (-0.60) Rev_Dispt -2.93 (-1.75)* -3.17 (-1.83)* 0.72 (0.28) 0.79 (0.30) Revt (x) Rev_Dispt 6.41 (2.01)** 6.09 (1.72)* 9.82 (1.97)* 7.29 (1.36) Intercept Revt (3) (4) D_GDPt -0.01 (-0.29) -0.04 (-1.29) D_Const -0.01 (-0.62) -0.04 (-1.35) D_Termt 0.02 (1.23) 0.00 (0.01) D_Yieldt -0.03 (-0.63) -0.07 (-0.87) D_CPIt 0.00 (0.20) 0.02 (0.79) D_Deft 0.03 (0.65) 0.10 (1.31) Adj. R2 0.04 F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0 1.64 0.02 0.05 0.10 0.94 6.16** 3.17* 59 UNEMP_FEt+1 Table 9: Continued Panel B: Measures of aggregate earnings and industrial production forecast errors IPROD_AFEt+1 (1) Intercept (2) IPROD_FEt+1 (3) (4) 1.73 (9.60)*** 1.82 (0.52) 0.18 (0.60) 0.08 (0.33) Revt 0.23 (0.51) 0.12 (0.27) -0.19 -(0.26) -0.15 (-0.23) Rev_Dispt 2.76 (0.09) -5.07 (-0.17) 44.89 (0.89) 55.57 (1.28) 154.02 (2.61)** 126.32 (2.09)** -254.37 (-2.64)*** -194.54 (-2.19)** Revt (x) Rev_Dispt D_GDPt -0.61 (-1.93)* 1.15 (2.48)** D_Const -0.52 (-1.51) 1.44 (2.83)*** D_Termt 0.32 (0.94) 0.17 (0.34) D_Yieldt -0.06 (-0.07) -0.12 (-0.10) D_CPIt -0.15 (-0.44) -0.31 (-0.63) D_Deft 1.34 (1.57) -3.21 (-2.56)** Adj. R2 0.14 F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0 9.70*** 0.26 0.08 0.36 5.58** 6.50** 3.38* This table reports the relation between one-quarter-ahead macroeconomist forecast errors (signed, and absolute) and uncertainty for 104 quarters from Q4:1985–Q3:2011. All the variables are defined in Table 1. Panel A reports the relation between one-quarter-ahead unemployment forecast errors and uncertainty. Panel B presents the relation between one-quarter-ahead industrial production forecast errors and uncertainty. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 60