state ownership, political risk, and asset prices

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State Ownership, Political Risk, and Asset Prices Andrea Beltratti, Bernardo Bortolotti and Valentina Milella Milan, 26 January 2007

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State Ownership, Political Risk, and Asset Prices. Andrea Beltratti, Bernardo Bortolotti and Valentina Milella Milan, 26 January 2007. Motivation. The government plays a fundamental role in most countries. It enjoys broad discretionary powers to tax, spend and regulate economic activity. - PowerPoint PPT Presentation

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State Ownership, Political Risk, and Asset PricesAndrea Beltratti, Bernardo Bortolotti and Valentina MilellaMilan, 26 January 2007

2

Motivation

The government plays a fundamental role in most countries. It enjoys broad discretionary powers to tax, spend and regulate economic activity.

However, the government may also hurt economic agents when new legislation is enacted to cater specific constituencies, when contracts are reneged, and when politicians interfere in the operating activity of firms.

This is Political Risk: the risk that an unexpected policy change affects the returns of a given asset.

3

Motivation

Large literature on political risk in emerging markets:

Political risk is a priced factor affecting expected returns and international market integration (Erb, Harvey and Viskanta 1996; Diamonte, Liew and Stevens 1996; Bekaert 1995).

Privatization resolution of political risk excess returns and domestic financial development (Perotti and van Ojien 2001; Perotti and Hiubers 1998).

To our knowledge, no previous study on the financial effects of political risk in developed countries. Nevertheless, political risk matters also in major stock markets and the associated losses may be quite large.

4

Examples

France: – February 2006: Enel bid on Suez blocked by the French government through the intervention of GdF

as white knight. Italy:

– December 2005: Bankitalia blocked the two takeovers attempts by Abn Amro and Bbva over Banca Antonveneta and BNL, respectively. Finally BNL was acquired by French BNP and Abn Amro succeeded.

– August 2006: Italian government denies approval to proposed merger between Autostrade-Abertis. Luxembourg:

– January 2006: Luxembourg together with France attempted to block Mittal from a takeover of Arcelor. Spain:

– February 2006: the Spanish government ousts the hostile takeover on Endesa by E.ON via Gas Natural. Nevertheless E.ON succeeds and the deal will be concluded on April 2007

Out of sample:– Venezuela: re-nationalization of the energy sector– USA: The United States rebuffs China over an attempt by the Chinese National Offshore Oil

Corporation (CNOOC) to take over Unocal; Dubai Ports World fails in its bid to acquire port operations in the US

– Poland: March 2006: Poland demands Unicredit to divest the Polish holdings of German HVB which had already acquired in a merger previously approved by EU regulators.

5

Research Questions

Likely, a political risk premium associated with government intervention exists also in developed economies. But,

How can we measure it? How large is it?

The central hypothesis:

Privatized companies (PC) - such as Suez - are particularly sensitive to political risk. Thus a price measure for political risk can be constructed using the returns of PC

6

Why are PC Sensitive to Political Risk?

Privatized Companies

Are typically large firms with a broad clientele.

Provide services of general interest.

Often manage strategic national infrastructures.

Are often used as policy tools to raise fiscal revenues, absorb unemployment, please costumers with affordable tariffs and universal services, and preserve national security in strategic supply.

The pricing of the shares of PC can also be designed to achieve key political objectives, notably re-election.

7

Theory

The extent to which PC are exposed to political risk depends upon the credibility of the institutional setting. Yet idiosyncratic factors such as the residual state ownership in the firm are key.

By keeping a stake, a market-oriented government can credibly signal its willingness not to interfere in the operating activity in the firm because it would suffer a loss. Then fully privatized firms should be more risky than companies where governments keep a residual stake, and as such they should yield a higher expected return (Perotti,1995).

8

Research Design

1. We track the evolution of government control rights (GCR) in firms privatized in EU15 countries from 1977 to Feb-2005.

2. We study the long-run performance of several portfolios built on different quartiles of GCR for the 1995-2005 period.

3. We test the effect of residual state ownership and control on expected returns of privatized companies.

4. Finally, we test the role of political risk in the excess returns of European equity markets.

9

The Sample of Privatized Companies

European privatized companies through public offers of shares in EU15 equity markets between 1977 and February 2005.

From 1977 to 2005 = 1,177 EU15 privatizations worth US$708bn equal to ½ of global revenues.

EU15 Share Issue Privatizations (SIPs) involved 220 companies and raised 70% of EU15 total privatization value (US$499bn).

