Andrade Chhaochharia Europe June2012

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The Euro and European Equity Market

(Dis)Integration

Sandro C. Andrade

University of Miami

Vidhi Chhaochharia

University of Miami

First Version: October 2010

This Version: June 2012

Abstract

We document a striking 2007-2011 reversal in the long-term trend towards equity market

integration in Europe. We link this equity market disintegration to three mutually reinforcing

Eurozone crises: a competitiveness/growth crisis, a sovereign debt crisis, and a banking crisis.

To that end, we use earnings yields dierentials to measure relative changes in equity valuation

levels, and sovereign spreads to describe time series and cross-sectional variation in the sever-

ity of the triple crisis. We nd that increases in Eurozone sovereign spreads are signicantly

associated with decreases in equity valuation levels in the Eurozone relative to the rest of Eu-

rope, particularly for: i) nancial rms, especially banks holding large amounts of distressed

sovereign debt; ii) non-nancial rms that are vulnerable to disruptions of local credit markets.

Our conclusions are buttressed by two event studies investigating the impact of sudden changes

in sovereign debt nancing conditions and banking sector resilience on equity valuation levels.

Paradoxically, while the Euro was in part conceived to further integrate European nancial

markets, it set the stage for the triple crisis which resulted in a reversal of European equity

market integration.


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1 Introduction

Recent research documents a multi-decade long trend towards the integration of

global equity markets, as evidenced by the convergence of equity valuation levels

around the world (Bekaert, Harvey, Lundblad, and Siegel 2011). Focusing on Eu-

rope in particular, Bekaert, Harvey, Lundblad, and Siegel (2012), henceforth BHLS,

conclude that equity valuation convergence is to a large extent driven by membership

in the European Union. Membership in the EU is associated with the removal of

barriers for mobility of capital, goods, and people, and with the harmonization of

rules and regulations across countries. In turn, rule harmonization and barrier re-

moval would lead to cross-border equalization of economic growth opportunities and

nancial risk-premia, thus resulting in convergence of equity valuation levels across

Europe.1

This paper documents that over the 2007 to 2011 period there is a spectacular

reversal of the multi-decade trend towards European equity valuation convergence.

Figure 1 illustrates the evolution of equity market segmentation (i.e., lack of inte-

gration) in Europe from 1987 to 2011. We plot our version of BHLSs pairwise seg-

mentation measure, based on absolute dierences of industry-level earnings yields.

Later in the paper we provide details on the construction of this measure. Note that

the 2007-2011 period displays a sharp reversal of the long-term trend towards lower

segmentation in Europe. As of December 2011, equity market integration in Europe

is at the same level as of the early 90s.

1 Bekaert, Harvey, Lundblad, and Siegel (2011,2012) measure equity valuation levels using earnings yieldsat the industry level. Using a simple dividend discount model, earnings yields can be expressed asr-g, wherer is a discount rate and gis the growth rate of earnings. Financial and economic integration equalize both

d h d d l d f l l l


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.0

2

.04

.06

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Jan 85 Jan 90 Jan 95 Jan 00 Jan 05 Jan 10

1987-2011

Figure 1: Pairwise Equity Market Segmentation in Europe

Interestingly, the recent equity market disintegration occurs even though Europe

has seen neither a reinstatement of previously removed barriers for trade and factormobility nor a reversal in the harmonization of rules and regulations. That is, it

appears that the drivers of the 2007-2011 divergence in equity valuation levels are

dierent from the drivers of the multi-decade long convergence before 2007.

We argue that the 2007-2011 divergence of equity valuations in Europe results

from the Euros three mutually reinforcing crises: a competitiveness/growth crisis,

a banking crisis, and a sovereign debt crisis (Shambaugh, 2012; Acharya, Drechsler,

and Schnabl, 2011). Ultimately, these crises appear to stem from the loss of exchange

rate and monetary autonomy associated with the introduction of a common currency


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and Drea, 2009). Paradoxically, while conceived in part to further integrate Europeannancial markets, the monetary union set the stage for a sharp reversal of existing

equity market integration. In Section 2 we discuss the competitiveness/growth crisis,

the banking crisis, and the sovereign debt crisis, their mutually reinforcing mecha-

nisms. We also explain the channels through which the Euros crises foster equity

market disintegration in Europe.

Our empirical analysis provides several results consistent with the Euros triple

crisis narrative for equity market disintegration in Europe. To that end, we use

sovereign CDS spreads as summary statistics of time-series and cross-sectional vari-

ation in the severity of the Euros triple crisis.2 Building on BHLS (2011, 2012), we

measure equity valuation levels using industry-level earnings yields. The valuation

levels of Eurozone rms are measured relative to comparable rms in the non-euro

Western Europe (UK, Switzerland, Denmark, Sweden, and Norway). We begin by

showing that equity valuation levels in the Eurozone are strongly correlated with

sovereign spreads. Higher spreads are associated with lower equity valuations. The

strong positive correlation holds both at the value-weighted aggregate stock market

and at the rm level. Our rm-level results include controls for leverage, size, and

protability, as well as rm and time xed eects.

Our narrative associates the disintegration of European equity markets in 2007-

2011 to the Euros triple crisis. An alternative narrative downplays the role sovereign

and banking crises, and posits that high levels of government debt resulting from s-

cal proigacy are to blame for relatively lower growth perspectives in the Eurozones

2 Alternatively we could have dened a pre- and a post-crisis period and pursue a dierence-in-dierencesmethodology. Note, however, that this alternative methodology cannot take into account time variation of

h f h h h d ( S b b h S b d


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periphery.

3

These lower growth perspectives translates to relatively lower equity val-uations in the Eurozones periphery. The association between lower equity valuation

levels and higher sovereign spreads documented by us would thus be spurious, as

both variables are caused by high government debt. According to this view, a high

public debt burden would lead to lower economic growth and hence lower valuation

levels even if the probability of sovereign default was zero.4

We explore cross-sectional variation in the association between sovereign spreads

and equity valuation dierentials in order to dierentiate our narrative from the al-

ternative narrative mentioned above. First we show that, for a given increase in

sovereign spreads, valuations of nancial rms decrease by much more than valua-

tions of non-nancial rms. This is consistent with our story that puts sovereign

and banking crises at the center of the of recent European equity market disinte-

gration, while the alternative story does not provide cross-sectional predictions for

the relationship between sovereign spreads and equity valuation levels. Moreover, we

use detailed data on sovereign debt holdings to link valuation levels to holdings of

sovereign debt directly.5 We nd that European banks holding more distressed sov-

ereign debt experience larger decreases in valuation levels associated with increases

in sovereign spreads. These results indicate a link between the Euros sovereign debt

crisis and equity valuations, over and above the eect of high government debt in

constraining growth.

3 Nobel laureate Robert Mundell (2011) writes: "The scal crisis of some Eurozone countries should notbe called the euro crisis. The socialist governments went into a scal binge under the protection of the euro."Reinhart and Rogo (2010) and Cecchetti, Mohanty, and Zampolli (2011) argue that high levels of publicdebt (above 85% or 90% of GDP) constrain economic growth. Therefore, also else equal, countries withpublic debt to GDP ratios above 90% would be expected to have weaker economic growth than countrieswith more moderate ratios.

