Flight to Quality in International Markets: Political Uncertainty and

Investors’ Demand for Financial Reporting Quality

Feng Chen

Rotman School of Management University of Toronto

feng.chen@rotman.utoronto.ca

Ole-Kristian Hope Rotman School of Management

University of Toronto okhope@rotman.utoronto.ca

Qingyuan Li

School of Economics and Management Wuhan University qyli@whu.edu.cn

Xin Wang

School of Business The University of Hong Kong

wangxacy@hku.edu

January 19, 2015

Acknowledgments

We greatly appreciate the helpful comments and suggestions from Mahfuz Chy, Fei Du, Bowe Hansen, Yue Li, Jeffrey Ng, Gordon Richardson, Mary Stanford, Barbara Su, Feng Tian, Dushyant Vyas, Guochang Zhang, Wuyang Zhao, and workshop participants at the Singapore Management University Accounting Symposium (2014), Indiana University, Texas Christian University, University of Alberta, University of Missouri, University of Oklahoma, University of Toronto, and Virginia Tech. Chen acknowledges the financial support from the Social Sciences and Humanities Research Council of Canada (SSHRC); Hope acknowledges the financial support of the Deloitte Professorship; Li acknowledges financial support from the School of Economics and Management at Wuhan University and the Education Ministry (NECT-12-0432); and Wang acknowledges the financial support from the General Research Fund of Hong Kong Research Grants Council (project No. 754312).

Flight to Quality in International Markets: Political Uncertainty and

Investors’ Demand for Financial Reporting Quality

Abstract

We examine whether international equity investors shift their portfolios toward stocks with

higher financial reporting quality during periods of high political uncertainty. Our study is

motivated by two primary factors. First, prior research shows evidence of investors’ “flight to

quality” (e.g., to less risky securities) during periods of uncertainty. Second, recent theoretical

research concludes that stocks with higher financial reporting quality are assessed as less

sensitive to systematic risk (such as political uncertainty). In our study, we employ national

elections as exogenous increases in systematic risk. Elections are accompanied by significantly

increased political uncertainty that is largely outside the control of firms and investors. In

addition, national elections take place at different points in time across countries, which controls

for possible confounding events such as global macro-economic trends. Using a large

international sample of mutual funds that focus on local markets, we find that international

mutual-fund managers shift their equity holdings to stocks with higher financial reporting quality

during election periods when political uncertainty is higher. The flight-to-quality effect is less

pronounced for elections with larger expected electoral margins in the pre-election period (i.e.,

when the incumbent is more likely to win the elections) and for countries with higher

transactions costs. In contrast, the effect is more pronounced when governments have greater

involvement in the local economy. Our inferences are robust to alternative proxies for political

uncertainty and financial reporting quality and to numerous other sensitivity analyses.

Key words: Political uncertainty; National elections; Flight to quality; Financial reporting

quality; International mutual funds.

1

Flight to Quality in International Markets: Political Uncertainty and

Investors’ Demand for Financial Reporting Quality

1. Introduction

In recent years, increasing attention has been paid to the quality of assets managed by

professional investors.1 Anecdotal evidence suggests that “flight to quality” is one of the main

drivers for institutions’ asset-allocation decisions during market uncertainty (McKay 2006;

McDonald 2007; Sechler 2009). 2 Consistent with practitioners’ investment allocation, Beber,

Brandt, and Kavajecz (2009) document the flight-to-quality phenomenon (in terms of high credit

quality and high liquidity) in the Euro-bond market. In his theoretical model, Vayanos (2004)

shows that risk-averse asset managers chase high-liquidity and low-volatility assets (i.e., flight to

quality) during high volatility periods, out of fear of investor redemptions. Our study contributes

to this line of research by examining the flight-to-quality behavior among fund managers in the

global equity markets and by considering the role of firms’ financial reporting quality.

Specifically, we investigate how mutual-fund managers allocate their equity investments in firms

with different financial reporting quality in local markets and in particular, whether and how their

asset allocation changes with political uncertainty in the local markets.

We argue that investors (i.e., mutual-fund managers in this study) concentrate their

holdings toward the stocks of firms with higher level of financial reporting quality when facing a

higher level of political uncertainty in the local markets. Prior research examines how the

economy-wide risk premium varies with firms’ disclosures (Easley and O’Hara 2004; Hughes,

Liu and Liu 2007; Lambert, Leuz, and Verrecchia 2007; Cheynel 2013). In particular, Lambert et

1 In the portfolio-management industry, professional consulting firms now scrutinize the attributes of portfolio holdings, including financial reporting quality. For example, Style Research (www.styleresearch.com) analyzes a portfolio’s overall quality using metrics such as return-on-equity ratio, leverage, and accounting accruals. 2 Caballero and Krishnamurthy (2008) discuss several high-profile examples of flight-to-quality cases.

2

al.(2007) show that with more precise accounting signals about a firm, investors assess a reduced

covariance of this firm’s cash flows with the cash flows of other firms, thus lowering the

perceived market risk inherent to the firm and the required cost of capital for the firm’s stock.

Similarly, Cheynel (2013) shows that under high uncertainty, firms’ disclosure can dilute their

cash-flow sensitivity to systematic risk, which in turn leads to decreased cost of capital and

increased market value for firms with high disclosure quality. These theoretical studies imply that

with increased economy-wide risk, such as amplified political uncertainty, investors could reduce

the undesirable exposure to increased economy-wide risk by allocating more investments to firms

with higher financial reporting quality as these firms’ cash flows are assessed to be less sensitive

to systematic risk compared with the cash flows of firms with lower financial reporting quality.

We focus on political uncertainty as an economy-wide risk because political factors can

shape economic outcomes and change financial risk, especially internationally (Rodrik 1991;

Bloom 2009; Pástor and Veronesi 2012, 2013). Equally importantly, compared with financial

factors, political factors are less likely to be endogenously determined by firms’ financial

reporting quality. Therefore, political uncertainty offers an ideal setting to investigate how

investors change their investment decisions in response to an increased level of uncertainty. Our

investigation thus sheds light on the role of firms’ financial reporting quality in affecting

investors’ investment-allocation decisions.

Our sample consists of 8,835 quarterly fund holdings in the periods around national

elections from 23 countries for 1,948 unique mutual funds that primarily invest in firms on one

local market. To measure quarterly fund-level asset allocation in terms of the underlying assets’

financial reporting quality, we follow a similar approach as Ali, Chen, Yao, and Yu (2008), who

compute an accruals-investing measure to test whether mutual funds trade on the accrual anomaly.

3

Specifically, we rank the accruals quality of all firms in each local market and calculate the

percentage of fund investments allocated to low, medium, and high categories of accruals quality.

Our primary fund-level flight-to-quality measure is the difference of fund investment weights

allocated to the high accruals-quality category versus the low accruals-quality category. This

research design facilitates the comparison across mutual funds from different countries. Equally

importantly, the use of relative accruals quality also helps mitigate the effect of possible market-

wide trends of firms’ accruals quality in the periods around elections.

As a second important feature of our research design, to measure political uncertainty, we

use national elections around the world as “natural experiments.” This choice offers several

advantages. First, elections have been shown to significantly increase political uncertainty (e.g.,

Julio and Yook 2012). Second, the timing of elections is outside the control of individual firms as

well as mutual-fund managers, mitigating potential endogeneity concerns. Third, the elections

take place at different points in time across countries, allowing the researcher to net out any

global macro-economic trends over time that could otherwise confound the relation between

political uncertainty and investors’ asset allocation.

The third important element of our research design is the requirement that the funds have

observations in a particular country in the period immediately prior to the election, during the

election period, and in the period immediately following the election. In this way each fund acts

as its own control vis-à-vis non-election periods.

Our multivariate empirical analyses include a number of controls motivated by prior

research. Importantly, we control for several characteristics of the fund holdings, including stock

return volatility, stock liquidity, and growth opportunities. We also control for fund size in all

empirical analyses. Furthermore, given that we have collected data on relevant time-varying

4

country-level variables, we are able to include both these specific country characteristics and

country fixed effects (as well as time-period fixed effects) in the regressions. In an alternative

econometric specification, we replace country fixed effects with fund fixed effects.

Consistent with our main hypothesis, we document a positive association between the

flight to higher financial reporting quality and political uncertainty. This relation is both strongly

statistically significant and economically meaningful.

Next, we conduct several cross-sectional analyses. First, we examine the degree of

uncertainty associated with national election events. That is, if the incumbent political party (or

president) is widely expected to win the election, there is lower uncertainty related to what

economic policies will be implemented following the election. Consistent with this argument, we

find that the effect of political uncertainty on the flight-to-quality investment behavior is less

pronounced for elections with a higher electoral margin (measured as the difference between the

expected vote share of the largest incumbent party and the expected vote share of the largest

opposition party prior to the election).

Second, we explore the conditioning effect of government involvement in the economy.

The effect of national elections on macro-economic uncertainty is likely to be more salient in

countries in which the government has greater influence over the local economy. Accordingly, we

expect the flight-to-quality effect to be stronger in countries with a greater government

involvement in the economy. Our empirical findings support this prediction.