10

Sampling Rules

Political risk spills over in M&A. In case of M&A activity we include in the sample the resulting company (in case of a merger) or the acquiror (in the case of a tender offer or an acquisition). We follow this rule only if the acquiring companies are listed in one or more EU15 stock markets and if the acquiror market capitalization is not more than double of the target company.

Political risk does not last forever. We exclude from the sample the companies turning 5 years after their full privatization (where we define a fully privatized company a former SOE in which the government does not hold any ultimate control right neither in terms of residual stakes, nor golden shares or special powers).

After this screening we end up with a final sample of 190 stocks

11

Measuring Government Control Rights in PC

We carry out a comprehensive analysis of the structure and evolution of GCR over the 10 years period 1994-2004 (GCR at the end of year t-1 are used to build portfolios for year t) for the 220 privatized companies through public offers of shares.

GCR are measured using the “weakest link concept” as in La Porta et al. (1999), Faccio and Lang (2002), Bortolotti and Faccio (2004).

For example if:

Then we posit that the government owns 25% of Firm B.

Government

50%

Firm A

25%

Firm B

12

Measuring GCR in PC

The % is determined by the minimum along the control chain; in case of multiple chains control rights are given by the sum of the minimum values of all control chains.

This methodology allows to take fully into account for pyramidal structures and cross-holdings.

13

Some Examples: Portugal Telecom

The evolution of the government control rights in Portugal Telecom (Portugal)

Portugal Telecom

Portuguese Government

100%

5%

BES

9.7%

BPI

2.5%

4.05%

1.8%

2.1 %

Caixa Geral de Depositos (CGD)

Portugal Telecom

Portuguese Government

100%

As of the end of 1994 As of the end of 2004

GCR 100% GCR 19%

14

Some Examples: Distrigaz

The evolution of the government control rights in Distrigaz (Belgium)

As of the end of 1994 As of the end of 2004

French State

Caisse des Dépôts et Consignations

(CDC)

SUEZ

3.1%

Tractebel

100%

Belgian Government

100%

Belgian Municipalities

100%

Publigaz Distrihold

50%

Société Générale de Belgique

61%

50.4%

Distrigaz

16.62% 16.75%

50%

57.53%

French State

Caisse des Dépôts et Consignations

(CDC)

SUEZ

3.1%

SUEZ/TRACTEBEL

46.77%

50%

Areva

CEA 100%

ERAP

3.21%

78.96%

Cogema 3.59%

100%

5.19%

100%

100%

100%

EdF 3.21%

AGF

1.7%

CréditAgricole

100%

3.4%

2.6%

CNPAssurances

1.6%

36.49%

Belgian Government

100%

2.2%

1.17%

Distrigaz

Publigaz

20.78% 20.94%

Distrihold50%

100%

Belgian Municipalities

GCR % 39.57 GCR 78.86%

15

The Evolution of GCR in PC

The evolution of GCR during the 1994-2004 period shows that the privatization process in EU15 has been at least partial and incomplete.

0%

5%

10%

15%

20%

25%

30%

35%

40%

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

0

20

40

60

80

100

120

140

Mean of Total Control Rights in Privatized Companies

Mean of Total Ownership Rights in Privatized Companies

Number of Privatized Companies

No. of PC increased, mean government ownership and control rights did not decrease.

16

The Distribution of GCR in PC

Government Control Rights (GCR)

Year Number of Companies

Mean Median Q1 Q4

1994 76 0.3195 0.2325 0.0000 0.5276

1995 85 0.3758 0.3140 0.0000 0.6368

1996 95 0.3095 0.2040 0.0000 0.5325

1997 101 0.2909 0.2010 0.0000 0.5194

1998 108 0.3033 0.2366 0.0023 0.5204

1999 121 0.3271 0.2800 0.0208 0.5350

2000 127 0.3187 0.3380 0.0285 0.5271

2001 128 0.3455 0.3425 0.0560 0.5517

2002 123 0.3451 0.3234 0.0611 0.5581

2003 123 0.3541 0.3404 0.0529 0.5564

2004 126 0.3379 0.3043 0.0643 0.5126

As of 2004,

½ of the companies of our sample have the government as influential shareholder with a 30% stake. ¼ of the firms are under government majority control.

Governments are reluctant to relinquish control over privatized firms.

17

Forming Portfolios Based on GCR

The data on the evolution of GCR in PC are used to form portfolios capturing a different degree of residual state ownership as a proxy for political risk:

1st we construct a broad Composite Portfolio (all 190 stocks).

