Political elections and the resolution of
uncertainty: The international evidence
Christos Pantzalis
a,*, David A. Stangeland
b,1,
Harry J. Turtle
c,2aCollege of Business Administration, University of South Florida, Tampa, FL 33620, USA b
Faculty of Management, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V4 c
College of Business and Economics, Washington State University, Pullman, WA 99164-4746, USA
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 ab-normal return during the two-week period prior to the election week. The positive re-action 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 ab-normal returns leading up to the elections (i) in less free countries won by the opposi-tion, 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
Journal of Banking & Finance 24 (2000) 1575±1604
www.elsevier.com/locate/econbase
*
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).
1Tel.: +1-204-474-6743; fax: +1-204-474-7545. 2Tel.: +1-509-335-3797; fax: +1-509-335-3857.
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 polit-ical 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 partici-pants revise their prior probability distributions of policies to be implement-ed 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 out-come 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 un-certainty inducing surprise. In this case, positive price changes should be ex-pected 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 an-ticipate 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.,
3Numerous articles in the popular press support this view. For example, see Fisher (1996), Martin (1996) and Price (1995) among others.
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 eventsÕties to ®nancial market performance has been the subject of a plethora of studies.4The 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.5Other 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.
(1985) and Stoken (1994) found evidence in support of the presidential election cycle theory.6 Foerster (1994) shows that the US presidential elec-tion 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 ex-amined 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 pres-idential elections with a few exceptions.8Our study is, to our knowledge, the ®rst study that examines stock market behavior around elections on an in-ternational 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 investorsÕexpectations:
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.
8Foerster and Schmitz (1997) looked at international stock returns
Õrelation 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.
Pricetÿ1
Pk
i1EPricetjoutcomei Probability outcomei
1ERtÿ1;t
;
wheretis the time period when the election result is ®nalized and realized by the market;kis the number of possible election outcomes; andERtÿ1;tis the
risk-adjusted expected return over the time period tÿ1 to t. The price that actually occurs at timetwill be based on investorsÕrevised expectations, given the outcome they observe on that date.
On average, the observed return over the period ending with the election should beERtÿ1;t. According to the UIH, though, this return is likely to be higher than the average return over periods where no event-induced un-certainty exists. When election-induced unun-certainty 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 pre-ceding 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 electionsÕimpacts 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 re-turns. 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
9We 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.
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) eco-nomic 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 re-duction 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 perfor-mance. 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 un-certainty 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 reso-lution ± 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-10Brown 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.
cally not readily or widely available; i.e., there is an information asymmetry between the electorate and the government. Perhaps the media are only par-tially independent of the government, or polls are uncommon and possibly not sophisticated enough to adequately capture voter sentiment prior to the elec-tion process. In addielec-tion, countries with a weak democratic tradielec-tion, or low economic freedom, may also be prone to such informational asymmetries. Thus, the election process results in a substantial increase of information dis-semination 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 re-vised 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 re-liable 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 clas-si®cations of elections, the hypothesis is the same and is based on the amount of uncertainty. Observed abnormal returns associated with uncertainty reso-lution should be higher for higher-uncertainty events than for lower-uncer-tainty 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 method-ology 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 election-related 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.
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.13The 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 indicesÕreturns.16
The MSCI database of weekly indices provides our sample of returns data. We obtain political elections information for all countries in the MSCI dat-abase 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).17This information is found in several editions of the Economist's World Atlas of Elections, Facts on File: World Political Al-manac, and theElections around the worlddatabase, and is veri®ed with articles
from theNew 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 electionsÕdata 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
13We 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.
14These 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.
15The 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).
17We 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.
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 perfor-mance 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 byFreedom House,
a nonpro®t, nonpartisan organization dedicated to promoting democracy around the world.20The 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
18We 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.
19An 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.
20Freedom 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 Housecompiles 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.
21The rankings provided by
Freedom House re¯ect mechanical computation and judgement. Additional details regarding the methodology used byFreedom HouseÕs survey teams can be found in the introductory section of each survey (and otherFreedom Housepublications).
