Economic Policy Uncertainty and Momentum

Ming Gu

School of Economics and WISE

Xiamen University

guming@xmu.edu.cn

Minxing Sun

Department of Finance

University of Memphis

msun@memphis.edu

Yangru Wu

Rutgers Business School – Newark and New Brunswick

Rutgers University

yangruwu@business.rutgers.edu

Weike Xu

Department of Finance

Clemson University

weikex@clemson.edu

June 9, 2017

Abstract

Using a news-based measure of economic policy uncertainty (EPU), we demonstrate that EPU

negatively forecasts momentum profits. An increase of one standard deviation in EPU is

associated with a 1.13% decrease in returns for the winner-minus-loser portfolio. The effect of

EPU on momentum is mainly driven by the short side portfolio. The predictive power of EPU on

momentum payoffs is robust after controlling for market states, business cycle, market volatility,

investor sentiment, market illiquidity, return dispersion and time-varying risk factors.

Furthermore, a global EPU index forecasts momentum profitability for the international equity

market and other asset classes. Our results suggest that economic policy uncertainty is an

important determinant of time-series variations in momentum profits.

Keywords: momentum; economic policy uncertainty; time-series variation of momentum

Economic Policy Uncertainty and Momentum

June 9, 2017

Abstract

Using a news-based measure of economic policy uncertainty (EPU), we demonstrate that EPU

negatively forecasts momentum profits. An increase of one standard deviation in EPU is

associated with a 1.13% decrease in returns for the winner-minus-loser portfolio. The effect of

EPU on momentum is mainly driven by the short side portfolio. The predictive power of EPU on

momentum payoffs is robust after controlling for market states, business cycle, market volatility,

investor sentiment, market illiquidity, return dispersion and time-varying risk factors.

Furthermore, a global EPU index forecasts momentum profitability for the international equity

market and other asset classes. Our results suggest that economic policy uncertainty is an

important determinant of time-series variations in momentum profits.

Keywords: momentum; economic policy uncertainty; time-series variation of momentum

1

Introduction

Momentum is the most robust and well-known anomaly in the finance literature.

Jegadeesh and Titman (1993) document that stocks with high returns over past 3 to 12 month

have abnormally high average returns over the next 3 to 12 month. This return pattern is robust

within different size groups (Fama and French, 2008) and significant in major stock markets

around the world (Rouwenhorst, 1998; Griffin, Ji and Martin, 2003; Chui, Titman and Wei,

2010).

It has also been shown that momentum profits are dependent on several state of economy

variables including business cycle (Chordia and Shivakumar, 2002), past market returns (Cooper,

Gutierrez and Hameed, 2004), investor sentiment (Antoniou, Doukas and Subrahmanyam, 2013),

market volatility (Wang and Xu, 2015), market illiquidity (Avramov, Cheng and Hameed, 2015)

and return dispersion (Stivers and Sun, 2010). Recently, Baker, Bloom and Davis (2016) develop

a new index of economic policy uncertainty (hereafter, EPU) based on newspaper coverage

frequency. They find that EPU increases stock price volatility and decreases investment and

employment in policy-sensitive sectors. Many studies show that EPU has the unique and

significant time-series effects on financial markets and corporate operations.1 For example,

Pastor and Veronesi (2013) demonstrate that political uncertainty proxied by the EPU index,

commands an equity risk premium, especially during the bad economy state. Brogaard and

Detzel (2015) also employ the EPU index to measure the policy uncertainty, and argue that EPU

is an economically important risk factor for equities. Thus, we raise a natural question whether

1 Other studies have linked policy uncertainty to stock prices (Pastor and Veronesi, 2012), daily jumps in stock and bond markets

(Baker, Bloom and Davis, 2013), aggregate bank credit growth (Bordo, Duca and Koch, 2016), merger and acquisition activities

(Bonaime, Gulen and Ion, 2016; Nguyen and Phan, 2016), corporate credit spreads (Kaviani, et. al, 2016), credit default swap

spreads and liquidity (Wang, Xu and Zhong, 2016), mutual fund flow-performance sensitivity (Starks and Sun, 2016), the term

structure of nominal interest rates (Leippold and Matthys, 2015) and bond option implied volatility (Ulrich, 2012).

2

momentum payoffs are related to EPU. We are interested in whether EPU have a unique power

to predict time-series variations in momentum profits.

At the same time, we are also interested in understanding how EPU will affect

momentum returns. Lou (2012) proposes the fund flow-based mechanism to understand price

momentum. He argues that past winning funds receive capital inflows and expand their existing

holdings (mainly in winning stocks); at the same time, past losing funds lose capital and have to

liquidate their holdings (mainly in losing stocks). As a result, performance-chasing mutual fund

flows can lead past winning stocks to keep outperforming past losing stocks. Meanwhile, several

recent studies show that investors infer mutual fund manager ability from signals of fund

performance. Such learning in turn affects fund flow-performance sensitivity (e.g., Berk and

Green, 2004; Pastor and Stambaugh, 2012; Starks and Sun, 2016). In particular, Starks and Sun

(2016) employ EPU index as proxy for policy uncertainty, and show that investors learning

about signals of fund performance weakens when uncertainty increases. Thus, investors have

more difficulty moving their investments to the mutual fund manager with superior return

generating ability during periods of higher uncertainty. In other word, investors may distribute

their investments randomly to the mutual funds regardless of their past performance in the case

of high policy uncertainty. Therefore, we expect that EPU may have the negative effect on

momentum. Specifically, when EPU is low, the flow-based mechanism that winning funds keep

investing in past winner stocks and losing funds liquidate their holdings in past loser stocks

continues to work, and generate the momentum; when EPU is high, the fund flow-based

mechanism may become disfunctional, and then momentum profits decline or even disappear.

Our findings can be summarized as follows. First, EPU negatively forecasts momentum

profits. An increase of one standard deviation in EPU is associated with a 1.13% decrease in

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returns for the winner-minus-loser portfolio. Specifically, the average monthly return in the low

EPU period is 1.67% (t-statistic=3.55), whereas the average momentum payoff in the high EPU

period is -0.18% (t-statistic=-0.31). The difference in returns between the low- and high-EPU

periods is economically large and statistical significant.

Second, the time-series predictive effect of EPU on momentum profits is mainly driven

by the short side portfolio. For instance, the difference in monthly raw returns between the low-

and high-EPU periods is -2.44 % (t-statistic=-2.82) for the short side portfolio, compared with -

0.58 % (t-statistic=-0.92) for the long side portfolio.

Third, we demonstrate that EPU has a unique power to predict momentum profits. The

forecast power of EPU on momentum profits is robust after controlling other time-series

variables, including business cycle, market states, investor sentiment, market volatility, market

illiquidity, cross-sectional return dispersion, and time-varying factors. More importantly, the

time-series regressions results show that EPU partially subsumes the explanatory power of

selected state variables.

Fourth, the EPU mimicking portfolio-UMP alone performs well as compared to the Fama

and French three factors in terms of a higher R-squared and a lower intercept, further supporting

that EPU has a strong power to explain momentum. We then decompose the EPU index into

three components, and find that the forecast power of momentum profits is mainly attributed to

the news-based component and the tax-related component. The inflation and government

spending component has little predictive power for momentum payoffs. Moreover, after

controlling for the macroeconomic uncertainty factor from Jurado, Ludvigson and Ng (2015), we

show that the effect of EPU on momentum remains unchanged, which implies that policy

4

uncertainty rather than economic uncertainty mainly drives the forecast power of EPU on

momentum.

Finally, using a global EPU index developed by Baker, Bloom and Davis (2016), we find

evidence that EPU can predict momentum profits in the international equity market and many

other asset classes. Additional robustness checks show that the effect of EPU on momentum is

robust after controlling for firm size, institutional ownership and analyst coverage.

Our paper makes several contributions to the literature. First, our study provides a new

time-series explanation for momentum. We demonstrate that the predictive power of EPU on

momentum profits is unaffected after controlling for previously documented state variables and

time-varying risk factors and robust in the global equity market and other asset classes. Second,

we shed light on the literature of how policy uncertainty impacts asset prices. Pastor and

Veronesi (2013) show that policy uncertainty commands equity risk premium, especially during

the bad economy state. Brogaard and Detzel (2015) document that policy uncertainty positively

forecasts equity risk premium. Our paper differs from other studies by examining whether EPU

can explain variations in profitability of stock price momentum. In addition, Addoum et al. (2015)

argue that momentum payoffs are concentrated among politically sensitive stocks and industries.

Our analysis differs from that of Addoum et al. (2015) in that we focus on a time-series

determinant of momentum.

The rest of this paper is organized as follows. In Section 2, we describe the data and

summary statistics. We investigate the predictive effect of EPU on momentum profits in Section

3. Further analyses including EPU mimicking portfolios, decomposition of EPU and

macroeconomic uncertainty are discussed in Section 4 and additional robustness checks are

conducted in Section 5. We provide a plausible explanation and conclusion remarks in Section 6.

5

2. Data and Descriptive Statistics

2.1 Measuring economic policy uncertainty

EPU index is a monthly news-based measure of economic policy uncertainty, developed

by Baker, Bloom and Davis (2016) and calculated as a weighted average of three components.

