Directory UMM :Data Elmu:jurnal:J-a:Journal Of Banking And Finance:Vol24.Issue10.2000:
Journal of Banking & Finance 24 (2000) 1575±1604
www.elsevier.com/locate/econbase
Political elections and the resolution of
uncertainty: The international evidence
Christos Pantzalis a,*, David A. Stangeland
Harry J. Turtle c,2
b,1
,
a
College of Business Administration, University of South Florida, Tampa, FL 33620, USA
Faculty of Management, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V4
College of Business and Economics, Washington State University, Pullman, WA 99164-4746, USA
b
c
Received 2 October 1997; accepted 26 July 1999
Abstract
We investigate the behavior of stock market indices across 33 countries around
political election dates during the sample period 1974±1995. We ®nd a positive abnormal return during the two-week period prior to the election week. The positive reaction of the stock market to elections is shown to be a function of a countryÕs degree of
political, economic and press freedom, and a function of the election timing and the
success of the incumbent in being re-elected. In particular, we ®nd strong positive abnormal returns leading up to the elections (i) in less free countries won by the opposition, and (ii) called early and lost by the incumbent government. These results are
consistent with the uncertain information hypothesis (UIH) of Brown et al. (Brown,
K.C., Harlow, W.V., Tinic, S.M., 1988. Journal of Financial Economics 22, 355±385)
and the model of election behavior of Harrington (Harrington, J.E., 1993. The
American Economic Review 83, 27±42). Ó 2000 Elsevier Science B.V. All rights
reserved.
JEL classi®cation: G14; G15
*
Corresponding author. Tel.: +1-813-974-2081; fax: +1-813-974-3030.
E-mail addresses: [email protected] (C. Pantzalis), [email protected] (D.A.
Stangeland), [email protected] (H.J. Turtle).
1
Tel.: +1-204-474-6743; fax: +1-204-474-7545.
2
Tel.: +1-509-335-3797; fax: +1-509-335-3857.
0378-4266/00/$ - see front matter Ó 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 4 2 6 6 ( 9 9 ) 0 0 0 9 3 - X
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Keywords: Elections; Uncertain information; Market indices
1. Introduction
Political events are a major in¯uence on ®nancial markets. Markets tend to
respond to new information regarding political decisions that may impact on a
nationÕs ®scal, and monetary policy. 3 As such, political events are closely
followed by investors who revise their expectations based on the outcome of
these events. Among the many political events followed by market participants,
political elections are particularly important because:
1. Elections provide voters (and investors) with an opportunity to in¯uence the
course of the medium- and long-term economic policies of a country. Voters
choose whether to re-elect incumbents based on their assessment of the
states of candidates, parties, and the nation prior to the election.
2. Elections are events that attract the attention of media, pollsters, and political and ®nancial analysts who ®lter information between politicians and the
public. This process disseminates information to ®nancial markets.
3. As the election outcome becomes more certain, ®nancial-market participants revise their prior probability distributions of policies to be implemented and the resulting economic eects.
Informational eciency requires that markets absorb news and political
trends into prices in anticipation of election outcomes. Much of the uncertainty
about the outcome may be resolved prior to the actual election date. Brown
et al. (1988) note that as uncertainty is reduced, price changes tend to be
positive on average. Therefore, if uncertainty is resolved as the election outcome draws near, positive price changes should be expected. In contrast, if the
outcome of the election does not allow investors to immediately assess the
eect on the countryÕs future, then the election outcome constitutes an uncertainty inducing surprise. In this case, positive price changes should be expected following the election as uncertainty about the policies to be
implemented by the election winner is resolved.
This study examines stock market behavior around political election dates
in dierent countries and addresses the following questions. Do markets anticipate election outcomes? To what extent, and under what circumstances, do
election outcomes resolve uncertainty? Are there commonalties in stock
market behavior around election outcomes between countries with dierent
degrees of political, economic and press freedom? Are economic factors a
major source of the marketsÕ response? Is the timing of the election, i.e.,
3
Numerous articles in the popular press support this view. For example, see Fisher (1996),
Martin (1996) and Price (1995) among others.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1577
whether an election is called earlier than originally scheduled, important in
explaining market response? Are market responses around election dates of
the same magnitude when incumbents win or lose the election? We explore
these questions using a standard event study methodology that examines
abnormal return behavior around election dates across 33 countries for the
period 1974±1995.
We ®nd a positive market reaction in the two-week period preceding election
dates. This positive abnormal return is strongest for elections with the highest
degrees of uncertainty, in particular, countries with low rankings of political,
economic, and press freedom, and elections in which the incumbent loses.
The remainder of the paper is organized as follows. In Section 2 we review
the literature linking political elections to ®nancial markets. Then, in Section 3,
we present our formal hypotheses. Section 4 describes the methodology and the
data sources. Section 5 describes the empirical results and Section 6 provides
concluding remarks.
2. Political elections and the stock market
The issue of political eventsÕ ties to ®nancial market performance has been
the subject of a plethora of studies. 4 The link between economic performance
and political business cycles was ®rst analyzed by Nordhaus (1975) and
MacRae (1977). NordhausÕ political business cycle hypothesis implies that
there is a signi®cant election-induced economic cycle in the US. 5 Other studies
have empirically examined the eects of economic events on presidential
election voting (cf. Atesoglu et al., 1982; Fair, 1978, 1982; Burdekin, 1988) and
generally found that economic variables (such as output growth, and in¯ation)
signi®cantly aect each partyÕs voting share in US presidential elections.
Several others provide evidence that expected stock returns are related to
economic factors (for example, Roze and Kinney, 1976; Fama and Schwert,
1977; Chen et al., 1986; Keim and Stambaugh, 1986; Campbell, 1987; Poterba
and Summers, 1988; Fama and French, 1988, 1989; Ferson, 1989; Chen, 1991;
Ferson et al., 1993).
The empirical literature on the link between stock market performance
and political elections dates back to Niederhofer et al. (1970) who studied
market behavior around US elections. Allivine and OÕNeill (1980), Huang
4
See, for example, Alesina and Sachs (1988), Allen (1986), Bachman (1992), Lamb et al. (1997)
and Niederhofer (1971) among others.
5
The empirical evidence in support of the political business cycle theory is inconclusive for the
United States. For example, Hibbs (1977, 1988), Chapell and Keech (1986), Richards (1986) and
Havrilesky (1987) reject it, while others such as Tufte (1978), Frey and Schneider (1978), Soh (1986)
and Haynes and Stone (1988) ®nd supportive evidence.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
(1985) and Stoken (1994) found evidence in support of the presidential
election cycle theory. 6 Foerster (1994) shows that the US presidential election eect also occurs for Canadian stocks. In a recent study, Foerster and
Schmitz (1997) provided evidence of the pervasiveness of the US presidential
election cycle in international stock market returns. 7 Reilly and Luksetich
(1980) found support for the Wall Street folklore that the market prefers
Republicans, at least in the short run after US presidential elections. They
also found weak support for the Wall Street perception that the market
declines after an incumbentÕs loss. Finally, another set of studies have examined market eciency issues around political election dates by examining
stock market responses to voter opinion polls and found mixed results (see,
among others, Gwilym and Buckle, 1994; Thompson and Ioannidis, 1987;
Gemmill, 1992).
Most of the above studies have focused on the US stock market and presidential elections with a few exceptions. 8 Our study is, to our knowledge, the
®rst study that examines stock market behavior around elections on an international scale. It utilizes data for 33 countries for the period 1974±1995 and
provides evidence regarding links between stock market performance and
elections on a global scale. Using the conceptual frameworks of Harrington
(1993) and Brown et al. (1988) we develop a rationale for the use of factors,
such as the electionÕs timing and outcome, the countryÕs economic performance
and the degree of political, economic, and press freedom to explain stock
market behavior around elections.
3. Hypotheses
Our study examines the interactions of the uncertain information hypothesis
(UIH) of Brown et al. (1988) and the election model of Harrington (1993).
Prior to the election day, market participants have a probability distribution
for possible election outcomes. We view the market price as the discounted
post-election price based on investorsÕ expectations:
6
A four-year political business cycle formed from politiciansÕ incentives to stimulate the
economy prior to a US presidential election and to pursue in¯ationary policies following the
election. US stocks were found to have larger prices in the third and fourth year of a presidential
term, while average returns in year 2 were found to be negative. Herbst and Slinkman (1984)
provided evidence in support of the existence of a four-year political-economic cycle.
7
Indeed, the evidence provided by Foerster (1994) suggests that the US election cycle is at least
as important for Canadian stocks as the Canadian election cycle. Foerster and Schmitz (1997)
found that the US election cycle eect persists beyond economic and seasonal variables.
8
Foerster and Schmitz (1997) looked at international stock returnsÕ relation to US election
cycles, while Gwilym and Buckle (1994) and Gemmill (1992) looked at the UK stock and options
markets eciency based on UK election opinion polls.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Pricetÿ1
Pk
i1
1579
EPricet joutcomei Probability outcomei
;
1 ERtÿ1;t
where t is the time period when the election result is ®nalized and realized by
the market; k is the number of possible election outcomes; and ERtÿ1;t is the
risk-adjusted expected return over the time period t ÿ 1 to t. The price that
actually occurs at time t will be based on investorsÕ revised expectations, given
the outcome they observe on that date.
On average, the observed return over the period ending with the election
should be ERtÿ1;t . According to the UIH, though, this return is likely to be
higher than the average return over periods where no event-induced uncertainty exists. When election-induced uncertainty is reduced (i.e., as the
election result becomes more certain) the risk-adjusted expected return falls
and stock prices rise. We expect the greatest degree of uncertainty resolution
and thus the highest observed returns in the time period immediately preceding the election date as this is when media coverage and campaigning are
at their peak. 9 Given that some uncertainty has been resolved, we expect
the cumulative abnormal returns (CARs) to remain positive in the time
period following the election week. Our ®rst hypothesis thus consists of two
parts:
H1a :
CARÿ2;0 > 0;
H1b :
CARÿ2;4 > 0:
It is possible that election outcomes only partially resolve prior uncertainty
and that the market needs time to assess electionsÕ impacts following the vote
count. If there is a signi®cant amount of uncertainty resolution following the
election date, we would expect to observe post-election positive abnormal returns. We examine the four-week period after the election week to test our
second hypothesis.
H2 :
CAR1;4 > 0:
The UIH also predicts that, on an average, price changes will be positive
(nonnegative) as uncertainty is resolved around unfavorable (favorable) events.