Table 1
Descriptive statistics for 129 international political elections for the sample period 1974±1995, by country
Number
Freedom rankingsa Descriptive statistics for CAR ÿ2
;4b
Political and civil
Economic Press Mean Median Minimum Maximum
Panel A. All countries
129 73 76.12 53 3 14 19 0.01940 0.00892 )0.33013 0.47819
Panel B. By country
Australia 8 6 57.75 6 2 14 10 0.01332 0.04765 )0.11862 0.08571
Austria 9 5 71.33 2 2 15 12 )0.00060 0.00892 )0.06852 0.05757
Belgium 6 2 41.17 4 3 15 10 0.10785 0.07364 0.01740 0.22472
Canada 5 1 60.60 3 2 15 11 )0.01133 0.02793 )0.20229 0.09776
Chile 1 1 48.00 0 4 13 30 0.25330 0.25330 0.25330 0.25330
Denmark 9 4 73.56 8 2 16 9 0.00550 )0.01734 )0.08477 0.15340
Finland 3 2 43.67 0 2 14 15 0.00043 )0.01808 )0.03014 0.04951
France 7 5 66.00 2 2 15 26 0.05463 0.07456 )0.33013 0.29013
Germany 6 5 101.30 2 3 15 11 )0.05013 )0.06346 )0.17493 0.11744
Greece 2 0 23.50 2 4 12 27 0.24162 0.24162 0.00736 0.47589
Indonesia 1 1 122.00 0 12 6 77 0.08030 0.08030 0.08030 0.08030
Ireland 1 0 41.00 1 2 15 19 0.08071 0.08071 0.08071 0.08071
Japan 7 4 100.30 6 3 13 20 )0.01323 0.07950 )0.31102 0.15484
Jordan 1 1 96.00 0 8 10 48 )0.10064 )0.10064 )0.10064 )0.10064
Korea (South)
2 2 100.50 0 4 7 25 0.06420 0.06420 0.00672 0.12169
Luxembourg 1 1 108.00 0 2 15 10 )0.01865 )0.01865 )0.01865 )0.01865
Malaysia 2 2 92.00 1 9 12 61 0.04197 0.04197 )0.01722 0.10116
Mexico 2 2 79.00 0 7 8 52 0.01898 0.01898 0.00692 0.03105
Netherlands 6 1 55.67 3 2 16 14 )0.01156 )0.01467 )0.11374 0.09517
New Zealand 2 1 50.00 0 2 16 6 )0.10723 )0.10723 )0.15194 )0.06252
Norway 5 3 64.80 0 2 15 5 0.03901 0.02993 )0.03004 0.12317
Peru 1 1 29.00 0 7 12 56 0.23070 0.23070 0.23070 0.23070
Philippines 2 1 76.50 0 5 10 46 0.16148 0.16148 0.10082 0.22215
Portugal 2 2 52.50 0 2 14 17 0.07033 0.07033 )0.02368 0.16433
Singapore 6 5 161.20 2 9 12 66 )0.00088 )0.01735 )0.08253 0.12148
Spain 6 4 64.50 5 3 15 19 )0.01835 )0.01871 )0.19478 0.16490
Sweden 7 3 61.71 0 2 16 10 0.01030 0.01206 )0.10738 0.11898
Switzerland 5 5 143.20 0 2 14 9 )0.03744 )0.00099 )0.29179 0.12705
Taiwan 1 1 96.00 0 4 11 28 )0.12608 )0.12608 )0.12608 )0.12608
Thailand 3 0 36.00 3 6 12 34 )0.00100 )0.05150 )0.10963 0.15813
Turkey 1 0 47.00 1 9 11 65 0.47819 0.47819 0.47819 0.47819
United Kingdom
4 3 91.00 2 3 16 22 0.00460 )0.01956 )0.08474 0.14227
United States 5 2 86.80 0 2 16 14 )0.00011 )0.01614 )0.02421 0.02933
a
Freedom rankings are compiled and reported byFreedom House. Low values ofpress freedomandpolitical and civil freedomsignify high levels of freedom. In contrast, high levels ofeconomic freedomsignify higher levels of freedom. In panel A, we report median freedom rankings across all countries.
bCumulative abnormal returns (CARs) are computed for the window
ÿ2;4around 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±16
04
return in the same country over the 100-week period prior to the ÿ4;4
window.