The first component is a normalized index of the volume of news articles discussing economic

policy uncertainty in 10 large newspapers. An article is considered as a policy uncertainty article

as long as it contains at least one of the terms ‘uncertainty’ or ‘uncertain’, at least one of the

terms ‘economic’ or ‘economy’ and at least one of the terms ‘congress’, ‘legislation’, ‘white

house’, ‘regulation’, ‘federal reserve’, or ‘deficit’. Each month, the total number of policy

uncertainty articles is normalized by the total number of articles in that newspaper. The second

component of the index is based on the present value of future scheduled tax code expirations

using data from the Congressional Budget Office. The third component of the index is based on

disagreement among professional forecasters over future government purchases and consumer

prices. It utilizes data in the Federal Reserve Bank of Philadelphia's Survey of Professional

Forecasters to measure the forecast dispersion for Consumer Price Index, Federal Expenditures

and State and Local Expenditures. The overall EPU index is obtained by applying the weights

1/2, 1/6 and 1/3 respectively to the above three components. The EPU index has been used

widely in academic studies and carried by several commercial data providers.2

2.2 The sample and momentum portfolio

2 Amengual and Xu (2014), Baker, Bloom and Davis (2013), Bloom (2014), Bordo, Duca and Koch (2016), Pastor

and Veronesi (2012), Starks and Sun (2016), Stock and Watson (2012) and Ulrich (2012). We download the EPU

index from the Economic Policy Uncertainty website. For more details regarding the EPU index, please refer to

http://www.policyuncertainty.com/. In addition, commercial provider such as Bloomberg, FRED, Haver and Reuters,

carries the EPU index.

6

Our sample consists of all common stocks (with share code of 10 or 11) listed on NYSE,

AMEX and NASDAQ obtained from the Center for Research in Security Prices (CRSP). The

sample period is from February 1985 to December 2014. We exclude stocks with price lower

than $5 at the beginning of portfolio formation period. Stock returns are adjusted for delisting by

using delisting return from CRSP. 3

To form momentum portfolios, we sort stocks based on their cumulative return from

month t-7 to month t-2 into ten portfolios, following previous literature (Jegadeesh and Titman,

1993). We skip one month between the end of the ranking period and the start of the holding

period to avoid the short-term reversals effect (Jegadeesh, 1990; and Lehmann, 1990). The decile

portfolio breakpoints are determined by sorting momentum using NYSE firms. To calculate the

momentum strategy returns, we long the winner portfolio, short the loser portfolio and hold the

portfolios for one month. Then we calculate the value-weighted average excess returns and the

risk-adjusted returns under the Fama-French three factor model for each portfolio.

< Table 1 >

Panel A of Table 1 presents the monthly average returns of the momentum strategy in the

full sample. For the monthly average excess returns, the long portfolio (W) earns 0.52 percent

and the short portfolio (L) yields -0.23 percent; the Long-Short (WML) portfolio earns 0.74

percent with the t-statistic of 1.94. For the Fama-French alpha, the winner portfolio yields -0.13

percent, the loser portfolio yields -1.26 percent; the Long-Short (WML) portfolio earns 1.12

percent and the t-statistic of 3.07. As can be seen in Panel A, the momentum strategy earns a

significantly positive abnormal return from 1985 to 2014.

3 Following Shumway (1997) and Shumway and Warther (1999), delisting return is -55% if trading on Nasdaq or -

30% if on NYSE/Amex when delisting is for performance-related reasons.

7

In Panel B of Table 1, we present the correlations between WML portfolio returns at

month t and the EPU index at month t-1. EPU is significantly negatively correlated with WML

portfolio returns, with a correlation of -0.16, implying that momentum payoffs are low when

EPU is high. In addition, we report the correlations between the WML portfolio returns at month

t and other state variables at month t-1. These variables have been proposed to predict time-

series variations in momentum payoffs in the previous literature, including UP market dummy

(Cooper, Gutierrez and Hameed, 2004), market volatility (Wang and Xu, 2015), business cycle

(Chordia and Shivakumar, 2002), investor sentiment (Antoniou, Doukas and Subrahmanyam,

2013), market illiquidity (Avramov, Cheng and Hameed, 2015) and return dispersion (Stivers

and Sun, 2010).

We define the UP market dummy variable as one if the prior two-year cumulative market

return is non-negative. Consistent with Cooper, Gutierrez and Hameed (2004), the correlation

between WML and UP market dummy is significantly positive (0.14), suggesting that

momentum strategy is stronger following UP market. The market volatility is defined as the

lagged 12-month daily return standard deviation. Wang and Xu (2015) demonstrate that the

momentum strategy returns are lower following high market volatility period. As can be seen,

WML is significantly negatively correlated with market volatility (-0.13), confirming findings in

Wang and Xu (2015). The NBER recession dummy variable is equal to one if periods represent

recession. Chordia and Shivakumar (2002) show that momentum profits tend to be lower

following recessions. The correlation between WML and NBER recession is -0.09, consistent

with Chordia and Shivakumar (2002). We also find evidence that WML is positively related to

Baker and Wrugler (2006)’s investor sentiment index, consistent with Antoniou, Doukas and

Subrahmanyam (2013) that momentum profits are higher when investors are optimistic. Finally,

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consistent with Stivers and Sun (2010), we demonstrate that momentum payoffs are negatively

correlated with lagged 3-month moving average of return dispersion4. In addition, these state of

economy variables are correlated, with correlations ranging from -0.48 to 0.40.

In sum, we document that the univariate correlation between WML and EPU is

significantly negative, implying that EPU may negatively predict the momentum payoffs.

However, it is critical to evaluate whether EPU has a distinctive power to forecast momentum

profits. We will show that the effect of EPU on momentum is robust after controlling for other

state variables in the later section.

3. Main Results

In this section, we provide a detailed analysis of how EPU forecasts momentum

profitability. First, we employ the portfolio-sort analysis and investigate momentum portfolio

returns and the Fama and French alphas among high and low EPU periods. Second, we conduct

time-series regression and Fama-Macbeth regression analyses. Third, we challenge our results by

controlling for other time series determinants of momentum profits and time-varying risk factors.

3.1 Portfolio sort analysis

We classify the whole sample periods into the high and low EPU months. High EPU

months are those in which the values of the EPU index in the previous month are above the

median value for the sample period and low EPU months are those with below the median values.

Then we compute the average excess returns and risk-adjusted returns for the low and high EPU

4 The correlation between WML and market illiquidity is insignificantly positive, which is inconsistent with

Avramov, Cheng and Hameed (2015). The reason is that we use a different sample period. When we use the same

sample period from 1925 to 2014 in Avramov, Cheng and Hameed (2015), we confirm a significantly negative

correlation between WML and market illiquidity.

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months. Following Stambaugh, Yu and Yuan (2012), the Fama-French alphas in the low and

high EPU periods are estimated using the following regression:

𝑅𝑡 = 𝛽𝐻𝛼𝐻,𝑡 + 𝛽𝐿𝛼𝐿,𝑡 + 𝛽1𝑀𝐾𝑇𝑡 + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝜀𝑡 (1)

where 𝛼𝐻,𝑡 and 𝛼𝐿,𝑡 are dummy variables that identify the high and low EPU periods.

Specifically, 𝛼𝐻,𝑡(𝛼𝐿,𝑡) is equal to 1 if the value of the EPU index in the previous month is above

(below) the median value. 𝑅𝑡 is the excess return at month t on either the long portfolio, the short

portfolio or the long-short portfolio. 𝑀𝐾𝑇𝑡 is the value-weighted market excess return at month t ,

𝑆𝑀𝐵𝑡 is return spread between low and high stocks at month t. 𝐻𝑀𝐿𝑡 is the return spread

between high and low value stocks at month t. The results for the average excess returns and

risk-adjusted returns are presented in Panel A and Panel B of Table 2, respectively.

< Table 2 >

Results in Table 2 reveal that momentum profits are only significant following low EPU

period. In Panel A, among the low EPU period, the average hedge portfolio return is 1.67 percent

per month with a t-statistic of 3.55, which is statistically significant at the 1% level. Among the

high EPU period, the mean return for WML portfolio is -0.18 percent per month with a t-statistic

of -0.31. The return difference between the low and high EPU months is 1.85 percent per month

with a t-statistic of 2.45, which is statistically significant at the 5% level.

We find a similar pattern for the Fama-French alpha. In Panel B, among the low EPU

months, the risk-adjusted return of WML portfolio is 1.97 percent per month with a t-statistic of

3.99. Among the high EPU months, the Fama-French alpha for WML portfolio is 0.26% and

insignificant. The difference in the risk-adjusted returns between the low and high EPU months

is 1.71 percent per month with a t-statistic of 2.35.

10

Table 2 shows that the predictive power of EPU on momentum profits is mainly driven

by the short side portfolio. In Panel A, the mean return difference between the low and high EPU

periods is insignificant for the long side, whereas the difference between two groups being -2.49

percent per month (t-statistic=-2.82) for the short side. In Panel B, the difference in risk-adjusted

returns between the low- and high-EPU months is 0.53% (t-statistic=1.64) for the long side

portfolio, while the difference between two groups being -1.17% per month (t-statistic=-2.34) for

the short side portfolio. The short side accounts for about 68% (1.17/1.71) of return difference

between long and high EPU periods.

The evidence in Table 2 suggests that EPU plays a crucial role to predict momentum

profits. We demonstrate that momentum profits are significant only when EPU is low. In

addition, the forecast power of EPU on momentum payoffs is mainly driven by the short

portfolio.

3.2 Time-series regression analysis

The results reported above are computed by averaging within low EPU and high EPU

months, where this classification is simply a binary measure. Here we conduct an alternative

analysis, using time-series regressions to investigate whether the level of EPU forecasts

momentum payoffs. We run the following regressions:

𝑅𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1+ 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝜀𝑡 (2)

where 𝑅𝑡 is the value weighted excess return at month t on either the long, the short, or the long-

short portfolio for momentum strategy; 𝐸𝑃𝑈𝑡−1 is the standardized value of the EPU index at

month t-1. The EPU index is scaled to have zero mean and a standard deviation of one. To

examine whether EPU can predict the momentum strategy, we regress the excess returns on the

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lagged EPU index as well as the Fama and French three factors. The regression results are

reported in Table 3.

< Table 3>

For the long side, the slope of EPU is insignificant in column (1), while the coefficient

estimate of EPU is significantly negative in column (2) after controlling for the Fama and French

three factors. Consistent with the portfolio sorting results in Table 2, we find that the short

portfolio has substantially lower returns following low levels of EPU. In column (3), the slope of

EPU is 1.27 (t-statistic= 2.22), implying that an increase of one standard deviation of EPU is

associated with a 1.27% increase in payoffs of the loser portfolio. After controlling for the Fama

and French three factors in column (4), the slope of EPU is 0.79 (t-statistic= 2.92).