In this case, the hypothesized return will be larger as uncertainty is resolved
9
We choose the two weeks prior to the election date plus the week including the election as the
period of examination. We ®nd our results are robust to other time windows; these results are
discussed in Section 5.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
because of a greater degree of risk aversion. 10 We classify elections with an
incumbent winning (losing) following poor (good) economic performance as an
unfavorable event. If the incumbent loses (wins) following poor (good) economic performance we classify the election result as a favorable event:
H3 :
CARunfavorable
event
> CARfavorable
event :
Underlying the UIH is the proposition that a reduction of uncertainty is
associated with positive observed returns and that greater uncertainty reduction yields greater observed returns. We now seek to classify our elections
into those associated with more or less uncertainty. In HarringtonÕs (1993)
model, voting behavior is determined jointly by the incumbentÕs policy and
performance. The less certain voters are of which policy they perceive as
best, the more easily they will switch loyalties and vote based on performance. According to Harrington, ``if voters are initially indierent as to
which policy is best ¼, voting is purely performance-based''. 11 Also, the
more policy-sensitive the electoral outcomes, the greater should be the extent
of policy manipulation by the incumbent for purposes of re-election. 12
Elections that are policy driven are associated with a large amount of uncertainty that is resolved as the election outcome becomes known. Thus
election outcomes that are performance driven (and where dierent policies
are seen as indistinct) are not associated with substantial uncertainty resolution ± there is not much uncertainty regarding the eects of policy changes
to resolve.
Another form of uncertainty in elections is due to limited information
available to the electorate. We examine three cases. First, we consider elections
held in countries with low rankings of political, economic and press freedom.
In these countries, information about the government and its policies is typi-
10
Brown et al. (1988, p. 356). In our case, we assume that equity in the country of the election
forms a signi®cant part of the marginal investorÕs portfolio (as in BHT). Bad news for a given
country (e.g., the expectation of an anti-business election outcome) reduces the value of the
portfolio and the investorÕs wealth. With decreasing absolute risk aversion, the lower level of wealth
following bad news results in greater absolute risk aversion and a larger risk premium is necessary.
Following good news and an increase in the investorÕs wealth, decreasing absolute risk aversion
implies a smaller premium is necessary to compensate for the same level of risk. Thus, risk
reduction following bad news should result in a greater price appreciation than risk reduction
following good news.
11
Harrington (1993, p. 33).
12
Using this line of reasoning, incumbents will most likely lose an election following poor
performance when the expected result from dierent policy alternatives is similar. Incumbents will
maintain enough votes to be re-elected when voters perceive a strong dierence between policy
alternatives; poor prior performance will be insucient to cause enough voters to switch
allegiances.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1581
cally not readily or widely available; i.e., there is an information asymmetry
between the electorate and the government. Perhaps the media are only partially independent of the government, or polls are uncommon and possibly not
sophisticated enough to adequately capture voter sentiment prior to the election process. In addition, countries with a weak democratic tradition, or low
economic freedom, may also be prone to such informational asymmetries.
Thus, the election process results in a substantial increase of information dissemination and a signi®cant decrease in uncertainty regarding future policies to
be followed.
Second, we consider elections held earlier than scheduled. A change in the
timing of an election gives the market less time to analyze new information
related to the election, and forces market participantsÕ expectations to be revised and re-evaluated in a shorter period of time. Note that when an election is
called early, this is also consistent with HarringtonÕs (1993) manipulation by
the incumbent during policy sensitive elections.
Third, we consider changes in political power. There is likely to be less reliable policy information available for a new government than there is for the
incumbent. Thus an expectation of an incumbent loss is associated with more
uncertainty than when an incumbent is reelected. For all these dierent classi®cations of elections, the hypothesis is the same and is based on the amount
of uncertainty. Observed abnormal returns associated with uncertainty resolution should be higher for higher-uncertainty events than for lower-uncertainty events:
H4 :
CARhigh-uncertainty
events
> CARlow-uncertainty
events :
4. Methodology and data
The aim of this study is to examine stock market behavior in dierent
countries around political election dates. We employ an event study methodology using a large sample of international election data spanning the 1974±
1995 period. We utilize weekly stock return data for individual country indices
and economic performance measures for individual countries in the election
year and the period prior to the election. Economic performance is measured
relative to prior economic performance within the country and relative to an
appropriate world index over the period prior to election. Several electionrelated attributes, such as the ability of the incumbent government to retain
power, the election timing, the relative degree of political, press, and economic
freedom of the country, and the countryÕs prior economic performance, are
characterized and analyzed. Section 4.1. describes the election and country
index returns sample. Section 4.2. describes the methodology for calculating
election period returns and details the tests performed.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
4.1. Data description and sources
The return data for this study are drawn from the Morgan Stanley Capital
International (MSCI) weekly data on value weighted equity indices for 45
countries and a world equity index. 13 The MSCI index values used here are in
US dollar terms. For major OECD countries, index levels are available from
January 1972 to December 1995. 14 For most developing countries, weekly
index levels are available from January 1988 to December 1995. 15 Although
the MSCI are not identical to the individual country indices, MSCI returns are
closely correlated to actual country indicesÕ returns. 16
The MSCI database of weekly indices provides our sample of returns data.
We obtain political elections information for all countries in the MSCI database for the periods in which returns are available for each country. Election
information includes the election date and the election outcome (i.e., whether
the incumbent won or lost). 17 This information is found in several editions of
the Economist's World Atlas of Elections, Facts on File: World Political Almanac, and the Elections around the world database, and is veri®ed with articles
from the New York Times. Economic performance measures on the in¯ation
rate, the unemployment rate, and the real GDP growth rate for each country
and the OECD and World averages are collected from several issues of the
Statistical Yearbook. The intersection of the electionsÕ data and the MSCI data
set results in a sample of 234 elections. Several countries included in the MSCI
database, such as China and Hong Kong, do not hold elections during the
13
We employ the non-dividend MSCI indices. MSCI also calculates index levels with dividend
reinvestment. Unfortunately, this adjustment occurs only once at the end of each month. Thus it
may result in a distortion of the election eect on returns.
14
These 22 countries are: Australia, Austria, Belgium, Canada, Denmark, Finland, France,
Germany, Hong Kong, Ireland, Italy, Japan, Malaysia, Netherlands, New Zealand, Norway,
Singapore, Spain, Sweden, Switzerland, United Kingdom, and USA. Index levels for Finland do
not begin until January 1987.
15
The 23 developing countries are Argentina, Brazil, Chile, China, Colombia, Greece, India,
Indonesia, Israel, Jordan, Korea, Luxembourg, Mexico, Pakistan, Peru, Philippines, Poland, South
Africa, Sri Lanka, Taiwan, Thailand, Turkey, and Venezuela. Returns data for a few of the
developing countries are incomplete. In particular, returns for Colombia, India, Israel, Pakistan,
Peru, Poland, South Africa, Sri Lanka and Venezuela begin on the ®rst week of January 1993.
16
For example, MSCI indices are weighted toward larger capitalization stocks, and, in order to
avoid double counting, they exclude investment companies and foreign incorporated companies.
Recently, the American Exchange oered investors the ability to directly purchase MSCI country
indices for 17 countries. This should increase the relevance of these indices for academic pursuits
because of increased liquidity and breadth of coverage. For a detailed description of the MSCI
database see Harvey (1991).
17
We only consider elections for the top oces in each country, i.e., presidential and/or
parliamentary elections. We do not account for related elections for lesser oces, e.g., the splitting
of the vote across parties between the US Presidential and Congressional elections.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1583
period of the study. Other countries are dropped because we are unable to
identify the election with our system of classi®cations (e.g., in Italy, complex
coalitions are often the election outcome) or because the economic performance data are incomplete or unavailable (e.g., Argentina, or Brazil). Finally,
a host of elections are dropped from the sample because there is not enough
data available for the 100-week estimation period. Our ®nal sample includes
129 elections spanning 33 countries over the period 1974±1995. 18
Table 1 reports a summary of the elections data by country. Of the 129
elections, the incumbent won 73 elections and lost 56 elections. Most of the
events (elections) are clustered in the European region (79), with 17 in the
`advanced Asia' region (Japan, Australia, New Zealand), 10 in North America
(USA, Canada) and 23 in the remaining countries. The average time in oce of
a government at the election date is about 76 months (with a range of 5±251
months). Out of the sample of 129 elections, we identi®ed 53 elections that were
held early. 19 An early election is quite common in some countries (e.g., in
Spain 5 out of 6 elections were held early), and very infrequent in others (e.g.,
in Sweden and Norway there were no early elections in a total of 12 elections).
Also reported in Table 1 are the political and civil freedom, economic freedom,
and press freedom rankings. These rankings were compiled by Freedom House,
a nonpro®t, nonpartisan organization dedicated to promoting democracy
around the world. 20 The combined political & civil rights rankings range from
2 (most free) to 14 (least free). The economic freedom rankings range from 6
(least free) to 16 (most free), while the press freedom rankings range from 5
(most free) to 66 (least free). 21 The last set of columns of Table 1 reports
descriptive statistics for the CARs by country, over the time period that starts
two weeks prior to the election week and ends four weeks after the election,
denoted ÿ2; 4. Abnormal returns were computed relative to the average
18
We recognize that our results are limited by the data available; thus, there may be selection
bias. Future research is warranted to re-examine these issues as more countries adopt democratic
processes and as ®nancial markets develop further.
19
An early election is de®ned as an election that took place at least three months prior to the
original date set at the beginning of the governmentÕs tenure.
20
Freedom House was established by Eleanor Roosevelt and Wendell Willkie in 1941. It
conducts programs to promote an engaged US foreign policy, monitor human rights and elections,
sponsor public education campaigns, oer training and technical assistance to promote democracy
and free market reforms, and support the rule of law, and eective local governance. Freedom
House compiles its rankings annually, based on comparative surveys covering a wide number of
countries around the globe. The survey and analysis that leads to the rankings are based on
universal criteria, not solely American or even Western concepts of freedom. Rather, the starting
point is the individual. Freedom House recognizes dierences across regions such as culture, diverse
national interests, and varying stages of economic development.
21
The rankings provided by Freedom House re¯ect mechanical computation and judgement.
Additional details regarding the methodology used by Freedom HouseÕs survey teams can be found
in the introductory section of each survey (and other Freedom House publications).
1584
Number
of
elections
Panel A. All countries
129
Panel B. By country
Australia
8
Austria
9
Belgium
6
Canada
5
Chile
1
Denmark
9
Finland
3
France
7
Germany
6
Greece
2
Indonesia
1
Ireland
1
Japan
7
Jordan
1
Korea
2
(South)
Incumbent
wins
Avg.
