How EPU drives long-term industry beta

Finance Research Letters 22 (2017) 249–258

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Finance Research Letters journal homepage: www.elsevier.com/locate/frl

How EPU drives long-term industry beta Honghai Yu a, Libing Fang a,∗, Donglei Du b, Panpan Yan a a b

School of Management and Engineering, Nanjing University, Nanjing, 210093, China School of Business Administration, University of New Brunswick, Fredericton, NB Canada E3B 5A3, Canada

a r t i c l e

i n f o

Article history: Received 26 December 2016 Revised 16 February 2017 Accepted 29 May 2017 Available online 2 June 2017 JEL Classification: E30 C32 C54 G18

a b s t r a c t Based on the DCC-MIDAS framework, we estimate the long-term beta of 10 industries driven by Economic Policy Uncertainty (EPU) in the US. The results demonstrate that EPU significantly drives industry beta. The driving effects on different industries are further analyzed along with the related stock market turmoil. The effects of 10 categories of specific EPU on industries’ beta are also presented. The findings help to characterize the systematic risk of industries facing different categories of economic policy uncertainty. © 2017 Elsevier Inc. All rights reserved.

Keywords: Industry beta EPU DCC-MIDAS MIDAS regression

1. Introduction Industry beta is essential to investors and fund managers, especially to those with long-term concerns. Industry portfolios are popular base assets in many strategic and tactical asset allocation models. Industry beta is also important to the practice of risk management, especially when monitoring and/or hedging portfolio market exposure. In this paper, we investigate how Economic Policy Uncertainty (EPU) drives industry beta in the US. EPU proposed by Baker et al. (2016) is the index of policy-related economic uncertainty based on newspaper coverage frequency. While in the US, the index includes also the two components based on (i) the present value of future scheduled tax code expirations and (ii) disagreement among professional forecasters over future government purchases and consumer prices. To some extent, it can affect industry-level corporate investment through financial decisions and supply-demand channels (Gulen and Ion, 2016). Therefore, EPU is expected to be a driving factor of industry beta. The literature has demonstrated EPU’s effect on the US stock market index. For example, Arouri et al. (2016) show that an increase in EPU significantly reduces stock returns, and this effect is stronger and persistent during periods of extreme volatility. Bekiros et al. (2016) present evidence that information on EPU matters when predicting the US equity premium. Liu and Zhang (2015)’s evidence suggests that higher EPU leads to significant increases in market volatility. However, relatively little attention has been paid to EPU’s impact on the stock market at the industry level. This paper uses DCC-MIDAS to estimate industry beta driven by EPU. MIDAS (MIxed DAta Sampling) regression is used to analyze the driving effect of ∗

Corresponding author. E-mail addresses: [email protected] (H. Yu), [email protected] (L. Fang), [email protected] (D. Du), [email protected] (P. Yan).

http://dx.doi.org/10.1016/j.frl.2017.05.012 1544-6123/© 2017 Elsevier Inc. All rights reserved.

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aggregated and category-specific EPU. MIDAS was introduced by Ghysels et al. (2007) to conduct bivariate regression and by Colacito et al. (2011) to model conditional volatility and correlation. It is especially suitable for our work due to the different frequency data of monthly EPU and daily return of industry portfolios. Baele and Londono (2013) also use the DCC-MIDAS model to investigate the dynamics and macroeconomic determinants of industry betas where EPU is not concerned. Based on the sector indices of Global Industry Classification Standard (GICS) Level 1, this paper contributes to the literature in three ways. First, the betas of Financials, Information Technology, and Materials are most likely higher than other industries driven by EPU, which presents that these industries are significantly affected by the economic policy uncertainty. However, the industries of Consumer Staples, Energy, and Utilities are always in the lowest group. The intuition is that these industries mainly supply necessary goods and services to people living and producing in an economy. The betas measuring the systematic risk of such industries are expected to be less driven by EPU. Therefore, our first contribution is the identification of the industries which are most/least driven by EPU, on average. Second, the DotCom Turmoil from late 1998 and the Financial Crisis from 2007 (both considered as subperiods in this study) mark structural changes in the driving effect of EPU. Along with the regimes switching from the Tranquil Period to these two great periods of turmoil, more and more industries not closely related to the turmoil, such as Health Care and Energy, become negatively driven by EPU. For the industries that are more directly involved in the turmoil, the betas are, in contrast, more highly driven by EPU before rather than during the tumultuous period. For example, the industry beta of Information Technology is positively related to EPU during the Tranquil Period, but is negative during the following two subperiods. The level, however, is especially high during the DotCom Turmoil. Similar results can be found in Financials. These findings imply that the effect of EPU on industry beta is probably a leading indicator of the turmoil that is closely related to the industry. Third, concerning the effect of category-specific EPU, we find that the categories of Taxes, Health Care, and National Security universally relate to the economy and show relatively higher explanation power of the industry betas over all the sample periods. However, the categories of EPU with more specific purpose significantly drive more industries’ beta only during the periods of great turmoil. For example, the categories of Fiscal Policy, Government Spending, and Entitlement Programs show less significant effects on the industries’ beta during the Tranquil Period, but show a greater effect on most of the industries during the periods of great turmoil. The category of Sovereign Debt is especially significant during the transformation of the 2007 Financial Crisis to the sovereign debt crisis in 2009, but not during the other two periods. The third contribution of this paper is to uncover the effects of the specific categories EPU on industries’ beta. The remainder of the paper is organized as follows. Sections 2 and 3 describe our methodology and sample data. The empirical results are presented and analyzed in Section 4. Section 5 presents the study’s conclusions.

2. Methodology 2.1. DCC-MIDAS with EPU We estimate industry beta using the DCC-MIDAS model proposed by Colacito et al. (2011). It is a natural extension combining the models of DCC (Engle, 2002) and GARCH-MIDAS (Engle et al., 2013). The MIDAS (MIxed DAta Sampling) specification is used here to link the smooth component of daily return correlations to monthly EPU. We denote ri, t and rm, t as the daily returns of industry and market index, respectively. The DCC style of bivariate vector rt = (ri,t , rm,t ) is specified as

rt ∼iid N (μ, Ht )

(1)

Ht = Dt Rt Dt

where Ht is the variance-covariance matrix; Dt is a diagonal matrix with standard deviations of Di, t and Dm, t ; Rt is conditional correlation matrix of the standardized innovations; μ = (μi,t , μm,t ) is the conditional mean of rt . Both of Di, t and Dm, t (denoted as Dk,t , k = ∀i, m) are composed of short- and long-term component

Dk,t =



δk,τ gk,t

(2)

where gk, t is the short-term component and follows (daily) GARCH(1,1) process

gk,t = (1 − αk − βk ) + αk

(rk,t−1 − μk )2 + βk gk,t−1 δk,τ

(3)

δ k, τ is the long-term component of conditional volatility and changes at quarterly frequency τ . It is set to be weighted sum of Lv lags of EPU over a long horizon,

log δk,τ = δk + θk

Lv  l=1

ϕl (ωk )Xτepu −l

(4)