More importantly, we document a significantly negative association between momentum

profits and EPU for the WML (long-short) portfolio5. As can be seen in in columns (5) and (6),

the slope coefficients on EPU are negative and significant. In column (5), the coefficient of EPU

is -1.13 with a t-statistic of -2.62, statistically significant at the 1% level. We find a similar result

after controlling for the Fama and French three factors in column (6). The coefficient of EPU is -

1.11 with a t-statistic of -2.89, implying that one standard deviation decrease in EPU is related to

a 1.11% monthly increase in abnormal return for the momentum strategy. The evidence in Table

3 demonstrates that EPU negatively predicts momentum profits and the forecast power is mainly

driven by firms in the short side portfolio6.

3.3 Fama-Macbeth regression analysis

5 We obtain qualitatively similar results using the UMD factor in the Kenneth French’s website. The UMD factor

constructed based on six value-weight portfolios formed on size and prior (2-12) returns. The winner-minus-loser

portfolio return is the average return on the two high prior return portfolios minus the average return on the two low

prior return portfolios 6 We find qualitatively similar results using equal-weighted portfolio returns.

12

In addition to time-series regression analysis, we conduct cross-sectional regression

analysis. Specifically, we first split the whole sample into the low- and high- EPU months. The

low (high) EPU months are those in which the value of EPU at the previous month is above

(below) median value. Then we run the Fama and MacBeth (1973) regressions for the full

sample period, the low- and high- EPU months as follows:

𝑅𝑡+1 = 𝛼 + 𝛽1𝑀𝑜𝑚𝑡−2,𝑡−7 + 𝛽2𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑡−1 + 𝜀𝑡 (3)

where Rt+1 is the value-weighted excess return at month t+1; Momt−2,t−7 is calculated as the

firm specific cumulative returns from month t-7 to month t-2. We include other firm

characteristics that have roles in explaining the cross-section of average stock returns in control

variables. Specifically, these variables are natural logarithm of book-to-market ratio (Fama and

French, 1992), natural logarithm of firm size (Banz, 1981), turnover ratio (Lee and Swaminathan,

2000), Idiosyncratic volatility (Ang et. al., 2006), short-term reversal (Jegadeesh, 1990), growth

profitability premium (Norvy-Marx, 2010), asset growth ratio (Cooper, Gulen and Schill, 2008),

net stock issuance (Loughran and Ritter, 1995), net operating assets (Hirshleifer et. al, 2004) and

investment-to-capital ratio (Xing, 2008). Definitions of variables are described in Appendix.

< Table 4 >

Table 4 reports the Fama-Macbeth regression results. Column 1 evaluates the forecast

power of momentum on future stock returns for the whole sample period. This result is

consistent with the portfolio-sorts analysis in Panel A of Table 1, momentum plays a strong role

in explaining the cross-section of average stock returns after controlling for other anomaly

variables. The coefficient of interest, the slope of momentum, is 0.77 (t-statistic=3.65) and

statistically significant at the 1 % level. Furthermore, we find that momentum is significant only

following low EPU period, confirming our findings in the portfolio sorts and time-series

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regression analyses. Specifically, the coefficient of momentum is 1.43 (t-statistic=6.42) for the

low EPU period in column (3), whereas the slope of momentum is 0.25 (t-statistic=0.76) for the

high EPU period in column (2). Our Fama and Macbeth regression analysis demonstrates that

momentum anomaly is significant only in the low EPU period.

3.4 Controlling for other time-series determinants of momentum

In this subsection, we investigate the role of EPU in forecasting momentum profits by

controlling for other time-series determinants. We consider six state variables, including market

state (Cooper, Gutierrez and Hameed, 2004), business cycle (Chordia and Shivakumar, 2002),

market volatility (Wang and Xu, 2015), investor sentiment (Antoniou, Doukas and

Subrahmanyam, 2013), market illiquidity (Avramov, Cheng and Hameed, 2015) and cross-

sectional return dispersion (Stivers and Sun, 2010).

To confirm the distinctive effect of EPU on time-series variations in momentum profits,

we regress monthly momentum returns on the lagged EPU index and one of the lagged state

variables. The regressions are as follow:

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1+ 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝛽5𝑋𝑡−1 + 𝜀𝑡 (4)

where 𝑅𝑤𝑚𝑙,𝑡 is the long-short hedge portfolio return for momentum strategy at month t; 𝐸𝑃𝑈𝑡−1

is the standardized value of EPU using the EPU index at month t-1. The EPU index is scaled to

have zero mean and a standard deviation of one. 𝑋𝑡−1 is one of the following variables at month

t-1: UP market dummy variable, NBER recession dummy variable, market volatility, investor

sentiment and market illiquidity. Specifically, UP market dummy variable (UP market) is

defined as one if the prior two-year cumulative market returns is non-negative. Business cycle

(BC) is a NBER recession dummy variable that equals one if periods represent recession. Market

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volatility is defined as the lagged 12-month daily return standard deviation. Investor sentiment

employs Baker and Wrugler (2006)’s investor sentiment index. Aggregate market illiquidity is

defined as the value-weighted average of each stock’s monthly Amihud illiquidity. The return

dispersion is the lagged three-month moving average of the cross-sectional standard deviation of

the monthly returns for the Fama and French 100 portfolios formed on size and book-to-market

ratio. The results are presented in Table 5.

< Table 5 >

As can be seen in Table 5, the predictive power of EPU on momentum profits is robust

after controlling for other time-series determinants of momentum. For example, the coefficients

of EPU are -0.90 (t-statistic = -2.21) and -0.84 (t-statistic = -2.35) for columns (1) and (2),

respectively. The coefficient of market state is significantly positive in column (2), consistent

with Cooper, Gutierrez and Hameed (2004) that momentum profits are higher in the bullish

market. However, the slope of market state is not significant in column (1), suggesting EPU

partially absorb the forecast power of the market state. The evidence in columns (1) and (2)

demonstrate that the predictive effect of EPU on momentum profits remain significantly negative

after controlling for the market state. Furthermore, from column (3) to column (12), we find that

EPU subsumes the predictive effect of business cycle, market volatility, investor sentiment,

market illiquidity and return dispersion. The slope coefficients of NBER recession, market

volatility, investor sentiment, market illiquidity and return dispersion become insignificant,

although the signs of these variables are as expected. Additionally, we control for these variables

together in columns (13) and (14). The coefficient on EPU is still negative and significant at the

10% level. In sum, the evidence in Table 5 indicates that EPU is a unique predictor of

momentum payoffs.

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3.5 Controlling for time-varying risk factor

The literature has documented that momentum profits have time-varying exposures to the

risk factors (Grundy and Martin, 2001; Korajczyk and Sadka, 2004). This subsection addresses

the concern that the effect of EPU could be explained by variations in the loadings on the Fama

and French three factors. Grundy and Martin (2001) find that the conditional factor risk for the

momentum portfolio is a linear function of the ranking-period return. Following Korajczyk and

Sadka (2004), we run the following regression to control the time-varying factor risk:

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1+ 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑀𝐾𝑇𝑡 ∗ 𝑀𝑀𝐾𝑇𝑡−2,𝑡−7 + 𝛽4𝑀𝐾𝑇𝑡 ∗ 𝑀𝑆𝑀𝐵𝑡−2,𝑡−7

+ 𝛽5𝑀𝐾𝑇𝑡 ∗ 𝑀𝐻𝑀𝐿𝑡−2,𝑡−7+𝛽6𝑆𝑀𝐵𝑡 + 𝛽7𝑆𝑀𝐵𝑡 ∗ 𝑀𝑆𝑀𝐵𝑡−2,𝑡−7 + 𝛽8𝑆𝑀𝐵𝑡

∗ 𝑀𝑀𝐾𝑇𝑡−2,𝑡−7+ 𝛽9𝑆𝑀𝐵𝑡 ∗ 𝑀𝐻𝑀𝐿𝑡−2,𝑡−7 + 𝛽10𝐻𝑀𝐿𝑡 + 𝛽11𝐻𝑀𝐿𝑡

∗ 𝑀𝐻𝑀𝐿𝑡−2,𝑡−7 + 𝛽12𝐻𝑀𝐿𝑡 ∗ 𝑀𝑀𝐾𝑇𝑡−2,𝑡−7 + 𝛽11𝐻𝑀𝐿𝑡 ∗ 𝑀𝑆𝑀𝐵𝑡−2,𝑡−7 + 𝜀𝑡

(5)

where 𝑅𝑤𝑚𝑙,𝑡 is the long-short hedge portfolio return for momentum strategy at month t;

𝑀𝑀𝐾𝑇𝑡−2,𝑡−7, 𝑀𝑆𝑀𝐵𝑡−2,𝑡−7 and 𝑀𝐻𝑀𝐿𝑡−2,𝑡−7 are the average cumulative returns of the Fama

and French three factors from the month t-7 to the month t-2. We present the results in Table 6.

< Table 6>

Specification (1) presents the regression results excluding the level of EPU. Consistent

with Grundy and Martin (2001) and Korajczyk and Sadka (2004), the momentum portfolios

indeed have time-varying exposures to the risk factors. In addition, the loadings of the risk

factors are generally higher following positive ranking-period factor returns. The evidence in the

specification (2) shows that the forecasting power of EPU on momentum profits is robust after

controlling for time-vary factor risks. The loading of the lagged level of EPU is -0.53 (t-statistic

16

=-2.03), which is negative and significant at the 5% level. The economic impact of implies that

one standard deviation increase in EPU reduces the momentum profits by 0.53% per month.

In sum, the findings in Tables 5 and 6 further confirm that EPU predicts the time-series

variations in momentum profits and the predictive power of EPU is robust after controlling for

other states variables and time-varying risk factors.