Number
tenure
of early
(months) elections
Freedom rankingsa
Political
and civil
Descriptive statistics for CAR ÿ2; 4b
Economic
Press
Mean
Median
Minimum
Maximum
73
76.12
53
3
14
19
0.01940
0.00892
)0.33013
0.47819
6
5
2
1
1
4
2
5
5
0
1
0
4
1
2
57.75
71.33
41.17
60.60
48.00
73.56
43.67
66.00
101.30
23.50
122.00
41.00
100.30
96.00
100.50
6
2
4
3
0
8
0
2
2
2
0
1
6
0
0
2
2
3
2
4
2
2
2
3
4
12
2
3
8
4
14
15
15
15
13
16
14
15
15
12
6
15
13
10
7
10
12
10
11
30
9
15
26
11
27
77
19
20
48
25
0.01332
)0.00060
0.10785
)0.01133
0.25330
0.00550
0.00043
0.05463
)0.05013
0.24162
0.08030
0.08071
)0.01323
)0.10064
0.06420
0.04765
0.00892
0.07364
0.02793
0.25330
)0.01734
)0.01808
0.07456
)0.06346
0.24162
0.08030
0.08071
0.07950
)0.10064
0.06420
)0.11862
)0.06852
0.01740
)0.20229
0.25330
)0.08477
)0.03014
)0.33013
)0.17493
0.00736
0.08030
0.08071
)0.31102
)0.10064
0.00672
0.08571
0.05757
0.22472
0.09776
0.25330
0.15340
0.04951
0.29013
0.11744
0.47589
0.08030
0.08071
0.15484
)0.10064
0.12169
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Table 1
Descriptive statistics for 129 international political elections for the sample period 1974±1995, by country
a
1
2
2
6
2
5
1
2
2
6
6
7
5
1
3
1
4
1
2
2
1
1
3
1
1
2
5
4
3
5
1
0
0
3
108.00
92.00
79.00
55.67
50.00
64.80
29.00
76.50
52.50
161.20
64.50
61.71
143.20
96.00
36.00
47.00
91.00
0
1
0
3
0
0
0
0
0
2
5
0
0
0
3
1
2
2
9
7
2
2
2
7
5
2
9
3
2
2
4
6
9
3
15
12
8
16
16
15
12
10
14
12
15
16
14
11
12
11
16
10
61
52
14
6
5
56
46
17
66
19
10
9
28
34
65
22
)0.01865
0.04197
0.01898
)0.01156
)0.10723
0.03901
0.23070
0.16148
0.07033
)0.00088
)0.01835
0.01030
)0.03744
)0.12608
)0.00100
0.47819
0.00460
)0.01865
0.04197
0.01898
)0.01467
)0.10723
0.02993
0.23070
0.16148
0.07033
)0.01735
)0.01871
0.01206
)0.00099
)0.12608
)0.05150
0.47819
)0.01956
)0.01865
)0.01722
0.00692
)0.11374
)0.15194
)0.03004
0.23070
0.10082
)0.02368
)0.08253
)0.19478
)0.10738
)0.29179
)0.12608
)0.10963
0.47819
)0.08474
)0.01865
0.10116
0.03105
0.09517
)0.06252
0.12317
0.23070
0.22215
0.16433
0.12148
0.16490
0.11898
0.12705
)0.12608
0.15813
0.47819
0.14227
5
2
86.80
0
2
16
14
)0.00011
)0.01614
)0.02421
0.02933
Freedom rankings are compiled and reported by Freedom House. Low values of press freedom and political and civil freedom signify high levels of
freedom. In contrast, high levels of economic freedom signify higher levels of freedom. In panel A, we report median freedom rankings across all
countries.
b
Cumulative abnormal returns (CARs) are computed for the window ÿ2; 4 around the election. Abnormal returns are computed relative to average
returns in the same country over the 100-week period prior to the window ÿ4; 4. All CARs are stated in decimal form.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Luxembourg
Malaysia
Mexico
Netherlands
New Zealand
Norway
Peru
Philippines
Portugal
Singapore
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
United
Kingdom
United States
1585
1586
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
return in the same country over the 100-week period prior to the ÿ4; 4
window.
The descriptive statistics for the full CAR ÿ2; 4 window dier dramatically across nations. For example, TurkeyÕs lone election produces a sevenweek CAR of 47.8%. Other large positive CARs in excess of 25% over this
seven-week window occurred in Chile, France and Greece. Extremely low
CARs of less than negative 25% occurred in France, Japan and Switzerland.
Notice that these extreme observations are not relegated to only small
emerging markets. Further, we note that vast dierences in CARs occur even
within the same country, at dierent points in time. For example, in GreeceÕs
two elections we observe one CAR of virtually zero, and another of almost
50%. Similarly, in France the observed seven-week CARs for seven elections
ranged from )33% to 29%. It is precisely this dispersion in CARs across nations and over time that we intend to investigate. 22
4.2. Event study of stock indices' returns around international election dates
We employ an event study methodology to examine country index reactions around the week of an election t 0. Two methods are used to calculate abnormal returns (ARs) and CARs: (i) mean-adjusted residuals on a
country-by-country basis using average country index weekly returns calculated over a 100-week period from t ÿ104 to t ÿ5, and (ii) a single factor
market model (see Brown and Warner, 1985), where the MSCI world index is
used as the proxy for the world market portfolio. The second method employs
Scholes and Williams (1977) alphas and betas with the market model parameters estimated using country index returns over the period from t ÿ104
to t ÿ5.
Weekly index values are reported as of the close of business every Friday.
We de®ne week zero as the week of the election or the week ending on the ®rst
Friday following the announcement of the election result. Thus, if an election
occurs on a Friday, Saturday, or Sunday, week zero is de®ned as the week
which ends on the following Friday; this is the ®rst week that ends with
knowledge of the election result. We de®ne the event window to be ÿ2; 0, the
three weeks starting at t ÿ2 (the second week before the election) and ending
at t 0 (the election week). We choose this window because it includes
the periods with the most potential for uncertainty resolution leading up to
22
Of course, this dramatic variability within the sample implies that relatively large economic
dierences from zero, and across subsamples, will be required to reject the null hypotheses
considered.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1587
an election 23 ARs and CARs are also estimated for a two-week pre-event
period
t ÿ4 to t ÿ3
and
a
four-week
post-event
period
t 1 to t 4. The two-week pre-event period is not included in the estimation window to avoid any possible election eects that might bias our
estimated parameters. 24 The four-week post-event period is examined to determine the magnitude of uncertainty resolution that occurs after the voting
outcome is known, e.g., in the case where the vote was close and required
coalition building, or if a runo election was required. 25 Finally we examine
the entire period ÿ2; 4 to determine whether the CARs represent persistent
or transitory eects. 26
5. Empirical results
In this section we present the event study results. We ®nd that event period
country index returns are generally positive and signi®cant, and that this eect
is strongest in the two weeks prior to the election week (i.e., t ÿ2 and
t ÿ1). We also ®nd that the eects are stronger when elections are classi®ed
based on several characteristics, such as election timing, country freedom
rankings, economic performance and election outcome, and for some interactions of these primary factors.
5.1. Pooled sample results
Table 2 reports AR results for the event weeks between week ÿ4 and week
4 and CARs for the two week pre-event period ÿ4; ÿ3, the event window
23
Because of how week 0 is de®ned, it may include returns from the ®nal days before the election
date (e.g., if there is a Thursday election, week 0 will include the returns from the Monday±
Thursday period prior to knowledge of the actual election outcome) or from days following the
election outcome (e.g., if there is a Friday election, the following Monday will be the ®rst trading
day re¯ecting market reactions to ®nal knowledge of the election outcome but week 0 will contain
returns from the following Tuesday±Friday period that occur after the election outcome is known).
We choose to include week 0 in our event window of analysis because of its potential to capture the
®nal resolution of uncertainty prior to knowledge of the election outcome (cases like the ®rst
example). We ®nd that excluding week 0 from the event window actually strengthens the
signi®cance of our results.
24
The length of the campaign varies from country to country, with a typical campaign lasting 4±
8 weeks. However, the most intense campaigning, media coverage and polling occur during the last
two weeks prior to the election.
25
Typically a runo election occurs 2±4 weeks after the initial vote.
26
We also examine CARs over the windows of ÿ4; ÿ1 and ÿ4; 4 instead of ÿ2; 0 and
ÿ2; 4 and ®nd the results to be similar in magnitude and signi®cance (although signi®cance is
weaker due to the relative lack of uncertainty resolution and the presence of additional noise in the
earlier weeks). These results are available from the authors upon request.
1588
Table 2
Announcement period return results for relative weeks )4 to +4 for country indices around 129 political elections
Week
Comparison period adjusteda
Median
AR (%)
Panel A. Abnormal return (AR) results
)4
0.14
0.15
)3
)0.33
)0.17
)2
0.50
0.57
)1
0.82
1.03
0
)0.19
)0.43
1
)0.06
0.20
2
0.43
0.41
3
)0.14
0.04
4
0.58
0.21
P-value
(t-test)
P-value
(Wilcoxon
Rank test)
Percentage
positive (%)
Average
AR (%)
Median
AR (%)
P-value
(t-test)
P-value
(Wilcoxon
Rank test)
0.6579
0.2169
0.0394
0.1073
0.4895
0.8294
0.1340
0.6763
0.6751
0.5996
0.3820
0.0159
0.0475
0.2935
0.7391
0.0909
0.6799
0.3833
52.71
46.51
62.02
58.14
41.86
52.71
55.81
50.39
53.49
)0.09
)0.28
0.31
0.99
)0.21
)0.10
0.22
)0.10
0.63%
0.01
)0.15
0.30
0.60
)0.55
)0.15
0.25
)0.14
0.05%
0.7849
0.2399
0.1790
0.0372
0.4240
0.6948
0.4027
0.7528
0.0430
0.6409
0.1554
0.1154
0.0276
0.2714
0.7998
0.5043
0.4041
0.2524
P-value
(Wilcoxon
Rank test)
Percentage
positive (%)
Average
CAR (%)
Median
CAR (%)
P-value
(t-test)
P-value
(Wilcoxon
Rank test)
Panel B. Cumulative abnormal return (CAR) results
Weeks
Average
Median
P-value
CAR (%)
CAR (%)
(t-test)
ÿ4; ÿ3
ÿ2; 0
1; 4
)0.18
1.12
0.81
)0.44
1.30
0.54
0.6923
0.0737
0.2501
0.6544
0.0161
0.3632
46.51
58.14
55.04
)0.36
1.09
0.64
)0.32
1.07
0.44
0.4108
0.0607
0.3223
0.2850
0.0422
0.6243
ÿ2; 4
1.93
0.89
0.0692
0.0868
55.81
1.74
1.24
0.0712
0.1493
a
Comparison period adjusted abnormal returns are computed relative to the average return in the same country over the 100-week period from week
ÿ104 to week ÿ5.
b
Market adjusted, equal weighted abnormal returns are computed using Scholes±Williams betas to adjust for nonsynchronous trading.
c
We examine whether average and median CARs are signi®cantly dierent from zero using two-tailed tests (t-test for averages and Wilcoxon test for
medians).