2nd we form two portfolios using the bottom and top quartiles of the distribution of GCR as breakpoints (GCRQ1 and GCRQ4).

3rd we calculate monthly value-weighted returns on these portfolios from the end of year t-1 to the end of year t using Datastream series for the 1995-2005 period. Stock prices are expressed in €.

18

Performance of Portfolios Based on GCR

0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

800Fe

b-9

5

Aug

-95

Feb-

96

Aug

-96

Feb-

97

Aug

-97

Feb-

98

Aug

-98

Feb-

99

Aug

-99

Feb-

00

Aug

-00

Feb-

01

Aug

-01

Feb-

02

Aug

-02

Feb-

03

Aug

-03

Feb-

04

Aug

-04

Feb-

05

Composite CGRQ1 CGRQ4 DJ Stoxx TMI

GCRQ1 €100 in Feb-95 would have grown to €420 in Feb-05GCRQ4 €100 in Feb-95 would have grown to €249 in Feb-05

19

Government Control Rights Portfolios Returns

Composite GCRQ1 GCRQ4 DJ TMI

R 1.01% 1.18% 0.75% 0.81%

σ 5.45% 4.80% 8.09% 4.74%

β 1.0752 0.9042 1.2821 1.0000

AR 12.76% 15.05% 9.36% 10.19%

Sharpe Ratio 0.55542 0.72975 0.33872 0.47143

The portfolio based on the lower GCR (GCRQ1) yield raw (R), annualized (AR), and risk-adjusted returns (Sharpe Ratio) higher than all other portfolios, and particularly GCRQ4.

GCRQ1 seems also to strongly outperform the Benchmark (DJ TMI)

20

Government Control Rights Portfolios Returns

We try to explain these differences in stock returns by using the conventional 3-factor Fama and French (1993) model where excess returns are explained by:

Market returns. Size (proxied by the return of small minus big

stock capitalization portfolios, SMB). Value (given by the high minus low book-to-market

portfolios, HML).

21

The Building Blocks

Starting from the constituents of the EU15 benchmark DJ TMI we constructed SIX PORTFOLIOS:

The median DJ TMI market capitalization is used to form 2 size groups, SMALL (S) and BIG (B).

The bottom 30%, the middle 40%, and the top 30% of book-to-market values are used as breakpoints to split the DJ TMI into 3 value groups, Low (Growth), Medium (Neutral), and High (Value)

From the intersection of the 2 size and the 3 value groups we construct the six building blocks that we define:

GrowthNeutralValue

SMALL (BIG)

22

The Building Blocks

Finally we used these six portfolios returns to form the SMB and HML factors:

SMB = [average (1,2,3) – average (4,5,6)] HML = [average (3,6) – average (1,4)]

654B

321S

HML

654B

321S

HML

23

Size-Value Portfolio Returns

EU data15 are consistent with US data:

controlling for Value on average Small Portfolios yield higher returns.

controlling for Size Value Portfolios returns tend to increase, especially in Big Portfolios.

Small Value

Small Neutral

Small Growth

Big ValueBig Neutral

Big Growth

SMB HML

R 1.27% 0.89% 1.00% 1.16% 1.07% 0.56% 0.09% 0.31%

σ 5.24% 5.18% 5.77% 5.50% 5.01% 4.81% 1.87% 2.93%

β 0.9488 0.9838 1.0856 1.0713 1.0110 0.95479 -0.0063 -0.0101

AR 16.35% 11.22% 12.68% 14.84% 13.62% 6.93% 1.09% 3.78%

Sharpe Ratio 0.75190 0.48790 0.53100 0.60910 0.59530 0.33650 -0.23790 0.11400

24

Performance Attribution Regressions and Results

α β SMB HML Adj. R2

Composite 0.0024 1.0745 a -0.1817 c -0.0696 87.59%

[1.32] [29.00] [-1.92] [-1.15]

GCRQ1 0.0037 c 0.9061 a -0.1267 0.1967 b 80.73%

[1.9] [22.30] [-1.22] [2.97]

GCRQ4 0.0025 1.2758 a -0.5076 b -0.5435 a 62.43%

[0.54] [13.18] [-2.05] [-3.44]

Market, size and value factors matter in explaining GCR portfolio returns.

Composite and GCRQ4 do not outperform. Interestingly, the estimated Jensen α for GCRQ1 yields

4,44 percent excess returns on a yearly basis. Thus companies privatized more fully outperform partly

privatized companies. We interpret this abnormal return as a premium for

political risk.

25

Testing Asset Pricing Model in EU15

The final question:

Is political risk a new common and un-diversifiable risk factor affecting the returns of EU15 markets?