4 Japan has very low sovereign CDS spreads even though its public debt to GDP ratio is above 200%


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Second, focusing on Eurozone non-nancial rms, we nd that rms that aremore vulnerable to disruption in credit markets (younger rms, rms with higher

debt rollover needs, and rms with less tangible assets) experience larger decreases

in valuation levels for a given increase in sovereign spreads. These results indicate

that disruptions in local credit markets play an important role in explaining lower

equity valuations in countries in which sovereign spreads are higher, and therefore inexplaining equity market disintegration in Europe. Again, these results conrm that

sovereign and banking crises play a central role in explaining why equity valuation

levels diverge in Europe.

Third, we nd that Eurozone peripherys non-nancial rms that are likely to

benet from an exchange rate depreciation associated with a potential Euro exit

(rms with high fraction of exports and low fraction of imports of intermediate in-

puts) in fact experience increases in valuation levels associated with sovereign spread

increases. This results shows that potential foreign exchange depreciations associ-

ated with exits from the monetary union, a reinforcing loop of the euros triple crisis,

play a role in explaining divergence of equity valuations in Europe.

Finally, we provide two event studies that highlight the importance of sovereign

debt and banking crises for the dispersion of valuation levels in the Europe. These

event studies focus on sudden improvements in sovereign debt nancing conditions

and in banking sector resilience. To the extent that other determinants of equity

market valuation levels (e.g., high levels of public debt) are xed in the very short

window around these sudden improvements, they help us establish a causal eect

going from sovereign and banking crises to equity valuations.


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The EFSF fund would provide more favorable debt nancing conditions for distressedEurozone countries. We nd that such announcement leads to a strong positive re-

action of Eurozones sovereign debt and equity markets relative to non-Euro Europe,

and that the equity market eect is stronger for nancial rms. The second event

study investigates markets response to the August 2011 announcement of a merger

of Greeces second and third largest banks, coupled with an injection of further cap-ital from Qatar. We nd that no reaction in sovereign debt markets, but a strong

positive reaction in Greeces equity market, particularly for nancial rms (excluding

those participating in the merger).

In sum, we document a striking reversal in the long-term trend towards equity

market integration in Europe, and link this recent disintegration in equity markets

to the Euros triple crises. The rest of the paper proceeds as follows. In Section 2

we discuss the Euros triple crises, and how it aects valuation levels. In Section 3

we describe our data. In Section 4 we use aggregated data to document the reversal

in Europes equity integration trend. In Section 5 we explore how sovereign spreads

- our summary statistics for time series and cross-sectional variation in the severity

of Europes triple crisis - relate to equity level dierentials in Europe. Section 6

concludes the paper.


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2 The Euros three crises and stock valuations

In this section we discuss the Euros three crisis, and their mutually reinforcing

mechanisms. We also detail the channels through which the Euro triple crisis foster

equity market disintegration in Europe. Figure 2 illustrates this sections discussion.

Competitiveness/growth crisis

The introduction of the Euro set the stage for a gradual loss of international com-

petitiveness in Greece, Ireland, Italy, Portugal, and Spain (henceforth the GIPSI). In

hindsight, the Eurozones one-size-ts-all monetary appear to have been too lax for

the GIPSIs before 2008.6 Relative to the rest of the Eurozone, Germany in particular,

these ve countries experienced large wage and price ination from the introduction

of the Euro to 2008 (De Grauwe 2011, Shambaugh 2012). Ination gradually eroded

the GIPSIs international competitiveness, as evidenced by the accumulation of large

and growing current account decits in the GIPSIs, mirrored by growing current ac-

count surpluses in other Eurozone countries, most notably Germany (Obstfeld 2012).

Trapped in a monetary union, the GIPSIs cannot depreciate their currencies in order

to lower the relative prices of its products and thereby spur demand for them.

The loss of competitiveness alone, associated with the impossibility of quick cor-

rection through instantaneous exchange rate depreciation, is sucient to generate

some dispersion across equity valuations within Europe. This is because growth op-

portunities in the GIPSIs would diverge from growth opportunities in the rest of

Europe, which leads to a reduction in GIPSI stocks valuation levels relative to the

6 Using a standard Taylor Rule, Nechio (2011) shows that while the ECBs monetary policy appearsadequate for the the core Eurozone until 2008, it was too lax for the Eurozones periphery. The diculty

f t bli hi f i t ll t li f th GIPSI d th t f th E di t d


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Sovereign debt crisis and banking crisis

In addition to setting the stage for the GIPSIs loss of competitiveness, and

precluding quick corrections via exchange rate depreciations, the introduction of

the Euro exacerbates the "nancial fragility" of public debt markets. This increases

the likelihood of sovereign debt crisis.

Since Calvos (1988) seminal analysis, it is known that public debt is vulnerable

to multiple equilibria and self-fullling expectations. If lenders believe public debt is

sustainable, then interest rates are low and public debt turns out to be sustainable.

Instead, if lenders question the willingness or ability of a government to fulll its

debt obligations, then interest rates are high and public debt is unsustainable.7

Importantly, for reasons not fully understood thus far, sovereign debt markets

appear to be much more "fragile" (unstable, prone to self-fullling crises) when a

government cannot control its own currency.8 The increased instability of public debt

markets associated with membership in a currency union opens up room for small

changes in fundamentals to have disproportionately large eects on debt pricing. This

is because small changes in fundamentals may set in motion self-reinforcing changes

in beliefs, amplifying the eect of the initial shock because beliefs can be self-fullling.

In the Eurozone case, the lower growth prospects in the GIPSIs resulting from loss of

competitiveness set in motion disproportionately large increases in sovereign spreads

7 Note that there is no quick and dirty method to evaluate a governments capacity and willingnessto pay back its debt. As Rodrik (2010) writes, they "depend on almost innite number of present andfuture contingencies. They depend not just on its tax and spending plans but also on the state of theeconomy, the external conjuncture, and the political context. All of these are highly uncertain, and requiremany assumptions to reach such form of judgment about creditworthiness. Using Allen and Gales (2004)terms, the diculty in assessing fundamental, "intrinsic" uncertainties related to sovereign sustainabilityallows "extrinsic uncertainties" (expectations/condence) to play a large role, and thus increases "nancialfragility". See Calvo (1988), Cole and Kehoe (2000), and Corsetti and Dedola (2011) for models of self-


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and a full-edged sovereign debt crisis.

There are three channels through which a sovereign debt crisis can aect stock

valuation levels. First, an increase in the probability of sovereign default signals

impending domestic banking crisis which would result in a large disruption in do-

mestic credit markets. This is consistent with evidence in Arteta and Hale (2008)

and Kohlscheen and OConnel (2007). Recent theories of sovereign default identify

a domestic banking crisis as the main cost of sovereign default.9 The mechanism

operates through two well-known "home-bias" phenomena associated with domes-

tic banks: domestic banks are responsible for a disproportionate large fraction of

lending to domestic non-nancial rms; and domestic banks hold disproportionate

large amounts of domestic sovereign debt. Because of practical diculties, govern-

ments cannot selectively default on foreign debtholders (Broner, Martin, and Ven-

tura, 2010). Therefore, sovereign default would necessarily reduce domestic bank

capital and hence disrupt domestic credit markets.10 The impending disruption of

domestic credit markets upon sovereign default reduces valuation levels through re-

duced growth opportunities and an increase in risk.