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where Xτ −l is the level of EPU during the period of τ − l; the weighting scheme of ϕ l (ωk ) is chosen to be beta function epu

(1 − l/Lv )ωk −1 ϕ l ( ω k ) = L v ωk −1 j=1 (1 − j/Lv )

(5)

The correlation matrix contains also two components. Specifically, the standardized residuals ηt = (ηi,t , ηm,t ), where ηt = Dt−1 (rt − μ ). It is possible to obtain short-term quasi-correlation matrix Qt whose element on off-diagonal position is

qi,m,t = (1 − ai − bi )ρ¯ i,m,τ + ai ηi,t−1 ηm,t−1 + bi qi,m,t−1

(6)

where ρ¯ i,m,τ is the slowly moving long-term correlation. The elements on diagonal position, qi, i, t and qm, m, t are obtained similarly. The short-term correlation is

qi,m,t √ qi,i,t qm,m,t

ρi,m,t = √

(7)

The long-term component of correlation is also driven by EPU in the following way

ρ i,m,τ =

exp(2zi,m,τ ) − 1 exp(2zi,m,τ ) + 1

zi,m,τ = δi,m + θi,m

Lc 

(8)

ϕl (ωi,m )Xτepu −l

(9)

l=1

where the weighting function ϕ l (ωi, m ) is similarly to ϕ l (ωk ) in Eq. (5). Combining the conditional volatility and correlation in Eqs. (4) and (8), the quarterly industry beta driven by EPU is,

βi,τ = ρ¯ i,m,τ

δi,τ δm,τ

(10)

2.2. The MIDAS regression To explicitly capture the effect of aggregated EPU on industry beta and disentangle it further, we investigate how β τ (i is omitted hereafter for simplicity) is related to the measure of aggregated EPU and each of the category specific one, xτ(n ) , where n = 3 for quarterly frequency. Since the different frequency of quarterly β τ and monthly xτ(n ) , we employ the MIDAS regression of Ghysels et al. (2007),

βτ = α0 + α1 B(L1/n ; θ )xτ(n) + ετ(n) B(L1/n ; θ )

S

(11) B(s; θ )Ls/n ,

L1/n x(n )

) = xτ(n−1 ; xτ(n ) represents /n

for τ = 1, . . . , T , where = s=0 is a lag operator such that τ monthly aggregated EPU and various category-specific EPU. The lag coefficients in B(s, θ ) of the corresponding lag operator Ls/n are parameterized as a function of a vector of parameters θ . In this paper, the lags number of xτ(n ) is one year, and following Ghysels and Wright (2009), we choose beta finite polynomials only one parameter as the lag function. L1/n

3. Data description Our analysis of industry beta is based on the Global Industry Classification Standard (GICS) Level 1 Sector indices at the daily frequency. GICS was developed in response to the global financial community’s demand for one complete, consistent set of global sector and industry definitions, thereby enabling seamless company, sector and industry comparisons across countries, regions, and the world. The industry portfolios are constructed by capitalization weighted from related companies. The 10 GICS industries are Consumer Discretionary (COND), Consumer Staples (CONS), Energy (ENRS), Financials (FINL), Health Care (HLTH), Industrials (INDU), Information Technology (INFT), Materials (MATR), Telecommunication Services (TELS), and Utilities (UTIL). The S&P 500, the index of market portfolios, is also popular among practitioners. All data comes from Bloomberg. The EPU index is monthly and is based on the 10 leading US newspapers’ coverage frequency. To investigate explicitly the different effect of category-specific policy on industry betas, we utilize 10 categories of specific EPU introduced by Baker et al. (2016). These categories are Monetary Policy (MON_POL), Fiscal Policy (FIS_POL), Taxes (TAX), Government Spending (GOV_SPE), Health Care (HEA_CAR), National Security (NAT_SEC), Entitlement Programs (ENT_PRO), Regulation (REG), Financial Regulation (FIN_REG), and Sovereign Debt (SOV_DEB). One additional category-specific EPU in Baker et al. (2016) is Trade Policy, which we did not include since there are many missing observations. The period analyzed runs from January 1993 to December 2015. Table 1 reports summary statistics of EPU, the returns of industries and S&P 500. The standard deviations of Financials (FINL) and Information Technology (INFT) are relatively higher than the others, which implies they are riskier during the sample period. The maximum and minimum show that the industries of FINL and INFT are also likely to boom and crash. The results of Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) test indicate that all return series are stationary at the 1% level. Furthermore, the results of ARCH test are also significant, suggesting that the industry shows a volatility clustering effect, which is the basis of GARCH modeling.

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Table 1 Descriptive statistics of the data. Mean(%) COND CONS ENRS FINL HLTH INDU INFT MATR TELS UTIL EPU S&P 500

0.034 0.030 0.028 0.021 0.037 0.030 0.040 0.021 0.006 0.011 0.933 0.027

Median 0.068 0.045 0.028 0.048 0.059 0.059 0.106 0.048 0.014 0.053 0.810 0.057

Maximum

Minimum −10.099 −9.296 −16.884 −18.639 −9.173 −9.215 −10.008 −12.934 −10.320 −8.996 0.373 −9.470

12.313 8.835 16.960 17.201 11.713 9.516 16.077 12.473 12.926 12.684 2.341 10.957

Stdev. 1.319 0.951 1.555 1.836 1.192 1.274 1.765 1.454 1.378 1.139 0.402 1.168

Skewness −0.033 −0.137 −0.290 −0.099 −0.120 −0.277 0.145 −0.208 0.065 −0.038 1.250 −0.240

Kurtosis 9.755 11.238 13.707 19.183 8.813 8.591 7.854 9.785 9.806 13.133 4.240 11.637

ADF

PP ∗∗∗

−56.771 −57.482∗ ∗ ∗ −59.455∗ ∗ ∗ −82.050∗ ∗ ∗ −57.256∗ ∗ ∗ −77.436∗ ∗ ∗ −56.596∗ ∗ ∗ −76.324∗ ∗ ∗ −56.533∗ ∗ ∗ −77.559∗ ∗ ∗ −5.681∗ ∗ ∗ −57.979∗ ∗ ∗

∗∗∗

−75.915 −79.773∗ ∗ ∗ −82.338∗ ∗ ∗ −83.453∗ ∗ ∗ −76.038∗ ∗ ∗ −77.598∗ ∗ ∗ −77.009∗ ∗ ∗ −76.657∗ ∗ ∗ −77.957∗ ∗ ∗ −77.650∗ ∗ ∗ −5.681∗ ∗ ∗ −81.627∗ ∗ ∗

ARCH(5)

ARCH(10)