Third, we investigate whether the effect is less prominent when equity transactions costs

are higher. Higher transactions costs make it more costly for fund managers to adjust fund

holdings. Therefore, the positive relation between flight to quality and political uncertainty is

expected to be less pronounced for countries with higher transactions costs. In line with this

5

hypothesis, we find that the effect of political uncertainty is reduced in countries with higher

transactions costs.3

In sensitivity analyses, we employ alternative proxies for both financial reporting quality

and political uncertainty. We further relax the requirement of sample funds having observations

in all three time periods (i.e., prior to, during, and following the elections), which increases the

sample size and thus the generalizability of the findings. We also assess the effects of political

uncertainty separately for fixed and flexible election schedules and for parliamentary and

presidential elections. Our inferences remain the same in these and several other robustness tests.

Furthermore, we add U.S. funds to our sample, which leads to a much larger sample size, and

conclusions remain unaltered. These results provide support for our hypothesis that fund

managers shift their holdings toward firms with higher financial reporting quality when they face

the macro-economic risk of political uncertainty. Hence, our evidence supports equity-market

flight to quality as a response to perceived political uncertainty.4

Our study contributes to the international accounting and finance literature by providing

evidence on the flight-to-quality phenomenon in the equity market. We find that equity investors

rebalance their holdings toward firms with better financial reporting quality in periods of higher

political uncertainty. Accordingly, our paper also responds to the call for research on investors’

investment behavior in the face of country opacity (Gelos and Wei 2005).

This article also relates to and extends the literature on the negative relation between

financial reporting quality and systematic risk embedded in stock prices (e.g., Ng 2011;

3 The findings of these partition analyses also provide further support for our primary hypothesis in that they show that the effect is greater (weaker) in subsamples for which we have ex-ante reasons to expect the relation to be stronger (weaker). 4 As explained in Section 4.3, we explicitly document that mutual-fund managers reallocate their assets during the election cycle, thus ruling out the possibility that our results are driven by the underlying firms changing their accounting practices (which would not be considered evidence of flight to quality).

6

Bhattacharya, Ecker, Olsson, and Schipper 2012). Using political uncertainty as a proxy for

systematic risk, we document fund managers’ preference for firms with higher financial reporting

quality when facing higher systematic risk.

Finally, there is limited prior research on how mutual-fund managers trade on the

accounting quality of underlying firms in a fund portfolio.5 Our study adds to the literature by

examining the effect of political uncertainty on fund managers’ preference for financial reporting

quality in an international setting.

The next section reviews related literature and develops the hypotheses. Section 3

describes the sample and the research design. Section 4 reports the empirical results and Section 5

concludes.

2. Prior Literature and Hypotheses Development

2.1 Economic Consequences of Political Uncertainty

Businesses often face a significant amount of uncertainty related to political factors. Of

direct interest to this study, the uncertainty associated with government policy decisions can

significantly increase the uncertainty related to firms’ future profitability. When making

investment decisions, investors thus recognize that political uncertainty has both a discount-rate

effect and a cash-flow effect on firms (Brogaard and Detzel 2014).

Prior literature has explored and documented the effects of political uncertainty on the real

economy. With high political uncertainty, companies tend to place a hold on potential investment

projects and freeze hiring (Durnev 2013; Gulen and Ion 2012; Julio and Yook 2012; Baker,

Bloom, and Davis 2013). Moreover, higher political uncertainty typically results in a higher cost

5 Ali et al. (2008) and Nallareddy and Ogneva (2014) consider the role of accounting quality for U.S. funds. Using an international setting, Maffett (2012) finds that firms with more opaque information environments experience more privately informed trading activities by institutional investors.

7

of finance (Gilchrist, Sim, and Zakrajsek 2010; Pástor and Veronesi 2012); the increased

financing cost discourages firms from taking on potential investment projects. The general

equilibrium model from Pástor and Veronesi (2012; 2013) shows that political uncertainty

commands a risk premium and makes stock returns more highly correlated across firms. They

conclude that political uncertainty is associated with lower stock prices, higher return volatility,

and larger systematic risk. The negative association between asset prices and political uncertainty

is confirmed empirically by Bansal, Khatchatrian, and Yaron (2005). Similarly, Brogaard and

Detzel (2014) find a negative contemporaneous association between current increases in

economic policy uncertainty and current market returns, and a positive relation between current

levels of economic policy uncertainty and future market returns. Furthermore, political

uncertainty pushes up the volatility and correlations of stock returns. Bittlingmayer (1998) and

Boutchkova, Doshi, Durnev, and Molchanov (2012) find a positive relation between political

uncertainty and stock-return volatility in a variety of settings.

2.2 Investors’ Flight to Quality in Response to Political Uncertainty

Prior studies find that investors tend to rebalance their portfolios in response to market

uncertainty. In particular, Beber et al. (2009) document investors’ tendency to hold government

bonds of high credit quality and high liquidity during crisis periods. For the stock market, high

political uncertainty results in a decline in stock prices and lower contemporaneous returns

(Brogaard and Detzel 2014). Furthermore, as discussed in the theoretical literature such as

Vayanos (2004) and Brunnermeier and Pederson (2009), fund managers become more risk averse

during periods of market uncertainty because higher market uncertainty translates into higher

stock-return volatility, which in turn increases the likelihood of fund-managers’ under-

8

performance and triggers costly performance-based withdrawals of funds. It would thus not be

surprising if fund managers rebalance their equity portfolios in response to changing

macroeconomic situations and investment opportunities. For example, they could change their

exposure to systematic risk if they believe that they have superior market-timing abilities (Huang,

Sialm, and Zhang 2011).

Political uncertainty imposes systematic risk on firms’ future performance. We

hypothesize that fund managers display flight-to-quality behavior by shifting their portfolios to

stocks with higher financial reporting quality when political uncertainty is high. As in Lambert et

al. (2007) and Ng (2011), we characterize firms’ financial reporting quality as information

precision about future cash flows, with more precise (i.e., lower variance) information being of

higher quality. Our hypothesis is based on two lines of reasoning. First, a firm’s higher financial

reporting quality helps reduce the firm’s perceived performance sensitivity to systematic risk and

that this makes the firm more attractive during periods of high systematic risk. Following the

Capital Asset Pricing Model (CAPM), Lambert et al. (2007) show that higher-quality accounting

information allows investors to better assess both the firm’s own variance of cash flows and the

covariance between the firm’s and other firms’ cash flows. Although higher quality information

reduces the assessed variance of the firm’s own cash flows, this effect is diversifiable in a large

economy. In contrast, the authors document that the negative effect of information quality on the

assessed covariance is non-diversifiable even in large economies; investors’ assessed covariance

decreases for firms that provide high-quality accounting reports. Therefore, Lambert et al. (2007)

point out that the direct effect of firms’ information quality on the cost of capital is through its

effect on the assessed covariance (i.e., systematic risk). Such a direct effect leads to lower cost of

9

capital.6 Following the same theme, Cheynel (2013) analytically shows that higher disclosure

dilutes firms’ cash-flow sensitivity to systematic risk, leading to a decreased cost of capital and

increased market value for disclosing firms relative to firms that disclose less.7

Second, Ng (2011) conjectures that the flight-to-quality phenomenon could be driven by

changing investor demand. Specifically, during periods of high market volatility (caused by

heightened political uncertainty), investor demand for stocks with lower financial reporting

quality would decline because these stocks are associated with greater uncertainty and adverse

selection. Likewise, market makers are less willing to provide liquidity to such stocks given the

concerns about adverse selection; this, in turn, would further reduce investors’ demand for these

stocks.

In summary, to reduce the undesirable effect of macro-economic factors such as an

exogenous increase in political uncertainty, we predict that fund managers will increase the

holdings of firms with high accounting quality, and thus reduce the assessed exposure of the

portfolio holdings to the increased systematic risk caused by heightened political uncertainty.8,9

Accordingly, we develop our flight-to-quality measure as the fund-level accounting-

quality weighted portfolio allocation and expect to observe that fund investment tilts toward

6 According to Lambert et al. (2007), if the unconditional covariance of a firm’s cash flows and other firms’ cash flows is positive, then high information quality reduces the cost of equity; otherwise, high information quality increases the cost of equity. On average, a firm’s unconditional covariance with other firms’ cash flows is expected to be positive (Samuelson 1967; Foster 1981). 7 Another stream of analytical literature (e.g., Barry and Brown 1985) links information quality and cost of equity capital through reduced estimation risk (or parameter uncertainty). 8 Using different arguments, Lang, Lins, and Maffett (2012) show that firms with higher disclosure quality experience greater stock liquidity, particularly during periods of high market uncertainties. Thus, fund managers who seek liquidity during the periods of political uncertainties may reallocate the fund assets to stocks with high financial reporting quality. Note that we control for the stock liquidity of underlying firms in our analyses. 9 Implicit in this discussion (and that in related studies) is the fact that portfolio-allocation choices are based on investors’ trade-off between the risk and expected returns (i.e., higher risk should be compensated with higher expected returns). It is during the periods of increased uncertainty associated with elections that the additional benefits of reduced cash-flow sensitivity to systematic risk are especially valued. Thus it would not be surprising if the investor shifts her portfolio to a different financial reporting quality mix after the uncertainty has been resolved.

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higher fund-level accounting quality when the country-level political uncertainty is higher. In

summary, our primary hypothesis is formally stated as follows:10

H1: Ceteris paribus, fund-level flight to quality measure is positively associated with political

uncertainty.

2.3 Cross-Sectional Variations in Flight to Quality

We investigate three factors that are predicted to affect the intensity of the effect of

political uncertainty on investors’ desire to shift their holdings to the stocks of firms with higher

financial reporting quality. Our first investigation is a direct extension of our primary hypothesis.