Second, even before the occurrence of sovereign default per se, higher probabil-

ity of sovereign default leads to bank deleveraging and an associated credit crunch

through higher bank funding costs and capital ight. Higher probability of sovereign

default increases the risk of lending to domestic banks, since these banks would suf-

fer large losses if sovereign default materializes. This increased risk raises domestic

9 See Brutti (2011), Gennaioli, Martin, and Rossi (2011), Bolton and Jeanne (2011), and Acharya,Drechsler, and Schnabl (2011). See Panizza, Sturzenegger, and Zettelmeyer (2009) and Levy-Yeyati andPanizza and (2011) for empirical evidence and illuminating discussions of the shortcomings of traditionaltheories of sovereign default.

10 A l f h l f d f l b k l h h


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banks current funding costs (Corsetti et al. 2012). In response to increased funding

costs, banks reduce lending activity. Bank deleveraging due to higher funding costs

thus reduces corporate investment and hence aects valuation levels through reduced

growth opportunities.

Third, governments response to the sovereign debt crisis may exacerbate the

competitiveness/growth crisis. If the cost of servicing public debt increases, govern-

ments may want to increase current and prospective primary budget surpluses in

order to signal that their debt to GDP ratios are sustainable over time. In the face

of a depressed economy with idle resources, reduced government spending may lead

to lower economic growth (De Long and Summers 2012).11 Therefore, governments

front loaded austerity response to a sovereign debt crisis may reduce valuation levels

through lower growth opportunities.

Banking crisis and sovereign debt crisis

Above we discussed how sovereign debt crisis can lead to lower valuation levels

through an impending banking crisis and through current bank deleveraging caused

by high bank funding costs. Here we explore how a banking crisis may lead to a

sovereign crisis, as pointed out by Acharya, Drechsler, and Schnabl (2011).

As mentioned before, the Eurozones one-size-ts-all monetary policy appears

to have been too lax for the GIPSIs from the introduction to the euro to 2008.

This created wage and price ination in these ve countries. In two of these ve

countries, Ireland and Spain, overly lax monetary policy may have contributed to

real estate booms not justied by fundamentals. The real estate burst after 2008

11 See also discussions in Acconcia, Corsetti, and Simonelli (2011), Cottarelli (2012), Shambaugh (2012),d K (2012) A i t l (2011) it "Th f thi t d f i i l lit


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left Irish and Spanish banks with a large number of potentially underperforming

real estate loans. In order to avoid severe disruptions associated with failures of

large banks, governments may bail out these banks creditors, by taking over a large

fraction of banks private debt into their balance sheets. Therefore, bank bailouts

may increase public debt by potentially large amounts, and contribute to a sovereign

debt crisis. The sovereign debt crisis aects growth perspectives and valuation levelsas previously discussed.

Euro exit reinforcing loop

The introduction of a single currency in a region not ready for it is the root

cause for the triple crises. Markets participants then conjecture that the ultimate

solution to the crises consists of exits from the monetary union. Such exits would

necessarily be associated with instantaneous and large real foreign exchange deval-

uations, particularly for the GIPSIs. Large devaluations would lead to immediate

insolvency not only of sovereigns but also of most local banks, whose liabilities would

be denominated in euros and assets in the new local currencies. Therefore, market

participants correctly anticipate that exits from the monetary union for periphery

countries would most likely be associated with forced re-denomination of liabilities

from euros to local currencies at non-market exchange rates. The possibility of Euro

exits thus leads to capital ight from peripheral countries, and associated increases

in sovereign debt spreads and bank funding costs (Levy-Yeyati et al., 2010; King,

2012). Therefore, the Euro exit loop associated with the triple crises reinforces both

sovereign and banking crises.

The euro exit loop leads to lower valuation levels for most rms in distressed


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Germany, Greece, Ireland, Italy, Netherlands, Portugal and Spain) and 5 West-

ern European countries that are not in the Eurozone (Denmark, Norway, Sweden,

Switzerland, and the UK).

We rst compute an earnings yield for each European stock in each month of our

sample. We use analysts earnings forecasts from I/B/E/S and stock price data from

Datastream. I/B/E/S tickers are matched to Datastream identiers in three steps:rst by ISIN, then by SEDOL, and nally by name (hand-matched).12 Both earnings

forecasts and stock prices are as of the third Thursday of each month, because this is

when I/B/E/S computes its summary statistics of earnings forecasts across analysts.

We use earnings forecasts rather than historical earnings to reduce measurement

errors, because realized earnings are equal to forecasted earnings plus noise. Liu,

Nissim, and Thomas (2002) compare earnings yields of individual U.S. companies

with their industry mean and nd that the dispersion of earnings yields calculated

from historical earnings is nearly twice that of earnings yields calculated from analyst

forecasts. Additionally Liu et al. (2007) show that earnings forecasts substantially

outperform historical earnings in describing valuations of European rms. Similarly,

Kim and Ritter (1999) nd much smaller IPO valuation errors using analyst earnings

forecasts rather than historical earnings.

In our baseline results, we compute the earnings yield using the average earnings

forecast for the scal years t, t+1, and t+2. We dont use earnings forecasts after

scal years t+2 for two reasons. First, because many stocks dont have forecasts

beyond scal year t+2. Second, because we want estimates of current earnings that

are uncontaminated by long-term earnings growth. In robustness tests we repeat our


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These alternative choices reduce the sample as there is a (small) drop in coverage

from t to t+1 and t+2. Following BHLS (2007, 2010, 2011), we discard negative

earnings and earnings yields above 100% per year.

Finding comparable stocks

Our methodology requires us to identify a set of comparable stocks for each

Eurozone stock in our sample. The closest comparable sample for our Eurozone

stocks are non- Euro Western European stocks. The countries in our comparable

sample include Denmark, Norway, Sweden, Switzerland and the United Kingdom.

Following BHLS (2007, 2010, 2011), we group rms by ICB Level 4 industries. There

are 39 such industries.

BHLS (2007, 2010, 2011) postulate that, in integrated markets, stocks in the same

industry have the same expected earnings growth rate and fundamental risk exposure.

This would make their earnings yields comparable. Furthermore, grouping rms by

industry is by far the most common way to perform cross-sectional comparisons of

stock multiples (e.g., Baker and Ruback, 1999; Kim and Ritter, 1999; Lie and Lie,

2002; Purnanandam and Swaminathan, 2004; Liu, Nissim, and Thomas, 2002 and

2007; Bris, Koskinen, and Nilsson, 2009).

To account for potential dierences in nancial risk (as oppose to business risk), in

our rm-level analysis we further group stocks in the same ICB Level 4 industry into

(net) leverage quartiles. Our rm-level results are robust to not-matching by leveragein addition to industry. For non-nancial rms, we dene leverage as total debt minus

cash divided by total assets as of the previous scal year. For nancials, we dene

leverage as total liabilities divided by total assets as of the end of the previous scal


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pairs and using both Eurozone and comparable stocks. Each Eurozone stock is then

matched to "comparable" stocks in the same industry and the same leverage quartile.

Table I provides information on our sample of Eurozone rms and the compa-

rable rms. Panel A, Table 1 shows that, on average each month, we have a total

of 1316 Eurozone stocks and a total of 1254 comparable stocks. Eurozone stocks

and comparables are similar in terms of average market capitalization. Specically,over our sample period the average market capitalization is $ 4304 mil for Eurozone

stocks and $ 3353 mil for comparable stocks. Panel B of Table 1 presents the average

monthly number of rms per industry in our sample. On average across industries,

each month we have 31 Eurozone stocks and 25 comparable stocks. sample.