∗∗∗

3.532∗ ∗ ∗ 5.653∗ ∗ ∗ 8.861∗ ∗ ∗ 8.526∗ ∗ ∗ 6.112∗ ∗ ∗ 3.473∗ ∗ ∗ 2.813∗ ∗ ∗ 2.776∗ ∗ ∗ 3.139∗ ∗ ∗ 2.324∗ ∗ ∗ 49.037∗ ∗ ∗ 6.645∗ ∗ ∗

5.640 7.424∗ ∗ ∗ 13.927∗ ∗ ∗ 12.964∗ ∗ ∗ 8.603∗ ∗ ∗ 4.007∗ ∗ ∗ 3.741∗ ∗ ∗ 3.650∗ ∗ ∗ 3.619∗ ∗ ∗ 2.668∗ ∗ 91.217∗ ∗ ∗ 10.042∗ ∗ ∗

Note: The reported statistics are the daily logarithmic returns based on the 10 GICS industry index, S&P 500 index and aggregated EPU index, respectively. These 10 GICS industries are Consumer Discretionary (COND), Consumer Staples (CONS), Energy (ENRS), Financials (FINL), Health Care (HLTH), Industrials (INDU), Information Technology (INFT), Materials (MATR), Telecommunication Services (TELS), and Utilities (UTIL). ADF and PP are the statistics of Augmented Dickey-Fuller and Phillips-Perron unit root tests, respectively, based on the lowest AIC value. The ARCH(k) statistically tests the significance of ARCH effect with k = 5,10. The data of EPU is available on www.policyuncertainty.com. ∗ Denotes significance at the 10% level. ∗ ∗ Denotes significance at the 5% level. ∗ ∗ ∗ Denotes significance at the 1% level. Table 2 Summary statistics of industry beta and its ranking group. Ranking number

Percentage of beta in each group

Industry

Mean

Min

Max

Stdev.

Mean

Max

Min

Stdev.

Top(%)

Middle(%)

Bottom(%)

COND CONS ENRS FINL HLTH INDU INFT MATR TELS UTIL

1.105 0.552 0.142 1.772 0.877 1.059 2.087 1.279 0.905 0.633

1.059 0.265 0.065 1.463 0.554 0.958 1.059 1.034 0.536 0.352

1.195 0.720 0.253 2.777 1.010 1.336 2.640 1.769 1.097 0.757

0.036 0.123 0.053 0.283 0.112 0.086 0.425 0.195 0.149 0.100

6.784 2.0 0 0 1.0 0 0 9.273 4.455 5.727 9.500 8.080 5.182 3.0 0 0

7 2 1 10 5 8 10 9 8 3

6 2 1 9 4 4 6 6 4 3

0.414 0.0 0 0 0.0 0 0 0.448 0.501 1.069 0.922 0.592 1.023 0.0 0 0

0 0 0 100 0 4.55 95.45 95.45 4.55 0

100 0 0 0 100 95.45 4.55 4.55 95.45 0

0 100 100 0 0 0 0 0 0 100

Note: The table reports summary statistics of industry beta estimated by the DCC-MIDAS-EPU model. The ranking number is the number of an indicated industry whose beta is cross-sectionally ranked from the lowest (1) to the highest (10). The ranking numbers of the industry beta period by period are the input of the statistics. The columns titled by “Percentage of beta in each group” list the percentage of the industries’ beta belong to the top (larger than 70th highest percentile), middle (between 30th and the 70th percentile) and bottom (less than 30th percentile) groups. The 10 GICS industries are Consumer Discretionary (COND), Consumer Staples (CONS), Energy (ENRS), Financials (FINL), Health Care (HLTH), Industrials (INDU), Information Technology (INFT), Materials (MATR), Telecommunication Services (TELS), and Utilities (UTIL).

4. Empirical results In this section, we present the empirical analysis of the estimated industry betas using the DCC-MIDAS-EPU model. To save space, we omitted the summary of the estimated coefficients. For the key parameters of θ in both models, most of them are significant, which means that EPU is significantly related to the dynamics of industry betas. The following subsection descriptively investigates the dynamics of the industry betas driven by EPU. 4.1. Descriptive analysis of the estimated industry beta Table 2 presents the results of summary statistics of the estimated industry betas. The mean of the Information Technology (INFT) is the largest and the Financials (FINL) is the second-largest, but Energy (ENRS) is the lowest. This result implies that the beta of the first two industries is highly driven by the uncertainty of economic policy. The standard deviations show that these two industries fluctuate much more and thus are more sensitive to EPU. The mean of ENRS, however, is to some extent immune to EPU. To investigate the relative ranking group of the industry betas, we calculate for each industry the number of times (relative to the whole sample) that its beta belongs to the top 30th, middle (between 30th and 70th), and bottom 30th percentile. The ranking results are shown in the right seven columns of Table 2. We find that there are six industries, always in the same group, where the percentage of beta equals 100%. The others switch to the next-nearest group, where the percentage of beta is only 4.55%. This finding implies that the beta of the industries driven by EPU is likely to persist at a relatively time-invariant level. More specifically, the betas of Financials (FINL), Information Technology (INFT), and Materials (MATR)

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Fig. 1. Long-term beta of the 10 GICS industries.

almost always remain in the top group, which means the betas of these industries are most related to the uncertainty of economic policy. In contrast, Consumer Staples (CONS), Energy (ENRS), and Utilities (UTIL) are always in the bottom group, and thus it can be concluded that the betas of these industries are least driven by EPU. Fig. 1 intuitively plots the dynamics of 10 industry betas. The beta of ENRS is shown to be persistently at the lowest level, as presented in Table 2. Since the output of the energy industry is essential but not well-suited to substitute for other industries in the long run, it is easy to understand why EPU drives the systematic risk measure of ENRS least. The betas of CONS and UTIL are also more likely to stay at relatively lower levels, as illustrated in Fig. 1. These two industries provide mainly goods and services to people’s lives and productivity, and the uncertainty of economic policy is therefore less related to them. For the industries of FINL and INFT, the betas fluctuate much more and are the most sensitive to EPU. Combined with their ranking group in Table 2, the results imply that the beta as systematic risk measure of the two industries is most likely driven by EPU. FINL is the industry most sensitive to the future of an economy. INFT is usually viewed as high-tech and consists of many newly emergent individual firms that are expected to improve the future of an economy. Therefore, the betas of these two industries are more sensitive and highly driven by EPU. 4.2. MIDAS regression and its structural change In this subsection, we explicitly explore the relationship of industry beta to EPU using MIDAS regression. However, it should be noted that the DotCom crash and 2007 financial crisis, two periods of turmoil, occurred over the sample period. It can also be seen in Fig. 1 that there is a collective jump in beta among the 10 industries in October 1998. After that point, all industry betas tend to fluctuate and become more divergent, especially during the DotCom crash. As shown by Fig. 2, EPU begins to suffer severe fluctuations in late 1998 and in 2007, which are generally recognized as the starting points of the two periods of turmoil, respectively. Therefore, we select October 1998 and July 2007 as the possible breakpoints and then divide the full sample period into three subperiods labeled the Tranquil Period, DotCom Turmoil, and Financial Crisis, respectively. Following Engle et al. (2013), we use the likelihood ratio statistic to test the structural breakpoints in MIDAS regression. The LRT statistic is defined as