In this direction:

We build 25 size-value portfolios using the constituents of DJ TMI and estimate the CAPM and its multifactor generalizations, including the GCRQ1 portfolio returns as a factor mimicking political risk.

The existence of a political risk beta would suggest that political risk is a new priced factor suitably captured by the returns of (fully) privatized firms.

26

The 25 Portfolios

The 25 portfolios are built much like the six portfolios:

In each year t-1 we sort DJ TMI stocks by end year market capitalization and book-to-market.

We use DJ TMI breakpoints for size and value to allocate stocks to 5 size-value quintiles.

From the intersection of the size-value quintiles we construct the 25 portfolios.

Finally we calculate value-weighted monthly returns on these portfolios from end year t-1 to the end of year t.

27

Risk and Return Properties of the 25 Portfolios

Sizequintile

Book-to-Market Equity (BE/ME) quintiles

Low 2 3 4 High

Means

Small 1.090% 1.015% 1.299% 0.781% 1.725%

2 0.774% 0.819% 0.755% 0.811% 1.505%

3 0.901% 0.822% 0.637% 0.784% 1.434%

4 0.444% 0.633% 0.541% 0.782% 1.438%

Big 0.474% 0.664% 0.880% 1.271% 0.640%

Standard Deviations

Small 6.713% 6.795% 6.384% 5.342% 5.372%

2 5.702% 5.070% 4.980% 5.304% 6.888%

3 6.888% 4.866% 4.946% 4.675% 5.261%

4 6.658% 4.313% 4.544% 4.732% 5.454%

Big 5.054% 4.891% 5.411% 6.014% 7.272%

Size ↑ Returns ↓; Value ↑ Returns ↑

Excess Returns on 25 Stock Portfolios

Formed on Size and Book-to-Market Equity

28

Econometric Tests

We estimate different asset pricing models and test the null of zero intercepts in time-series and cross-section tests:

F test under normality assumptions.

Chi-square tests: “general” and “specific”, with different assumptions on correlation and heteroskedasticity of errors.

Classical Fama-MacBeth (1973) approach, estimating average unconditional risk premia and zero intercepts in cross-section tests.

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Empirical Results

We strongly reject the CAPM both in time series and cross-section tests.

The Fama-French model is not rejected in cross-section, but is rejected in time series tests.

Similar results are obtained when factor loadings for sectors are included, with 4 significant sector premium.

When the factor loading for political risk is included, the model is still rejected in time series test. But interestingly, we find an average premium for political risk, while sector premia disappear.

Overall, we find a strong evidence supporting the value premium.

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Testing Factor Models

[1] [2] [3] [4]

α 0.021 -0.000 -0.016 -0.006[2.218] [-0.008] [-1.445] [-0.513]

β -0.018 0.000 0.017 0.007[-1.734] [0.024] [1.406] [0.543]

SMB

0.004 0.004 0.003

[1.605] [1.669] [1.148]

HML

0.012 0.014 0.015

[3.231] [3.476] [3.690]

Basic Materials

0.018 0.007

[1.284] [0.524]

Consumer Goods

0.032 0.020

[2.339] [1.446]

Consumer Services

0.023 0.012

[2.043] [1.161]

Financials

0.029 0.019

[2.031] [1.282]

Health Care

0.009 0.004

[0.685] [0.261]

Industrials

0.023 0.016

[2.094] [1.536]

Oil & Gas

0.030 0.026

[1.595] [1.424]

Technology

0.022 0.011

[1.661] [0.756]

Telecommunication

0.012 0.002

[0.812] [0.155]

Utilities

0.031 0.021

[1.534] [1.030]

GCRQ1

0.024

[1.852]

General test χ 2

Specific Test χ 2

56.50[0.000]

53.83

58.25[0.000]

54.43

112.80[0.000]

67.45

52.00[0.000]

65.71

[0.001] [0.001] [0.000] [0.000]

F1.691 1.673 1.851 1.781

[0.037] [0.040] [0.020] [0.027]

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Conclusions

We have linked the evolution of control rights in privatized firms in EU15 to their financial performance

We have found that companies privatized more fully require a significant excess return likely to compensate exposure to political risk. Thus this portfolio returns can be used as price measure for political risk.

The new aggregate risk factor related to political interference seems useful to explain the required returns of European stocks, even if pricing risk in the EU15 remains a challenging task.

Policy implications: curbing political risk by improving the commitment capabilities of the institutional setting may boost market value, and contribute to accomplish full (and sustainable) privatization.

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