TABLE 1

For each of the Eurozone stocks and for each month, we compute the earnings

yield of the comparable stocks in the same industry and the same leverage quartile.

So, for each stock j in a Eurozone country at a given point in time, we have its

own earnings yield EYEZ;jt and its comparable earnings yieldEYcomp;jt . In our rm-

level analysis, the dierential earnings yield EYEZ;jt EYcomp;jt is the main dependent

variable. In total, across all Eurozone countries and all months, we have 108837

observations of rm-level earnings yield dierentials.

Aggregation

Following BHLS (2007, 2010, 2011), we do not match by leverage quartile in addi-

tion to industry and use value-weighted averaging to aggregate EYEZ;jt andEYcomp;jt


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.02

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Europe North America/Asia

.02

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Eurozone Non-Eurozone

1987-2011Figure 4: Pairwise Equity Market Segmentation

Figure 4 shows that the disintegration trend is essentially an European rather

global phenomenon. In the rst panel we plot the average pairwise segmentation

across developed countries in the rest of the world (North America and Asia-Pacic),

along with the European average pairwise segmentation plotted before in Figure 1.

Details about this global sample are on Table CC in the Appendix. Note that

the cross-border divergence of equity valuations after the summer of 2007 is more

pronounced in Europe than in the rest of the developed world. In the second panel we

show that, within Europe, the equity market disintegration is essentially an Eurozone

phenomenon. We plot the average pairwise segmentation across the 11 Eurozone

countries and the 5 Western European non-Euro countries. The graph shows much


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-.

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4

0.

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Jan2000 Jan2002 Jan2004 Jan2006 Jan2008 Jan2010 Jan2012

Austria Belgium Finland France

Germany Greece Ireland Italy

Netherlands Portugal Spain EY_difference

2001-2011Figure 5: Average Eurozone minus Non-Eurozone Earnings Yield Differentials and 10-year CDS spreads

Figure 5 plots signed as opposed to absolute (non-signed) dierences of valuation

levels, along with Eurozone CDS spreads. The gure shows that, on average, valua-

tions levels decreased in the Eurozone relative to the 5 non-Euro Western European

countries from 2005 to 2011. We plot the dierence between the average earnings

yield in the Eurozone and the average earnings yield in the rest of developed Europe,

after aggregating industry earnings yields to the country level via value-weighting.

We use equally-weighted averages across countries in the Euro and non-Euro groups.

Results are similar when we use value-weighted averages of countries in each group

before taking the dierence.

Note that there are two interesting patterns in Figure 5. First, the decrease in


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the Eurozone are almost identical to those in the non-Euro countries. On average in

that period, the dierence in earnings yields is just -0.004. But in the last quarter

of 2011 the dierence increases to 0.018. Given that the average earnings yield in

the two country groups in the 2005 to 2011 period is about 9% (see Table 2), the

dierence in earnings yields at the end of 2011 amounts roughly 24% of rm values

(0.018-(-0.04)0.09). Second, the time series pattern of the (average) earnings yield

dierential is similar to the time series patters on Eurozone CDS spreads.

5 On the drivers of European equity market (dis)integration

In this section we explore cross-sectional and time series variation in earnings yielddierentials in order to shed light on the drivers of the 2007-2011 European equity

market disintegration.

5.1 Are earnings yield dierentials correlated with spreads? Country-

level

In this section we present linear regressions of the earnings yield dierences on sov-

ereign debt spreads to investigate whether equity market valuations and sovereign

spreads are at all correlated. We report Driscoll-Kraay (1998) standard errors with

12 lags. These standard errors are robust to heteroskedasticity, autocorrelation with

12 lags within each cluster, and contemporaneous cross-sectional correlation across

clusters.


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Table 3 presents our linear regression results of country level earnings yield dier-

ences on spreads. The data are comprised of 84 months for each of the 11 Eurozone

countries, totalling 924 data points. We regress the country level earnings yield dif-

ferences onto the log spread and other control variables. In column (1) we regress

country level earnings yield dierences on log spreads. First we nd that the spreads

and valuation dierentials are signicantly positive correlated. In column (2) we add

the square of the log spread to investigate whether there is a non-linear relation be-

tween the spreads and earnings yield dierences. We nd that square term is negative

and statistically signicant at the 10% level indicating that there is a concave rela-

tion between the earnings yield dierences and spreads. Using the maximum spread

in the sample and the estimated coecients, we nd that the relationship between

earnings yield dierentials and spreads is increasing at all points in the sample.

To simplify our analysis we use a monotonic and concave transformation of the

log spread as our main independent variable. This is because we later interact spread

coecients with a number of rm characteristics. It is much easier to interpret the

sign of the interaction terms when we have only one spread variable, than if we had to

use both log spread and its square. In column (3) we report regression results of the

square root of the log spread on country level earnings yield dierences. As expected,

we nd that the square root of the log spread is signicantly positively associated

with the earnings yield dierences. Specically the coecient on the square root of

the log spread is 0.095 which is statistically signicant at the 1% level.

In terms of economic signicance, the coecient on the square root log spread

in column (3) translates to a 1 standard deviation increase in the square root of

h l d d h f h ld d f


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the sample average translates into a valuation discount of 4.6% or an increase in

earnings yield dierence of 0.4%. Therefore the relation between the spread and

valuation dierences is economically and statistically large.

In Column (4) we add country xed eects and in Column (6) we add month-year

xed eects and in column (7) we add both country and time eects together. Ad-

ditionally, below column (7) we report our results where we cluster by both countryand time. The square root of the log spread remains economically and statisti-

cally signicant at the 5% level in all these regressions. To ensure that the spread

is capturing the worsening of the crises in the Eurozone we add additional control

variables to check whether the spread is just proxying for worsening macroeconomic

growth prospects or worsening competitiveness or high debt/GDP ratios in some ofthe Eurozone countries.

In column (5) we add the dierence slope yield curve as measure of macroeconomic

growth prospects of the Eurozone relative to the non- Eurozone Europe. Specically

the dierence slope yield curve is the dierence between the slope of the riskless yield

curve in the Eurozone and the slope of yield curve in the non-Eurozone countries.

The slope is calculated as the dierence between a 10-year and 3-month rates. In the

Eurozone the 3-month rate is the yield of a 3-month German government bond and

the 10-year rate is the yield of a 10-year German government bond less the 10-year

German CDS spread. In the non-Eurozone we calculate the slope for each of the 5

countries and aggregate by value-weighting where the stock market capitalizations

of the individual countries are used as weights. We nd that the spread remains

statistically signicant at the 1% level. It is important to note that the increase in

d l h f


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There is signicant variability in the rm level earnings yield dierences as indicated

in high standard deviation (0.059). The average spread at the rm level is 0.059. In

Panel B of Table 4 we report summary statistics for earnings yield dierences and

rm characteristics for non-nancial rms only. On average we nd that the average

leverage-matched earnings yields are 0.005 which is very close to the overall sample

average.