LRT = −2 LLFf ull −

 i=sub−samples



LLFi ∼ χ 2 (df )

(12)

where df is the number of parameters times one less than the number of sub-samples, which corresponds to the number of restrictions; LLFfull and LLFi are the Log Likelihood Function value of MIDAS regression over the full sample and the ith subsample. The results of the LRT test confirm the existence of the selected two breakpoints (please refer to the supplementary material for more details). Table 3 reports the explicit relation of industry beta to EPU over the periods of the full sample and the three sub-samples. The first interesting result is that EPU is positively related to the beta of all industries during the Tranquil Period; however, it is negatively related to some industries during the two great periods of turmoil, the DotCom Turmoil and the Financial Crisis. In particular, more industries are negatively related to EPU during the Financial Crisis. Furthermore, EPU positively drives the betas of eight industries during the Tranquil Period, except for Health Care (HLTH) and Financials (FINL). The HLTH industry is subject to people’s basic demand and thus inelasticity to the change of economic policy. The FINL industry,

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Fig. 2. The dynamics of EPU. Table 3 MIDAS regression of industry beta on the aggregated EPU. Industry

The α 1 of MIDAS regression Full-sample

Tranquil period

DotCom turmoil

Financial crisis

Full-sample

Tranquil period

DotCom turmoil

Financial crisis

COND

0.103∗ ∗ ∗ (0.007) −0.347∗ ∗ ∗ (0.024) 0.155∗ ∗ ∗ (0.009) 0.660∗ ∗ ∗ (0.058) −0.264∗ ∗ ∗ (0.024) 0.212∗ ∗ ∗ (0.019) −1.189∗ ∗ ∗ (0.086) 0.556∗ ∗ ∗ (0.037) −0.417∗ ∗ ∗ (0.030) −0.229∗ ∗ ∗ (0.018)

0.850∗ ∗ ∗ (0.114) 0.059∗ ∗ ∗ (0.004) 0.606∗ ∗ ∗ (0.074) 0.264 – 0.172 – 0.700∗ ∗ ∗ (0.126) 1.031∗ ∗ ∗ (0.389) 0.858∗ ∗ ∗ (0.125) 0.201∗ ∗ ∗ (0.030) 0.449∗ ∗ ∗ (0.052)

−1.456∗ ∗ ∗ (0.098) 0.327∗ ∗ ∗ (0.016) −0.661∗ ∗ ∗ (0.072) 3.824∗ ∗ ∗ (0.240) −0.267∗ ∗ ∗ (0.047) 0.660∗ ∗ ∗ (0.034) −0.155∗ ∗ ∗ (0.025) −0.314 (0.288) 1.097∗ ∗ ∗ (0.057) 0.642∗ ∗ ∗ (0.033)

0.049∗ ∗ ∗ (0.004) −0.168∗ ∗ ∗ (0.013) −0.136∗ ∗ ∗ (0.044) 1.843∗ ∗ ∗ (0.171) −0.334∗ ∗ ∗ (0.025) 0.155∗ ∗ ∗ (0.013) −0.003∗ ∗ ∗ (0.001) 1.547∗ ∗ ∗ (0.142) −0.185∗ ∗ ∗ (0.014) −0.635∗ ∗ ∗ (0.046)

0.728

0.757

0.874

0.826

0.711

0.909

0.927

0.834

0.770

0.791

0.725

0.226

0.651

0.111

0.891

0.782

0.646

0.214

0.505

0.843

0.660

0.634

0.922

0.808

0.701

0.300

0.543

0.735

0.729

0.730

0.034

0.786

0.706

0.713

0.922

0.840

0.670

0.806

0.923

0.855

CONS ENRS FINL HLTH INDU INFT MATR TELS UTIL

R2 of MIDAS regression

Note: The table reports the estimated α 1 and R2 of MIDAS regression for each industry over the full sample and three sub-samples (Standard Errors in Parentheses), which are divided by the DotCom Turmoil from September 1998 and the Financial Crisis from June 2007. The 10 GICS industries are Consumer Discretionary (COND), Consumer Staples (CONS), Energy (ENRS), Financials (FINL), Health Care (HLTH), Industrials (INDU), Information Technology (INFT), Materials (MATR), Telecommunication Services (TELS), and Utilities (UTIL). The sample covers from January 1993 until December 2015. ∗ Denotes significance at the 10% level. ∗ ∗ Denotes significance at the 5% level. ∗ ∗ ∗ Denotes significance at the 1% level.

however, is less related to EPU during the Tranquil Period and its R2 of the regression is also very low. One surprising result is that the beta of FINL is more driven by EPU during the DotCom Turmoil than during the Financial Crisis. We explain this finding because during the Financial Crisis the systematic risk of FINL comes more from market factors, such as the market liquidity and funding liquidity, rather than EPU (Brunnermeier and Pedersen, 2009). Another surprising result is that Information Technology (INFT) is also more driven by EPU during the Tranquil Period than during the DotCom Turmoil. Combining the above two findings, the results implies that the effect of EPU on industry beta is probably a leading indicator of future turmoil, since EPU increases the beta of relevant industries before the periods of great turmoil. 4.3. Effect of category-specific EPU on industry betas In addition to the aggregated EPU, Baker et al. (2016) also construct the category-specific EPU indices to measure the uncertainty of specific policy, such as monetary, fiscal, tax and government regulation. We believe that category-specific

H. Yu et al. / Finance Research Letters 22 (2017) 249–258

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Table 4 MIDAS regression during the tranquil period. MON_POL

FIS_POL

Panel A: α 1 of MIDAS regression 0.167 COND 0.433∗ ∗ (0.178) – ∗∗∗ 0.032∗ ∗ ∗ CONS 0.042 (0.008) (0.009) 0.120∗ ENRS 0.323∗ ∗ ∗ (0.122) (0.061) FINL −0.212 0.559∗ ∗ ∗ (0.166) (0.191) HLTH −0.046 0.066 (0.080) – 0.134 INDU 0.347∗ ∗ (0.165) – INFT −0.193 1.027∗ ∗ ∗ (0.253) (0.293) 0.161 MATR 0.437∗ ∗ (0.183) – ∗∗∗ 0.111∗ ∗ ∗ TELS 0.125 (0.040) (0.036) 0.092 UTIL 0.232∗ ∗ ∗ (0.090) –

TAX

GOV_SPE

HEA_CAR

NAT_SEC

ENT_PRO

REG

FIN_REG

SOV_DEB

0.733∗ ∗ ∗ (0.088) 0.035∗ ∗ ∗ (0.008) 0.511∗ ∗ ∗ (0.058) 0.682∗ ∗ ∗ (0.216) 0.349∗ ∗ ∗ (0.090) 0.685∗ ∗ ∗ (0.093) 1.321∗ ∗ ∗ (0.298) 0.775∗ ∗ ∗ (0.104) 0.057 – 0.114∗ ∗ (0.045)