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Average
ARc (%)
Market adjusted, equal weighted equity
(Scholes±Williams betas)b
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1589
ÿ2; 0, the four-week post-event period 1; 4, and the period ÿ2; 4 for
the total sample of 129 elections. The reported results are based on the countryby-country adjustment (comparison period) method, and the market adjusted
method.
The average AR for the total sample using the comparison period adjusted
method is statistically signi®cant at the 5% level for week ÿ2. 27 The Wilcoxon
rank test reveals that for weeks ÿ2 and ÿ1 the median ARs are signi®cantly
higher than zero (again at the 5% level). Using the market model, we ®nd
signi®cance (at the 5% level) for average ARs over weeks ÿ1 and 4 and for
the median AR for week ÿ1. The event period ÿ2; 0 average CAR based on
the comparison period-adjusted method is 1.12%; the ÿ2; 4 average CAR is
1.93% (both are signi®cant at the 10% level). The ÿ2; 0 and ÿ2; 4 median
CARs are also signi®cantly larger than zero at the 5% and 10% levels, respectively. Because the CARs using the market-adjusted method are very
similar to the comparison period results, the former are omitted for brevity in
the remainder of the paper.
The results in Table 2 are consistent with the ®rst hypothesis; there is a
positive market reaction in the two-week period leading up to election dates
and this eect persists through the four-week period following the election.
Note, though, that the CARs over the post election period 1; 4 are not
statistically signi®cant and therefore we cannot reject the null hypothesis in
favor of hypothesis two. The pattern described above is similar for both
methods of CAR computation.
In order to identify the factors determining election period abnormal returns, additional analysis is required. In the following sections we examine
whether the positive abnormal returns are driven by factors that constitute
characteristics of the election process and the socio-economic environment in
which the elections take place. Such factors are proxied by country freedom
rankings, pre-election economic performance, election outcome, election timing, and interactions between these factors.
5.2. Results by election timing, country freedom ranking, economic performance,
and election outcome
We begin the analysis related to the third and fourth hypotheses by incorporating dierent individual factors that may shape the nature of the election
and the amount of uncertainty related to election outcomes. The ®rst such
factor examined is the timing of the election. The incumbent government may
have the option of calling an early election to improve their chances of
27
P-values in all tables are from two-tailed tests and these are the default P-values reported in
the text. Given the nature of our hypotheses, it is actually appropriate to use less conservative onetailed tests.: We explicitly state when one-tailed test P-values are used in the text.
1590
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
retaining control, or an early election may result because of pressure applied by
the parliament or other extraordinary country-speci®c events. We argue that
the magnitude of uncertainty resolution immediately prior to early-held elections is greater than in the case of on-time elections. We therefore group
elections that were held three or more months prior to the originally scheduled
date as `early'. The remaining elections are categorized as `not early'.
The average and median CARs for the ÿ2; 0 and ÿ2; 4 windows for the
early and not-early elections can be found in Panel A of Table 3. 28 The ÿ2; 0
average and median CARs are signi®cantly positive at the 10% level for the
early group, however for the not-early group only the median ÿ2; 0 CAR is
signi®cantly larger than zero. Using the F statistic to test equality of means and
the Kruskal±Wallis statistic to test equality of medians, we ®nd that the early
and not-early subsamples are not signi®cantly dierent from each other. Thus,
based on the election-timing classi®cation, we cannot reject the null hypothesis
(of equal CARs) in favor of hypothesis four (that CARs for high-uncertainty
events are greater than CARs for low-uncertainty events).
Another possible explanation for the existence of abnormal returns around
election dates is that the eect is concentrated in countries where information
about the government and its policies is not usually readily available. The
countries that ®t this pro®le are aggregated to form the `less-free' group; the
remaining are grouped as `free'. A country is categorized as free if it is free
according to at least two out of three Freedom House measures on political,
economic, or press freedom; otherwise we classify it as less free. 29 Panel B of
Table 3 shows that the average and median CARs are signi®cantly larger than
zero for the less-free group only. In addition, both the average and median
ÿ2; 0 CAR and the average ÿ2; 4 CAR for the less-free group are greater
than the free group (signi®cant at the 5% level for the ÿ2; 0 CAR and at the
10% level for the ÿ2; 4 CAR). This is consistent with the notion that
countries that are less free are associated with more informational asymmetries, and therefore more uncertainty resolution in the market near the election
date. Based on the freedom classi®cation, we reject the null hypothesis in favor
of hypothesis four.
The next factor we examine is past economic performance. Three dimensions of economic performance are measured: the in¯ation rate, the real GDP
growth rate, and the unemployment rate. Previous research, such as Fair (1978,
1982) and Burdekin (1988), ®nds that these variables are important determi28
CARs for the windows ÿ4; ÿ3 and 1; 4 were also calculated for each panel of Table 3.
These results are not presented as the means and medians for these windows are not signi®cantly
dierent from zero at the 10% level.
29
For each Freedom House measure we determine the median score and assign `free' or `less free'
depending on which side of the median a country falls. Ties at the median are classi®ed according
to an additional qualitative assessment provided by Freedom House in their surveys.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1591
Table 3
Examining cumulative abnormal returns by primary classi®cations for the event window ÿ2; 0
and the window ÿ2; 4
CAR ÿ2; 0
Panel A. By election timing
Election called early N 53
Election not called early N 76
P-value for dierences in subsamplesb
Panel B. By country freedom measures
Free based on 2 out of 3 measures
N 97
Less-free N 32
P-value for dierences in subsamples
CAR ÿ2; 4
Meana
Median
Mean
Median
1.61%
0.79%
0.525
1.39%
1.29%
0.966
2.67%
1.43%
0.566
1.60%
0.79%
0.818
0.24%
0.96%
0.75%
0.74%
3.81%
0.013
2.75%
0.033
5.54%
0.051
4.31%
0.150
Panel C. By economic performance
Good economic performance on 2 or
more measures N 66
Poor economic performance N 63
P-value for dierences in subsamples
0.76%
1.10%
0.80%
0.63%
1.51%
0.549
1.72%
0.510
3.13%
0.274
1.60%
0.383
Panel D. By incumbent performance
Incumbent wins N 73
Incumbent loses N 56
P-value for dierences in subsamples
0.66%
1.74%
0.393
0.96%
1.97%
0.239
1.42%
2.62%
0.574
0.69%
1.67%
0.842
a
Comparison period adjusted abnormal returns are used for all cumulative abnormal returns. We
examine whether average and median CARs are signi®cantly dierent from zero using two-tailed
tests (t-test for averages and Wilcoxon test for medians).
b
F-test P-values are reported for dierences in subsample means, and the Kruskal±Wallis test
P-values are reported for dierences in medians. Reported P-values are from two-tailed tests.
*
Signi®cance at the 10% level.
**
Signi®cance at the 5% level.
***
Signi®cance at the 1% level.
nants of voter decisions. We assume that voters assess each of the three factors
by: (i) comparing the current economic variable to its value in the previous
year, (ii) comparing the average value of the economic indicator for the period
of the current administrationÕs tenure to that of the period of the previous
administrationÕs tenure, and (iii) comparing the average value of the economic
indicator for the current administrationÕs period of reign with the average value
of the indicator for the world over the same period. 30 A countryÕs performance
30
World averages are OECD averages. We employ the industrial countriesÕ OECD average
in¯ation and GDP growth for comparisons of the world with industrial countries, and the
developing countriesÕ OECD average in¯ation and real GDP growth for the comparison of the
world with developing countries. We use only one world unemployment rate (all OECD countriesÕ
average) for comparison with all countries.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
with regards to an individual economic variable is characterized as `good' if at
least two of the three comparisons were favorable, otherwise performance for
that variable is classi®ed as `poor'. 31 For example, if in¯ation during the
election year was: higher than the previous yearÕs in¯ation, lower on an average
for the current administrationÕs governing period compared to the average
in¯ation rate during the last governmentÕs tenure, and lower than the average
OECD in¯ation rate for the period, then the performance with respect to in¯ation will be classi®ed as good (i.e., in¯ation was low). We divide the total
sample into two subsamples based on each countryÕs aggregate economic
performance. A countryÕs aggregate economic performance is classi®ed as good
(poor) if at least two of the three individual economic variables (in¯ation, real
GDP growth, and unemployment) are classi®ed as good (poor).
The CARs for the good and the poor aggregate economic performance
subsamples are shown in Panel C of Table 3. The magnitude of the marketÕs
reaction to elections is signi®cantly greater than zero only when the past economic performance was poor. The average and median ÿ2; 4 CAR in that
case is 3.13% and 1.60%, respectively (both are signi®cant at the 10% level).
The median ÿ2; 0 CAR is 1.72% (signi®cant at the 5% level). The average and
median CARs for the good performance case are not signi®cantly dierent
from zero. These results suggest that the positive election eect is primarily
concentrated in cases where the countryÕs economic performance was poor,
however, tests of the dierences of means or medians indicate that these two
subsamples are not signi®cantly dierent from each other. 32
Panel D of Table 3 reports CAR results by election outcome. When the
incumbent loses the election the average (1.74%) and median (1.97%) CARs are
positive for the ÿ2; 0 window (signi®cant at the 10% and 1% levels, respectively). On the other hand, the average and median CARs are not signi®cant
when the incumbent wins the election. It appears that the marketÕs response is
greater when the election outcome constitutes a change in the status quo (i.e.,
the incumbent loses) which may be associated with more uncertainty. However, similar to panels A and C, the means and medians are not signi®cantly
dierent across the two subsamples.
Thus we have several classi®cations (denoted primary factors) that impact the
magnitude of the marketÕs response around political elections. The discussions
of hypotheses three and four suggest that the interactions of economic performance and election outcome are also potentially important. In addition, the
degree of uncertainty surrounding an election may be related to a combination
31
Low in¯ation, low unemployment, and high real GDP growth are classi®ed as good
performance; while high in¯ation, high unemployment and low real GDP growth are classi®ed as
poor performance.
32
These ®ndings are qualitatively unchanged for similar classi®cations based on alternative
de®nitions of good and poor economic performance that do not use all three factors.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
of the factors investigated. In the next sections we examine various interactions
of the primary factors and how they aect the cumulative abnormal returns.