4. Further analyses of the momentum-EPU relation

In this subsection, we provide further analyses of the relationship between EPU and

momentum profits. First, we investigate whether an asset pricing model with the EPU factor can

explain the momentum anomaly in Section 4.1. Second, we examine which component of the

EPU index drives the impact of EPU on momentum profits in Section 4.2. Third, we test whether

the relationship between EPU and momentum profits is driven by policy uncertainty in Section

4.3. Finally, the predictive power of EPU on momentum in global equity markets and other asset

classes are discussed in Section 4.4.

4.1 The EPU factor-mimicking portfolio

4.1.1 Construction of the EPU mimicking factor-UMP

We project EPU onto the traded return space to form an EPU factor-mimicking portfolio,

UMP. Then we aim to test whether a contemporaneous UMP factor can explain momentum. To

construct the EPU factor-mimicking portfolio, following Adrian, Etula and Muir (2014), we then

run the following regression:

log (𝐸𝑃𝑈𝑡) = 𝑎 + 𝑏′[𝑀𝐾𝑇, 𝑆𝑀𝐵, 𝐻𝑀𝐿, 𝑅𝑀𝑊, 𝐶𝑀𝐴]𝑡 + 𝜀𝑡 (6)

17

where [𝑀𝐾𝑇, 𝑆𝑀𝐵, 𝐻𝑀𝐿, 𝑅𝑀𝑊, 𝐶𝑀𝐴] are the Fama and French five factors (Fama and French,

2015)7. We choose these return factors because they represent a large spread of the return space

(from -34.6% to 22.3%). By construction, 𝐶𝑜𝑣(log (𝐸𝑃𝑈𝑡), 𝑅𝑡) = 𝑐𝑜𝑣(𝑈𝑀𝑃𝑡, 𝑅𝑡) +

𝑐𝑜𝑣(𝜀𝑡, 𝑅𝑡) = 𝑐𝑜𝑣(𝑈𝑀𝑃𝑡, 𝑅𝑡), for all 𝑅𝑡 ∈ [𝑀𝐾𝑇, 𝑆𝑀𝐵, 𝐻𝑀𝐿, 𝑅𝑀𝑊, 𝐶𝑀𝐴]𝑡 . Ideally, the error

term, 𝜀𝑡 , is orthogonal to the space of returns so that the covariance of any asset with EPU is

identical to its covariance with the UMP. Thus, the EPU mimicking factor, 𝑈𝑀𝑃𝑡, is the fitted

value of the regression. In addition, we normalize the weights, 𝑏′ , to sum to one for the

convenience of units. The monthly return for the UMP factor is estimated by

𝑈𝑀𝑃𝑡 = 𝑎 + 𝑏 ̅′[𝑀𝐾𝑇, 𝑆𝑀𝐵, 𝐻𝑀𝐿, 𝑅𝑀𝑊, 𝐶𝑀𝐴]𝑡

where 𝑏̅′ =𝑏

∑ 𝑏= [−0.31, 0.38, −1.95, 1.17,1.72].

4.1.2 The pricing results using the UMP

We examine whether the contemporaneous EPU mimicking factor-UMP can reduce

momentum alpha. In addition, we compare the performance of explaining momentum using

several factor models. Specifically, we regress the long-short portfolio returns of momentum on

the several factors and compute the alpha and R-squared for each model. The regressions are as

follows:

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝑈𝑀𝑃𝑡 + 𝜀𝑡 (7)

where 𝑅𝑤𝑚𝑙,𝑡 is the long-short hedge portfolio return of momentum strategy at month t; 𝑈𝑀𝑃𝑡 is

the EPU mimicking factor estimated in the subsection 4.1.1. We report the results in Table 7.

< Table 7>

7 We download the five factors from Kenneth French’s website.

18

The evidence in Table 7 delivers a clear message that the contemporaneous EPU

mimicking factor-UMP explains momentum well. For example, the monthly alpha in column (1)

is 0.59% (t-statistic=1.61). The single UMP factor model has higher explaining power for

momentum profits than the Fama and French three-factor model. Specifically, the R-squared in

column (1) is 0.10, compared with 0.08 in column (2). Furthermore, we add the UMP factor to

the Fama-French three-factor model in column (3). Alpha is 0.74% (t-statistic=1.77) in column

(3), compared to 1.13% (t-statistic=3.08) in column (2). The R-squared increases from 0.08 in

column (2) to 0.11 in column (3). Overall, the UMP performs well compared with the Fama and

French three factors in terms of a relatively high R-squared and a low alpha. Table 7 shows that

returns of momentum substantially decrease after adjusting for the policy uncertainty risk,

suggesting that the EPU mimicking factor-UMP has the power to explain momentum anomaly.

4.2 Decomposing EPU into three components

The EPU measured by the EPU index has three components. The first component, EPU

news-based component, is a normalized index of the volume of news articles discussing

economic policy uncertainty in 10 large newspapers. The second component, EPU related to tax-

code, is based on the present value of future scheduled tax code expirations using data from the

Congressional Budget Office. The third component, EPU related to CPI and government

purchase, is based on disagreement among professional forecasters over future government

purchases and consumer prices. As the EPU index is a weighted average of three components, it

is important to determine which component contributes the predictive effect of momentum

profits. We repeat the time-series regression analysis in Table 3 for each component of the EPU

index. For each component of the EPU index, we run the following regressions:

19

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑡−1 + 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝜀𝑡 (8)

where 𝑅𝑤𝑚𝑙,𝑡 is the value weighted hedge portfolio at month t for momentum strategy.

𝐸𝑃𝑈 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑡−1 is the standardized value for each component of the EPU index at month t-

1. Each component of the EPU index is scaled to have zero mean and a standard deviation of one.

We report the results in Table 8.

< Table 8>

The evidence shows that the major explaining power of EPU on momentum payoffs

comes from the news-based component and tax-based component. In column (2), the slope of

EPU news-based component is -1.14 with a t-statistic of -2.85, suggesting that a one standard

deviation increase in the EPU news-based component is associated with a 1.14% decrease in

hedge portfolio return for momentum. Columns (3) and (4) suggest that uncertainty related to

tax-code also contributes to the predictive effect of momentum payoffs. For instance, in column

(3), the coefficient estimate of EPU tax-code based component is -0.71 with a t-statistic of -2.08,

significant at the 5% level. After controlling for the Fama and French three factors in column (4),

the slope of uncertainty related to tax reduces to -0.64 (t-statistic=-1.94) and significant at the 10%

level. Furthermore, we find that uncertainty related to inflation and government purchase does

not have explaining power for momentum payoffs. The slopes of inflation and government

purchase components are negative but insignificant in columns (5)-(8).

4.3 Controlling for macroeconomic uncertainty

Which uncertainty contributes to the significant predictive effect of EPU on momentum,

economic uncertainty or policy uncertainty? We address this question in this subsection, by

introducing the Jurado, Ludvigson and Ng (2015) index to measure economic uncertainty. Jurado,

20

Ludvigson and Ng (2015)’s measure is constructed using the aggregation of individual

conditional volatilities, which are estimated based on unpredictable component of the future

value of 132 macroeconomic series. We download the one-month, three-month and 12-month-

ahead economic uncertainty indices (EU1, EU2 and EU3) from Sydney Ludvigson’s website.

Then we run the following regressions to control for economic uncertainty:

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝑋𝑡−1 + 𝛽2𝐸𝑃𝑈𝑡−1 + 𝛽3𝑀𝐾𝑇𝑡 + 𝛽4𝑆𝑀𝐵𝑡 + 𝛽5𝐻𝑀𝐿𝑡 + 𝜀𝑡 (9)

where 𝑋𝑡−1 is one of the three economic uncertainty indices. Each index is scaled to have zero

mean and a standard deviation of one. We present the results in Table 9.

< Table 9>

We first examine whether economic uncertainty can forecast the momentum payoffs, in

which we find mixed results. We find no evidence that economic uncertainty predicts momentum

profits using monthly long-short hedge portfolio returns regress on economic uncertainty alone.

For instance, in columns (1), (5) and (9), the slopes of three economic uncertainty measures are

all negative but insignificant. However, we find that economic uncertainty negatively forecasts

momentum profits after adding the Fama and French three factors in regressions. For example,

among columns (2), (6) and (10), the coefficients of economic uncertainty are all significantly

negative. Then we test whether the negative predictability of EPU on momentum profits is robust

after controlling for economic uncertainty. Our regression results find that the effect of EPU on

momentum payoffs remains unaffected. Furthermore, EPU subsumes the effect of economic

uncertainty on momentum in models using the Fama and French three factors and EPU as

regressors. For instance, in columns (4) and (8), the coefficients of economic uncertainty are

significant only at the 10% level. In column (12), the slope of economic uncertainty is

21

insignificant. Table 9 demonstrates that the predicting effect of EPU on momentum is robust

after controlling for economic uncertainty.

4.4 Global equity and other asset classes

A recent study of Asness, Moskowitz and Pedersen (2013) documents strong momentum

effects exist not only among global equity markets but also in other asset classes including global

equity index futures, currencies, global government bonds and commodity futures. In this

subsection, we investigate whether the predictive power of EPU on momentum is robust in

global equity markets and other asset classes.

We use the monthly Global Economic Policy Uncertainty index (GEPU) to capture the

overall policy uncertainty in the global economy from January 1997 to December 2016. The

GEPU is a GDP-weighted average of national EPU indices for 18 countries: Australia, Brazil,

Canada, Chile, China, France, Germany, India, Ireland, Italy, Japan, the Netherlands, Russia,

South Korea, Spain, Sweden, the United Kingdom and the United States8. We download the

monthly global momentum factor from Kenneth French’s website. The momentum returns for

other assets are from Asness, Moskowitz and Pedersen (2013), which contain equity country

index futures across 18 developed equity markets, 10 currencies across developed markets, 10

country government bonds and 27 different commodity futures9. The market index for each asset

is as follows: it is the global market excess return from Kenneth French’s website for equity, the

MSCI World Index for country index futures, an equal-weighted average of the securities for

8 See Baker, Bloom and Davis (2016) for a detailed discussion of how to construct national EPU indices. The GEPU

index is downloaded from the Economic Policy Uncertainty website. 9 We download the updated monthly momentum returns for different assets from AQR capital management:

https://www.aqr.com/library/data-sets/value-and-momentum-everywhere-portfolios-monthly. This data set is an

updated and extended version of Asness, Moskowitz and Pedersen (2013). Details on data description and sources

can be found in Asness, Moskowitz and Pedersen (2013).