0.055 – 0.008 (0.008) 0.137∗ (0.079) 0.091 – 0.039 – 0.054 – 0.116 – 0.055 – 0.001 (0.016) 0.027 (0.031)

0.340∗ ∗ ∗ (0.048) 0.021∗ ∗ ∗ (0.003) 0.237∗ ∗ ∗ (0.031) 0.187∗ (0.103) 0.118∗ ∗ ∗ (0.044) 0.278∗ ∗ ∗ (0.053) 0.401∗ ∗ (0.157) 0.343∗ ∗ ∗ (0.054) 0.078∗ ∗ ∗ (0.011) 0.178∗ ∗ ∗ (0.019)

0.536 – 0.048∗ ∗ ∗ (0.004) 0.377 – 0.336 – 0.205 – 0.447∗ ∗ ∗ (0.080) 0.686 – 0.538 – 0.161∗ ∗ ∗ (0.025) 0.282 –

−0.038 (0.097) −0.002 (0.007) −0.022 (0.068) 0.083 – 0.227∗ ∗ ∗ (0.086) −0.021 (0.087) 0.055 (0.139) −0.034 (0.099) −0.017 (0.032) −0.025 (0.050)

0.203 – 0.040∗ ∗ ∗ (0.008) 0.155∗ (0.082) 0.724∗ ∗ ∗ (0.178) 0.395∗ ∗ ∗ (0.066) 0.660∗ ∗ ∗ (0.080) 1.313∗ ∗ ∗ (0.236) 0.737∗ ∗ ∗ (0.097) 0.121∗ ∗ ∗ (0.038) 0.107 –

1.302∗ ∗ ∗ (0.301) 0.080∗ ∗ ∗ (0.020) 0.937∗ ∗ ∗ (0.206) −0.191 (0.185) 0.629∗ ∗ ∗ (0.201) 1.163∗ ∗ ∗ (0.273) 2.066∗ ∗ ∗ (0.659) 1.344∗ ∗ ∗ (0.305) 0.243∗ ∗ ∗ (0.089) 0.648∗ ∗ ∗ (0.160)

−0.019 (0.016) −0.001 (0.001) −0.014 (0.011) −0.034∗ (0.018) −0.017∗ ∗ (0.009) −0.019 (0.014) −0.045 (0.030) −0.019 (0.016) −0.002 (0.004) −0.009 (0.008)

0.820 0.516 0.822 0.399 0.508 0.796 0.559 0.806 0.294 0.267 0.579

0.044 0.056 0.146 0.080 0.068 0.052 0.053 0.042 0.0 0 0 0.039 0.058

0.769 0.780 0.782 0.157 0.287 0.635 0.286 0.729 0.748 0.833 0.601

0.736 0.898 0.745 0.193 0.325 0.633 0.328 0.706 0.714 0.775 0.605

0.009 0.006 0.006 0.049 0.278 0.003 0.009 0.007 0.020 0.014 0.040

0.138 0.573 0.164 0.490 0.681 0.792 0.640 0.764 0.361 0.146 0.475

0.514 0.480 0.540 0.056 0.357 0.508 0.358 0.524 0.294 0.483 0.411

0.075 0.040 0.080 0.160 0.183 0.097 0.115 0.072 0.014 0.061 0.090

Panel B: R2 of MIDAS regression COND CONS ENRS FINL HLTH INDU INFT MATR TELS UTIL AVERAGE

0.248 0.578 0.281 0.086 0.018 0.197 0.032 0.240 0.348 0.270 0.230

0.168 0.410 0.176 0.328 0.079 0.133 0.426 0.149 0.359 0.192 0.242

Note: The table reports the MIDAS regression of industry beta on category-specific EPU over the Tranquil Period from January 1993 to September 1998 (Standard Errors in Parentheses). The 10 GICS industries are Consumer Discretionary (COND), Consumer Staples (CONS), Energy (ENRS), Financials (FINL), Health Care (HLTH), Industrials (INDU), Information Technology (INFT), Materials (MATR), Telecommunication Services (TELS), and Utilities (UTIL). The 10 categories of specific EPU are Monetary Policy (MON_POL), Fiscal Policy (FIS_POL), Taxes (TAX), Government Spending (GOV_SPE), Health Care (HEA_CAR), National Security (NAT_SEC), Entitlement Programs (ENT_PRO), Regulation (REG), Financial Regulation (FIN_REG), and Sovereign Debt (SOV_DEB). ∗ Denotes significance at the 10% level. ∗ ∗ Denotes significance at the 5% level. ∗ ∗ ∗ Denotes significance at the 1% level.

EPU has a greater effect on the decision and thus on the future cash flow of the related industry. Therefore, we disentangle the effect of aggregated EPU on industry beta by investigating the effect of category-specific EPU. MIDAS regression of Eq. (11) is also used for this purpose. The results are presented in Tables 4–6. We notice that the effect of the various category-specific EPUs is primarily similar to the aggregated EPU. It is consistent with the values of approximately 60% in the correlation matrix of the categories of specific EPU (please refer to the supplementary tables for the detail). Panel A of Tables 4–6 shows the estimated α 1 of MIDAS regression in Eq. (11), which indicates the relation of industry beta to category-specific EPU. In particular, Monetary Policy (MON_POL) and Financial Regulation (FIN_REG) are significantly related to the beta of Financials (FINL) only during the periods of great turmoil but not during the Tranquil Period. These two policy categories are strongly related to the systematic risk of FINL during economic turmoil. Systemic weaknesses with the governance of financial regulation–the system associated with designing, implementing, and reforming financial policies–contributed to the global financial crisis (Levine et al., 2012). It also brought into sharp focus the inadequacies of the contemporary model of financial regulation both at the national and the global levels. The US government designed in summits a series of national and global financial regulation measures to increase disclosure, strengthen the capital base, and target the enhancement of financial markets’ discipline, such as the Dodd-Frank Act. Similarly, when crises emerge, monetary policy actions, such as changes in the Federal funds rate, have the most direct and immediate effects on the financial markets. For instance, a vehement reaction to a Fed action is associated with the 25-basis-point intermeeting rate cut on October 15, 1998, which was made in response to unsettled conditions in the financial markets (Bernanke and Kuttner, 2005). Therefore, the effects of category-specific EPU of MON_POL and FIN_REG are insignificant on the industry of FINL during the Tranquil Period, but significant during the last two periods. Additionally, the effect of Sovereign Debt (SOV_DEB) is only significant during the period of Financial Crisis. It is well known that the global financial crisis transformed into a sovereign debt crisis, especially in the Euro area in 2009. After the

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Table 5 MIDAS regression during the DotCom Turmoil. MON_POL