The descriptive statistics for the full CAR ÿ2;4window dier dramat-ically across nations. For example, TurkeyÕs lone election produces a seven-week 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
na-tions 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 reac-tions around the week of an election t0. Two methods are used to cal-culate abnormal returns (ARs) and CARs: (i) mean-adjusted residuals on a country-by-country basis using average country index weekly returns calcu-lated over a 100-week period fromt ÿ104 tot 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 pa-rameters estimated using country index returns over the period fromt 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 att ÿ2 (the second week before the election) and ending
at t0 (the election week). We choose this window because it includes
the periods with the most potential for uncertainty resolution leading up to
22Of 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.
an election23 ARs and CARs are also estimated for a two-week pre-event
period t ÿ4 tot ÿ3 and a four-week post-event period
t 1 tot 4. The two-week pre-event period is not included in the es-timation window to avoid any possible election eects that might bias our estimated parameters.24 The four-week post-event period is examined to de-termine 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.25Finally we examine the entire period ÿ2;4to 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 inter-actions of these primary factors.
5.1. Pooled sample results
Table 2 reports AR results for the event weeks between weekÿ4 and week
4 and CARs for the two week pre-event period ÿ4;ÿ3, the event window
23Because 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;ÿ1and ÿ4;4instead of ÿ2;0and
ÿ2;4and ®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.
Table 2
Announcement period return results for relative weeks)4 to +4 for country indices around 129 political elections
Week Comparison period adjusteda Market adjusted, equal weighted equity
(Scholes±Williams betas)b
Panel A. Abnormal return (AR) results
)4 0.14 0.15 0.6579 0.5996 52.71 )0.09 0.01 0.7849 0.6409
)3 )0.33 )0.17 0.2169 0.3820 46.51 )0.28 )0.15 0.2399 0.1554
)2 0.50 0.57 0.0394 0.0159 62.02 0.31 0.30 0.1790 0.1154
)1 0.82 1.03 0.1073 0.0475 58.14 0.99 0.60 0.0372 0.0276
0 )0.19 )0.43 0.4895 0.2935 41.86 )0.21 )0.55 0.4240 0.2714
1 )0.06 0.20 0.8294 0.7391 52.71 )0.10 )0.15 0.6948 0.7998
2 0.43 0.41 0.1340 0.0909 55.81 0.22 0.25 0.4027 0.5043
3 )0.14 0.04 0.6763 0.6799 50.39 )0.10 )0.14 0.7528 0.4041
4 0.58 0.21 0.6751 0.3833 53.49 0.63% 0.05% 0.0430 0.2524
Panel B. Cumulative abnormal return (CAR) results
Weeks Average
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 weekÿ5.
b
Market adjusted, equal weighted abnormal returns are computed using Scholes±Williams betas to adjust for nonsynchronous trading.
c
ÿ2;0, the four-week post-event period 1;4, and the period ÿ2;4for
the total sample of 129 elections. The reported results are based on the country-by-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 weekÿ2.27The Wilcoxon rank test reveals that for weeksÿ2 andÿ1 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 weeksÿ1 and 4 and for the median AR for weekÿ1. The event period ÿ2;0average CAR based on
the comparison period-adjusted method is 1.12%; the ÿ2;4average CAR is
1.93% (both are signi®cant at the 10% level). The ÿ2;0and ÿ2;4median
CARs are also signi®cantly larger than zero at the 5% and 10% levels, re-spectively. 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 re-turns, 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 tim-ing, 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 incor-porating 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 defaultP-values reported in the text. Given the nature of our hypotheses, it is actually appropriate to use less conservative one-tailed tests.:We explicitly state when one-tailed testP-values are used in the text.
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 elec-tions is greater than in the case of on-time elecelec-tions. 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;0and ÿ2;4windows for the
early and not-early elections can be found in Panel A of Table 3.28The 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;0CAR is
signi®cantly larger than zero. Using theFstatistic 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 Housemeasures on political, economic, or press freedom; otherwise we classify it as less free.29Panel 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;0CAR and the average ÿ2;4CAR for the less-free group are greater
than the free group (signi®cant at the 5% level for the ÿ2;0CAR 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 asymme-tries, 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 dimen-sions 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
determi-28
CARs for the windows ÿ4;ÿ3and 1;4were 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.
29For each
Freedom Housemeasure 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 byFreedom Housein their surveys.