22

currency, the Bloomberg Barclays global treasury total return index for government bonds and

the S&P Goldman Sachs Commodity Index (S&P GSCI) for commodity.

Additionally, we add nonstock assets and all assets in our analysis. The momentum

returns for nonstock assets are calculated by taking average of momentum premiums across all

nonstock assets. Similarly, the momentum profits for all assets are obtained by taking average of

momentum returns for each asset. To investigate the predictive effect of GEPU on momentum

for each asset, we regress the monthly momentum returns on the GEPU alone. Then we add the

market excess returns for each asset in our regression analysis to control for the market risk.

Table 10 reports the regression results.

< Table 10>

We find evidence that the GEPU significantly negatively forecasts momentum profits

among five asset classes including global stocks, country index futures, commodity, nonstock

assets and all assets. For instance, the coefficient estimator of GEPU is -0.73 (t-statistic=-2.74)

for global stocks in column (1), implying that a one-standard-deviation increase in GEPU is

associated with a 0.73% decrease in global momentum returns. Additionally, the coefficients on

GEPU for country index futures, commodity, nonstock assets and all assets are significantly

negative. The results are unaffected after controlling for the market excess returns. However, the

relations between GEPU and momentum returns are insignificant for government bonds and

currency. From Table 10, we show that the effects of EPU on momentum profits are robust in the

global equity market and among many other asset classes.

5. Other Robustness Checks

23

We conduct several additional robustness checks in this subsection. The relationships

between EPU and momentum at a variety of holding periods are discussed in Section 5.1. We

also examine the forecasting power of EPU on momentum profits in three subsamples based on

size, institutional ownership and analyst coverage.

5.1 EPU and momentum at various holding periods

We examine the predictability of EPU on momentum payoffs using different holding

period returns. The holding periods for momentum are ranging from one- to twelve-months. For

each holding period, we compute the average value-weighted hedge portfolio returns and the

risk-adjusted returns for the low- and high-EPU months. Figure 1 presents the results for average

hedge portfolio return and risk-adjusted returns up to twelve-months holding period. It is clear to

observe that differences in average hedge portfolio returns between the low- and high-EPU

periods decrease with holding periods. We present the time-series predict regressions using

different holding period returns in Table 11 using the same setting as Table 2. For instance, in

Panel A of Table 11, the regression coefficients of EPU are -1.13 (t-statistic = -2.62) for one

month, -0.49 (t-statistic =-1.65) for six months and -0.24 (t-statistic =-0.89) for twelve months,

respectively. Panel A suggests that EPU could negatively predict momentum profits for up to 6

months. After controlling for the Fama and French three factors in Panel B, the negative

predictive of EPU on momentum also persists around 6 months.10

Overall, we show that the

predictive power of EPU on momentum profits decreases with portfolio holding periods. The

negative relation between EPU and momentum profits are significant up to next 6 months.

10

In an un-reported table, the raw return difference in monthly momentum profits between the low- and high-EPU

months is 1.86% for the one-month holding period, compared to 0.22% for the 12-months holding period. The risk-

adjusted return difference in momentum profits between the low- and high-EPU months is 1.71% for the one-month

holding period, compared to 0.10% for the 12-months holding period.

24

< Figure 1>

< Table 11>

5.2 Subsamples by firm size, institutional ownership and analyst coverage

We investigate the predictability of EPU on time-series variations in momentum profits

in subsamples based on three characteristics: size, institutional ownership and analyst coverage.

Small (large) stocks are those whose sizes are lower (higher) than NYSE 50 percentile. Low

(high) institutional ownership stocks are those whose institutional ownership is below (above)

the median value for each quarter. Similarly, low (high) analyst coverage stocks are those whose

analyst coverage is below (above) the median value for each month.

For each subsample, we regress the value-weight long-short portfolio returns on the

standardized value of EPU as well as the Fama and French three factors. Table 12 presents the

results for size, institutional ownership and analyst coverage in Panels A, B and C, respectively.

We find that EPU negatively predicts momentum profits for each subsample. For instance, in

Panel A, for large stocks, the slope of EPU is -1.15 (t-statistic=-2.82) in column (4), suggesting

that an increase of one standard deviation in EPU is related to a 1.15% decreases in momentum

profits. Also, the coefficient estimate of EPU is -1.07 (t-statistic=-2.57) in column (2) for small

stocks. We find consistent results for low and high institutional ownership stocks as well as low

and high analyst coverage firms. In Panel B, the coefficient of EPU is -1.10 (t-statistic=-2.68) in

column (4), compared with -1.16 (t-statistic=-2.37) in column (2). In Panel C, the slope of EPU

is -1.07 (t-statistic=-2.60) in column (4), as opposed to -0.83 (t-statistic=-2.09) in column (2).

< Table 12>

25

Overall, we further show that EPU negatively forecasts momentum profits for small and

large stocks; for low and high institutional ownership firms; and for low and high analyst

coverage firms.

6. Conclusion

Economic policy uncertainty plays a significant role in predicting momentum profits.

Using a news-based measure of EPU, we demonstrate that EPU negatively forecasts momentum

profits. Specifically, an increase of one standard deviation in EPU is associated with a 1.13%

decrease in momentum returns for the winner-minus-loser portfolio. We show that momentum is

significant only following low levels of EPU. The difference in returns between the low- and

high-EPU periods is significant. Furthermore, we find that the effect of EPU on momentum is

mainly driven by the short-side portfolio. The difference in returns between the low-and high-

EPU periods is -2.49 % (t-statistic=-2.82) for the short portfolio, compared with -0.58 % (t-

statistic=-0.92) for the long portfolio.

To understand our findings, we borrow the fund flow-based mechanism of momentum

from Lou (2012) that winning funds keep investing in past winner stocks and losing funds

liquidate their holdings in past loser stocks. Combining with the implication of Starks and Sun

(2016) that investors’ learning about signals of fund performance weakens when uncertainty

increases, the flow-based mechanism may work only in the state of Low EPU, leading to the

significant payoffs of momentum.

We further demonstrate that the predictive power of EPU on momentum remains

significantly negative after controlling for the state of economy variables and time-varying risk

factors. EPU subsumes the predictive effect of business cycle, market volatility, investor

26

sentiment, market illiquidity and return dispersion. We also construct an EPU mimicking

portfolio and show that the single mimicking factor model explains momentum profits well.

Further analysis shows that the explanatory power of EPU on momentum payoffs mainly comes

from the news-based component and tax-based component; the effect of EPU on momentum is

mainly attributed to the policy uncertainty rather than economic uncertainty. Finally, we show

that a global EPU index helps explain momentum profits in the global equity market and other

asset classes. Overall, our findings suggest that EPU is an important determinant of time-series

variations in momentum profits.

27

Appendix: Definition of variables

EPU and other state of economy variables:

EPU: the EPU index in Baker, Bloom and Davis (2016). The data are obtained from

http://www.policyuncertainty.com/index.html.

WML: the raw average hedge portfolio return for the momentum strategy. Momentum at month t is the

cumulative continuously compounded return from month t-7 to month t-2.

UP market dummy: one if the prior two-year cumulative market return is non-negative.

Market volatility: the lagged 12-month daily return standard deviation.

Recession dummy: one if periods represent recession provide by NBER website.

Market illiquidity: the value-weighted average of each stock’s monthly Amihud illiquidity (2002) ratio.

The illiquidity ratio is defined as the absolute value of daily stock return scaled by the dollar trading

volume of the stock on that day.

Investor sentiment: Baker and Wrugler (2006)’s investor sentiment index based on first principal

component of five sentiment proxies where each of the proxies has first been orthogonalized with respect

to a set of six macroeconomic indicators.

Return dispersion: the lagged three-month moving average of the cross-sectional standard deviation of the

monthly returns for the Fama and French 100 portfolios formed on size and book-to-market ratio.

Control variables in Fama-Macbeth regressions:

Firm size (SIZE): total market capitalization at month t-1.

Book-to-market ratio (BM): the book equity in the prior fiscal year divided by market equity at month t-1.

Turnover ratio (TO): the average monthly turnover from month t-12 to month t-3.

Idiosyncratic volatility (IVOL): the standard deviation of residuals from a regression of daily excess

returns on the Fama-French three factor model.

Short-term reversal (STREV): prior one-month return at month t-1.

Gross profitability premium (GP): the ratio of sales minus cost of goods sold over total assets.

Asset growth (AG): the percentage change in total assets.

Net stock issuance (NSI): the split-adjusted shares outstanding at month t-1 divided by lagged 12-month

split-adjusted shares outstanding.

28

Net operating assets (NOA): total operating assets minus total operating liabilities scaled by the average

total assets over the past two years.

Investment-to-capital (IK): the ratio of capital expenditure over property, plant and equipment.

Others

UMP factor: the EPU mimicking portfolio returns.

GEPU: the GEPU Index is a GDP-weighted average of national EPU indices for 18 countries: Australia,

Brazil, Canada, Chile, China, France, Germany, India, Ireland, Italy, Japan, the Netherlands, Russia,

South Korea, Spain, Sweden, the United Kingdom, and the United States.