TABLE 5

Table 5 contains results of our rm-level regressions. As a rst step we replicate

our country level analysis at the rm level. We include rm level controls for assets

and protability. Additionally all rm level regressions include rm and time xed

eects. Column (1) of Table 5 reports regression results of leverage-matched earnings

yield dierences on the log spread. The coecient on the spread is 0.233 which is

statistically signicant at the 1% level. To account for the concave relation between

earnings yield dierences and spread we use the square root of the log spread as our

main independent variable. Column (3), of Table 5 reports regression results of the

valuation dierential on the square root of the log spread. Again we nd a strong

positive association between earnings yield dierences and the spread indicating that

higher earnings yield dierences in the Eurozone relative to Non-Eurozone European

countries is associated with an increase in sovereign debt spreads. Next we explore

the cross sectional variation in the association between the sovereign spread and

equity market valuation dierentials in order to highlight the role of the sovereign

and banking crisis in the Eurozone area.


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Following Acharya, Drechsler, and Schnabl (2011), we scale a banks sovereign

debt holdings by the ratio of the banks Tier 1 capital in both 2010 and 2011, and

compute a spread-weighted and scaled exposure to sovereign debt for both 2010 and

2011. Finally, we compute the change in this spread-weighted exposure for each of

the 28 banks from 2010 to 2011. The formula is:

Pcountries

Holdingscountry

Tier 1 CapitalCountry Spread

In Table 6 we correlate the change in sovereign debt holdings dened above with

the rm-level change in earnings yields over the same 2010 to 2011 period. We

nd that an increase in the spread weighted sovereign debt holding of banks in the

Eurozone is associated with a signicant decrease in equity market valuation of these

banks as compared to the non-Eurozone European banks. Specically in column (1)

we regress the change in the spread weighted sovereign debt holdings on a change in

the bank earnings yield and nd that these banks experience as decrease in valuationof 0.882 which is statistically signicant at the 1% level and economically large. This

result is robust to inclusion of additional control variables like log assets, average

earnings yields and country xed eects (columns (2) to (4) of Table 6).

TABLE 6

To mitigate the potential eect of outliers in our small sample we also run median

regressions. These results are reported in columns (5)(8) of Table 6. We nd that


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results conrm a direct link between sovereign debt holdings and equity valuation

levels.

Non-nancials with higher vulnerability to credit market disruptions

Table 7 provides evidence consistent with the idea that non-nancial rms that

are more vulnerable to disruption in credit markets experience larger decreases in

valuation levels for a given increase in the sovereign spread. We measure vulnera-

bility to credit market disruptions in three alternative ways: rms with higher debt

rollover needs (high net leverage), rms with less pledgeable hard collateral (low as-

set tangibility), and rms less likely to have a long credit history (younger rms).13

All regressions include assets, protability, rm and time xed eects as controls.

In column (1) of Table 7 we interact the sovereign spread with a dummy for high

leverage. The dummy for high leverage takes a value 1 if rm leverage at time t is

higher than median leverage for the sample of Eurozone rms at time t. We nd that

rms with higher leverage experience larger valuation discounts for a given level of

the sovereign spread. Specically the coecient on the interaction term of the spread

and high leverage is 0.100 and is statistically signicant at the 1% level. Given the

nite maturity of debt, rms with high leverage are more likely to need to roll over

their debt and therefore would have to access the external markets. Such rms would

be more aected on account of a banking crisis.

TABLE 7

In column (2) we interact a dummy for low asset tangibility with the sovereign


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for the GIPSI countries. In such an event it is possible to identify rms that might

potentially benet from a currency depreciation and therefore might experience an

increase in equity market valuations as the sovereign spread increases. Firms that

have a high fraction of foreign sales coupled with low fraction of intermediate input

imports are rms that could benet from the Euro exit. These rms are earning most

of the revenue from export earnings and are using domestic inputs for production.

TABLE 8

In Table 8 we interact the sovereign spread with a dummy for rms that have

high foreign sales and low intermediate input imports. We obtain foreign sales data

from Datastream and supplement the data with OSIRIS. Foreign sales is dened

as the fraction of total sales that come from foreign markets. Since rm level data

on intermediate imports is not available we use industry level data on intermediate

input imports. We obtain this data from OECDstat Extracts and is dened as the

contribution that imports make in the production of exports of goods and services

in the mid-2000s.

Column (1) of Table 8 has the sovereign spread interacted with the dummy for

high foreign sales and dummy for low intermediate input imports. We nd that

for a given level of the spread, rms in the Eurozone with high foreign sales and

low intermediate input imports experience an increase in their valuation levels (thecoecient 0.118 is positive). This eect is statistically signicant at the 1% level.

In column (2) and (3) of Table 8 we break up our sample into the GIPSI and

GIPSI t i d th i th t b l C t i


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pared to Germany for example. Therefore it is reasonable to conjecture that the

aforementioned foreign exchange eect should be more pronounced in rms in the

GIPSI countries. Conrming our conjecture, we nd that rms in the GIPSI that

have high foreign sales and low intermediate input imports experience a valuation

increase of 0.184 which is statistically signicant at the 1% level. In contrast the

non-GIPSI rms with the same characteristics do not experience a signicant value

increase.

5.3 Event studies

Because our focus has been on equity market valuation dispersion and its relation

with the sovereign spread in the Eurozone we cannot ignore the fact that could be

many alternative determinants that could be aecting both equity market valuations

and causing sovereign spreads to be increasing. Most notably, the major alternative

narrative for Euro crisis is predicated upon high levels of debt to GDP ratios causing

slower economic growth. In that case, even if the probability of sovereign default

was near zero (e.g., Japan), high debt levels would reduce equity valuation levels

because of reduced growth opportunities. It could be the case, therefore, the high

sovereign debt levels cause both low valuations and high sovereign spreads, and that

the correlation of spreads and valuations is spurious.

Endogeneity issues as the one described above are notably dicult to fully address

in nancial economics. It is hard to nd unequivocally clean instrument variables or

natural experiments. Nonetheless, we attempt to mitigate concerns about endogene-

ity using two event studies, described below.


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Announcement of EFSF fund creation

As Claessens, Tong, and Zuccardi (2011), we use the May 2010 announcement of

the creation of the European Financial Stability Facility (EFSF) as a quasi-natural

experiment generating a sudden improvement in debt nancing conditions for trou-

bled Eurozone economies. On Saturday May 8 2010, European governments an-

nounced a 750 billion euro rescue package for distressed countries. The package

amounted to contingent credit lines to countries which would allow distressed coun-

tries to lengthen the maturity of their debts and pay below market interest rates.

Importantly, the package did not contemplate debt reduction itself. Therefore, it is

unlikely that any observed price reaction in stock markets and in the sovereign debt

market was triggered by a direct eect of a reduction in high debt-to-GDP ratios forexample.

We use daily data to evaluate the market reaction to the announcement of the

creation of the EFSF fund. Note that over these very short windows of time other

determinants of equity market valuations are less likely to change substantially. The

change in sovereign spreads is calculated as the change in the 10-year CDS spread

between May 10 and May 7th of 2010. We calculate stock returns on May 10, 2010

using daily data from Datastream ICB Level 4 Industry stock indices for all sample

countries. We rst compute the value-weighted stock return in each of the Eurozone

countries. Then we subtract the comparable stock return in the comparables sample

to dene industry- adjusted Eurozone stock return. The calculation of the compa-

rable stock return in non-Eurozone countries is analogous to our previous analysis.