5.3. Results across favorable and unfavorable events
The third hypothesis is drawn from the UIH of Brown et al. (1988) who
state tha
www.elsevier.com/locate/econbase
Political elections and the resolution of
uncertainty: The international evidence
Christos Pantzalis a,*, David A. Stangeland
Harry J. Turtle c,2
b,1
,
a
College of Business Administration, University of South Florida, Tampa, FL 33620, USA
Faculty of Management, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V4
College of Business and Economics, Washington State University, Pullman, WA 99164-4746, USA
b
c
Received 2 October 1997; accepted 26 July 1999
Abstract
We investigate the behavior of stock market indices across 33 countries around
political election dates during the sample period 1974±1995. We ®nd a positive abnormal return during the two-week period prior to the election week. The positive reaction of the stock market to elections is shown to be a function of a countryÕs degree of
political, economic and press freedom, and a function of the election timing and the
success of the incumbent in being re-elected. In particular, we ®nd strong positive abnormal returns leading up to the elections (i) in less free countries won by the opposition, and (ii) called early and lost by the incumbent government. These results are
consistent with the uncertain information hypothesis (UIH) of Brown et al. (Brown,
K.C., Harlow, W.V., Tinic, S.M., 1988. Journal of Financial Economics 22, 355±385)
and the model of election behavior of Harrington (Harrington, J.E., 1993. The
American Economic Review 83, 27±42). Ó 2000 Elsevier Science B.V. All rights
reserved.
JEL classi®cation: G14; G15
*
Corresponding author. Tel.: +1-813-974-2081; fax: +1-813-974-3030.
E-mail addresses: [email protected] (C. Pantzalis), [email protected] (D.A.
Stangeland), [email protected] (H.J. Turtle).
1
Tel.: +1-204-474-6743; fax: +1-204-474-7545.
2
Tel.: +1-509-335-3797; fax: +1-509-335-3857.
0378-4266/00/$ - see front matter Ó 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 4 2 6 6 ( 9 9 ) 0 0 0 9 3 - X
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Keywords: Elections; Uncertain information; Market indices
1. Introduction
Political events are a major in¯uence on ®nancial markets. Markets tend to
respond to new information regarding political decisions that may impact on a
nationÕs ®scal, and monetary policy. 3 As such, political events are closely
followed by investors who revise their expectations based on the outcome of
these events. Among the many political events followed by market participants,
political elections are particularly important because:
1. Elections provide voters (and investors) with an opportunity to in¯uence the
course of the medium- and long-term economic policies of a country. Voters
choose whether to re-elect incumbents based on their assessment of the
states of candidates, parties, and the nation prior to the election.
2. Elections are events that attract the attention of media, pollsters, and political and ®nancial analysts who ®lter information between politicians and the
public. This process disseminates information to ®nancial markets.
3. As the election outcome becomes more certain, ®nancial-market participants revise their prior probability distributions of policies to be implemented and the resulting economic eects.
Informational eciency requires that markets absorb news and political
trends into prices in anticipation of election outcomes. Much of the uncertainty
about the outcome may be resolved prior to the actual election date. Brown
et al. (1988) note that as uncertainty is reduced, price changes tend to be
positive on average. Therefore, if uncertainty is resolved as the election outcome draws near, positive price changes should be expected. In contrast, if the
outcome of the election does not allow investors to immediately assess the
eect on the countryÕs future, then the election outcome constitutes an uncertainty inducing surprise. In this case, positive price changes should be expected following the election as uncertainty about the policies to be
implemented by the election winner is resolved.
This study examines stock market behavior around political election dates
in dierent countries and addresses the following questions. Do markets anticipate election outcomes? To what extent, and under what circumstances, do
election outcomes resolve uncertainty? Are there commonalties in stock
market behavior around election outcomes between countries with dierent
degrees of political, economic and press freedom? Are economic factors a
major source of the marketsÕ response? Is the timing of the election, i.e.,
3
Numerous articles in the popular press support this view. For example, see Fisher (1996),
Martin (1996) and Price (1995) among others.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1577
whether an election is called earlier than originally scheduled, important in
explaining market response? Are market responses around election dates of
the same magnitude when incumbents win or lose the election? We explore
these questions using a standard event study methodology that examines
abnormal return behavior around election dates across 33 countries for the
period 1974±1995.
We ®nd a positive market reaction in the two-week period preceding election
dates. This positive abnormal return is strongest for elections with the highest
degrees of uncertainty, in particular, countries with low rankings of political,
economic, and press freedom, and elections in which the incumbent loses.
The remainder of the paper is organized as follows. In Section 2 we review
the literature linking political elections to ®nancial markets. Then, in Section 3,
we present our formal hypotheses. Section 4 describes the methodology and the
data sources. Section 5 describes the empirical results and Section 6 provides
concluding remarks.
2. Political elections and the stock market
The issue of political eventsÕ ties to ®nancial market performance has been
the subject of a plethora of studies. 4 The link between economic performance
and political business cycles was ®rst analyzed by Nordhaus (1975) and
MacRae (1977). NordhausÕ political business cycle hypothesis implies that
there is a signi®cant election-induced economic cycle in the US. 5 Other studies
have empirically examined the eects of economic events on presidential
election voting (cf. Atesoglu et al., 1982; Fair, 1978, 1982; Burdekin, 1988) and
generally found that economic variables (such as output growth, and in¯ation)
signi®cantly aect each partyÕs voting share in US presidential elections.
Several others provide evidence that expected stock returns are related to
economic factors (for example, Roze and Kinney, 1976; Fama and Schwert,
1977; Chen et al., 1986; Keim and Stambaugh, 1986; Campbell, 1987; Poterba
and Summers, 1988; Fama and French, 1988, 1989; Ferson, 1989; Chen, 1991;
Ferson et al., 1993).
The empirical literature on the link between stock market performance
and political elections dates back to Niederhofer et al. (1970) who studied
market behavior around US elections. Allivine and OÕNeill (1980), Huang
4
See, for example, Alesina and Sachs (1988), Allen (1986), Bachman (1992), Lamb et al. (1997)
and Niederhofer (1971) among others.
5
The empirical evidence in support of the political business cycle theory is inconclusive for the
United States. For example, Hibbs (1977, 1988), Chapell and Keech (1986), Richards (1986) and
Havrilesky (1987) reject it, while others such as Tufte (1978), Frey and Schneider (1978), Soh (1986)
and Haynes and Stone (1988) ®nd supportive evidence.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
(1985) and Stoken (1994) found evidence in support of the presidential
election cycle theory. 6 Foerster (1994) shows that the US presidential election eect also occurs for Canadian stocks. In a recent study, Foerster and
Schmitz (1997) provided evidence of the pervasiveness of the US presidential
election cycle in international stock market returns. 7 Reilly and Luksetich
(1980) found support for the Wall Street folklore that the market prefers
Republicans, at least in the short run after US presidential elections. They
also found weak support for the Wall Street perception that the market
declines after an incumbentÕs loss. Finally, another set of studies have examined market eciency issues around political election dates by examining
stock market responses to voter opinion polls and found mixed results (see,
among others, Gwilym and Buckle, 1994; Thompson and Ioannidis, 1987;
Gemmill, 1992).
Most of the above studies have focused on the US stock market and presidential elections with a few exceptions. 8 Our study is, to our knowledge, the
®rst study that examines stock market behavior around elections on an international scale. It utilizes data for 33 countries for the period 1974±1995 and
provides evidence regarding links between stock market performance and
elections on a global scale. Using the conceptual frameworks of Harrington
(1993) and Brown et al. (1988) we develop a rationale for the use of factors,
such as the electionÕs timing and outcome, the countryÕs economic performance
and the degree of political, economic, and press freedom to explain stock
market behavior around elections.
3. Hypotheses
Our study examines the interactions of the uncertain information hypothesis
(UIH) of Brown et al. (1988) and the election model of Harrington (1993).
Prior to the election day, market participants have a probability distribution
for possible election outcomes. We view the market price as the discounted
post-election price based on investorsÕ expectations:
6
A four-year political business cycle formed from politiciansÕ incentives to stimulate the
economy prior to a US presidential election and to pursue in¯ationary policies following the
election. US stocks were found to have larger prices in the third and fourth year of a presidential
term, while average returns in year 2 were found to be negative. Herbst and Slinkman (1984)
provided evidence in support of the existence of a four-year political-economic cycle.
7
Indeed, the evidence provided by Foerster (1994) suggests that the US election cycle is at least
as important for Canadian stocks as the Canadian election cycle. Foerster and Schmitz (1997)
found that the US election cycle eect persists beyond economic and seasonal variables.
8
Foerster and Schmitz (1997) looked at international stock returnsÕ relation to US election
cycles, while Gwilym and Buckle (1994) and Gemmill (1992) looked at the UK stock and options
markets eciency based on UK election opinion polls.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Pricetÿ1
Pk
i1
1579
EPricet joutcomei Probability outcomei
;
1 ERtÿ1;t
where t is the time period when the election result is ®nalized and realized by
the market; k is the number of possible election outcomes; and ERtÿ1;t is the
risk-adjusted expected return over the time period t ÿ 1 to t. The price that
actually occurs at time t will be based on investorsÕ revised expectations, given
the outcome they observe on that date.
On average, the observed return over the period ending with the election
should be ERtÿ1;t . According to the UIH, though, this return is likely to be
higher than the average return over periods where no event-induced uncertainty exists. When election-induced uncertainty is reduced (i.e., as the
election result becomes more certain) the risk-adjusted expected return falls
and stock prices rise. We expect the greatest degree of uncertainty resolution
and thus the highest observed returns in the time period immediately preceding the election date as this is when media coverage and campaigning are
at their peak. 9 Given that some uncertainty has been resolved, we expect
the cumulative abnormal returns (CARs) to remain positive in the time
period following the election week. Our ®rst hypothesis thus consists of two
parts:
H1a :
CARÿ2;0 > 0;
H1b :
CARÿ2;4 > 0:
It is possible that election outcomes only partially resolve prior uncertainty
and that the market needs time to assess electionsÕ impacts following the vote
count. If there is a signi®cant amount of uncertainty resolution following the
election date, we would expect to observe post-election positive abnormal returns. We examine the four-week period after the election week to test our
second hypothesis.
H2 :
CAR1;4 > 0:
The UIH also predicts that, on an average, price changes will be positive
(nonnegative) as uncertainty is resolved around unfavorable (favorable) events.