29

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33

Table 1: Descriptive Statistics of Momentum and Economic Policy Uncertainty

At the beginning of each month t, all NYSE, AMEX and NASDAQ common stocks are sorted into decile portfolios

based on past six month returns (the cumulative return from month t-7 to month t-2). The decile portfolio

breakpoints are determined by sorting momentum using NYSE firms. The sample excludes stocks with price lower

than $5 at the beginning of portfolio formation and stock returns are adjusted for delisting bias. The sample period is

from February 1985 to December 2014. We buy the winner portfolio, sell the loser portfolio and hold for one month.

Then we calculated the value-weighted excess return and risk-adjusted returns for the winner-minus-loser (WML)

portfolio. The economic policy uncertainty (EPU) is measured by the news-based measure developed by Baker,

Bloom and Davis (2016). WML is the raw average hedge portfolio return for the momentum strategy. UP market

dummy (UP MKT) is defined as one if the prior two-year cumulative market return is non-negative. The market

volatility (MKT VOL) is defined as the lagged 12-month daily return standard deviation. The business cycle (BC) is a

NBER recession dummy variable defined as one if periods represent recession. The aggregate market illiquidity

(MKT ILLIQ) defined as the value-weighted average of each stock’s monthly Amihud illiquidity. The sentiment

variable (SENT) employs Baker and Wrugler (2006)’s investor sentiment index. The return dispersion (RD) is the

lagged three-month moving average of the cross-sectional standard deviation of the monthly returns for the Fama

and French 100 portfolios formed on size and book-to-market ratio. The t-statistics in parentheses are calculated

based on heteroskedasticity-consistent standard errors of White (1980).

Panel A presents the long, short and long-short excess returns and risk-adjusted returns of the momentum strategy.

Panel B provides the correlations of the momentum strategy profits, the economic policy uncertainty index (EPU)

and other market state variables.

34

Panel A: Momentum Strategy

Momentum Excess Returns Fama-French Alpha

Long (W) ret (%) 0.52 -0.13

t-stat (1.64)

(-0.86)

Short (L) ret (%) -0.23

-1.26

t-stat (-0.52)

(-4.90)

Long-Short (WML) ret (%) 0.74

1.12

t-stat (1.94)

(3.07)

Panel B: Correlation of EPU index and other indexes

WML EPU UP MKT MKT VOL REC MKT ILLIQ SENT RD

WML 1.00

EPU -0.16 1.00

UP MKT 0.14 -0.28 1.00

MKT VOL -0.13 0.40 -0.48 1.00

BC -0.09 0.27 -0.32 0.28 1.00

MKT ILLIQ 0.01 -0.13 0.06 -0.11 -0.03 1.00

SENT 0.11 -0.19 0.19 -0.11 0.00 0.06 1.00

RD -0.07 0.04 -0.30 0.40 0.26 0.00 0.19 1.00

35

Table 2: Portfolio sorting of momentum and EPU

This table presents the value-weighted excess returns and risk-adjusted returns of the momentum strategy during the

periods of low and high economic policy uncertainty (EPU). EPU is captured by the EPU index developed by Baker,

Bloom and Davis (2016). The low and high levels of EPU are classified based on the median value for the sample

period. The sample period is from February 1985 to December 2014. The risk-adjusted returns in the low and high

EPU periods are calculated as the follows:

𝑅𝑡 = 𝛽𝐻𝛼𝐻,𝑡 + 𝛽𝐿𝛼𝐿,𝑡 + 𝛽1𝑀𝐾𝑇𝑡 + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝜀𝑡

where 𝛼𝐻,𝑡 and 𝛼𝐿,𝑡 are the dummy variables that identify high and low EPU periods. Specifically, 𝛼𝐻,𝑡(𝛼𝐿,𝑡) is equal

to 1 if the value of the EPU index in the previous month is above (below) the median value. 𝑅𝑡 is the excess return at

month t on either the long portfolio, the short portfolio or the long-short portfolio; 𝑀𝐾𝑇𝑡 is the value-weighted

market excess return at month t ; 𝑆𝑀𝐵𝑡 is return spread between small and big size stocks at month t; 𝐻𝑀𝐿𝑡 is the

return spread between high and low value stocks at month t. Panel A and Panel B report the excess returns and risk-

adjusted returns of momentum strategy between the low and high EPU periods. The t-statistics in parentheses are

calculated based on heteroskedasticity-consistent standard errors of White (1980).

Panel A: Excess Returns between the low and high EPU periods

Low High Low-High

Long ret (%) 0.22

0.81

-0.58

t-stat (0.45)

(2.06)

(-0.92)

Short ret (%) -1.45

0.99

-2.49

t-stat (-2.85)

(1.41)

(-2.82)

Long-short ret (%) 1.67

-0.18

1.85

t-stat (3.55) (-0.31) (2.45)

Panel B: Fama-French Alphas between low and high EPU periods

Low

High

Low-High

Long ret (%) 0.12 -0.41 0.53

t-stat (0.54)

(-1.81)

(1.64)

Short ret (%) -1.84

-0.67

-1.17

t-stat (-5.41)

(-1.79)

(-2.34)

Long-short ret (%) 1.97

0.26

1.71

t-stat (3.99) (0.49) (2.35)

36

Table 3: Time series regression of momentum and EPU

This table examines the predictive effect of EPU on momentum profits using the following regressions:

𝑅𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1 + 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝜀𝑡

where 𝑅𝑡 is the value weighted excess return at month t on either the long, the short, or the long-short portfolio for

momentum strategy. EPU is captured by the EPU index developed by Baker, Bloom and Davis (2016). 𝐸𝑃𝑈𝑡−1 is

the standardized value of the EPU index at month t-1, where the EPU index is scaled to have zero mean and a

standard deviation of one. 𝑀𝐾𝑇𝑡 is the value-weighted market excess return at month t ; 𝑆𝑀𝐵𝑡 is return spread

between small and big size stocks at month t; 𝐻𝑀𝐿𝑡 is the return spread between high and low value stocks at month

t. The t-statistics in parentheses are calculated based on heteroskedasticity-consistent standard errors of White

(1980). *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively. The sample period is from

February 1985 to December 2014.

Long (W) Short (L) Long-Short (WML)

(1) (2) (3) (4) (5) (6)

EPU

0.14 -0.32**

1.27** 0.79***

-1.13*** -1.11***

(0.45) (-2.00)

(2.22) (2.92)

(-2.62) (-2.89)

MKT

0.99***

1.46***

-0.47***

(21.18)

(18.48)

(-4.28)

SMB

0.47***

0.30**

0.16

(5.89)

(2.13)

(0.79)

HML

-0.20**

0.12

-0.32

(-1.99) (0.83) (-1.38)

37

Table 4: Fama-Macbeth regression analysis of momentum and EPU

This table presents the results of Fama-Macbeth regression analysis. We split the time periods into low and high

EPU months. EPU is captured by the EPU index developed by Baker, Bloom and Davis (2016).The low (high) EPU

months are those in which the value of EPU at the previous month is above (below) median value. We run the Fama

and Macbeth (1973) regressions for all periods and high and low EPU periods as follows:

𝑅𝑡+1 = 𝛼 + 𝛽1𝑀𝑜𝑚𝑡−2,𝑡−7 + 𝛽2𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑡−1 + 𝜀𝑡

where Rt+1 is holding period return at month t+1; Momt−2,t−7 is calculated as the firm specific cumulative returns

from month t-7 to month t-2. These control variables are natural logarithm of book-to-market ratio (BM), natural

logarithm of firm size (SIZE), turnover ratio (TO), Idiosyncratic volatility (IVOL), short-term reversal (STREV),

growth profitability premium (GP), asset growth ratio (AG), net stock issuance (NSI), net operating assets (NOA)

and investment-to-capital ratio (IK). Definitions of variables are described in Appendix. The t-statistics are reported

in parentheses. *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively. The sample period is

from February1985 to December 2014.

All High EPU Low EPU

MOM

0.77

0.25

1.43

(3.65)

(0.76)

(6.42)

BM

0.14

0.17

0.10

(2.56)

(2.42)

(1.18)

SIZE

0.02

0.00

0.05

(0.70)

(-0.02)

(1.03)

TO

-3.25

-1.65

-5.31

(-2.17)

(-0.78)

(-2.54)

IVOL -4.16

-3.25

-5.34

(-5.44 )

(-3.38 )

(-4.31 )

STREV

-2.34

-3.15

-1.29

( -6.31)

( -6.05)

( -2.54)

GP

0.70

0.80

0.57

(4.63)

(4.03)

(2.45)

AG

-0.33

-0.47

-0.15

(-4.17)

(-4.13)

(-1.40)

NSI

-0.82

-0.74

-0.92

( -4.63 )

( -2.92 )

( -3.83 )

NOA

-0.52

-0.49

-0.56

( -3.45)

( -2.48)

( -2.39)

IK

-0.14

-0.05

-0.26

( -2.22 ) ( -0.51 ) ( -3.49 )

38

Table 5: Controlling for other time-series determinants of momentum profits

This table investigates the forecast power of economic policy uncertainty (EPU) on excess returns of the momentum

strategies after controlling for other market state variables. EPU is captured by the EPU index developed by Baker,

Bloom and Davis (2016). The loadings on economic policy uncertainty periods are calculated as the follows:

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1 + 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝛽5𝑋𝑡−1 + 𝜀𝑡

where 𝑅𝑤𝑚𝑙,𝑡 is the long-short hedge portfolio return for momentum strategy at month t; 𝐸𝑃𝑈𝑡−1 is the standardized

value of the EPU index at month t-1, where the EPU index is scaled to have zero mean and a standard deviation of

one; 𝑀𝐾𝑇𝑡 is the value-weighted market excess return at month t; 𝑆𝑀𝐵𝑡 is return spread between small and big size

stocks at month t; 𝐻𝑀𝐿𝑡 is the return spread between high and low value stocks at month t. Other controls include

UP market dummy variable (UP MKT) defined as one if the prior two-year cumulative market return is non-

negative, the business cycle (BC) is a NBER recession dummy variable defined as one if periods represent recession,

the market volatility (MKT VOL) defined as the lagged 12-month daily return standard deviation, the sentiment

variable (SENT) employing Baker and Wrugler (2006)’s investor sentiment index and the aggregate market

illiquidity (MKT ILLIQ) defined as the value-weighted average of each stock’s monthly Amihud illiquidity. The

return dispersion (RD) is the cross-sectional standard deviation of the monthly returns for the Fama and French 100

portfolios formed on size and book-to-market ratio. The t-statistics in parentheses are calculated based on

heteroskedasticity-consistent standard errors of White (1980). *, ** and *** indicate significance at 10%, 5% and 1%

levels, respectively. The sample period is from February 1985 to December 2014.