That is, we rst calculate the value-weighted return in each of the 39 industries for

l d h l l h


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comparable May 10, 2010 stock return in each of the Eurozone countries. Table 9,

Panel A presents our results.

TABLE 9

Table 9 shows that, on average, we nd that for all 11 Eurozone countries there

is a large positive reaction in sovereign debt and stock markets. The average sov-

ereign spread between May 7 and May 10, 2010 decreases by 72 basis points and

the industry-adjusted stock market returns are equal to 2.9% on May 10th, 2010.

Additionally, note that nancial rms experience a larger stock return increase equal

to 6.4%, as compared to 2% for non-nancials. Notably all markets except Germany

experience an unequivocal increase in stock returns for that day.

Quite remarkably, the magnitude of contemporaneous stock returns and sovereign

spread changes around the EFSF announcement is in line with our panel date es-

timates in Column 10 of Table 3. More specically, if we assume no change in

(short-term) earnings forecasts from May 7th to May 10th, we are able to computean imputed change in earnings yield dierentials associated with the stock returns in

Table 9. We can correlate such imputed change with the contemporaneous change

in the square root of log spreads.

Figure 6 shows the outcome of this exercise. On the x-axis we plot the change in

square root log spread for each Eurozone country from May 7th to May 10th. On the

y-axis we plot the correspondent (imputed) change om earnings yield dierentials,

assuming no change in short-term earnings forecasts. The slope of the best t line

i l t 0 057 Thi l i k bl l t th 0 046 l i C l 10 f


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mostly by variation in spreads rather than by variations of a third, omitted variable.

Alpha Bank and EFG Eurobank merger announcement

Alpha Bank and EFG Eurobank are respectively the second and third largest

banks in Greece. On August 29th, 2011 an almost overnight merger agreement was

reached between these two banks. Of the two banks Alpha bank was in considerably

better shape as compared to EFG Eurobank and a merger between them would

make the banking sector in Greece more resilient. Importantly, the merger was

also associated with a capital injection of $ 500 million from Qatar into the merged

bank. Remarkably, the overall Greek stock market went up by 12.5% on August

29th, 2011, upon the merger announcement. The one-day stock market performance


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in a single day adjusting for contemporaneous returns in other industries across the

Eurozone. Non-nancial rms went up by 8%. On the other hand, the 10-year

CDS spread of Greece decreased by just 12 basis points, an statistically insignicant

outcome (t-stat=0.21).

This event study indicates that stock valuation levels are depressed in Greece

due to a potential banking crises resulting from a sovereign debt crisis. The sud-

den increase in the perceived resilience of the banking sector reduces the expected

magnitude of the banking crisis, even though it does not change the probability of

sovereign default.

6 Conclusion

This paper documents a striking 2007-2011 reversal in the long term trend towards

equity market integration in Europe. This equity market disintegration is on account

of three mutually reinforcing crises in the Eurozone: a growth/competitiveness crisis,

a sovereign debt crisis, and a banking crisis. The root cause for this triple crisis isintroduction of a single currency in a region not ripe for it. Paradoxically, while

conceived in part to further integrate European nancial markets, the monetary

union set the stage for a sharp reversal of existing equity market integration.

Our empirical analysis provides several results consistent with the Euro triple-

crises narrative for equity market disintegration in Europe. To that end, we use

sovereign debt spreads as summary statistics for time-series and cross-sectional vari-

ation in the severity of the triple crises. First, we nd that equity valuation levels

( )


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sovereign spreads, valuations of nancial rms decrease by much more than valua-

tions of non-Financial rms. We provide further evidence by focusing on a sample

of European banks for whom we have sovereign debt holdings data. We show that

banks holding more distressed sovereign debt experience larger decreases in valuation

levels associated with increases in sovereign spreads, wich demonstrate a direct link

between the Euros sovereign debt crisis and equity valuations.

Third, focusing on Eurozone non-nancial rms, and nd that rms that are

more vulnerable to credit market disruptions (younger rms, rms with higher debt

rollover needs, and rms with less tangible assets) experience larger decreases in

valuation levels for a given increase in sovereign spreads. Fourth, we show that

Eurozones periphery non-nancial rms that are likely to benet from an exchangerate depreciation associated with a potential Euro exit (rms with high fraction

of exports and low fraction of imports of intermediate inputs) actually experience

valuation levels increasesassociated with sovereign spread increases.

Finally, we provide two event studies that highlight the importance of sovereign

debt and banking crises for the growing dispersion of valuation levels in the Europe.

These event studies focus on sudden improvements in sovereign debt nancing con-

ditions and in banking sector resilience. To the extent that other determinants of

equity market valuation levels (e.g., high levels of public debt) are xed in the very

short window around these sudden improvements, they help us establish a causal

eect going from sovereign and banking crises to equity valuations.


40/59

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Panel B: Industry Classification


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47

Industry Eurozone Comparables Industry Eurozone Comparables

Aerospace & Defense 9 16 Industrial Metals & Mining 23 7

Alternative Energy 18 5 Industrial Transportation 33 35

Automobiles & Parts 34 7 Leis ure Goods 20 9

Banks 54 45 Life Insurance 8 10

Beverages 8 6 Media 78 66

Chemicals 37 21 Mining 4 27

Construction & Materials 72 51 Mobile Telecommunications 9 7

Electricity 27 14 Nonlife Insurance 21 26

Electronic & Electrical Equipment 53 46 Oil & Gas Producers 12 20

Financial Services 57 75 Oil Equipment, Services & Distribution 6 18

Fixed Line Telecommunications 13 9 Personal Goods 32 13

Food & Drug Retailers 13 9 Pharmaceuticals & Biotechnology 29 40

Food Producers 23 33 Real Estate Investment & Services 43 50

Forestry & Paper 14 8 Real Estate Investment Trusts 34 15

Gas, Water & Multiutilities 20 8 Software & Computer Services 139 113

General Industrials 25 17 Support Services 51 130

General Retailers 43 60 Technology Hardware & Equipment 51 37

Health Care Equipment & Services 40 36 Tobacco 2 2

Household Goods & Home Construction 32 31 Travel & Leisure 42 62

Industrial Engineering 88 68 Average 31 25

Table II


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48

Summary Statistics on Country level Characteristics: 2005-2011

The table reports summary statistics for country level earnings yields, sovereign bond spreads and other country level characteristics. The earnings yield

is firm-level earnings yield aggregated up to the country level by value-weighting. The comparable earnings yield uses the value-weighted average ofeach industry in the comparables sample. The sample consists of monthly observations from 2005-2011. Country level earnings yield differences

measures the earnings yield of the Eurozone countries relative the non-Eurozone European countries. Sovereign spreads are measured by the 10 year

CDS spreads denominated in Euros. The difference slope yield curve is the difference the slope of the yield curve of the Eurozone and non-Eurozone

European countries. Unit labor costs are measured as the ratio of the compensation per employee and labor productivity. The government debt is

measured as total government debt scaled by GDP at constant prices. Both unit labor cost and government debt data are collected from Eurostat. Panel

A reports summary statistics and panel B reports correlations among the different variables.