Within countries, we expect the hypothesized effect to be less pronounced for elections with a

higher probability of the incumbent government party (or president) winning the election. When

the incumbent government is expected to win the election with a higher electoral margin, the

political uncertainty inherent to the election is lower because the winning party is less likely to

change the economic policies. Following prior literature (e.g., Durnev 2013), we calculate

electoral margin as the difference between the expected vote share of the largest government

party (or president) and the expected vote share of the largest opposition party (or presidential

candidate) prior to the election. Our second hypothesis is as follows:

H2: Ceteris paribus, the positive association between fund-level flight to quality and political

uncertainty, as stated in H1, is less pronounced for elections with larger pre-election

electoral margin enjoyed by the incumbent government party (or president).

10 All hypotheses are stated in the alternative.

11

Next, we explore the extent of state control over the economy. If the government is involved

in the economy to a larger extent, political uncertainty is expected to lead to a higher level of

market-wide economy risk, which in turn strengthens the incentives for fund managers to shift to

stocks with higher financial reporting quality. Following Bushman and Piotroski (2006), we use

the sum of government enterprises and investments scaled by GDP to develop the proxy for

government involvement. This variable not only measures the extent of government ownership,

but also reflects the threat of government involvement in non-state-owned firms (where greater

government ownership in an economy implies a higher likelihood of future involvement). Our

third hypothesis is as follows:

H3: Ceteris paribus, the positive association between fund-level flight to quality and political

uncertainty, as stated in H1, is more pronounced when governments have greater

involvement in the economy.

Last, we expect that high transactions costs impose a constraint on the flight-to-quality

phenomenon. Higher transactions costs make it more costly for fund managers to adjust their

portfolios (e.g., Thapa and Poshakwale 2010). In addition, higher transactions costs result in a

lower level of market liquidity, leading to a lack of trading opportunities for fund managers to

adjust their portfolios. Our final hypothesis is:

H4: Ceteris paribus, the positive association between flight to quality and political uncertainty,

as stated in H1, is less pronounced when transactions costs are higher.

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3. Sample Selection and Research Design

3.1 Data and Sample Selection

We obtain mutual-fund stockholdings data from Thomson Reuters from 1996 to 2009.11

This mutual-fund database contains information on equity mutual funds worldwide. The database

provides three data files: (a) the Fund Master File, containing the fund number, fund name,

management company name, country code, and report date; (b) the Security Master File,

containing the security number, security name, country code, security price in U.S. dollars, and

shares outstanding; and (c) the Portfolio Holdings File, containing the fund number, security

number, number of shares held by the fund, and net changes in shares held since prior report dates.

First, similar to Chan, Covrig, and Ng (2005), we require that the fund investment is

concentrated on one single country. Specifically, for our sample of mutual funds, at least 80% of

the equity investment must be in one country. This requirement implies that political uncertainty

is an un-diversifiable (or hard-to-diversify) risk for the sample funds and hence is relevant for

fund managers’ holding decision. Second, similar to Kacperczyk, Sialm, and Zheng (2008), we

select those funds whose portfolios contain at least 15 firms with available accounting

information (i.e., data to compute accruals quality). With this selection criterion, our measure of

fund-level financial reporting quality is based on the aggregation of a large number of individual

firms’ accruals quality for each fund holding. To calculate the firm-level accruals quality, we use

firm-specific financial information from the Worldscope database and match between the firms in

the Worldscope database and the underlying firms of the fund-holdings database. Some fund-

level control variables are based on the underlying firms’ trading data. For example, stock returns

and shares turnover are obtained from the Datastream database. Third, as our interest is in

11 These data are not available from WRDS and are expensive to purchase; thus we do not have data beyond 2009. However, as the research questions we examine are not restricted to a specific time period we do not view this as a major limitation.

13

examining international political uncertainty, we exclude the funds of U.S. stocks from our

primary analyses.12 Finally, we only include those funds investing in countries for which we have

election-event data and require the presence of every fund’s quarterly holdings in the pre-election

periods, during the election, and in the post-election periods.13

Our final sample consists of 8,835 quarterly fund observations for 1,948 funds from 23

countries. Specifically, our research design of quasi-natural experiments is based on national

election events, entailing 2,594 fund-quarter observations immediately before the national

election period, 3,647 fund-quarter observations during the election period (which spans seven

months, as we explain below), and again 2,594 fund-quarter observations immediately after the

election period. The fund-level financial reporting quality is based on 464,197 underlying firm-

quarters (125,207 unique firms) in the fund holdings. As shown in Table 1, there is variation in

the distribution of our fund-quarter observations across countries. The three countries with the

largest number of observations are Japan, Canada, and the U.K, for which the number of

observations ranges from 1,450 to 2,364 fund quarters. In contrast, the three countries with the

fewest observations are Denmark, the Netherlands, and Chile, for which the number of

observations ranges from three to eleven fund quarters.14

12 In Section 4.3 we repeat the analyses after including U.S. funds. The exclusion of U.S. elections from our main analysis also helps to mitigate the confounding effects resulted from the global macro-economy trends, given that the U.S. government has a significant influence on the global economy and hence U.S. elections could result in a world-wide change in economic factors. 13 Specifically, in addition to being present during the election period, each fund in our sample is required to have one quarterly holding as the pre-election observation and one as the post-election observation. Note that there could be more than one quarterly holding for this fund during the election period. 14 In some of our sensitivity analyses, we have considerably larger sample sizes and we also consider the effect of the sample being concentrated in certain countries (see Section 4.3).

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3.2 Measures of Political Uncertainty and Fund-level Financial Reporting Quality

3.2.1 Political Uncertainty

As explained, our primary measure of political uncertainty is national elections, and we

obtain data on election events from the Polity IV database maintained by the Center for

International Development and Conflict Management at the University of Maryland. Importantly,

election timings vary from country to country; thus we are not likely to merely pick up some

global macro-economic factors across our sample countries. In addition, the timing of elections is

exogenous to an individual firm (and largely exogenous to a fund). We use the fund holdings with

filing months immediately before and immediately after the election period, allowing pre-election

(post-election) sampling period up to 12 months before (after) the election period. Following

Julio and Yook (2012), we define Election as an indicator variable equal to one if the filing

months of fund quarters lie between four months prior to the election month and two months after

the election month, zero otherwise.15 Our research design assumes national elections will induce

exogenous variations in political uncertainty. In the additional analyses, we employ alternative

measures of political uncertainty. It is also important to note that we require the particular fund to

be present in the period immediately before, during, and right after the election period. This

requirement ensures that we compare between the election period and non-election periods for the

same funds. As one of our sensitivity analyses, we lift this restriction and re-run the analysis

using a larger sample size (Section 4.3). Moreover, by adopting this quasi-natural experiment

design of exogenous national elections, we ensure that any documented effect is due to fund

managers’ flight-to-quality behavior.

15 In untabulated robustness checks, instead of defining Election as the [-4, +2] period surrounding the election month, we change the window to [-3, +3], [-3, +2], [-4, +4], and [-2, +2] surrounding the election month. Our inferences hold with these alternative Election windows.

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3.2.2 Flight to (Financial Reporting) Quality Measures

As in Ng (2011) and Bhattacharya et al. (2012), we identify measures that capture the

precision of earnings signals. Specifically, we use accruals quality as the proxy for individual

firms’ financial reporting quality. Using the underlying firms’ accounting quality, we then

aggregate the firm-level accounting quality values for all firms in a fund holding to develop the

fund-level measure of flight to quality.

To proxy for accounting quality, in our primary analyses we use the absolute value of the

performance-adjusted discretionary accruals as developed by Kothari, Leone, and Wasley (2005).

Specifically, we estimate the following model by country for each industry and year with at least

10 observations (consistent with Kothari et al. 2005 and Ecker, Francis, Olsson, and Schipper

2013):

, 0 1 , 1 2 , 3 , 4 , ,(1/ )i t i t i t i t i t i tTAccr Assets Rev PPE ROAα α α α α e−= + + ∆ + + +

where ,i tTAccr is total accruals, measured as the change in non-cash current assets minus the

change in current non-interest bearing liabilities, minus depreciation and amortization expense for

firm i at year t, scaled by lagged total assets ( , 1i tAssets − ); ,i tRev∆ is the annual change in revenues

scaled by lagged total assets; ,i tPPE is property, plant, and equipment for firm i at year t, scaled

by lagged total assets; i,tROA is return on assets for firm i at year t. The residuals from the

regression model are discretionary accruals. We use the absolute values of discretionary accruals

from the most recent year as a proxy for the financial reporting quality of underlying firms in a

fund holding in a certain quarter.

The construction of our fund-level flight-to-quality measure involves the following steps.

To form accounting-quality categories, we adopt the bottom 30%, middle 40%, and top 30%

breakpoints for the absolute discretionary accruals of all firms from each respective local market

16

every year, similar to the approach implemented by Hirshleifer, Hou, and Teoh (2012). The

underlying stocks in a specific fund portfolio are allocated into one of the three categories (i.e.,

low, medium, or high) of accruals quality with the high category referring to those stocks ranked

as the bottom group of absolute discretionary accruals in the local market. Then, to compute the

portfolio weights of each accruals quality category in a fund portfolio for fund quarter t, we sum

up individual firms’ investment percentage as , ,

n

r t i ti r

W ω∈

=∑ , where ,i tω is the value-weighted

percentage of firm i in fund quarter t while n is the number of firms that belong to the accruals

quality category r, using accounting information from the most recent year. Finally, to measure

the fund-level flight to (financial reporting) quality (FQKLW) for fund quarter t, we compute the

difference of the two portfolio weights between high and low accruals quality categories.16 Our

primary flight-to-quality measure essentially assigns linear coefficients of -1, 0, and 1 to the

portfolio weights of low, medium, and high accruals quality categories, respectively. A high

FQKLW indicates that the fund tilts its equity holdings toward firms with high financial

reporting quality.