39

Market State Business Cycles Market Volatility Investor Sentiment Market Illiquidity Return Dispersion All Variables

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

EPU -0.90** -0.84** -1.02*** -0.93*** -0.88** -0.88** -1.01** -1.01*** -1.14*** -1.11*** -1.11*** -1.08*** -0.76* -0.72*

(-2.21) (-2.35) (-2.60) (-2.67) (-2.04) (-2.24) (-2.32) (-2.60) (-2.61) (-2.87) (-2.60) (-2.80) (-1.74) (-1.86)

MKTRF

-0.48*** -0.49*** -0.47*** -0.46*** -0.47*** -0.49***

-0.50***

(-4.41) (-4.31) (-4.32) (-4.32) (-4.27) (-4.39)

(-4.51)

SMB

0.20 0.20 0.18 0.16 0.16 0.20

0.23

(0.98) (0.94) (0.87) (0.77) (0.79) (0.99)

(1.16)

HML

-0.31 -0.32 -0.30 -0.34 -0.32 -0.29

-0.32

(-1.39) (-1.39) (-1.32) (-1.49) (-1.38) (-1.22)

(-1.35)

UP MKT 2.21 2.68** 1.31 1.72

(1.62) (2.07) (0.89) (1.23)

BC

-1.37 -2.43 -0.63 -1.57

(-0.68) (-1.32) (-0.29) (-0.79)

MKT VOL

-0.36 -0.33

1.05 1.03

(-1.52) (-1.54)

(1.01) (1.03)

SENT

1.02 0.97 -0.13 -0.05

(0.96) (0.89) (-1.08) (-0.46)

MKT ILLIQ

-0.09 -0.03 -0.11 0.04

(-0.87) (-0.31) (-0.47) (0.17)

RD

-0.34 -0.49 -0.24 -0.39

(-0.52) (-0.85) (-0.31) (-0.59)

40

Table 6: Controlling for time-varying risk factor

This table examines the predictive effect of EPU after adjusting time-varying exposures to the Fama and French

three factors. Following Korajczyk and Sadka (2004), we run the following regression to control for time-varying

factor risk:

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1+ 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑀𝐾𝑇𝑡 ∗ 𝑀𝑀𝐾𝑇𝑡−2,𝑡−7 + 𝛽4𝑀𝐾𝑇𝑡 ∗ 𝑀𝑆𝑀𝐵𝑡−2,𝑡−7 + 𝛽5𝑀𝐾𝑇𝑡 ∗

𝑀𝐻𝑀𝐿𝑡−2,𝑡−7+𝛽6𝑆𝑀𝐵𝑡 + 𝛽7𝑆𝑀𝐵𝑡 ∗ 𝑀𝑆𝑀𝐵𝑡−2,𝑡−7 + 𝛽8𝑆𝑀𝐵𝑡 ∗ 𝑀𝑀𝐾𝑇𝑡−2,𝑡−7+ 𝛽9𝑆𝑀𝐵𝑡 ∗ 𝑀𝐻𝑀𝐿𝑡−2,𝑡−7 +

𝛽10𝐻𝑀𝐿𝑡 + 𝛽11𝐻𝑀𝐿𝑡 ∗ 𝑀𝐻𝑀𝐿𝑡−2,𝑡−7 + 𝛽12𝐻𝑀𝐿𝑡 ∗ 𝑀𝑀𝐾𝑇𝑡−2,𝑡−7 + 𝛽11𝐻𝑀𝐿𝑡 ∗ 𝑀𝑆𝑀𝐵𝑡−2,𝑡−7 + 𝜀𝑡

where 𝑅𝑤𝑚𝑙,𝑡 is the long-short hedge portfolio return for momentum strategy at month t;

𝑀𝑀𝐾𝑇𝑡−2,𝑡−7, 𝑀𝑆𝑀𝐵𝑡−2,𝑡−7 and 𝑀𝐻𝑀𝐿𝑡−2,𝑡−7 are the average cumulative returns of the Fama and French three

factors from the month t-7 to the month t-2; 𝐸𝑃𝑈𝑡−1 is the standardized value of the EPU index at month t-1, where

the EPU index is scaled to have zero mean and a standard deviation of one; 𝑀𝐾𝑇𝑡 is the value-weighted market

excess return at month t; 𝑆𝑀𝐵𝑡 is return spread between small and big size stocks at month t; 𝐻𝑀𝐿𝑡 is the return

spread between high and low value stocks at month t. Model (1) reports the regression results excluding the lagged

level of EPU. Model (2) reports the estimate coefficients after controlling for time-varying factor. *, ** and ***

indicate significance at 10%, 5% and 1% levels, respectively. The sample period is from February 1985 to

December 2014.

(1) (2)

EPU

-0.53**

(-2.03)

MKTRF -0.44*** -0.43***

(-5.55) (-5.44)

MKTRF*MMKTRF 0.24*** 0.23***

(5.64) (5.71)

MKTRF*MSMB 0.01 -0.01

(0.09) (-0.12)

MKTRF*MHML 0.12** 0.12**

(2.45) (2.38)

SMB -0.37** -0.36**

(-2.49) (-2.40)

SMB*MSMB 0.41*** 0.41***

(4.35) (4.32)

SMB*MMKTRF 0.09 0.08

(1.24) (1.17)

SMB*MHML 0.03 0.03

(0.43) (0.49)

HML -0.32** -0.32**

(-1.98) (-2.03)

HML*MHML 0.39*** 0.38***

(5.30) (5.31)

HML*MMKTRF -0.18** -0.17**

(-2.48) (-2.47)

HML*MSMB 0.27*** 0.25**

(2.87) (2.58)

41

Table 7: The EPU mimicking portfolio-UMP

This table reports the results of the following regressions:

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝑈𝑀𝑃𝑡+ 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝜀𝑡

Where 𝑅𝑤𝑚𝑙,𝑡 is the long-short hedge portfolio return for momentum strategy at month t; 𝑈𝑀𝑃𝑡 is the monthly

return of the EPU factor-mimicking portfolio at month t, 𝑀𝐾𝑇𝑡 is the value-weighted market excess return at month

t; 𝑆𝑀𝐵𝑡 is return spread between small and big size stocks at month t; 𝐻𝑀𝐿𝑡 is the return spread between high and

low value stocks at month t. The construction of the EPU factor-mimicking portfolio-UMP is described in section

4.1.1. The t-statistics in parentheses are calculated based on heteroskedasticity-consistent standard errors of White

(1980). *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively. The sample period is from

February 1985 to December 2014.

(1) (2) (3)

MKTRF

-0.48*** -0.21*

(-4.19) (-1.65)

SMB

0.15 0.10

(0.69) (0.45)

HML

-0.29 0.04

(-1.26) (0.14)

UMP 0.47***

0.39**

(4.52)

(2.48)

Alpha 0.59 1.12*** 0.74*

(1.61) (3.07) (1.77)

R-squared 0.10 0.08 0.11

42

Table 8: Decomposing EPU into different components

This table presents the time-series regression results of momentum payoffs and components of EPU. EPU is

captured by the EPU index developed by Baker, Bloom and Davis (2016). The EPU index has three components.

The first component, EPU news component, is a normalized index of the volume of news articles discussing

economic policy uncertainty in 10 large newspapers. The second component, EPU tax component, is based on the

present value of future scheduled tax code expirations using data from the Congressional Budget Office. The third

component, EPU CPI and government purchase, is based on disagreement among professional forecasters over

future government purchases and consumer prices. For each component of the EPU index, we run the following

regressions: 𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑡−1 + 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝜀𝑡

where 𝑅𝑤𝑚𝑙,𝑡 is the value weighted hedge portfolio at month t for momentum strategy. 𝐸𝑃𝑈 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑡−1 is the

standardized value for each component of the EPU index at month t-1. Each component of the EPU index is scaled

to have zero mean and a standard deviation of one. 𝑀𝐾𝑇𝑡 is the value-weighted market excess return at month t ;

𝑆𝑀𝐵𝑡 is return spread between small and big size stocks at month t; 𝐻𝑀𝐿𝑡 is the return spread between high and

low value stocks at month t. The t-statistics in parentheses are calculated based on heteroskedasticity-consistent

standard errors of White (1980). *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively. The

sample period is from February 1985 to December 2014.