Panel A: Summary Statistics

Panel B: Correlations

Earnings Yield

(Eurozone)

Comparable

Earnings Yield

Country Level

Earnings Yield

Difference Spread Log Spread

Square

Root Log

Spread

Difference

Slope Yield

Curve

Unit Labor

Costs

Government

Debt

Mean 0.093 0.091 0.002 0.0107 0.010 0.073 1.058 104.8 73.33

Median 0.088 0.087 0.0001 0.0029 0.003 0.054 1.363 105.9 68.10

Std Dev 0.022 0.016 0.013 0.0355 0.029 0.069 0.788 9.7 27.77

N 924 924 924 924 924 924 924 891 924

Earnings Yield

(Eurozone)

Comparable

Earnings Yield

Country Level

Earnings Yield

Difference Spread Log Spread

Square

Root Log

Spread

Difference

Slope Yield

Curve

Unit Labor

Costs

Government

Debt

Earnings Yield (Eurozone) 1

Comparable Earnings Yield 0.83 1

Country Level Earnings Yield 0.70 0.18 1

Spread 0.38 0.14 0.48 1

Log Spread 0.40 0.16 0.49 0.99 1

Square Root Log Spread 0.53 0.32 0.53 0.83 0.87 1

Difference Slope Yield Curve -0.43 -0.25 -0.43 -0.33 -0.36 -0.66 1

Unit Labor Costs 0.17 0.08 0.19 0.14 0.15 0.23 0.01 1

Government Debt 0.15 0.19 0.01 0.41 0.44 0.55 -0.32 -0.08 1

Table III


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49

Are earnings yield di fferences correlated with sovereign spreads from 2005-2011 in the Eurozone?

The table reports regressions of monthly country- level earnings yield differences on sovereign debt spreads. Country level earnings yield differences

measures the earnings yield of the Eurozone countries relative the non-Eurozone European countries. Sovereign spreads are measured by the 10 yearCDS spreads denominated in Euros. Column (1) reports OLS regression of the log spread on country level earnings differences. Column (2) includes the

squared term of the log spread along with the log spread in the regression. In column (3) we report regression results using the square root of the log

spread. In column (4) we include the square root of the log spread and country fixed effects and in column (5) we include the difference of the slope yield

curve as an additional explanatory variable. In column (6) we include the square root of the log spread and time fixed effects and in column (7) we

include country and time fixed effects together and lastly in column (8) we additionally include controls for overall government debt and unit labor costs.

In column (9) we report results where the earnings yields for each of the Eurozone firms are matched to Non-Eurozone European firms based on an

industry and leverage quartile match. In column (10) we transform the square root of the log spread and earnings yield differences into their first

differences. All standard errors are Driscoll-Kraay standard errors adjusted for 12 lags and are reported in parentheses below the coefficients. In column

(7) we additionally report standard errors clustered by country and time. ***, **,* represent significance at the 1%, 5% and 10% respectively.

Dependent Variable:

Log Spread 0.207 *** 0.316 ***

(0.027) (0.071)

Log Spread Squared -0.346 *(0.170)

Square Root Log Spread 0.095 *** 0.108 *** 0.096 *** 0.070 *** 0.089 *** 0.112 *** 0.097 ***

(0.007) (0.006) (0.009) (0.012) (0.011) (0.014) (0.013)

Difference Slope Yield Curve -0.001 *

(0.001)

Government Debt (%) -0.020 **

(0.006)

Unit Labor Costs (%) 0.060 **

(0.019)

Square Root Log Spread 0.046 **

(0.017)

Country Fixed Effects N N N Y Y N Y Y Y Y

Time Fixed Effects N N N N N Y Y Y Y Y

N 924 924 924 924 924 924 924 891 924 913

R2 0.243 0.262 0.279 0.543 0.546 0.326 0.578 0.582 0.592 0.163

0.089 **

(0.035)

2-way clustering

(country & time)

Country Level

Earnings Yield

Difference

Country Level Earnings Yield Difference

Leverage-Matched

Country Level

Earnings Yield

Difference

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

Table IV


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50

Summary Statistics on Firm level Characteristics: 2005-2011

The table reports summary statistics for leverage-matched firm level earnings yields, sovereign bond spreads and other firm level characteristics. The

sample consists of monthly observations from 2005-2011. Sovereign spreads are measured by the 10 year CDS spreads denominated in Euros. Panel Areports summary statistics for all firms in the sample and panel B reports summary statistics for non-financials.

Panel A: All fi rms

Panel B: Non-financials

Leverage-

matched

earnings yieldsdifferences Log Assets Profitability

LogSpread Beta

Mean 0.006 13.64 0.102 0.059 1.005

Median -0.001 13.39 0.099 0.049 0.949

Std Dev 0.059 2.25 0.093 0.049 0.949

N 110586 110586 108837 110586 96495

Leverage-

matched

earnings yields

differences Log Assets Profitability Leverage Tangibility Age

Foreign

Sales (%)

Intermediate

input

imports

Mean 0.005 13.29 0.113 0.104 0.234 47 49.07 0.234

Median -0.001 13.11 0.109 0.137 0.184 28 50.84 0.236

Std Dev 0.055 2.00 0.091 0.272 0.194 45.510 28.25 0.129

N 92268 92268 91600 92268 92215 72935 70733 91413

Table V

A i i ld di ff f Fi i l ff t d b i d th N Fi i l ?


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Are earnings yield di fferences of Financials more affected by sovereign spreads than Non-Financials?

The table reports regressions of monthly firm level leverage matched earnings yield differences on sovereign debt spreads. The earnings yield

differences are based on a leverage and industry match of firms from the Eurozone sample with the non-Eurozone European sample. Sovereign spreadsare measured by the 10 year CDS spreads denominated in Euros. Column (1) reports OLS regression of the log spread on firm level earnings

differences. Column (2) includes the squared term of the log spread in the regression. In column (3) we report results for the square root of log spread. In

column (4) we include an interaction of the spread with a financial sector dummy. In column (5) we report regression we include an interaction of the

spread with firm level betas. All regression specifications include controls for assets, profitability firm and time fixed effects. Standard errors are clustered

at the firm level and are reported in parentheses below the coefficients. ***, **,* represent significance at the 1%, 5% and 10% respectively.

Dependent Variable:

Log Spread 0.233 *** 0.531 ***

(0.043) (0.089)

Log Spread Squared -0.830 ***

(0.205)

Square Root Log Spread 0.174 *** 0.151 *** 0.122 ***

(0.025) (0.025) (0.039)

Square Root Log Spread * Financial Sector 0.131 *** 0.142 ***

(0.041) (0.041)

Square Root Log Spread * Beta 0.014

(0.031)

Log Assets 0.003 0.003 0.003 0.003 0.002

(0.003) (0.003) (0.003) (0.003) (0.003)

Profitability 0.002 0.004 0.004 0.005 0.005

(0.009) (0.009) (0.009) (0.009) (0.010)

Firm Fixed Effects Y Y Y Y Y

Time Fixed Effects Y Y Y Y Y

N 108837 108837 108837 108837 95012

R2 0.529 0.530 0.531 0.532 0.522

Leverage-matched Firm Level Earnings Yield Difference

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


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Table VII

Are earnings yield di fferences of fi rms with higher ref inancing needs or financially const rained more affected by sovereign spreads?


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Are earnings yield di fferences of fi rms with higher ref inancing needs or financially const rained more affected by sovereign spreads?