FIS_POL

Panel A: α 1 of MIDAS regression −1.062∗ ∗ ∗ COND −1.038∗ ∗ ∗ (0.099) (0.075) 0.242∗ ∗ ∗ CONS 0.243∗ ∗ ∗ (0.021) (0.011) −0.472∗ ∗ ∗ ENRS −0.465∗ ∗ ∗ (0.060) (0.058) 2.796∗ ∗ ∗ FINL 2.721∗ ∗ ∗ (0.263) (0.169) −0.187∗ ∗ ∗ HLTH −0.199∗ ∗ ∗ (0.033) (0.037) 0.495∗ ∗ ∗ INDU 0.474∗ ∗ ∗ (0.043) (0.020) −0.109∗ ∗ ∗ INFT −0.126∗ ∗ ∗ (0.017) (0.019) MATR −0.260 −0.190 (0.207) (0.220) ∗∗∗ 0.807∗ ∗ ∗ TELS 0.778 (0.067) (0.037) 0.473∗ ∗ ∗ UTIL 0.453∗ ∗ ∗ (0.040) (0.022)

TAX

GOV_SPE

HEA_CAR

NAT_SEC

ENT_PRO

REG

FIN_REG

SOV_DEB

−0.997∗ ∗ ∗ (0.070) 0.227∗ ∗ ∗ (0.010) −0.442∗ ∗ ∗ (0.054) 2.613∗ ∗ ∗ (0.165) −0.176∗ ∗ ∗ (0.034) 0.463∗ ∗ ∗ (0.019) −0.103∗ ∗ ∗ (0.017) −0.164 (0.205) 0.755∗ ∗ ∗ (0.037) 0.443∗ ∗ ∗ (0.021)

−1.025∗ ∗ ∗ (0.091) 0.236∗ ∗ ∗ (0.014) −0.455∗ ∗ ∗ (0.064) 2.745∗ ∗ ∗ (0.196) −0.173∗ ∗ ∗ (0.040) 0.483∗ ∗ ∗ (0.027) −0.100∗ ∗ ∗ (0.021) −0.175 (0.224) 0.789∗ ∗ ∗ (0.047) 0.462∗ ∗ ∗ (0.028)

−1.173∗ ∗ ∗ (0.120) 0.263∗ ∗ ∗ (0.024) −0.508∗ ∗ ∗ (0.077) 3.005∗ ∗ ∗ (0.340) −0.201∗ ∗ ∗ (0.045) 0.542∗ ∗ ∗ (0.044) −0.113∗ ∗ ∗ (0.025) 0.183 (0.306) 0.875∗ ∗ ∗ (0.085) 0.515∗ ∗ ∗ (0.049)

−0.450∗ ∗ ∗ (0.059) 0.108∗ ∗ ∗ (0.010) −0.195∗ ∗ ∗ (0.035) 1.256∗ ∗ ∗ (0.124) −0.077∗ ∗ ∗ (0.019) 0.224∗ ∗ ∗ (0.020) −0.044∗ ∗ ∗ (0.011) −0.108 (0.103) 0.365∗ ∗ ∗ (0.032) 0.212∗ ∗ ∗ (0.019)

−0.940∗ ∗ ∗ (0.082) 0.214∗ ∗ ∗ (0.016) −0.404∗ ∗ ∗ (0.054) 2.484∗ ∗ ∗ (0.226) −0.167∗ ∗ ∗ (0.031) 0.430∗ ∗ ∗ (0.031) −0.100∗ ∗ ∗ (0.017) −0.109 (0.190) 0.718∗ ∗ ∗ (0.054) 0.420∗ ∗ ∗ (0.031)

−1.613∗ ∗ ∗ (0.139) 0.367∗ ∗ ∗ (0.026) −0.710∗ ∗ ∗ (0.102) 4.196∗ ∗ ∗ (0.378) −0.275∗ ∗ ∗ (0.061) 0.728∗ ∗ ∗ (0.049) −0.165∗ ∗ ∗ (0.032) −0.198 (0.337) 1.204∗ ∗ ∗ (0.091) 0.705∗ ∗ ∗ (0.052)

−0.678∗ ∗ ∗ (0.096) 0.160∗ ∗ ∗ (0.017) −0.288∗ ∗ ∗ (0.062) 1.881∗ ∗ ∗ (0.234) −0.099∗ ∗ ∗ (0.034) 0.324∗ ∗ ∗ (0.032) −0.059∗ ∗ ∗ (0.018) 0.083 (0.125) 0.543∗ ∗ ∗ (0.059) 0.316∗ ∗ ∗ (0.034)

−0.010 (0.059) −0.004 (0.010) −0.027 (0.032) −0.040 – −0.017 (0.016) −0.011 (0.020) −0.007 (0.009) 0.026 (0.074) −0.013 (0.020) −0.008 (0.020)

0.865 0.939 0.676 0.891 0.449 0.949 0.517 0.019 0.932 0.933 0.717

0.800 0.903 0.608 0.862 0.364 0.909 0.419 0.018 0.900 0.898 0.668

0.750 0.790 0.578 0.716 0.377 0.820 0.379 0.011 0.767 0.776 0.597

0.656 0.785 0.506 0.773 0.340 0.798 0.357 0.032 0.810 0.796 0.585

0.807 0.856 0.647 0.798 0.484 0.856 0.519 0.010 0.845 0.848 0.667

0.805 0.862 0.603 0.792 0.385 0.867 0.460 0.010 0.841 0.846 0.647

0.605 0.728 0.403 0.664 0.202 0.749 0.245 0.014 0.723 0.721 0.505

0.001 0.011 0.021 0.008 0.031 0.016 0.019 0.004 0.011 0.011 0.013

Panel B: R2 of MIDAS regression COND CONS ENRS FINL HLTH INDU INFT MATR TELS UTIL AVERAGE

0.773 0.806 0.666 0.774 0.546 0.792 0.642 0.046 0.803 0.799 0.665

0.865 0.941 0.674 0.898 0.440 0.951 0.510 0.022 0.937 0.937 0.717

Note: The table reports the MIDAS regression of industry beta on category-specific EPU over the DotCom Turmoil from October 1998 to June 2007 (Standard Errors in Parentheses). The 10 GICS industries are Consumer Discretionary (COND), Consumer Staples (CONS), Energy (ENRS), Financials (FINL), Health Care (HLTH), Industrials (INDU), Information Technology (INFT), Materials (MATR), Telecommunication Services (TELS), and Utilities (UTIL). The 10 categories of specific EPU are Monetary Policy (MON_POL), Fiscal Policy (FIS_POL), Taxes (TAX), Government Spending (GOV_SPE), Health Care (HEA_CAR), National Security (NAT_SEC), Entitlement Programs (ENT_PRO), Regulation (REG), Financial Regulation (FIN_REG), and Sovereign Debt (SOV_DEB). ∗ Denotes significance at the 10% level. ∗ ∗ Denotes significance at the 5% level. ∗ ∗ ∗ Denotes significance at the 1% level.