For the purpose of sensitivity analyses we construct two additional flight-to-quality

measures. One is based on the underlying firms’ standard deviation of discretionary working-

capital accruals estimated from the model of Dechow and Dichev (2002). The other is the

weighted-average rank of the low, medium, and high accruals quality categories in a fund

portfolio. We discuss these additional measures in Section 4.3.

16 FQKLW is similar in spirit to the fund-level accruals investing measure constructed by Ali et al. (2008), which is the weighted average accruals decile rank of stocks held by a fund. Assigning other coefficient values to the portfolio weights of low, medium, and high financial reporting quality categories, particularly a non-zero rank value to the portfolio weights of medium category, would render mathematically equivalent measures of flight to quality (see Section 4.3).

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3.3 Research Design

3.3.1 Regression Model for H1

We test H1 by estimating the following empirical model:17

0 1i j n iFQKLW Election Controlsβ β β e= + + + (1)

where:

FQKLWi = The fund-level flight-to-quality measure, computed in the following steps. First, based on the absolute values of discretionary accruals for all underlying stocks in a fund portfolio, we sort the underlying stocks into three categories (i.e., low, medium, or high) of accruals quality, using the bottom 30%, middle 40%, and top 30% breakpoints of the absolute discretionary accruals for all firms from each respective local market every year. Then, to compute the portfolio weights of each accruals quality category (r) in a fund portfolio, we sum up individual firms’

investment percentage as , ,

n

r t i ti r

W ω∈

=∑ , where ,i tω is the value-weighted

percentage of firm i in fund quarter t while n is the number of firms that belong to the accruals quality category r. Finally, we compute the difference of the two portfolio weights between high and low accruals quality categories.

Election = An indicator variable that equals one if the filing months of a fund quarter are four months prior to and up to two months after the election month, and zero otherwise.

We include several control variables that are motivated by prior research. First, we control

for fund characteristics. Specifically, because fund size affects fund portfolio choice (Chevalier

and Ellison 1997), we control for the size of the funds (Size), measured as the natural logarithm of

the market value of all stocks in the fund holdings. In addition, any flight-to-quality behavior may

be motivated by fund-mangers’ liquidity concerns (Vayanos 2004; Beber et al. 2009). Thus, we

include the weighted average stock liquidity of underlying firms (Turnover) for each fund holding,

measured as the weighted average of shares traded scaled by the number of outstanding shares

17 The Appendix provides detailed definitions of all variables.

18

during the previous year. Following prior studies (e.g., Boutchkova et al. 2012), we further

control for the weighted average return volatility of a fund’s underlying stocks in the prior quarter

(Volatility). By controlling for return volatility, we relieve the possible concern that our political

uncertainty measure is confounded by other macroeconomic factors. Furthermore, we calculate

the weighted average of book-to-market ratios of underlying firms (BM) and use it to control for

fund investment types. We winsorize all fund-level variables at the 1% and 99% levels.

We also control for time-varying country-level variables. First, motivated by Andrade and

Chhaochharia (2010), we control for trade development (Trade, measured by aggregate exports

and imports of goods and services), foreign direct investment (FDI), and the extent of financial

development (FinDev, proxied by equity-market capitalization), all scaled by the GDP of the

respective countries. These three variables are indicators of market liquidity and are also related

to lower transactions costs, thereby they may affect fund portfolio choice. Second, following

Chinn and Ito (2008), we control for the degree of capital-account openness (Openness). Third,

we include the index of law and order in a country (Law). Last, we follow Bhattacharya, Daouk,

and Welker (2003) and include the GDP growth rate (GDPGr) of the local economy because fund

investors’ portfolio choices could be mechanically related to that country’s growth rate. In

addition, the timing of elections could depend on the state of the economy (Shi and Svensson

2006). Similarly, we include PerCapita for GDP per capita in each sample nation.

Finally, since our sample includes multiple quarterly observations from the same funds,

we include filing-month fixed effects in all regressions to mitigate any time-related dependence

issue. We also include country fixed effects, which is a common approach to controlling for

country-specific effects and addressing correlated omitted country-level variable problems (e.g.,

Gelos and Wei 2005). Different from most prior research, we include both country fixed effects

19

and specific time-varying country controls in the same regressions. As an alternative econometric

specification, we also report results of regressions in which we replace country fixed effects with

fund fixed effects; thus the interpretation of the estimated coefficient on Election is strictly as a

“within-fund effect.”18 The reported t-values are based on standard errors that are clustered by

fund.

3.3.2 Regression Model for Cross-Sectional Analyses

The impact of political uncertainty on fund managers’ stock picking could vary under

different circumstances. We test H2-H4 by adding each conditioning variable and its interaction

with Election. Hypotheses H2-H4 are tested using the following regression:

0 1 2 3i j j j j n iFQKLW Election CondVar Election CondVar Controlsβ β β β β e= + + + × + + (2)

where:

CondVar = The cross-sectional variables for H2-H4: Electoral margin for the incumbent government party (or president) (Margin) for H2; government involvement (Govt) for H3; and transactions costs (TradeCost) for H4. The Appendix provides detailed definitions.

4. Empirical Results

4.1 Descriptive Statistics

Table 1 presents the sample size, the number of funds, and the median values of variables

for each of the twenty-three countries included in the primary sample. The flight-to-quality

measure, FQKLW, has a median value ranging from -0.205 for Denmark to 0.460 for Canada. A

negative (positive) value of -0.205 (0.460) indicates that for a typical Denmark (Canada) fund, the

18 For some countries, we have a small number of sample funds. It is not surprising that the country fixed effects are correlated with fund fixed effects. Therefore, in our main analyses, we utilize the specification with country fixed effects.

20

investment percentage in the low accruals-quality category is 20.5% higher (46% lower) than that

in the high accruals-quality category.

Turning to electoral margins (Margin), we observe that India has the lowest median vote

margins (= -0.286), suggesting high degrees of electoral uncertainty in that country (i.e., the

incumbent is more likely to be replaced). In contrast, both South Africa and Malaysia display

high median electoral margins (0.568 and 0.540, respectively). With respect to governments’

involvement in national economy (Govt), several countries, such as Australia, Canada, Italy, and

the UK have the lowest scores (= 0), whereas Malaysia has the highest score of 1. The yearly

country-level trading cost estimates (TradeCost) are based on commissions, fees, and market

impact costs, as compiled by Elkins/McSherry Co., and the values are the lowest in Japan (=

0.198) and highest in Korea (= 0.774). In addition, sample countries exhibit large variations in

financial development, trade development, foreign direct investment, degree of capital-account

openness, and GDP growth rate. For example, several European nations, such as Switzerland,

Netherlands, and Germany, have low annual GDP growth rates (i.e., close to zero), while two

Asian countries in the sample, India and Korea, exhibit the highest GDP growth rates (7.9% and

7.2%, respectively). On the other hand, three emerging markets, Brazil, South Africa, and

Thailand have the lowest scores of law and order, while several developed countries such as

Australia, Canada, the Netherlands, and Norway have the highest score, 1.792.

Table 2, Panel A presents holdings-based style characteristics for the mutual funds. The

mean of FQKLW is 0.200, suggesting that the sample funds on average invest 20% more in the

high accruals-quality category than in the low accruals-quality category. The fund size, measured

as the market value of stock holdings, has a mean value of 103 million U.S. dollars. The average

book-to-market ratio of underlying firms in a fund portfolio is 0.62. Panel B presents the Pearson

21

correlations among the variables. Consistent with H1, this table shows a positive and statistically

significant correlation between the fund-level flight-to-quality measure (FQKLW) and the proxy

for political uncertainty (Election).

4.2 Results for H1 (Primary Hypothesis)

Figure 1 presents the median value of fund asset allocation weights in the high vs. low

accruals-quality categories around national elections. As Figure 1 shows, the median portfolio

weight of high accruals-quality stocks increases from 0.361 in the pre-election period to 0.407 in

the election period, and then goes down to 0.347 in the post-election period. In contrast, fund

managers appear to reduce the portfolio weight of low accruals-quality stocks in an election

period, as low accruals-quality stocks account for only 0.132 of a typical sample fund portfolio,

down from 0.198 before the election period, but then back to 0.152 following the election period.

Thus, the median value of flight-to-quality measure (i.e., the investment-weight difference

between high and low accruals-quality categories) shows a similar pattern of change. Specifically,

it increases from 0.156 in the pre-election period to 0.253 in the election period, representing a 62%

jump in the intensity of flight to quality in response to political uncertainty, and then decreases to

0.188 in the post-election period.