In this case, the hypothesized return will be larger as uncertainty is resolved
9
We choose the two weeks prior to the election date plus the week including the election as the
period of examination. We ®nd our results are robust to other time windows; these results are
discussed in Section 5.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
because of a greater degree of risk aversion. 10 We classify elections with an
incumbent winning (losing) following poor (good) economic performance as an
unfavorable event. If the incumbent loses (wins) following poor (good) economic performance we classify the election result as a favorable event:
H3 :
CARunfavorable
event
> CARfavorable
event :
Underlying the UIH is the proposition that a reduction of uncertainty is
associated with positive observed returns and that greater uncertainty reduction yields greater observed returns. We now seek to classify our elections
into those associated with more or less uncertainty. In HarringtonÕs (1993)
model, voting behavior is determined jointly by the incumbentÕs policy and
performance. The less certain voters are of which policy they perceive as
best, the more easily they will switch loyalties and vote based on performance. According to Harrington, ``if voters are initially indierent as to
which policy is best ¼, voting is purely performance-based''. 11 Also, the
more policy-sensitive the electoral outcomes, the greater should be the extent
of policy manipulation by the incumbent for purposes of re-election. 12
Elections that are policy driven are associated with a large amount of uncertainty that is resolved as the election outcome becomes known. Thus
election outcomes that are performance driven (and where dierent policies
are seen as indistinct) are not associated with substantial uncertainty resolution ± there is not much uncertainty regarding the eects of policy changes
to resolve.
Another form of uncertainty in elections is due to limited information
available to the electorate. We examine three cases. First, we consider elections
held in countries with low rankings of political, economic and press freedom.
In these countries, information about the government and its policies is typi-
10
Brown et al. (1988, p. 356). In our case, we assume that equity in the country of the election
forms a signi®cant part of the marginal investorÕs portfolio (as in BHT). Bad news for a given
country (e.g., the expectation of an anti-business election outcome) reduces the value of the
portfolio and the investorÕs wealth. With decreasing absolute risk aversion, the lower level of wealth
following bad news results in greater absolute risk aversion and a larger risk premium is necessary.
Following good news and an increase in the investorÕs wealth, decreasing absolute risk aversion
implies a smaller premium is necessary to compensate for the same level of risk. Thus, risk
reduction following bad news should result in a greater price appreciation than risk reduction
following good news.
11
Harrington (1993, p. 33).
12
Using this line of reasoning, incumbents will most likely lose an election following poor
performance when the expected result from dierent policy alternatives is similar. Incumbents will
maintain enough votes to be re-elected when voters perceive a strong dierence between policy
alternatives; poor prior performance will be insucient to cause enough voters to switch
allegiances.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1581
cally not readily or widely available; i.e., there is an information asymmetry
between the electorate and the government. Perhaps the media are only partially independent of the government, or polls are uncommon and possibly not
sophisticated enough to adequately capture voter sentiment prior to the election process. In addition, countries with a weak democratic tradition, or low
economic freedom, may also be prone to such informational asymmetries.
Thus, the election process results in a substantial increase of information dissemination and a signi®cant decrease in uncertainty regarding future policies to
be followed.
Second, we consider elections held earlier than scheduled. A change in the
timing of an election gives the market less time to analyze new information
related to the election, and forces market participantsÕ expectations to be revised and re-evaluated in a shorter period of time. Note that when an election is
called early, this is also consistent with HarringtonÕs (1993) manipulation by
the incumbent during policy sensitive elections.
Third, we consider changes in political power. There is likely to be less reliable policy information available for a new government than there is for the
incumbent. Thus an expectation of an incumbent loss is associated with more
uncertainty than when an incumbent is reelected. For all these dierent classi®cations of elections, the hypothesis is the same and is based on the amount
of uncertainty. Observed abnormal returns associated with uncertainty resolution should be higher for higher-uncertainty events than for lower-uncertainty events:
H4 :
CARhigh-uncertainty
events
> CARlow-uncertainty
events :
4. Methodology and data
The aim of this study is to examine stock market behavior in dierent
countries around political election dates. We employ an event study methodology using a large sample of international election data spanning the 1974±
1995 period. We utilize weekly stock return data for individual country indices
and economic performance measures for individual countries in the election
year and the period prior to the election. Economic performance is measured
relative to prior economic performance within the country and relative to an
appropriate world index over the period prior to election. Several electionrelated attributes, such as the ability of the incumbent government to retain
power, the election timing, the relative degree of political, press, and economic
freedom of the country, and the countryÕs prior economic performance, are
characterized and analyzed. Section 4.1. describes the election and country
index returns sample. Section 4.2. describes the methodology for calculating
election period returns and details the tests performed.
1582
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
4.1. Data description and sources
The return data for this study are drawn from the Morgan Stanley Capital
International (MSCI) weekly data on value weighted equity indices for 45
countries and a world equity index. 13 The MSCI index values used here are in
US dollar terms. For major OECD countries, index levels are available from
January 1972 to December 1995. 14 For most developing countries, weekly
index levels are available from January 1988 to December 1995. 15 Although
the MSCI are not identical to the individual country indices, MSCI returns are
closely correlated to actual country indicesÕ returns. 16
The MSCI database of weekly indices provides our sample of returns data.
We obtain political elections information for all countries in the MSCI database for the periods in which returns are available for each country. Election
information includes the election date and the election outcome (i.e., whether
the incumbent won or lost). 17 This information is found in several editions of
the Economist's World Atlas of Elections, Facts on File: World Political Almanac, and the Elections around the world database, and is veri®ed with articles
from the New York Times. Economic performance measures on the in¯ation
rate, the unemployment rate, and the real GDP growth rate for each country
and the OECD and World averages are collected from several issues of the
Statistical Yearbook. The intersection of the electionsÕ data and the MSCI data
set results in a sample of 234 elections. Several countries included in the MSCI
database, such as China and Hong Kong, do not hold elections during the
13
We employ the non-dividend MSCI indices. MSCI also calculates index levels with dividend
reinvestment. Unfortunately, this adjustment occurs only once at the end of each month. Thus it
may result in a distortion of the election eect on returns.
14
These 22 countries are: Australia, Austria, Belgium, Canada, Denmark, Finland, France,
Germany, Hong Kong, Ireland, Italy, Japan, Malaysia, Netherlands, New Zealand, Norway,
Singapore, Spain, Sweden, Switzerland, United Kingdom, and USA. Index levels for Finland do
not begin until January 1987.
15
The 23 developing countries are Argentina, Brazil, Chile, China, Colombia, Greece, India,
Indonesia, Israel, Jordan, Korea, Luxembourg, Mexico, Pakistan, Peru, Philippines, Poland, South
Africa, Sri Lanka, Taiwan, Thailand, Turkey, and Venezuela. Returns data for a few of the
developing countries are incomplete. In particular, returns for Colombia, India, Israel, Pakistan,
Peru, Poland, South Africa, Sri Lanka and Venezuela begin on the ®rst week of January 1993.
16
For example, MSCI indices are weighted toward larger capitalization stocks, and, in order to
avoid double counting, they exclude investment companies and foreign incorporated companies.
Recently, the American Exchange oered investors the ability to directly purchase MSCI country
indices for 17 countries. This should increase the relevance of these indices for academic pursuits
because of increased liquidity and breadth of coverage. For a detailed description of the MSCI
database see Harvey (1991).
17
We only consider elections for the top oces in each country, i.e., presidential and/or
parliamentary elections. We do not account for related elections for lesser oces, e.g., the splitting
of the vote across parties between the US Presidential and Congressional elections.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1583
period of the study. Other countries are dropped because we are unable to
identify the election with our system of classi®cations (e.g., in Italy, complex
coalitions are often the election outcome) or because the economic performance data are incomplete or unavailable (e.g., Argentina, or Brazil). Finally,
a host of elections are dropped from the sample because there is not enough
data available for the 100-week estimation period. Our ®nal sample includes
129 elections spanning 33 countries over the period 1974±1995. 18
Table 1 reports a summary of the elections data by country. Of the 129
elections, the incumbent won 73 elections and lost 56 elections. Most of the
events (elections) are clustered in the European region (79), with 17 in the
`advanced Asia' region (Japan, Australia, New Zealand), 10 in North America
(USA, Canada) and 23 in the remaining countries. The average time in oce of
a government at the election date is about 76 months (with a range of 5±251
months). Out of the sample of 129 elections, we identi®ed 53 elections that were
held early. 19 An early election is quite common in some countries (e.g., in
Spain 5 out of 6 elections were held early), and very infrequent in others (e.g.,
in Sweden and Norway there were no early elections in a total of 12 elections).
Also reported in Table 1 are the political and civil freedom, economic freedom,
and press freedom rankings. These rankings were compiled by Freedom House,
a nonpro®t, nonpartisan organization dedicated to promoting democracy
around the world. 20 The combined political & civil rights rankings range from
2 (most free) to 14 (least free). The economic freedom rankings range from 6
(least free) to 16 (most free), while the press freedom rankings range from 5
(most free) to 66 (least free). 21 The last set of columns of Table 1 reports
descriptive statistics for the CARs by country, over the time period that starts
two weeks prior to the election week and ends four weeks after the election,
denoted ÿ2; 4. Abnormal returns were computed relative to the average
18
We recognize that our results are limited by the data available; thus, there may be selection
bias. Future research is warranted to re-examine these issues as more countries adopt democratic
processes and as ®nancial markets develop further.
19
An early election is de®ned as an election that took place at least three months prior to the
original date set at the beginning of the governmentÕs tenure.
20
Freedom House was established by Eleanor Roosevelt and Wendell Willkie in 1941. It
conducts programs to promote an engaged US foreign policy, monitor human rights and elections,
sponsor public education campaigns, oer training and technical assistance to promote democracy
and free market reforms, and support the rule of law, and eective local governance. Freedom
House compiles its rankings annually, based on comparative surveys covering a wide number of
countries around the globe. The survey and analysis that leads to the rankings are based on
universal criteria, not solely American or even Western concepts of freedom. Rather, the starting
point is the individual. Freedom House recognizes dierences across regions such as culture, diverse
national interests, and varying stages of economic development.
21
The rankings provided by Freedom House re¯ect mechanical computation and judgement.
Additional details regarding the methodology used by Freedom HouseÕs survey teams can be found
in the introductory section of each survey (and other Freedom House publications).
1584
Number
of
elections
Panel A. All countries
129
Panel B. By country
Australia
8
Austria
9
Belgium
6
Canada
5
Chile
1
Denmark
9
Finland
3
France
7
Germany
6
Greece
2
Indonesia
1
Ireland
1
Japan
7
Jordan
1
Korea
2
(South)
Incumbent
wins
Avg.