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.30A countryÕs performance
Table 3
Examining cumulative abnormal returns by primary classi®cations for the event window ÿ2;0
and the window ÿ2;4 P-value for dierences in subsamplesb 0.525 0.966 0.566 0.818
Panel B. By country freedom measures Free based on 2 out of 3 measures
N97
0.24% 0.96% 0.75% 0.74%
Less-free N32 3.81% 2.75% 5.54% 4.31%
P-value for dierences in subsamples 0.013 0.033 0.051 0.150
Panel C. By economic performance Good economic performance on 2 or more measures N66
0.76% 1.10% 0.80% 0.63%
Poor economic performance N63 1.51% 1.72% 3.13% 1.60% P-value for dierences in subsamples 0.549 0.510 0.274 0.383
Panel D. By incumbent performance
Incumbent wins N73 0.66% 0.96% 1.42% 0.69%
Incumbent loses N56 1.74% 1.97% 2.62% 1.67%
P-value for dierences in subsamples 0.393 0.239 0.574 0.842 aComparison 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-testP-values are reported for dierences in subsample means, and the Kruskal±Wallis test P-values are reported for dierences in medians. ReportedP-values are from two-tailed tests. *
World averages are OECD averages. We employ the industrial countriesÕOECD average in¯ation and GDP growth for comparisons of the world with industrial countries, and the developing countriesÕOECD 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.
with regards to anindividualeconomic 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., inin-¯ation was low). We divide the total
sample into two subsamples based on each countryÕs aggregate economic
performance. A countryÕsaggregateeconomic 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 eco-nomic performance was poor. The average and median ÿ2;4CAR in that case is 3.13% and 1.60%, respectively (both are signi®cant at the 10% level). The median ÿ2;0CAR 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;0window (signi®cant at the 10% and 1% levels,
respec-tively). 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. How-ever, 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 perfor-mance 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.
32These ®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.
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 that when investors have decreasing absolute risk aversion the association of positive returns with reductions in uncertainty should be magni®ed when
uncertainty is resolved around `bad' news events compared to ÔgoodÕ news
events. 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. Panel A of Table 4 presents CARs for favorable and unfavorable subsamples. The mean CARs for the unfavorable
Table 4
Examining favorable and unfavorable event cumulative abnormal returns for the event window
ÿ2;0and the window ÿ2;4. A favorable event is de®ned as an incumbent loss (win) following poor (good) economic performance
CAR ÿ2;0 CAR ÿ2;4
Meana Median Mean Median
Panel A. By favorableness of outcome
Favorable outcome N76 0.84% 1.45% 1.48% 1.21%
Unfavorable outcome N53 1.53% 1.27% 2.60% 0.74%
P-value for dierences in subsamplesb 0.588 0.893 0.602 0.712
Panel B. By favorableness of outcome for the subsample of less-free countries
Favorable outcome N21 4.20% 2.43% 5.70% 0.74%
Unfavorable outcome N11 3.05% 5.22% 3.06% 7.95%
P-value for dierences in subsamples 0.728 0.953 0.939 0.827
Panel C. By favorableness of outcome for the subsample of early elections
Favorable outcome N31 1.11% 0.54% 1.27% )1.58%
Unfavorable outcome N22 2.31% 2.39% 4.65% 5.21%
P-value for dierences in subsamples 0.534 0.304 0.372 0.097
Panel D. By favorableness of outcome and election timing for the subsample of less-free countries Not early and favorable N5 5.20% 4.82% 8.84% 10.12% Early and unfavorable N11 4.83% 6.92% 7.66% 8.03% P-value for dierences in subsamples 0.912 0.865 0.825 0.692 aComparison 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).
bF-testP-values are reported for dierences in subsample means, and the Kruskal±Wallis test P-values are reported for dierences in medians. ReportedP-values are from two-tailed tests. *Signi®cance at the 10% level.
**Signi®cance at the 5% level.
Table 5
Low- versus high-uncertainty event cumulative abnormal returns for the event window ÿ2;0and the window ÿ2;4
CAR ÿ2;0 CAR ÿ2;4
Meana Median Mean Median
Panel A. By country freedom and election outcome
Country of election is less-free and incumbent lost N11 8.46% 6.69% 14.31% 10.37%
Country of election is free and incumbent won N52 0.37% 0.87% 1.61% 0.82%
P-value for dierences in subsamplesb 0.001 0.002 0.002 0.025
Panel B. By election outcome for the subset of less-free countries
Incumbent lost N11 8.46% 6.69% 14.31% 10.37%
Incumbent won N21 1.37% 1.24% 0.94% 0.67%
P-value for dierences in subsamples 0.025 0.034 0.026 0.054
Panel C. By country freedom for the subset of elections where the incumbent lost
Country of election is less-free N11 8.46% 6.69% 14.31% 10.37%
Country of election is free N45 0.10% 1.10% )0.24% 0.59%
P-value for dierences in subsamples 0.000 0.001 0.001 0.012
Panel D. By country freedom and election timing
Country of election is less-free and election is held early
N15
4.07% 3.06% 5.31% 0.74%
Country of election is free and election is not held early
N59
)0.01% 1.10% 0.19% 0.52%
P-value for dierences in subsamples 0.075 0.168 0.191 0.595
1594
C.