Table 3 reports the results of multivariate analyses for our primary hypothesis: the

association between political uncertainty and fund-level flight to quality. Column (1) presents the

OLS regression results with fund-level control variables only, as well as country and filing-month

fixed effects; Column (2) presents the results after adding time-varying country-level control

variables. The adjusted R2s are 45.5% and 46.8%, respectively. Recall that we control for both

country and time (filing-month) fixed effects. In addition, we control for four fund characteristics

22

and seven time-varying country variables. More importantly, the estimated coefficients on

Election are positive and statistically significant at the 0.01 level (using two-sided tests) in both

columns.19 Given that Election is a country-level variable and that the timing of elections varies

across our sample, it is unlikely that these findings are driven by endogeneity.

In Column (3) we replace the country fixed effects with fund fixed effects, for a within-

fund estimation of the effect of Election. As the table shows, this alternative econometric

specification increases the adjusted R2 to 64.9%. The coefficient on Election retains a similar

magnitude and continues to be significant at the 0.01 level. With fund fixed effects, as expected

some fund-level variables such as Volatility, BM, and Size become statistically insignificant.

The coefficients on the control variables across the three regressions in Table 3 generally

carry expected signs. Specifically, the coefficient on Turnover is significantly negative,

suggesting that flight to liquidity and flight to higher financial reporting quality serve as

substitutes. The variables Volatility and BM are proxies of mutual fund investment styles. The

negative coefficient on Volatility and the positive coefficient on BM suggest that for the fund

managers who tend to select value firms and firms with lower return volatility, their fund

holdings usually show a larger difference between high versus low accruals quality stocks.

However, unsurprisingly, when fund fixed effects are included in Column (3), both fund-style

variables become insignificant.

Overall, the findings are consistent with fund managers increasing (decreasing) their

holdings of firms with high (low) financial reporting quality and thus reducing the assessed

exposure of their portfolio holdings to the systematic risk caused by political uncertainty.

19 The coefficient estimate of 0.027 for Election (Column 2) implies that the flight-to-quality measure is on average 0.027 higher in the election period than that of non-election periods (i.e., both pre- and post-election periods), which is approximately 13.5% of the mean flight-to-quality value.

23

4.3 Robustness Checks and Additional Analyses

Although the regressions reported in Table 3 are based on exogenous (and time-varying)

national elections and include a number of controls and fixed effects motivated by prior research,

to potentially be able to make stronger causal inferences and to generalize our findings, we

provide additional evidence based on alternative measures of financial reporting quality, an

alternative sample filtering, the inclusion of U.S. funds in the sample, and alternative proxies for

political uncertainty. We report the results for these robustness tests in Table 4. These analyses

contain all the control variables included in Table 3 but for brevity we do not tabulate them.

As shown in Panel A of Table 4, we first assess the sensitivity of our findings to the

choice of an alternative flight-to-quality measure, FQDD, as the dependent variable, which is

based on the standard deviation of individual firms’ discretionary working-capital accruals

(Dechow and Dichev 2002).20 Column (1) of Panel A shows that the estimated coefficient on

Election is positive and statistically significant at the 0.05 level (coefficient = 0.011 and t-value =

2.07). In Column (2) of Panel A, we use a variation of our primary flight-to-quality measure,

WKLW, which is the weighted average rank of the low, medium, and high accruals-quality

categories (rank = 1, 2, and 3, respectively). The weights are the respective portfolio weights of

the three accruals-quality categories in a fund portfolio.21 The coefficient on Election remains

positive and statistically significant (Coefficient = 0.025 and t-value = 4.78). Thus, we conclude 20 We use the working-capital accruals model in Dechow and Dichev (2002), further modified by McNichols (2002), and estimate the following model by country and for each industry that has at least 15 observations:

, 0 1 , 1 2 , 3 , 1 4 , 5 , ,i t i t i t i t i t i t i tTCAccr OCF OCF OCF Rev PPEα α α α α α e− +

= + + + + ∆ + + where TCAccr is total current accruals, measured as the change in non-cash current assets minus the change in current non-interest bearing liabilities, scaled by lagged total assets; OCF is cash flow from operations, measured as the sum of net income, depreciation and amortization, and changes in current liabilities, minus changes in current assets, scaled by lagged total assets; Rev∆ is the annual change in revenues scaled by lagged total assets; PPE is property, plant, and equipment, scaled by lagged total assets. We use the standard deviation of the residuals from the most recent year as the second proxy for the financial reporting quality of underlying firms. We calculate the standard deviation using rolling time intervals requiring a minimum of three and a maximum of five years of data. The higher data requirement of this model results in a smaller sample size (N = 5,866). 21 The WKLW measure is similar to the fund-level accruals-investing measure constructed by Ali et al. (2008).

24

that our inferences are not limited to using performance-adjusted abnormal accruals or using the

difference between the investments in high and low accruals-quality firms for the fund-level

measure of flight to quality.

Next, our sample selection procedure requires that sample funds have a presence in the

period before, during, and after the election event. Such a requirement, while an important feature

of our research design, reduces the sample size considerably. Thus, we check the sensitivity of our

conclusions to this constraint in Panel B of Table 4. Specifically, by requiring the fund-quarter

observations to appear in either pre- or post-election period (but still appear in the election

period), and by expanding the sampling window to a period from one year prior to until one year

after the election period, we have a much larger sample of 19,051 fund-quarter observations. In

Column (1), we find a positive and significant coefficient on Election for this sample (Coefficient

= 0.031 and t-value = 7.61).

As a second way to enlarge our sample and enhance the generalizability of our

conclusions, we include U.S. funds in our sample (and hence the U.S. election events). Our

interest in this study is in examining the role of financial reporting quality in an international

setting. However, given the importance of the U.S. market to the world economy, as an additional

analysis we test whether our main result is robust to the inclusion of U.S. mutual funds. We apply

similar sample-selection filters for the U.S. mutual-fund sample as in our main tests. The total

sample size increases to 69,915 with the U.S. funds dominating the sample. The regression still

shows a positive and highly significant coefficient on the flight-to-quality measure during the

election periods as evidenced by the positive coefficient on Election in Column (2) of Panel B.22

22 In untabulated analysis we find that Election also loads positively and significantly when using only the U.S. sample. More importantly, given that Japan comprises the largest contributor to the primary sample, in untabulated analysis we drop Japan from our sample. We find that the coefficient on Election continues to be positive and statistically significant when Japan is excluded. Finally, to mitigate potential heteroskedasticity across countries, we

25

Third, we re-estimate the regressions using two alternative variables of political

uncertainty. Although we view Election as a strong research-design choice that has been used in

authoritative studies, to generalize our contribution, we consider alternative ways of measuring

political uncertainty. Importantly, using alternative proxies for political uncertainty allows us to

increase the sample size considerably, thus potentially enhancing the generalizability of our study

(N = 39,553).23 In Column (1) of Panel C, we use the aggregate index of political uncertainty in

the quarter prior to the fund-filing quarter (PU), measured as the negative natural logarithm of the

sum of four subcategories from the International Country Risk Guide (ICRG) political risk ratings

(i.e., government stability, socioeconomic condition, military in politics, and democratic

accountability). 24 A higher value of PU signifies a higher level of political uncertainty. The

regression results using PU show a positive coefficient of 0.435 (t-value = 12.36), supporting our

conclusions based on national election events.

Next, in Column (2) of Panel C, we use PolCrisis, defined as the country-level index of

political instability and violence/terrorism, compiled by Worldwide Governance Indicators. This

measure reflects the perceived likelihood that the government will be destabilized or overthrown

by unconstitutional or violent means. We find a positive coefficient on PolCrisis (coefficient =

0.043 with t-value = 3.48). Thus, our results confirm that political uncertainty (as proxied for by

either political risk ratings or the political instability index) leads to investors’ flight to higher

accruals-quality stocks in the global stock markets.

run weighted least squares regressions in which we use the number of observations per country as an inverse weight. The inference remains intact. 23 Although we prefer Election as a proxy for political uncertainty for the reasons provided, one advantage of using PU (and PolCrisis) is that we can include countries that either do not hold regular elections or for which we do not have sufficient observations to satisfy the requirement of having observations before, during, and after the election events (and thus the sample size increases). The additional countries include Belgium, Mexico, Philippines, Poland, and most importantly China. 24 The ICRG ratings have been widely used in previous studies (e.g., Gelos and Wei 2005; Bekaert, Harvey, and Lundblad 2005). We also use the overall ICRG political-risk indicator as another proxy for political uncertainty and obtain similar results.

26

Besides the aforementioned sensitivity tests, we also implement additional analyses based

on different sample partitions. Because elections could be endogenous to economic outcomes, we

first divide the national elections in our sample based on whether they have fixed or flexible

timing, using the classification of Julio and Yook (2012). In our sample, there are 11 fixed and 47

flexible elections. We run the regressions separately for fixed and flexible election events. As

shown in Table 5, Panel A, the coefficients on Election are positive and statistically significant

across both subsets. Similarly, we partition our sample by whether the election is parliamentary or

presidential. As in Julio and Yook (2012), our election events consist of only presidential elections

for those countries in which the executive authority belongs to presidents, and only parliamentary

elections for those countries in which parliaments possess the executive authority. Results are

tabulated in Panel B. We find that the coefficients on Election are positive and statistically

significant across both subsets. Therefore, we conclude that our conclusions are not sensitive to

the timing or the type of national elections.