Number
tenure
of early
(months) elections
Freedom rankingsa
Political
and civil
Descriptive statistics for CAR ÿ2; 4b
Economic
Press
Mean
Median
Minimum
Maximum
73
76.12
53
3
14
19
0.01940
0.00892
)0.33013
0.47819
6
5
2
1
1
4
2
5
5
0
1
0
4
1
2
57.75
71.33
41.17
60.60
48.00
73.56
43.67
66.00
101.30
23.50
122.00
41.00
100.30
96.00
100.50
6
2
4
3
0
8
0
2
2
2
0
1
6
0
0
2
2
3
2
4
2
2
2
3
4
12
2
3
8
4
14
15
15
15
13
16
14
15
15
12
6
15
13
10
7
10
12
10
11
30
9
15
26
11
27
77
19
20
48
25
0.01332
)0.00060
0.10785
)0.01133
0.25330
0.00550
0.00043
0.05463
)0.05013
0.24162
0.08030
0.08071
)0.01323
)0.10064
0.06420
0.04765
0.00892
0.07364
0.02793
0.25330
)0.01734
)0.01808
0.07456
)0.06346
0.24162
0.08030
0.08071
0.07950
)0.10064
0.06420
)0.11862
)0.06852
0.01740
)0.20229
0.25330
)0.08477
)0.03014
)0.33013
)0.17493
0.00736
0.08030
0.08071
)0.31102
)0.10064
0.00672
0.08571
0.05757
0.22472
0.09776
0.25330
0.15340
0.04951
0.29013
0.11744
0.47589
0.08030
0.08071
0.15484
)0.10064
0.12169
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Table 1
Descriptive statistics for 129 international political elections for the sample period 1974±1995, by country
a
1
2
2
6
2
5
1
2
2
6
6
7
5
1
3
1
4
1
2
2
1
1
3
1
1
2
5
4
3
5
1
0
0
3
108.00
92.00
79.00
55.67
50.00
64.80
29.00
76.50
52.50
161.20
64.50
61.71
143.20
96.00
36.00
47.00
91.00
0
1
0
3
0
0
0
0
0
2
5
0
0
0
3
1
2
2
9
7
2
2
2
7
5
2
9
3
2
2
4
6
9
3
15
12
8
16
16
15
12
10
14
12
15
16
14
11
12
11
16
10
61
52
14
6
5
56
46
17
66
19
10
9
28
34
65
22
)0.01865
0.04197
0.01898
)0.01156
)0.10723
0.03901
0.23070
0.16148
0.07033
)0.00088
)0.01835
0.01030
)0.03744
)0.12608
)0.00100
0.47819
0.00460
)0.01865
0.04197
0.01898
)0.01467
)0.10723
0.02993
0.23070
0.16148
0.07033
)0.01735
)0.01871
0.01206
)0.00099
)0.12608
)0.05150
0.47819
)0.01956
)0.01865
)0.01722
0.00692
)0.11374
)0.15194
)0.03004
0.23070
0.10082
)0.02368
)0.08253
)0.19478
)0.10738
)0.29179
)0.12608
)0.10963
0.47819
)0.08474
)0.01865
0.10116
0.03105
0.09517
)0.06252
0.12317
0.23070
0.22215
0.16433
0.12148
0.16490
0.11898
0.12705
)0.12608
0.15813
0.47819
0.14227
5
2
86.80
0
2
16
14
)0.00011
)0.01614
)0.02421
0.02933
Freedom rankings are compiled and reported by Freedom House. Low values of press freedom and political and civil freedom signify high levels of
freedom. In contrast, high levels of economic freedom signify higher levels of freedom. In panel A, we report median freedom rankings across all
countries.
b
Cumulative abnormal returns (CARs) are computed for the window ÿ2; 4 around the election. Abnormal returns are computed relative to average
returns in the same country over the 100-week period prior to the window ÿ4; 4. All CARs are stated in decimal form.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Luxembourg
Malaysia
Mexico
Netherlands
New Zealand
Norway
Peru
Philippines
Portugal
Singapore
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
United
Kingdom
United States
1585
1586
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
return in the same country over the 100-week period prior to the ÿ4; 4
window.
The descriptive statistics for the full CAR ÿ2; 4 window dier dramatically across nations. For example, TurkeyÕs lone election produces a sevenweek CAR of 47.8%. Other large positive CARs in excess of 25% over this
seven-week window occurred in Chile, France and Greece. Extremely low
CARs of less than negative 25% occurred in France, Japan and Switzerland.
Notice that these extreme observations are not relegated to only small
emerging markets. Further, we note that vast dierences in CARs occur even
within the same country, at dierent points in time. For example, in GreeceÕs
two elections we observe one CAR of virtually zero, and another of almost
50%. Similarly, in France the observed seven-week CARs for seven elections
ranged from )33% to 29%. It is precisely this dispersion in CARs across nations and over time that we intend to investigate. 22
4.2. Event study of stock indices' returns around international election dates
We employ an event study methodology to examine country index reactions around the week of an election t 0. Two methods are used to calculate abnormal returns (ARs) and CARs: (i) mean-adjusted residuals on a
country-by-country basis using average country index weekly returns calculated over a 100-week period from t ÿ104 to t ÿ5, and (ii) a single factor
market model (see Brown and Warner, 1985), where the MSCI world index is
used as the proxy for the world market portfolio. The second method employs
Scholes and Williams (1977) alphas and betas with the market model parameters estimated using country index returns over the period from t ÿ104
to t ÿ5.
Weekly index values are reported as of the close of business every Friday.
We de®ne week zero as the week of the election or the week ending on the ®rst
Friday following the announcement of the election result. Thus, if an election
occurs on a Friday, Saturday, or Sunday, week zero is de®ned as the week
which ends on the following Friday; this is the ®rst week that ends with
knowledge of the election result. We de®ne the event window to be ÿ2; 0, the
three weeks starting at t ÿ2 (the second week before the election) and ending
at t 0 (the election week). We choose this window because it includes
the periods with the most potential for uncertainty resolution leading up to
22
Of course, this dramatic variability within the sample implies that relatively large economic
dierences from zero, and across subsamples, will be required to reject the null hypotheses
considered.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1587
an election 23 ARs and CARs are also estimated for a two-week pre-event
period
t ÿ4 to t ÿ3
and
a
four-week
post-event
period
t 1 to t 4. The two-week pre-event period is not included in the estimation window to avoid any possible election eects that might bias our
estimated parameters. 24 The four-week post-event period is examined to determine the magnitude of uncertainty resolution that occurs after the voting
outcome is known, e.g., in the case where the vote was close and required
coalition building, or if a runo election was required. 25 Finally we examine
the entire period ÿ2; 4 to determine whether the CARs represent persistent
or transitory eects. 26
5. Empirical results
In this section we present the event study results. We ®nd that event period
country index returns are generally positive and signi®cant, and that this eect
is strongest in the two weeks prior to the election week (i.e., t ÿ2 and
t ÿ1). We also ®nd that the eects are stronger when elections are classi®ed
based on several characteristics, such as election timing, country freedom
rankings, economic performance and election outcome, and for some interactions of these primary factors.
5.1. Pooled sample results
Table 2 reports AR results for the event weeks between week ÿ4 and week
4 and CARs for the two week pre-event period ÿ4; ÿ3, the event window
23
Because of how week 0 is de®ned, it may include returns from the ®nal days before the election
date (e.g., if there is a Thursday election, week 0 will include the returns from the Monday±
Thursday period prior to knowledge of the actual election outcome) or from days following the
election outcome (e.g., if there is a Friday election, the following Monday will be the ®rst trading
day re¯ecting market reactions to ®nal knowledge of the election outcome but week 0 will contain
returns from the following Tuesday±Friday period that occur after the election outcome is known).
We choose to include week 0 in our event window of analysis because of its potential to capture the
®nal resolution of uncertainty prior to knowledge of the election outcome (cases like the ®rst
example). We ®nd that excluding week 0 from the event window actually strengthens the
signi®cance of our results.
24
The length of the campaign varies from country to country, with a typical campaign lasting 4±
8 weeks. However, the most intense campaigning, media coverage and polling occur during the last
two weeks prior to the election.
25
Typically a runo election occurs 2±4 weeks after the initial vote.
26
We also examine CARs over the windows of ÿ4; ÿ1 and ÿ4; 4 instead of ÿ2; 0 and
ÿ2; 4 and ®nd the results to be similar in magnitude and signi®cance (although signi®cance is
weaker due to the relative lack of uncertainty resolution and the presence of additional noise in the
earlier weeks). These results are available from the authors upon request.
1588
Table 2
Announcement period return results for relative weeks )4 to +4 for country indices around 129 political elections
Week
Comparison period adjusteda
Median
AR (%)
Panel A. Abnormal return (AR) results
)4
0.14
0.15
)3
)0.33
)0.17
)2
0.50
0.57
)1
0.82
1.03
0
)0.19
)0.43
1
)0.06
0.20
2
0.43
0.41
3
)0.14
0.04
4
0.58
0.21
P-value
(t-test)
P-value
(Wilcoxon
Rank test)
Percentage
positive (%)
Average
AR (%)
Median
AR (%)
P-value
(t-test)
P-value
(Wilcoxon
Rank test)
0.6579
0.2169
0.0394
0.1073
0.4895
0.8294
0.1340
0.6763
0.6751
0.5996
0.3820
0.0159
0.0475
0.2935
0.7391
0.0909
0.6799
0.3833
52.71
46.51
62.02
58.14
41.86
52.71
55.81
50.39
53.49
)0.09
)0.28
0.31
0.99
)0.21
)0.10
0.22
)0.10
0.63%
0.01
)0.15
0.30
0.60
)0.55
)0.15
0.25
)0.14
0.05%
0.7849
0.2399
0.1790
0.0372
0.4240
0.6948
0.4027
0.7528
0.0430
0.6409
0.1554
0.1154
0.0276
0.2714
0.7998
0.5043
0.4041
0.2524
P-value
(Wilcoxon
Rank test)
Percentage
positive (%)
Average
CAR (%)
Median
CAR (%)
P-value
(t-test)
P-value
(Wilcoxon
Rank test)
Panel B. Cumulative abnormal return (CAR) results
Weeks
Average
Median
P-value
CAR (%)
CAR (%)
(t-test)
ÿ4; ÿ3
ÿ2; 0
1; 4
)0.18
1.12
0.81
)0.44
1.30
0.54
0.6923
0.0737
0.2501
0.6544
0.0161
0.3632
46.51
58.14
55.04
)0.36
1.09
0.64
)0.32
1.07
0.44
0.4108
0.0607
0.3223
0.2850
0.0422
0.6243
ÿ2; 4
1.93
0.89
0.0692
0.0868
55.81
1.74
1.24
0.0712
0.1493
a
Comparison period adjusted abnormal returns are computed relative to the average return in the same country over the 100-week period from week
ÿ104 to week ÿ5.
b
Market adjusted, equal weighted abnormal returns are computed using Scholes±Williams betas to adjust for nonsynchronous trading.
c
We examine whether average and median CARs are signi®cantly dierent from zero using two-tailed tests (t-test for averages and Wilcoxon test for
medians).