financial crisis, the governments issued large amount of sovereign debt in the context of quantitative easing. Starting with Greece in 2009, the crisis has prompted European policy-makers to take extraordinary measures to limit the crisis fall-out on the affected countries and prevent its further spreading. Over-issued debt has received the attention of the US government, increasing the uncertainty pertaining to relative policy. Moreover, the EPU of Government Spending (GOV_SPE) and Entitlement Programs (ENT_PRO) show no significant results for most of the industries but have significant effects after the Tranquil Period. Since the middle of 1980s, entitlement programs have accounted for more than half of all federal spending (Johnson, 2013). These two categories of policy involve universal industries; the most important examples of entitlement programs at the federal level would include Social Security, Medicare, and Medicaid, most Veterans’ Administration programs, federal employee and military retirement plans, unemployment compensation, food stamps, and agricultural price support programs (Naughton et al., 2015). GOV_SPE together with ENT_PRO significantly increase and significantly affect the industry betas when markets show abnormal fluctuation. As presented in Panel B, the R2 of the category-specific EPU of Fiscal Policy (FIS_POL), GOV_SPE, and ENT_PRO are relatively low during the Tranquil Period, but increase during the periods of great turmoil. This finding is reasonable since the related categories of policy are usually adopted to stimulate economic development. In addition, the R2 of the categories of Taxes (TAX), Health Care (HEA_CAR), and National Security (NAT_SEC) is always relatively high over all three of the subperiods. The periods of great turmoil change little with regards to the effect of these categories on industry beta. In particular, NAT_SEC and HEA_CAR are subject to extensive regulation at both the federal and the state level. Tax policy has not generated financial crisis and the implementation of tax policy has always been a tricky business. Although tax policy adjusts in response to the crisis, the effect is not so distinct. Finally, R2 of SOV_DEB shifts from 0.1 to 0.5 after the Financial Crisis. As an indicator of explanation power, high R2 indicates a better goodness of fit for industry beta. SOV_DEB is attracting more attention from governments. The correlations between SOV_DEB and other category-specific EPU remain low before the Financial Crisis and sharply increase subsequently.

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Table 6 MIDAS regression during the financial crisis. MON_POL

FIS_POL

Panel A: α 1 of MIDAS regression 0.026∗ ∗ ∗ COND 0.043∗ ∗ ∗ (0.010) (0.002) −0.091∗ ∗ ∗ CONS −0.144∗ ∗ ∗ (0.035) (0.008) −0.058∗ ∗ ENRS −0.204∗ ∗ ∗ (0.048) (0.025) 1.002∗ ∗ ∗ FINL 1.689∗ ∗ ∗ (0.406) (0.097) −0.182∗ ∗ ∗ HLTH −0.282∗ ∗ ∗ (0.070) (0.015) 0.086∗ ∗ ∗ INDU 0.128∗ ∗ ∗ (0.034) (0.007) −0.002∗ ∗ ∗ INFT −0.003∗ ∗ ∗ (0.001) (0.0 0 0) 0.856∗ ∗ ∗ MATR 1.316∗ ∗ ∗ (0.343) (0.076) −0.103∗ ∗ ∗ TELS −0.150∗ ∗ ∗ (0.040) (0.008) −0.347∗ ∗ ∗ UTIL −0.512∗ ∗ ∗ (0.135) (0.028)

TAX

GOV_SPE

HEA_CAR

NAT_SEC

ENT_PRO

REG

FIN_REG

SOV_DEB

0.027∗ ∗ ∗ (0.002) −0.092∗ ∗ ∗ (0.008) −0.060∗ ∗ ∗ (0.023) 0.993∗ ∗ ∗ (0.099) −0.183∗ ∗ ∗ (0.015) 0.085∗ ∗ ∗ (0.008) −0.002∗ ∗ ∗ (0.0 0 0) 0.844∗ ∗ ∗ (0.078) −0.102∗ ∗ ∗ (0.008) −0.346∗ ∗ ∗ (0.028)

0.016∗ ∗ ∗ (0.002) −0.054∗ ∗ ∗ (0.007) −0.033∗ (0.019) 0.612∗ ∗ ∗ (0.081) −0.109∗ ∗ ∗ (0.014) 0.053∗ ∗ ∗ (0.006) −0.001∗ ∗ ∗ (0.0 0 0) 0.533∗ ∗ ∗ (0.064) −0.063∗ ∗ ∗ (0.007) −0.208∗ ∗ ∗ (0.026)

0.018∗ ∗ ∗ (0.002) −0.062∗ ∗ ∗ (0.007) −0.024 (0.021) 0.675∗ ∗ ∗ (0.081) −0.124∗ ∗ ∗ (0.013) 0.059∗ ∗ ∗ (0.006) −0.001∗ ∗ ∗ (0.0 0 0) 0.583∗ ∗ ∗ (0.065) −0.071∗ ∗ ∗ (0.007) −0.241∗ ∗ ∗ (0.022)

0.063∗ ∗ ∗ (0.011) −0.214∗ ∗ ∗ (0.037) −0.182∗ ∗ ∗ (0.067) 2.387∗ ∗ ∗ (0.443) −0.424∗ ∗ ∗ (0.073) 0.192∗ ∗ ∗ (0.036) −0.004∗ ∗ ∗ (0.001) 1.927∗ ∗ ∗ (0.370) −0.230∗ ∗ ∗ (0.041) −0.803∗ ∗ ∗ (0.137)

0.018∗ ∗ ∗ (0.002) −0.062∗ ∗ ∗ (0.008) −0.025 (0.020) 0.677∗ ∗ ∗ (0.092) −0.124∗ ∗ ∗ (0.016) 0.060∗ ∗ ∗ (0.007) −0.001∗ ∗ ∗ (0.0 0 0) 0.596∗ ∗ ∗ (0.073) −0.071∗ ∗ ∗ (0.008) −0.241∗ ∗ ∗ (0.028)

0.033∗ ∗ ∗ (0.003) −0.112∗ ∗ ∗ (0.010) −0.082∗ ∗ ∗ (0.029) 1.279∗ ∗ ∗ (0.124) −0.222∗ ∗ ∗ (0.019) 0.102∗ ∗ ∗ (0.011) −0.002∗ ∗ ∗ (0.0 0 0) 1.037∗ ∗ ∗ (0.105) −0.121∗ ∗ ∗ (0.011) −0.417∗ ∗ ∗ (0.036)

0.014∗ ∗ ∗ (0.002) −0.020 – −0.044∗ ∗ ∗ (0.016) 0.211∗ ∗ ∗ (0.062) −0.041 – 0.019 – −0.001∗ ∗ ∗ (0.0 0 0) 0.190 – −0.023 – −0.079 –