Finally, to rule out the possibility that our main results are driven by the underlying firms

changing their accounting practices (which would not be considered evidence of flight to quality

on behalf of investors), we examine portfolio turnover of our sample mutual funds over the time

interval between the election period and the post-election period. The evidence of portfolio

turnover will lend credence to the notion that fund managers’ stock choices, instead of underlying

firms’ possible changes in their financial reporting practices, drive our results. Portfolio turnover

of a fund is calculated as market-value increases due to purchasing additional shares during the

time interval, plus market-value decreases due to selling additional shares, scaled by total market

value of stock holdings for the fund at the start of the time interval. The test of portfolio turnover

entails 2,594 fund quarters, consistent with the explanation from the sample-selection section

27

(Section 3.1). Table 6, Panel A presents the descriptive statistics. The mean (median) of portfolio

turnover between election and the post-election period is 0.750 (0.518). To put the mean and

median values from Panel A in perspective, we similarly compute the portfolio-turnover ratio

between the two consecutive fund quarters that immediately follow the election period (i.e., a

non-election time interval) and present the descriptive statistics in Panel B. Compared with the

statistics of portfolio turnover during the election time interval, the mean and median of portfolio

turnover during the non-election time interval are significantly lower at 0.321 and 0.201,

respectively.

In sum, these sensitivity tests and additional analyses provide further support for our

prediction that fund holdings shift to firms with better financial reporting quality when there is a

higher level of political uncertainty.25

4.4 Results for H2-H4 (Cross-Sectional Analyses)

We now turn to tests of our cross-sectional predictions and the results are shown in Table

7. Column (1) shows how the effect of political uncertainty varies with the anticipated electoral

margin for the incumbent government party (or president). While the main effect of Election

continues to be positive and significant, the interaction effect (Election×Margin) is negative and

significant at the 0.05 level (Coefficient = -0.120 with t-value = -1.97), suggesting that the effect

of political uncertainty is mitigated when the incumbent is more likely to win the election. This

finding supports H2.

25 Similar to prior research (e.g., Beber et al. 2009), we only consider within-asset class shifting within local markets. As a practical matter, the fund database only contains the information on equity investments. Such a data constraint limits our ability to investigate any possible cross-asset shifting by fund managers during the periods of high political uncertainty (e.g., asset allocation to cash or bonds). However, our inferences could be affected only if such cross-asset shifting were correlated with our flight-to-quality measure. Even if related, potential cross-asset shifting is likely to work against finding support for our hypothesis.

28

Similarly, Column (2) assesses the conditioning effect of government involvement in the

economy. Consistent with the idea that a prominent government role in the economy should

exacerbate the effect of political uncertainty (H3), we observe a positive and significant

interaction term (Election×Govt) (Coefficient = 0.047 with t-value = 2.02), while the main effect

of Election remains positive and significant.

Column (3) shows a negative and significant coefficient on Election×TradeCost

(Coefficient = -0.214 with t-value = -5.46), implying that the flight-to-quality phenomenon is less

pronounced when fund managers face higher transactions costs related to the rebalancing of fund

holdings, thus supporting H4.

Finally, in Column (4) we include all three conditioning variables and their interactions

with Election simultaneously. All three interactions continue to have the predicted signs and are

statistically significant. The results for H2-H4 also lend further credence to the findings reported

for H1. That is, we find that the results are significantly more (or less) pronounced in the

subsamples for which theory and prior research predict that the effects should be more (or less)

relevant.

4.5 Additional Analyses

4.5.1 Alternative Asset-Allocation Strategies

Since an individual stock’s beta reflects the extent of exposure to systematic risk, it is

possible that equity investors shun high-beta stocks in response to heightened political

uncertainty. We construct a variable, DifBeta, which captures the beta-based asset-allocation

strategy. Similar to the construction of our fund-level flight-to-quality measure, we adopt the

bottom 30%, middle 40%, and top 30% breakpoints for betas of all firms from each local market

29

every year, and then assign the underlying stocks in a specific fund portfolio into one of the three

beta categories (i.e., low, medium, or high). DifBeta is measured as the difference of the two

portfolio weights between high and low beta categories. A higher value of DifBeta indicates that

the fund tilts its equity holdings toward stocks with high historical betas. In an untabulated test,

we find that DifBeta is negatively associated with FQKLW, which is consistent with Lambert et

al. (2007) that higher financial reporting quality of fund holdings mitigates the exposure to

systematic risk. More importantly, the coefficient on Election remains positive and statistically

significant after controlling for DifBeta.

As a second possible investment strategy, equity investors may turn away from

politically sensitive stocks in response to heightened political uncertainty. Following Julio and

Yook (2012), we classify firms in the tobacco products, pharmaceuticals, health-care services,

defense, petroleum and natural gas, telecommunications, and transportation industries as

politically sensitive stocks. The portfolio weights of politically sensitive stocks remain low

across election and non-election periods. Specifically, the median portfolio weight of politically

sensitive stocks increases from 7.4% in the pre-election period to 8.4% during the election

period, and then drops to 7.2% in the post-election period (untabulated). Thus, it does not appear

likely that reducing investment in politically sensitive stocks is driving our results.

4.5.2 Effects of Country-Level Disclosure

Finally, we investigate how the general disclosure level in a country affects the strength of

the association between political uncertainty and flight to quality. When the general disclosure

level is already high in the local capital market, the shift from high category to low category

stocks may not reduce the risk exposure of fund holdings significantly. Therefore, we expect a

30

weaker effect of political uncertainty for a country with higher country-level disclosure scores.

We use the disclosure index data from Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2008)

and add the variable Disclosure and its interaction term with Election to the regression model.

The regression results (untabulated) show that the main effect of Election continues to be positive

and statistically significant, and that the interaction effect (Election×Disclosure) is negative and

significant at the 0.01 level. These results are consistent with our expectation that the impact of

political uncertainty on fund managers’ flight to quality is less prominent when a country’s

general disclosure level is high.

5. Concluding Remarks

This is the first study to investigate whether mutual-fund managers shift their equity

holdings to stocks that have higher financial reporting quality during periods of political

uncertainty. Our investigation is motivated by recent evidence on “flight to quality” as a driver of

portfolio-allocation decisions, as well as recent analytical work on how higher accounting-quality

firms have a lower level of assessed performance sensitivity to systematic risk.

We consider national elections as our primary proxy for political uncertainty. Elections

have been used in prior finance research for measuring political uncertainty because of their

potentially important effects on the local economic environment, and the fact that they are

exogenous to the underlying firms (and mutual-fund managers). Using a large sample of mutual

funds from 23 countries (and more in some of our additional analyses), we find strong evidence

supporting the hypothesis that investors rebalance their portfolios toward fund holdings with

higher financial reporting quality during periods of heightened political uncertainty. This finding

is robust to the inclusion of numerous control variables and fixed effects and to the use of

31

alternative proxies for financial reporting quality and political uncertainty as well as alternative

sample choices. Further supporting our primary hypothesis, we find that the positive relation

between fund-level flight to higher accounting quality and political uncertainty is less pronounced

when there is limited prior outcome uncertainty regarding the elections and when transactions

costs are higher. In addition, we show that the effect is greater when the government plays a more

prominent role in the local economy. Overall, we conclude that flight to quality exists in

international equity markets, and that portfolio investors consider the underlying firms’ financial

reporting quality to be an important dimension of the overall quality of their portfolios.

32

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Appendix: Variable Definitions This appendix provides the definitions of variables and data sources. For those variables for which we do not mention specific data sources, we obtain the data from Thomson Reuters, Worldscope, CRSP, or COMPUSTAT.

Dependent Variables

FQKLW = Our primary fund-level flight-to-quality measure, computed in the following steps. First, based on the absolute values of discretionary accruals from Kothari et al. (2005) for all underlying stocks in a fund portfolio, we sort the underlying stocks into three categories (i.e., low, medium, or high) of accruals quality, using the bottom 30%, middle 40%, and top 30% breakpoints for all firms from each respective local market every year. Then, to compute the portfolio weights of each accruals quality category (r) in a fund portfolio, we sum up individual firms’ investment percentage

as , ,

n

r t i ti r

W ω∈

=∑ , where ,i tω is the investment percentage of firm i in fund quarter t

while n is the number of firms belongs to the accruals quality category r. Finally, we compute the difference of the two portfolio weights between high and low accruals quality categories. [Sources: Worldscope and Thomson Reuters]

FQDD = An alternative measure of fund-level flight-to-quality, computed similarly to FQKLW, but based on the standard deviation of discretionary accruals from Dechow and Dichev (2002). [Sources: Worldscope and Thomson Reuters]

WKLW = An alternative measure of fund-level flight-to-quality, computed similarly to FQKLW, but is the weighted average rank of the low, medium, and high accruals quality categories in a fund portfolio, with the rank being 1, 2, and 3 respectively, and the weights being the portfolio weights of the three accruals quality categories. [Sources: Worldscope and Thomson Reuters]

Test Variables

Election = An indicator variable that equals to one if the filing months of a fund quarter are within the time interval between four months prior to, and two months after the national election month, and zero otherwise. [Source: the Polity IV database]

PU = An aggregate index of political uncertainty in the previous quarter, measured as the negative natural logarithm of sum of four subcategories (i.e., the government stability, socioeconomic condition, military in politics and democratic accountability) from the ICRG political risk ratings. [Source: The International Country Risk Guide (ICRG)]

PolCrisis

= A country-level index of political instability and violence/ terrorism, which reflects perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism. It is multiplied by -1 so that larger values correspond to higher political uncertainty. [Source: The Worldwide Governance Indicators]