Pantzalis
et
al.
/
Journal
of
Banking
&
Finance
24
(2000)
1575±16
Panel E. By election timing for the subset of less-free countries
Election is held early N15 4.07% 3.06% 5.31% 0.74%
Election is not held early N17 3.57% 2.43% 5.73% 5.51%
P-value for dierences in subsamples 0.873 0.806 0.943 0.692
Panel F. By election timing and election outcome
Election is held early and incumbent lost N28 3.76% 3.18% 7.17% 5.33%
Election is not held early and incumbent won N48 1.42% 1.47% 3.38% 3.05%
P-value for dierences in subsamples 0.185 0.306 0.200 0.338
Panel G. By election outcome for the subset of early elections
Incumbent lost N28 3.76% 3.18% 7.17% 5.33%
Incumbent won N25 )0.81% )1.45% )2.36% )2.29%
P-value for dierences in subsamples 0.014 0.004 0.009 0.008
Panel H. By election timing for the subset of elections where the incumbent lost
Election is held early N28 3.76% 3.18% 7.17% 5.33%
Election is not held early N28 )0.29% 0.36% )1.92% )1.73%
P-value for dierences in subsamples 0.038 0.046 0.011 0.008
aComparison 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).
bF-testP-values are reported for dierences in subsample means, and the Kruskal±Wallis testP-values are reported for dierences in medians.
ReportedP-values are from two-tailed tests.
*
Signi®cance at the 10% level.
**
Signi®cance at the 5% level.
***
Signi®cance at the 1% level.
C.
Pantzalis
et
al.
/
Journal
of
Banking
&
Finance
24
(2000)
1575±16
04
subsample are slightly higher than for the favorable subsample, but the me-dians are lower. We ®nd neither the means nor meme-dians to be signi®cantly dierent across the two subsamples.
We also investigate whether this eect may only be prevalent among high-uncertainty events. These results are presented in Panels B±D. In all cases, except one, we ®nd no signi®cant dierences between the favorable and un-favorable subsamples so we cannot reject the null hypothesis of equal CARs in favor of hypothesis three. The one exception is for higher-uncertainty early elections (Panel C) where the unfavorable subsample median ÿ2;4CAR is
greater than the favorable subsample median (signi®cant at the 10% level).
5.4. Results across high- and low-uncertainty events
According to the UIH of Brown et al. (1988), greater positive returns should be associated with greater reductions in uncertainty; this is the fourth hypothesis we test. As reported in Table 3, the comparisons of the more- and less-free subsamples partially con®rm the fourth hypothesis. However, the dierences between high- and low-uncertainty events are insigni®cant when comparing the subsamples based on election timing and election outcome. In Table 5 we report the results for subsamples constructed according to inter-actions of the primary election classi®cations. We use the interinter-actions to create subsamples with either very high or very low levels of uncertainty.
In Panel A of Table 5 we ®nd elections with incumbent losses in less-free countries to have signi®cantly higher CARs than elections with incumbent victories in more-free countries. This is consistent with our expectation that the ®rst (second) group is associated with the highest (lowest) amount of uncer-tainty resolution. The signi®cant dierences between groups, however, may be due to the higher CARs associated with elections in less-free countries as doc-umented in Table 3. In Panel B, we restrict our sample to less-free countries and compare election outcomes. We ®nd that losses by incumbents are associated with higher CARs (signi®cantly dierent ÿ2;0CARs at a 5% level for both
means and medians, and signi®cantly dierent ÿ2;4CARs at a 5% and 10%
level for means and medians, respectively). In Panel C, we restrict our sample to elections with the incumbent loss outcome to determine whether the country freedom measure is still important. We ®nd the less-free countriesÕ CARs re-main greater than the more-free countriesÕCARs (all dierences are signi®cant at the 1% level except the medians of the ÿ2;4CARs that are signi®cantly dierent at the 5% level). Based on these results, we are able to conclude that, when analyzed together, both country freedom and election outcome have in-cremental value in measuring election uncertainty and its resolution.