The table reports regressions of monthly firm level leverage matched earnings yield differences on sovereign debt spreads and firm characteristics for a

sample of non-financial firms. Column (1) reports OLS regression of the log spread and its interaction with the high leverage dummy. Column (2) addsthe interaction of the log spread with the low tangibility dummy. In column (3) we report regression results where we interact log spread with the low age

dummy. In column (4) we include the interaction of the spread with low tangibility and high leverage together. In column (5) we include the interaction of

the spread with low age and leverage high together. All regression specifications include controls for assets, profitability firm and time fixed effects.

Standard errors are clustered at the firm level and are reported in parentheses below the coefficients. ***, **,* represent significance at the 1%, 5% and

10% respectively.

Square Root Log Spread 0.084 *** 0.143 *** 0.121 *** 0.039 0.042

(0.026) (0.026) (0.031) (0.026) (0.036)

Square Root Log Spread * High leverage 0.100 *** 0.126 *** 0.097 ***

(0.024) (0.025) (0.029)

High leverage -0.002 -0.005 ** -0.001

(0.002) (0.002) (0.003)

Square Root Log Spread * Low tangibility 0.119 *** 0.140 ***

(0.027) (0.029)

Low tangibility -0.008 *** -0.011 ***

(0.003) (0.003)

High leverage * Low tangibility 0.002

(0.003)

Square Root Log Spread * Low age 0.067 ** 0.076 **

(0.032) (0.033)

Low age -0.003 -0.003

(0.005) (0.005)

High leverage * Low age -0.001

(0.003)

Log Assets 0.001 0.0003 -0.0003 0.001 -0.001

(0.003) (0.003) (0.004) (0.003) (0.004)

Profitability 0.019 * 0.017 * 0.016 0.019 ** 0.019 *

(0.009) (0.009) (0.011) (0.009) (0.011)

Firm Fixed Effects Y Y Y Y Y

Time Fixed Effects Y Y Y Y Y

N 91564 91600 71465 91564 71436

R2 0.544 0.545 0.537 0.547 0.538

Leverage &

Tangibility

(4)

Leverage &

Firm Age

(5)

Leverage Tangibility Firm Age

(1) (2) (3)

Table VIII

Are earnings yields of f irms with high foreign sales and low imports of intermediate inputs less affected by sovereign spreads?


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Are earnings yields of f irms with high foreign sales and low imports of intermediate inputs less af fected by sovereign spreads?

The table reports regressions of monthly firm level leverage matched earnings yield differences on sovereign debt spreads and firm characteristics for a

sample of non-financial firms. Column (1) reports OLS regression of the log spread with the high sales dummy and the dummy for low intermediate

inputs imports. In column (2) and column (3) we break up the sample into GIIPS and non-GIIPS countries respectively. All regression specifications

include controls for assets, profitability firm and time fixed effects. Standard errors are clustered at the firm level and are reported in parentheses below

the coefficients. ***, **,* represent significance at the 1%, 5% and 10% respectively.

Dependent Variable:

Square Root Log Spread 0.177 *** 0.122 *** 0.235 ***

(0.034) (0.046) (0.063)

Square Root Log Spread * High Foreign Sales * Low int. input imports -0.118 *** -0.184 *** -0.037

(0.029) (0.053) (0.042)

High Foreign Sales * Low int. input imports 0.003 0.004 0.000

(0.003) (0.007) (0.003)

Log Assets 0.000 0.001 -0.001

(0.004) (0.009) (0.004)

Profitability 0.029 *** 0.019 0.031 ***

(0.011) (0.032) (0.012)

Firm Fixed Effects Y Y Y

Time Fixed Effects Y Y Y

N 67281 16409 50872

R2 0.580 0.614 0.566

(2) (3)

Leverage-matched Firm Level

Earnings Yield Difference

(1) GIIPS Non-GIIPS

Table IX

Event Studies


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Event Studies

Panel A reports event study results for the announcement of the ESFS creation in on May 10, 2010 and Panel B reports event study results for the Greek

bank merger on 29th August 2011. We report the difference between returns in the Eurozone and comparable results in the Non-Euro European

countries, adjusted for industry composition (ICB Level 4 classification). In parentheses below the coefficients we report the industry-adjusted return

differential on the event day divided by volatility of daily industry-adjusted return differentials in the previous year. . ***, **,* represent significance at the

1%, 5% and 10% respectively.

Panel A: May 10th

2010: EFSF creation

May 7 May 10

Austria 88 74 -13 *** 0.020 ** 0.031 ** 0.012

(-4.10) (2.35) (2.54) (1.44)

Belgium 111 90 -20 *** 0.031 *** 0.099 *** 0.015 **

(-7.15) (3.97) (5.83) (1.98)

Finland 33 32 -1 0.023 ** 0.045 *** 0.020 **

(-0.82) (2.51) (4.07) (2.09)

France 83 71 -12 *** 0.033 *** 0.083 *** 0.022 ***

(-5.43) (5.90) (8.85) (3.95)

Germany 63 55 -8 *** -0.017 ** -0.014 -0.017 **

(-4.73) (-2.29) (-1.52) (-2.24)

Greece 863 493 -370 *** 0.022 0.038 0.011

(-20.48) (1.16) (1.33) (0.80)

Ireland 243 176 -68 *** 0.027 *** 0.097 ** 0.021 **

(-10.57) (2.69) (2.01) (2.29)

Italy 219 151 -68 *** 0.037 *** 0.066 *** 0.024 ***

(-13.02) (5.48) (6.49) (3.68)

Netherlands 58 49 -9 *** 0.018 *** 0.110 *** 0.002

(-3.33) (2.94) (6.09) (0.29)

Portugal 394 233 -161 *** 0.052 *** 0.036 ** 0.055 ***

(-15.13) (5.76) (2.23) (6.43)

Spain 230 165 -65 *** 0.069 *** 0.107 *** 0.051 ***

(-9.91) (-9.14) (8.29) (7.79)

Ave ra ge 217 144 -72 0.029 0.064 0.020

( May10- May7)

Industry adjusted stock returns differentials

Financials Non-Financials

10-y CDS spread (in bps)

All

Panel B: Aug 29th

2011: Alpha Bank and EFG Eurobank merger


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Aug 26 Aug 29

Greece 2239 2227 12 0.125 *** 0.223 *** 0.080 ***

(0.21) (7.61) (8.29) (6.14)

10-y CDS spread (in bps) Industry adjusted stock returns differentials

( Aug 29- Aug 26) All Financials Non-Financials


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AppendixTable:VariableDescriptionLow intermediate inputs of imports Dummy variable that takes a value 1 if the Import Content of Exports for a firm is

hi h th th di I t C t t f E t f th fi i th t

OECDstat Extracts One cross-

ti i


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higher than the median Import Content of Exports of other firms in the same country

and 0 otherwise.

section using

Mid 2000sdata.

Spread-weighted sovereign debtholdings

Change of spread-weighted sovereign debt holdings (scaled by Bank Tier 1 capital)

from 2010 to 2011.

EBA 2010 and2011 Stress Tests,Markit, and

Datastream

One cross-section using2010 and 2011

data

Industry-adjusted return difference Difference between value-weight stock return in the Eurozone country and the value-

weighted return of Non-Euro European countries, adjusted by industry composition.

Datastream Event studies

Andrade Chhaochharia Europe June2012

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Transcript of Andrade Chhaochharia Europe June2012