Margin = The difference between the anticipated vote share of the largest government party and that of the largest opposition party, prior to an election. Larger values of electoral margin indicate less electoral uncertainty. [Source: the World Bank’s Database of Political Institutions]

36

Govt = A government involvement score based on government enterprises and investment as a percentage of GDP. Data on the number, composition and share of output supplied by state-operated enterprises and government investment as a share of total output are used to construct a score from 0 (high percentage) to 10 (low percentage) ratings. We subtract the original value from 10, and scale the value into a range of 0 to 1. Larger values correspond to more government enterprises and investment. [Source: Economic Freedom of the World: 2012 Annual Report]

TradeCost = Country-level equity trading costs, which include the average commission paid, the average fee (i.e., costs incurred for obtaining additional services such as the post-trade settlement), and the average cost of market impact (i.e., the difference between the price at which a trade is executed and the average of the stock’s high, low, opening and closing prices during the trade), all multiplied by 100. [Source: annual issues of Standard & Poor’s Global Stock Markets Factbook (2003-2008) and annual issues of Standard & Poor’s Emerging Stock Markets Factbook (1998-2002)]

Control Variables

Size = Natural logarithm of the market value of fund holdings in millions of U.S. dollars from the prior quarter. [Source: Datastream]

Turnover = Turnover of underlying stocks in a fund portfolio, calculated as the value-weighted average of average monthly shares traded scaled by outstanding shares for underlying stocks during the previous year, with weights being the investment percentage of respective stocks in a fund portfolio. [Source: Datastream]

Volatility = Stock return volatility of underlying stocks in a fund portfolio, calculated as the value-weighted average of standard deviation of daily returns for underlying stocks during the previous year, with weights being the investment percentage of respective stocks in a fund portfolio. [Source: Datastream]

BM = Book-to-market ratio of underlying stocks in a fund portfolio, calculated as the value-weighted average of book value to market value for underlying stocks during the previous year, with weights being the investment percentage of respective stocks in a fund portfolio. [Source: Worldscope and Datastream]

Trade = The extent of trade development, measured as the sum of export and import of goods and services for host countries, scaled by gross domestic product of respective countries in the prior year. [Source: World Development Indicators 2011]

FDI = The extent of foreign direct investment development, measured as net inflows of foreign direct investment of host countries, scaled by gross domestic product of respective countries in the prior year. [Source: World Development Indicators 2011]

Openness = Degree of capital account openness as measured by Chinn-Ito Financial Openness Index in the previous year. This index is based on binary variables that tabulate restrictions on cross-border financial transactions as reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions. Higher values indicate lower investment regulation. [Source: http:// web. pdx.edu/ ~ito/Chinn-Ito_website.htm]

Law = Natural logarithm of law and order, which is an assessment of the strength and impartiality of the legal system, while the sub-component is an assessment of popular observance of the law; higher values indicate stronger judicial systems. [Source: The International Country Risk Guide (ICRG)]

37

FinDev = The extent of financial development, measured as equity market capitalization of host countries, scaled by gross domestic product of respective countries in the prior year. [Source: World Development Indicators 2011]

GDPGr = Annual percentage growth rate of gross domestic product (GDP) at market prices in the prior year, based on constant local currency. [Source: World Development Indicators 2011]

PerCapita = Natural logarithm of GDP per capita divided by 10,000 (based on Year 2000 constant U.S. dollars). [Source: World Development Indicators 2011]

38

Figure 1: Median Fund Asset Allocation Weights in High vs. Low Accruals Quality Categories around National Elections

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Pre Election PostLow High High-Low

39

Table 1: Sample Composition and Median Characteristics by Country

This table shows the mutual fund sample distribution by country and presents the median values of country-level variables. The sample consists of 8,835 quarterly fund observations for 1,948 funds from 23 countries during the period 1996-2009.

Country #

Elections #

Funds # Fund-Quarters

FQKLW Trade FDI Openness FinDev Law GDPGr PerCapita Margin Govt TradeCost

AUSTRALIA 3 107 513 0.317 0.408 0.043 1.132 1.161 1.792 0.038 0.854 -0.008 0.000 0.321 BRAZIL 2 41 127 -0.115 0.266 0.018 0.423 0.653 0.899 0.040 -0.894 0.049 0.200 0.477 CANADA 7 219 2,138 0.460 0.719 0.023 2.456 1.187 1.792 0.028 0.934 0.071 0.000 0.319 CHILE 1 3 11 0.054 0.683 0.056 2.456 1.097 1.609 0.056 -0.514 0.037 0.000 0.697 DENMARK 1 1 3 -0.205 0.870 0.058 2.456 0.551 1.792 0.007 1.101 0.119 0.300 0.357 FINLAND 2 3 12 -0.153 0.736 0.058 2.456 1.037 1.792 0.020 0.909 -0.123 0.800 0.415 FRANCE 2 107 380 0.137 0.556 0.038 2.456 0.878 1.609 0.018 0.789 0.152 0.600 0.338 GERMANY 2 151 583 0.308 0.676 0.015 2.456 0.441 1.609 0.007 0.844 0.125 0.400 0.293 GREECE 2 30 97 0.115 0.575 0.007 2.456 0.554 1.216 0.044 0.293 0.011 0.200 0.615 INDIA 2 100 353 -0.096 0.449 0.026 -1.159 0.537 1.386 0.079 -2.675 -0.286 0.600 0.555 ITALY 3 25 104 0.031 0.518 0.013 2.456 0.469 1.386 0.017 0.682 0.147 0.000 0.307 JAPAN 7 588 2,364 0.033 0.221 0.001 2.456 0.790 1.609 0.017 1.356 0.032 0.200 0.198 KOREA 1 10 30 0.380 0.692 0.004 -0.106 0.433 1.386 0.072 0.221 -0.030 0.400 0.774 MALAYSIA 3 38 129 -0.030 1.994 0.033 -0.106 1.523 1.322 0.058 -0.824 0.540 1.000 0.581 NETHERLANDS 2 2 7 0.209 1.288 0.058 2.456 0.917 1.792 0.003 0.889 0.106 0.000 0.238 NORWAY 1 5 16 0.213 0.760 0.012 2.456 0.404 1.792 0.020 1.336 -0.218 0.800 0.327 SINGAPORE 2 6 21 0.053 3.718 0.166 2.456 1.287 1.792 0.042 0.861 0.368 0.300 0.591 SOUTH AFRICA 3 39 144 0.189 0.534 0.005 -1.159 2.079 0.916 0.044 -1.120 0.568 0.200 0.510 SPAIN 3 17 52 0.120 0.568 0.029 2.456 0.822 1.504 0.031 0.416 0.105 0.600 0.320 SWEDEN 2 18 61 0.286 0.859 0.049 2.456 0.715 1.792 0.025 1.056 0.135 0.400 0.304 SWITZERLAND 2 46 219 0.252 0.803 0.036 2.456 2.173 1.609 0.002 1.271 0.175 0.000 0.225 THAILAND 3 6 21 0.049 1.365 0.046 -0.106 0.723 0.916 0.050 -1.444 0.140 0.400 0.541 UK 2 386 1,450 0.273 0.540 0.037 2.456 1.332 1.749 0.029 1.021 0.090 0.000 0.517 TOTAL 58 1,948 8,835

40

Table 2: Descriptive Statistics and Correlations

This table presents the descriptive statistics and the correlation coefficients for the variables used in the main tests. Panel A reports the descriptive statistics. Panel B shows Pearson correlation for all the variables used in the main tests. Size is reported in millions of dollars. All other variables are defined in the Appendix. *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively, using two-tailed tests. Panel A: Descriptive Statistics N 25% Mean Median 75% STD FQKLW 8,835 0.030 0.200 0.204 0.418 0.275 Election 8,835 0.000 0.413 0.000 1.000 0.492 Size (Raw Value) 8,835 4.961 103.389 20.500 65.490 348.529 Turnover 8,835 0.006 0.010 0.009 0.012 0.008 Volatility 8,835 0.015 0.025 0.021 0.026 0.025 BM 8,835 0.472 0.623 0.576 0.720 0.240 Trade 8,835 0.272 0.538 0.540 0.687 0.319 FDI 8,835 0.002 0.027 0.022 0.043 0.031 Openness 8,835 2.456 2.092 2.456 2.456 0.948 FinDev 8,835 0.707 1.037 1.036 1.306 0.412 Law 8,835 1.609 1.641 1.609 1.792 0.206 GDPGr 8,835 0.013 0.024 0.024 0.030 0.019 PerCapita 8,835 0.857 0.792 0.953 1.316 0.878 Margin 8,557 0.049 0.089 0.090 0.125 0.106 Govt 8,835 0.000 0.191 0.200 0.300 0.212 TradeCost 8,835 0.210 0.343 0.302 0.510 0.144

41

Table 2 (Cont’d)

Panel B: Pearson Correlations

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) FQKLW

(2) Election 0.131***

(3) Size -0.047*** 0.027**

(4) Turnover -0.003 -0.025** -0.122***

(5) Volatility -0.026** -0.129*** -0.299*** 0.029***

(6) BM 0.001 0.023** 0.005 0.069*** 0.039***

(7) Trade 0.252*** 0.065*** -0.107*** -0.080*** -0.033*** -0.008

(8) FDI 0.158*** 0.105*** -0.187*** 0.285*** 0.163*** -0.146*** 0.391***

(9) Openness 0.220*** 0.028*** 0.088*** 0.121*** 0.025** -0.099*** -0.072*** 0.073***