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
Average
ARc (%)
Market adjusted, equal weighted equity
(Scholes±Williams betas)b
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1589
ÿ2; 0, the four-week post-event period 1; 4, and the period ÿ2; 4 for
the total sample of 129 elections. The reported results are based on the countryby-country adjustment (comparison period) method, and the market adjusted
method.
The average AR for the total sample using the comparison period adjusted
method is statistically signi®cant at the 5% level for week ÿ2. 27 The Wilcoxon
rank test reveals that for weeks ÿ2 and ÿ1 the median ARs are signi®cantly
higher than zero (again at the 5% level). Using the market model, we ®nd
signi®cance (at the 5% level) for average ARs over weeks ÿ1 and 4 and for
the median AR for week ÿ1. The event period ÿ2; 0 average CAR based on
the comparison period-adjusted method is 1.12%; the ÿ2; 4 average CAR is
1.93% (both are signi®cant at the 10% level). The ÿ2; 0 and ÿ2; 4 median
CARs are also signi®cantly larger than zero at the 5% and 10% levels, respectively. Because the CARs using the market-adjusted method are very
similar to the comparison period results, the former are omitted for brevity in
the remainder of the paper.
The results in Table 2 are consistent with the ®rst hypothesis; there is a
positive market reaction in the two-week period leading up to election dates
and this eect persists through the four-week period following the election.
Note, though, that the CARs over the post election period 1; 4 are not
statistically signi®cant and therefore we cannot reject the null hypothesis in
favor of hypothesis two. The pattern described above is similar for both
methods of CAR computation.
In order to identify the factors determining election period abnormal returns, additional analysis is required. In the following sections we examine
whether the positive abnormal returns are driven by factors that constitute
characteristics of the election process and the socio-economic environment in
which the elections take place. Such factors are proxied by country freedom
rankings, pre-election economic performance, election outcome, election timing, and interactions between these factors.
5.2. Results by election timing, country freedom ranking, economic performance,
and election outcome
We begin the analysis related to the third and fourth hypotheses by incorporating dierent individual factors that may shape the nature of the election
and the amount of uncertainty related to election outcomes. The ®rst such
factor examined is the timing of the election. The incumbent government may
have the option of calling an early election to improve their chances of
27
P-values in all tables are from two-tailed tests and these are the default P-values reported in
the text. Given the nature of our hypotheses, it is actually appropriate to use less conservative onetailed tests.: We explicitly state when one-tailed test P-values are used in the text.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
retaining control, or an early election may result because of pressure applied by
the parliament or other extraordinary country-speci®c events. We argue that
the magnitude of uncertainty resolution immediately prior to early-held elections is greater than in the case of on-time elections. We therefore group
elections that were held three or more months prior to the originally scheduled
date as `early'. The remaining elections are categorized as `not early'.
The average and median CARs for the ÿ2; 0 and ÿ2; 4 windows for the
early and not-early elections can be found in Panel A of Table 3. 28 The ÿ2; 0
average and median CARs are signi®cantly positive at the 10% level for the
early group, however for the not-early group only the median ÿ2; 0 CAR is
signi®cantly larger than zero. Using the F statistic to test equality of means and
the Kruskal±Wallis statistic to test equality of medians, we ®nd that the early
and not-early subsamples are not signi®cantly dierent from each other. Thus,
based on the election-timing classi®cation, we cannot reject the null hypothesis
(of equal CARs) in favor of hypothesis four (that CARs for high-uncertainty
events are greater than CARs for low-uncertainty events).
Another possible explanation for the existence of abnormal returns around
election dates is that the eect is concentrated in countries where information
about the government and its policies is not usually readily available. The
countries that ®t this pro®le are aggregated to form the `less-free' group; the
remaining are grouped as `free'. A country is categorized as free if it is free
according to at least two out of three Freedom House measures on political,
economic, or press freedom; otherwise we classify it as less free. 29 Panel B of
Table 3 shows that the average and median CARs are signi®cantly larger than
zero for the less-free group only. In addition, both the average and median
ÿ2; 0 CAR and the average ÿ2; 4 CAR for the less-free group are greater
than the free group (signi®cant at the 5% level for the ÿ2; 0 CAR and at the
10% level for the ÿ2; 4 CAR). This is consistent with the notion that
countries that are less free are associated with more informational asymmetries, and therefore more uncertainty resolution in the market near the election
date. Based on the freedom classi®cation, we reject the null hypothesis in favor
of hypothesis four.
The next factor we examine is past economic performance. Three dimensions of economic performance are measured: the in¯ation rate, the real GDP
growth rate, and the unemployment rate. Previous research, such as Fair (1978,
1982) and Burdekin (1988), ®nds that these variables are important determi28
CARs for the windows ÿ4; ÿ3 and 1; 4 were also calculated for each panel of Table 3.
These results are not presented as the means and medians for these windows are not signi®cantly
dierent from zero at the 10% level.
29
For each Freedom House measure we determine the median score and assign `free' or `less free'
depending on which side of the median a country falls. Ties at the median are classi®ed according
to an additional qualitative assessment provided by Freedom House in their surveys.
C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
1591
Table 3
Examining cumulative abnormal returns by primary classi®cations for the event window ÿ2; 0
and the window ÿ2; 4
CAR ÿ2; 0
Panel A. By election timing
Election called early N 53
Election not called early N 76
P-value for dierences in subsamplesb
Panel B. By country freedom measures
Free based on 2 out of 3 measures
N 97
Less-free N 32
P-value for dierences in subsamples
CAR ÿ2; 4
Meana
Median
Mean
Median
1.61%
0.79%
0.525
1.39%
1.29%
0.966
2.67%
1.43%
0.566
1.60%
0.79%
0.818
0.24%
0.96%
0.75%
0.74%
3.81%
0.013
2.75%
0.033
5.54%
0.051
4.31%
0.150
Panel C. By economic performance
Good economic performance on 2 or
more measures N 66
Poor economic performance N 63
P-value for dierences in subsamples
0.76%
1.10%
0.80%
0.63%
1.51%
0.549
1.72%
0.510
3.13%
0.274
1.60%
0.383
Panel D. By incumbent performance
Incumbent wins N 73
Incumbent loses N 56
P-value for dierences in subsamples
0.66%
1.74%
0.393
0.96%
1.97%
0.239
1.42%
2.62%
0.574
0.69%
1.67%
0.842
a
Comparison period adjusted abnormal returns are used for all cumulative abnormal returns. We
examine whether average and median CARs are signi®cantly dierent from zero using two-tailed
tests (t-test for averages and Wilcoxon test for medians).
b
F-test P-values are reported for dierences in subsample means, and the Kruskal±Wallis test
P-values are reported for dierences in medians. Reported P-values are from two-tailed tests.
*
Signi®cance at the 10% level.
**
Signi®cance at the 5% level.
***
Signi®cance at the 1% level.
nants of voter decisions. We assume that voters assess each of the three factors
by: (i) comparing the current economic variable to its value in the previous
year, (ii) comparing the average value of the economic indicator for the period
of the current administrationÕs tenure to that of the period of the previous
administrationÕs tenure, and (iii) comparing the average value of the economic
indicator for the current administrationÕs period of reign with the average value
of the indicator for the world over the same period. 30 A countryÕs performance
30
World averages are OECD averages. We employ the industrial countriesÕ OECD average
in¯ation and GDP growth for comparisons of the world with industrial countries, and the
developing countriesÕ OECD average in¯ation and real GDP growth for the comparison of the
world with developing countries. We use only one world unemployment rate (all OECD countriesÕ
average) for comparison with all countries.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
with regards to an individual economic variable is characterized as `good' if at
least two of the three comparisons were favorable, otherwise performance for
that variable is classi®ed as `poor'. 31 For example, if in¯ation during the
election year was: higher than the previous yearÕs in¯ation, lower on an average
for the current administrationÕs governing period compared to the average
in¯ation rate during the last governmentÕs tenure, and lower than the average
OECD in¯ation rate for the period, then the performance with respect to in¯ation will be classi®ed as good (i.e., in¯ation was low). We divide the total
sample into two subsamples based on each countryÕs aggregate economic
performance. A countryÕs aggregate economic performance is classi®ed as good
(poor) if at least two of the three individual economic variables (in¯ation, real
GDP growth, and unemployment) are classi®ed as good (poor).
The CARs for the good and the poor aggregate economic performance
subsamples are shown in Panel C of Table 3. The magnitude of the marketÕs
reaction to elections is signi®cantly greater than zero only when the past economic performance was poor. The average and median ÿ2; 4 CAR in that
case is 3.13% and 1.60%, respectively (both are signi®cant at the 10% level).
The median ÿ2; 0 CAR is 1.72% (signi®cant at the 5% level). The average and
median CARs for the good performance case are not signi®cantly dierent
from zero. These results suggest that the positive election eect is primarily
concentrated in cases where the countryÕs economic performance was poor,
however, tests of the dierences of means or medians indicate that these two
subsamples are not signi®cantly dierent from each other. 32
Panel D of Table 3 reports CAR results by election outcome. When the
incumbent loses the election the average (1.74%) and median (1.97%) CARs are
positive for the ÿ2; 0 window (signi®cant at the 10% and 1% levels, respectively). On the other hand, the average and median CARs are not signi®cant
when the incumbent wins the election. It appears that the marketÕs response is
greater when the election outcome constitutes a change in the status quo (i.e.,
the incumbent loses) which may be associated with more uncertainty. However, similar to panels A and C, the means and medians are not signi®cantly
dierent across the two subsamples.
Thus we have several classi®cations (denoted primary factors) that impact the
magnitude of the marketÕs response around political elections. The discussions
of hypotheses three and four suggest that the interactions of economic performance and election outcome are also potentially important. In addition, the
degree of uncertainty surrounding an election may be related to a combination
31
Low in¯ation, low unemployment, and high real GDP growth are classi®ed as good
performance; while high in¯ation, high unemployment and low real GDP growth are classi®ed as
poor performance.
32
These ®ndings are qualitatively unchanged for similar classi®cations based on alternative
de®nitions of good and poor economic performance that do not use all three factors.
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C. Pantzalis et al. / Journal of Banking & Finance 24 (2000) 1575±1604
of the factors investigated. In the next sections we examine various interactions
of the primary factors and how they aect the cumulative abnormal returns.
5.3. Results across favorable and unfavorable events
The third hypothesis is drawn from the UIH of Brown et al. (1988) who
state tha