0.008∗ ∗ ∗ (0.001) −0.027∗ ∗ ∗ (0.004) −0.017 (0.011) 0.333∗ ∗ ∗ (0.046) −0.054∗ ∗ ∗ (0.009) 0.027∗ ∗ ∗ (0.004) −0.001∗ ∗ ∗ (0.0 0 0) 0.281∗ ∗ ∗ (0.037) −0.031∗ ∗ ∗ (0.005) −0.097∗ ∗ ∗ (0.018)

0.795 0.804 0.173 0.764 0.816 0.799 0.682 0.782 0.832 0.822 0.727

0.643 0.646 0.084 0.654 0.656 0.684 0.625 0.685 0.696 0.662 0.603

0.732 0.738 0.039 0.691 0.752 0.741 0.615 0.715 0.778 0.784 0.659

0.503 0.505 0.186 0.476 0.506 0.468 0.460 0.456 0.486 0.510 0.456

0.648 0.655 0.049 0.649 0.669 0.699 0.543 0.684 0.708 0.691 0.600

0.812 0.810 0.199 0.765 0.810 0.743 0.714 0.751 0.797 0.804 0.720

0.572 0.327 0.193 0.263 0.339 0.341 0.454 0.317 0.361 0.355 0.352

0.551 0.542 0.069 0.623 0.535 0.577 0.529 0.639 0.569 0.481 0.511

Panel B: R2 of MIDAS regression COND CONS ENRS FINL HLTH INDU INFT MATR TELS UTIL AVERAGE

0.339 0.337 0.380 0.346 0.328 0.301 0.369 0.309 0.301 0.304 0.332

0.787 0.797 0.145 0.776 0.810 0.811 0.698 0.797 0.835 0.821 0.728

Note: The table reports the MIDAS regression of industry beta on category specific EPU over the DotCom Turmoil from July 2007 to December 2015 (Standard Errors in Parentheses). The 10 GICS industries are Consumer Discretionary (COND), Consumer Staples (CONS), Energy (ENRS), Financials (FINL), Health Care (HLTH), Industrials (INDU), Information Technology (INFT), Materials (MATR), Telecommunication Services (TELS), and Utilities (UTIL). The 10 categories of specific EPU are Monetary Policy (MON_POL), Fiscal Policy (FIS_POL), Taxes (TAX), Government Spending (GOV_SPE), Health Care (HEA_CAR), National Security (NAT_SEC), Entitlement Programs (ENT_PRO), Regulation (REG), Financial Regulation (FIN_REG), and Sovereign Debt (SOV_DEB). ∗ Denotes significance at the 10% level. ∗ ∗ Denotes significance at the 5% level. ∗ ∗ ∗ Denotes significance at the 1% level.

5. Conclusion Industry beta is one of the key input variables to risk management, asset allocation, and capital budgeting decisions. It has been widely supported that industry beta is sensitive to changes in common macroeconomic conditions. In this paper, we explore how Economic Policy Uncertainty (EPU) drives the industry beta in US. We use DCC-MIDAS to estimate the industry beta driven by EPU and investigate their relationship by means of bivariate MIDAS regression. The effect of 10 categories of specific EPU on the industry betas is also explored by MIDAS regression, thus disentangling the effect of aggregated EPU. Over the sample period, the DotCom Turmoil and Financial Crisis occurred, which may structurally change the effect of aggregated and category-specific EPU. We divide the sample into three subperiods, Tranquil Period, DotCom Turmoil and Financial Crisis, and reexamine the results in the sub-samples. The results can be concluded as the following. First, in the US, EPU significantly drives industry beta on average. Second, the driving effect substantially changes in line with market turmoil. The industry beta driven by EPU is probably a leading indicator of turmoil when it is especially high. However, during the period of turmoil, the beta of the relevant industries is less driven by EPU since some other simultaneous factors, such as market liquidity and economic distress, are believed by the literature to be more important. Third, the categories of specific EPU with more universal and fundamental effect on the economy, such as Taxes, Health Care, and National Security, demonstrate relatively more explanation of industry beta over all sample periods. The beta of more industries is significantly affected during periods of turmoil by the categories with more specific purpose, such as Monetary Policy, Fiscal Policy, Government Spending and Entitlement Programs, and Sovereign Debt. We expect these findings will be helpful to asset allocation decisions, especially to investment across industries with long-term concern. Policy-makers are also expected to benefit. For example, they should be cautious when an individual industry beta is greatly boosted by EPU.

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Acknowledgments We are grateful to the editors and anonymous reviewers for their helpful comments which greatly improved the earlier draft. This work was supported by the National Science Foundation of China [grant numbers 71472085, 71401071, 71672081]. Supplementary material Supplementary material associated with this article can be found, in the online version, at 10.1016/j.frl.2017.05.012. References Arouri, M.E.H., Estay, C., Rault, C., Roubaud, D., 2016. Economic policy uncertainty and stock markets: long-run evidence from the US. Finance Res. Lett. 18, 136–141. Baele, L., Londono, J.M., 2013. Understanding industry betas. J. Empirical Finance 22, 30–51. Baker, S.R., Bloom, N., Davis, S.J., 2016. Measuring economic policy uncertainty. Q. J. Econ. 131 (4), 1593–1636. Bekiros, S., Gupta, R., Majumdar, A., 2016. Incorporating economic policy uncertainty in US equity premium models: a nonlinear predictability analysis. Finance Res. Lett. 18, 291–296. Bernanke, B.S., Kuttner, K.N., 2005. What explains the stock market’s reaction to federal reserve policy? J. Finance 60 (3), 1221–1257. Brunnermeier, M.K., Pedersen, L.H., 2009. Market liquidity and funding liquidity. Rev. Financial Stud. 22 (6), 2201–2238. Colacito, R., Engle, R.F., Ghysels, E., 2011. A component model for dynamic correlations. J. Econom. 164 (1), 45–59. Engle, R.F., 2002. Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J. Bus. Econ. Stat. 20 (3), 339–350. Engle, R.F., Ghysels, E., Sohn, B., 2013. Stock market volatility and macroeconomic fundamentals. Rev. Econ. Stat. 95 (3), 776–797. Ghysels, E., Sinko, A., Valkanov, R., 2007. MIDAS Regressions: further results and new directions. Econom. Rev. 26 (1), 53–90. Ghysels, E., Wright, J.H., 2009. Forecasting professional forecasters. J. Bus. Econ. Stat. 27 (4), 504–516. Gulen, H., Ion, M., 2016. Policy uncertainty and corporate investment. Rev. Financial Stud. 29 (3), 523–564. Johnson, P.M., 2013. A glossary of political economy terms. Ref. Rev. 27 (5), 22–23. Levine, R., Loury, G., Rubinstein, Y., Shleifer, A., 2012. The governance of financial regulation: reform lessons from the recent crisis. Int. Rev. Finance 12 (1), 39–56. Liu, L., Zhang, T., 2015. Economic policy uncertainty and stock market volatility. Finance Res. Lett. 15, 99–105. Naughton, J., Petacchi, R., Weber, J., 2015. Public pension accounting rules and economic outcomes. J. Account. Econ. 59 (2-3), 221–241.