(10) FinDev 0.195*** 0.024** -0.179*** 0.229*** 0.181*** -0.030*** 0.301*** 0.402*** 0.000

(11) Law 0.373*** 0.073*** -0.019* 0.129*** 0.058*** -0.121*** 0.036*** 0.220*** 0.638*** 0.108***

(12) GDPGr -0.119*** -0.005 -0.200*** 0.000 0.098*** 0.123*** 0.145*** 0.180*** -0.620*** 0.192*** -0.286***

(13) PerCapita 0.171*** 0.012 0.137*** 0.160*** -0.016 -0.062*** -0.169*** -0.004 0.903*** 0.089*** 0.580*** -0.655***

(14) Margin 0.170*** 0.046*** -0.097*** 0.001 0.025** 0.124*** 0.434*** 0.080*** 0.059*** 0.429*** -0.222*** -0.080*** 0.088***

(15) Govt -0.402*** -0.080*** -0.036*** -0.219*** 0.124*** -0.008 -0.009 -0.245*** -0.441*** -0.308*** -0.427*** 0.302*** -0.434*** -0.166***

(16) TradeCost 0.059*** -0.073*** -0.358*** 0.140*** 0.257*** -0.039*** 0.285*** 0.423*** -0.453*** 0.323*** -0.174*** 0.568*** -0.523*** 0.047*** 0.180***

42

Table 3: The Relation between National Elections and Fund-level Flight to Quality

This table reports the OLS regressions of a fund-level flight-to-quality measure (FQKLW) on national elections and control variables. All variables are defined in the Appendix.*, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively, using two-tailed tests. Filing-month and country fixed effects (FE) are included in the first two specifications; filing-month and fund fixed effects are included in the third column. Standard errors are clustered by fund.

(1) (2) (3) Election 0.018*** 0.027*** 0.023*** (3.65) (5.26) (4.14) Size 0.004 0.005** -0.006 (1.47) (2.24) (-1.23) Turnover -1.543*** -1.038** -1.449*** (-3.11) (-2.01) (-2.62) Volatility -0.606*** -0.652*** -0.290 (-3.26) (-3.38) (-1.51) BM 0.116*** 0.105*** 0.014 (6.41) (6.00) (0.74) Trade 0.066*** 0.033 (4.68) (0.18) FDI -1.534*** -1.060*** (-8.27) (-4.76) Openness 0.005 -0.072 (0.18) (-0.93) FinDev 0.058** 0.131*** (2.00) (2.83) Law -0.056 -0.085 (-0.79) (-0.79) GDPGr -0.204 1.029 (-0.36) (1.51) PerCapita -0.023 -0.982*** (-0.77) (-3.16) Intercept -0.291*** -0.256** 1.590*** (-3.40) (-2.15) (2.80) Country FE Yes Yes Fund FE Yes Filing Month FE Yes Yes Yes N 8,835 8,835 8,835 Adj. R2 0.455 0.468 0.649

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Table 4: The Relation between National Elections and Fund-level Flight to Quality – Robustness Checks

This table reports the OLS regressions of a fund-level flight-to-quality measure (FQKLW, FQDD, or WKLW) on national elections and control variables. All variables are defined in the Appendix.*, **, and *** represent the significance at the 10%, 5%, and 1% levels, respectively, using two-tailed tests. Filing month and country fixed effects (FE) are included. Standard errors are clustered by fund. Panel A: Alternative Measures of Flight to Accruals Quality (1) (2)

Dep. Var. = FQDD Dep. Var. = WKLW Election 0.011** 0.025*** (2.07) (4.78) Controls Yes Yes Country FE Yes Yes Filing month FE Yes Yes N 5,866 8,835 Adj. R2 0.501 0.446 Panel B: Increased Sample Size by Relaxing Sampling Requirements (Column 1) or by Adding the U.S. Fund Sample (Column 2) (1) (2)

Dep. Var. = FQKLW Dep. Var. = FQKLW Election 0.031*** 0.004*** (7.61) (4.49) Controls Yes Yes Country FE Yes Yes Filing month FE Yes Yes N 19,051 69,915 Adj. R2 0.467 0.561 Panel C: Alternative Proxies for Political Uncertainty (1) (2)

Dep. Var. = FQKLW Dep. Var. = FQKLW PU 0.435*** (12.36) PolCrisis 0.043*** (3.48) Controls Yes Yes Country FE Yes Yes Filing month FE Yes Yes N 39,553 39,553 Adj. R2 0.380 0.376

44

Table 5: The Relation between National Elections and Fund-level Flight to Quality - Considering Timing and Type of Elections

This table reports the OLS regressions of a fund-level flight-to-quality measure (FQKLW) on national elections and control variables. The sample is portioned by the timing of elections (fixed vs. flexible elections) in Panel A, and by the type of elections (presidential vs. parliamentary elections) in Panel B. All variables are defined in the Appendix.*, **, and *** represent the significance at the 10%, 5%, and 1% levels, respectively, using two-tailed tests. Filing month and country fixed effects (FE) are included. Standard errors are clustered by fund. Panel A: By the Timing of Elections

Panel B: By the Type of Elections

Fixed Flexible Election 0.024** 0.030*** (2.01) (5.77) Controls Yes Yes Country FE Yes Yes Filing month FE Yes Yes N 844 7,991 Adj. R2 0.551 0.507

Presidential Parliamentary Election 0.130*** 0.039*** (3.69) (7.09) Controls Yes Yes Country FE Yes Yes Filing month FE Yes Yes N 548 8,287 Adj. R2 0.716 0.486

45

Table 6: Descriptive Statistics of Portfolio Turnover for Sample Mutual Funds This table presents the descriptive statistics of portfolio turnover for mutual funds in the sample during the following two separate time intervals: first between the election and post-election period (Panel A), and then between the two consecutive fund quarters which immediately follow the election period (Panel B). Portfolio turnover ratio is calculated as market value increases due to purchasing additional shares during a given time interval, plus market value decreases due to selling additional shares, then scaled by total market value of stock holdings for the fund at the start of the time interval. Panel A: Portfolio Turnover between the Election Period and the Post-Election Period (N = 2,594)

25% Mean Median 75% STD Portfolio turnover 0.256 0.750 0.518 0.924 0.833 Panel B: Portfolio Turnover between the Two Immediate Post-Election Periods (N = 2,594)

25% Mean Median 75% STD Portfolio turnover 0.077 0.321 0.201 0.444 0.339

46

Table 7: Cross-Sectional Variations in the Relation between National Elections and the Fund-level Flight to Quality Measure

This table reports the OLS regressions of a fund-level flight-to-quality measure (FQKLW) on national elections, three cross-sectional variables (CondVar), and their interaction items with the national election indicator. Specifically, CondVar refers to electoral margins (Margin) in Column (1), the extent of government involvement (Govt) in Column (2), and equity trading costs (TradeCost) in Column (3). In addition, Column (4) includes all three conditional variables and their interactions with Election. All other variables are defined in the Appendix. *, **, and *** represent the significance at the 10%, 5%, and 1% levels, respectively, using two-tailed tests. Filing month and country fixed effects (FE) are included. Standard errors are clustered by fund. (1) (2) (3) (4) Election 0.044*** 0.018** 0.104*** 0.097*** (5.19) (2.55) (7.17) (5.70) Margin -0.798*** -0.891*** (-6.47) (-6.88) Election×Margin -0.120** -0.088* (-1.97) (-1.78) Govt -0.086 -0.218*** (-1.33) (-3.40) Election×Govt 0.047** 0.077*** (2.02) (2.71) TradeCost 0.334*** 0.029 (4.35) (0.33) Election×TradeCost -0.214*** -0.195*** (-5.46) (-4.51) Size 0.006** 0.005** 0.005** 0.006** (2.37) (2.25) (2.24) (2.45) Turnover -1.489*** -1.002* -1.161** -1.461*** (-2.95) (-1.95) (-2.27) (-2.94) Volatility -0.890*** -0.645*** -0.680*** -0.916*** (-4.70) (-3.35) (-3.59) (-4.83) BM 0.096*** 0.104*** 0.103*** 0.091*** (5.24) (5.90) (5.87) (4.92) Trade 0.163*** 0.061*** 0.042*** 0.158*** (8.34) (3.92) (2.91) (7.14) FDI -1.936*** -1.476*** -1.478*** -1.748*** (-10.35) (-7.37) (-7.91) (-8.90) Openness -0.031 0.001 0.014 -0.041** (-1.48) (0.03) (0.60) (-2.03) FinDev 0.185*** 0.065** 0.071*** 0.207*** (5.73) (2.13) (2.61) (6.39) Law -0.295*** -0.046 -0.096 -0.304*** (-3.52) (-0.65) (-1.41) (-3.56) GDPGr -0.836 -0.205 -0.165 -0.682 (-1.57) (-0.37) (-0.30) (-1.29)

47

Table 7 (Cont’d)

PerCapita 0.010 -0.017 0.015 0.020 (0.35) (-0.56) (0.47) (0.54) Intercept 0.279* -0.256** -0.405*** 0.340** (1.86) (-2.15) (-3.42) (2.10) Country FE Yes Yes Yes Yes Filing month FE Yes Yes Yes Yes N 8,557 8,835 8,835 8,557 Adj. R2 0.478 0.468 0.471 0.481