(1) (2) (3) (4) (5) (6) (7) (8)

MKTRF

-0.48***

-0.47***

-0.48***

-0.48***

(-4.27)

(-4.22)

(-4.18)

(-4.22)

SMB

0.18

0.15

0.15

0.14

(0.86)

(0.72)

(0.68)

(0.66)

HML

-0.32

-0.29

-0.29

-0.30

(-1.38)

(-1.27)

(-1.26)

(-1.29)

EPU news component -1.08** -1.14***

(-2.38) (-2.85)

EPU tax component

-0.71** -0.64*

(-2.08) (-1.94)

EPU Gov.Purch. Component

-0.44 -0.26

(-1.34) (-0.81)

EPU CPI component

-0.32 -0.34

(-0.95) (-1.01)

43

Table 9: Controlling for macroeconomic uncertainty

This table investigates the predictive effect of EPU on momentum after controlling for macroeconomic uncertainty. We employ the Jurado, Ludvigson and Ng

(2015) index as the macroeconomic uncertainty measure. We obtain the one-month, three-month and 12-month-ahead economic uncertainty indices (EU1, EU2

and EU3) from Sydney Ludvigson’s website. We run the following regressions to control for economic uncertainty:

𝑅𝑤𝑚𝑙,𝑡 = 𝛼 + 𝛽1𝑋𝑡−1 + 𝛽2𝐸𝑃𝑈𝑡−1 + 𝛽3𝑀𝐾𝑇𝑡 + 𝛽4𝑆𝑀𝐵𝑡 + 𝛽5𝐻𝑀𝐿𝑡 + 𝜀𝑡

where 𝑅𝑤𝑚𝑙,𝑡 is the value weighted hedge portfolio at month t for momentum strategy. 𝑋𝑡−1is the value of one of the three economic uncertainty indices. Each

index is scaled to have zero mean and a standard deviation of one. 𝐸𝑃𝑈𝑡−1 is the standardized value of the EPU index at month t-1, where the EPU index is

scaled to have zero mean and a standard deviation of one; 𝑀𝐾𝑇𝑡 is the value-weighted market excess return at month t ; 𝑆𝑀𝐵𝑡 is return spread between small and

big size stocks at month t; 𝐻𝑀𝐿𝑡 is the return spread between high and low value stocks at month t. The t-statistics in parentheses are calculated based on

heteroskedasticity-consistent standard errors of White (1980). *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively. The sample period is

from February 1985 to December 2014.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

EPU

-0.93** -0.74**

-0.96** -0.77**

-1.03*** -0.86**

(-2.38) (-2.15)

(-2.45) (-2.22)

(-2.61) (-2.48)

MKTRF

-0.53***

-0.51***

-0.53***

-0.51***

-0.53***

-0.51***

(-4.49)

(-4.41)

(-4.47)

(-4.39)

(-4.38)

(-4.32)

SMB

0.19

0.19

0.19

0.19

0.18

0.18

(0.92)

(0.94)

(0.90)

(0.93)

(0.85)

(0.89)

HML

-0.32

-0.33

-0.32

-0.33

-0.32

-0.33

(-1.39)

(-1.45)

(-1.40)

(-1.45)

(-1.38)

(-1.44)

EU1 -0.88 -1.30** -0.55 -1.02*

(-1.43) (-2.45) (-0.90) (-1.92)

EU2

-0.82 -1.26** -0.50 -0.98*

(-1.33) (-2.34) (-0.81) (-1.82)

EU3

-0.64 -1.08* -0.32 -0.81

(-1.02) (-1.95) (-0.53) (-1.46)

44

Table 10: The Global equity and other asset classes

This table tests the predictive power of a global EPU (GEPU) index on momentum profits among seven asset classes: global stocks (GS), country index futures

(EQ), currency (FX), government bonds (FI), commodity (CM), nonstock assets (NA) and all assets (AA) The global EPU index is scaled to have zero mean and

a standard deviation of one. For each asset, we first regress the monthly momentum returns on the GEPU alone. Then we add the market excess returns for each

asset in our regression analysis to control for the market risk. The detailed descriptions of data are provided in section 4.4. The t-statistics in parentheses are

calculated based on heteroskedasticity-consistent standard errors of White (1980). *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively.

The sample period is January 1997 to December 2016.

Global Stocks (GS) Country index (EQ) Currency (FX) Fixed Income (FI) Commodity (CM) Nonstock Assets (NA) All Asset classes (AA)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

GEPU -0.73*** -0.67*** -0.53*** -0.50*** -0.07 -0.08 -0.06 -0.05 -0.85*** -0.78** -0.24*** -0.24*** -0.45*** -0.45***

(-2.74) (-2.64) (-2.95) (-2.84) (-0.43) (-0.52) (-1.09) (-1.02) (-2.62) (-2.44) (-2.71) (-2.77) (-3.47) (-3.52)

Mkt excess returns for GS -0.23***

(-2.87)

Mkt excess returns for EQ

-0.13**

(-2.54)

Mkt excess returns for FX

0.14

(-0.87)

Mkt excess returns for FI

0.20***

(-2.89)

Mkt excess returns for CM

0.08

(-0.97)

Mkt excess returns for NA

-0.01

(-0.22)

Mkt excess returns for AA

-0.09

(-1.18)

45

Table 11: EPU and momentum among various holding period returns

This table examines the predictive effect of EPU on momentum profits using the following regressions:

𝑅𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1 + 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝜀𝑡

where 𝑅𝑡 is the value-weighted hedge portfolio returns for momentum at month t for each holding period(ranging from one- to twelve-months). 𝐸𝑃𝑈𝑡−1 is the

standardized value of the EPU index at month t-1. The EPU index is scaled to have zero mean and a standard deviation of one. 𝑀𝐾𝑇𝑡 is the value-weighted

market excess return at month t ; 𝑆𝑀𝐵𝑡 is return spread between small and big size stocks at month t; 𝐻𝑀𝐿𝑡 is the return spread between high and low value

stocks at month t. *, ** and *** indicate significance at 10%, 5% and 1% levels, respectively. The sample period is from February 1985 to December 2014.

Holding Month(s) 1 2 3 4 5 6 7 8 9 10 11 12

Panel A: 𝑅𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1 + 𝜀𝑡

EPU -1.13*** -0.76** -0.71** -0.67** -0.56* -0.49* -0.43 -0.37 -0.29 -0.25 -0.23 -0.24

(-2.62) (-2.39) (-2.32) (-2.25) (-1.89) (-1.65) (-1.42) (-1.20) (-0.99) (-0.90) (-0.86) (-0.89)

Panel B: 𝑅𝑡 = 𝛼 + 𝛽1𝐸𝑃𝑈𝑡−1+ 𝛽2𝑀𝐾𝑇𝑡 + 𝛽3𝑆𝑀𝐵𝑡 + 𝛽4𝐻𝑀𝐿𝑡 + 𝜀𝑡

EPU -1.11*** -0.79** -0.75** -0.73** -0.62** -0.56* -0.51* -0.44 -0.36 -0.33 -0.31 -0.31

(-2.89) (-2.56) (-2.53) (-2.46) (-2.12) (-1.92) (-1.73) (-1.52) (-1.34) (-1.27) (-1.25) (-1.28)

MKTRF -0.47*** -0.30*** -0.29*** -0.27*** -0.25*** -0.23*** -0.23*** -0.22*** -0.22*** -0.21*** -0.21*** -0.21***

(-4.28) (-3.11) (-3.05) (-2.93) (-2.88) (-2.80) (-2.88) (-2.97) (-3.13) (-3.27) (-3.33) (-3.36)

SMB 0.16 0.14 0.15 0.14 0.11 0.09 0.06 0.03 0.00 -0.03 -0.05 -0.07

(0.79) (0.73) (0.83) (0.79) (0.68) (0.59) (0.45) (0.27) (0.03) (-0.26) (-0.49) (-0.77)

HML -0.32 -0.30 -0.36** -0.38** -0.41*** -0.44*** -0.47*** -0.50*** -0.51*** -0.54*** -0.55*** -0.56***

(-1.38) (-1.60) (-1.99) (-2.19) (-2.60) (-3.01) (-3.48) (-3.93) (-4.42) (-4.91) (-5.07) (-5.09)

46

Table 12: Robustness check: firm Size, institutional ownership and analyst coverage

The table reports the time-series regression analysis of EPU and momentum using different subsamples based on

size, institutional ownership and analyst coverage. Low (large) stocks are those whose size is smaller (higher) than

NYSE 50 percentile. Low (high) institutional ownership stocks are those whose institutional ownership is below

(above) the median value for each quarter. Low (high) analyst coverage stocks are those whose analyst coverage is

below (above) the median value for each month. For each subgroup, we regress value-weighted long-short portfolio

returns on the standardized value of EPU as well as the Fama and French three factors. The t-statistics in parentheses

are calculated based on heteroskedasticity-consistent standard errors of White (1980). *, ** and *** indicate

significance at 10%, 5% and 1% levels, respectively. The sample period is from February1985 to December 2014.

Panel A: firm size

Small Large

(1) (2) (3) (4)

EPU -1.09** -1.07** -1.15** -1.15***

(-2.38) (-2.57) (-2.57) (-2.82)

MKTRF

-0.44***

-0.46***

(-3.53)

(-3.99)

SMB

0.13

0.21

(0.63)

(0.97)

HML

-0.26

-0.34

(-1.07)

(-1.47)

Panel B: institutional ownership

Low High

(1) (2) (3) (4)

EPU -1.24** -1.16** -1.17*** -1.16***

(-2.26) (-2.37) (-2.65) (-2.92)

MKTRF

-0.52***

-0.48***

(-3.79)

(-4.30)

SMB

0.03

0.18

(0.16)

(0.83)

HML

-0.22

-0.32

(-0.88)

(-1.38)

Panel C: analyst coverage

Low High

(1) (2) (3) (4)

EPU -0.87* -0.83** -1.09** -1.07***

(-1.91) (-2.09) (-2.41) (-2.60)

MKTRF

-0.40***

-0.45***

(-3.44)

(-3.71)

SMB

0.00

0.18

(0.02)

-0.86

HML

-0.36

-0.32

(-1.42) (-1.33)

47

Figure 1: EPU and Momentum for various holding periods

The figure reports the predictive effect of EPU on momentum profits using various holding periods. The holding

periods for momentum are ranging from one- to twelve-months. For each holding period, we present average

monthly momentum profits for the low- and high-EPU months. The sample period is from February 1985 to

December 2014.

Panel A: The raw returns of momentum strategy

Panel B: The risk-adjusted returns of momentum strategy

-0.50

0.00

0.50

1.00

1.50

2.00

1 2 3 4 5 6 7 8 9 10 11 12

Av

era

ge

Hed

ge

Po

rtfo

lio

Ret

urn

s

(%)

Holding Periods (months)

High EPU

Low EPU

0.00

0.50

1.00

1.50

2.00

2.50

1 2 3 4 5 6 7 8 9 10 11 12

FF

Alp

ha

s (%

)

Holding Periods (months)

High EPU

Low EPU