In Panels D and E of Table 5 we investigate whether election timing be-comes a signi®cant factor when interacting with the country freedom classi®-cations. The only signi®cant dierence across high- and low-uncertainty
subsamples is the means of the ÿ2;0 CARs (P-value of 0.075) when
com-paring early elections in less-free countries to on-time elections in more-free countries.33 It is dicult to draw conclusions on the election timing eect given the lack of signi®cant dierences across subsamples in Panel E (where only less-free countries are examined). It is possible that the dierence between subsamples in Panel D is due to the dierence between the less- and more-free subsamples and not due to the interaction with election outcome. We are therefore unable to conclude that election timing has an incremental impact on measuring election uncertainty when country freedom is also utilized.
In Panels F, G, and H, we examine interactions of election timing and elec-tion outcome. Panel F reports the comparison of early elecelec-tions with an in-cumbent losing to on-time elections with an inin-cumbent winning. We expect the ®rst (second) group to be associated with the highest (lowest) amount of un-certainty resolution but ®nd the dierences between groups to be only of mar-ginal signi®cance (at a 10% level using one-tailed tests for equality of means). However, in Panels G and H, there are signi®cant dierences between the subsamples examined. Panel G reports comparisons across election outcomes (incumbent win or loss) for early elections. As expected the CARs are signi®-cantly higher for the higher-uncertainty group containing elections with the incumbent losing (signi®cant at the 5% level for the mean ÿ2;0CAR and at
the 1% level for the median ÿ2;0 CAR and both the mean and median
ÿ2;4CAR). Panel H reports comparisons across election timing (early or
on-time) for elections with losing incumbents. Here we ®nd the early elections to have CARs signi®cantly higher than the on-time elections (signi®cant at the 5% level for the mean and median ÿ2;0CAR and the mean ÿ2;4CAR and at
the 1% level for the median ÿ2;4CAR). These results are consistent with
hypothesis four; they indicate that, when evaluated together, election timing and election outcome possess incremental value in measuring election uncertainty.
Overall, our ®ndings are consistent with hypotheses one and four: positive abnormal returns are associated with uncertainty resolution prior to election dates, and greater CARs are associated with higher-uncertainty elections. Our results do not con®rm hypotheses two or three: following elections there is a lack of signi®cant uncertainty resolution as measured by CARs, and there is little evidence of the UIH contention that CARs are higher for unfavorable events.34
33
Using a one-tailed test (consistent with hypothesis four), the median ÿ2;0 and mean
ÿ2;4CARs are also signi®cantly dierent (P-values of 0.084 and 0.096) across subsamples. 34
In a follow-up to their UIH paper, Brown et al (1993) examine changes to both total and systematic risk around major ®nancial events. Our ®ndings are consistent with the results for their subsample of risk-increasing events that are followed by eventual risk reductions. In addition, their coecient, ``which measures the incremental impact of risk changes caused by favorable surprises, is not signi®cantly dierent from zero, suggesting that risk changes following favorable and unfavorable events have the same impact on post-event stock price reactions'' (Brown et al., 1993, p. 114).
Table 6
Cross-sectional analysis of cumulative abnormal returnsafor the ÿ2;0and ÿ2;4event windows. OLS regressions of CARs on dummy variables
for various election classi®cations. Each regression considers all elections
Panel A. Primary classi®cations of electionsb;c
Event window for CAR dependent variable
Intercept Early election Free country Poor economic
performance
Panel B. Categorizing elections by Country Freedom, Incumbent Performance, and Favorable Outcomesd;e
Panel C. Categorizing elections by Timing, Incumbent Performance, and Favorable Outcomesf
Comparison period adjusted abnormal returns are used for all cumulative abnormal returns.
bCumulative abnormal returns for all events are regressed against an intercept and dummy variable for elections called early, elections in free countries,
elections in countries with poor economic performance, and elections in which the incumbent loses
CARjb0b1Earlyjb2Freejb3Poorjb4Lossjej:
cCoecient values are reported for the independent variables witht-statisticP-values (two-tailed tests) in parentheses.
dFavorable outcomes are de®ned as the combination of poor economic performance with an incumbent losing, or the combination of good economic
performance with an incumbent winning.
eCumulative abnormal returns for all events are regressed against an intercept and dummy variables for less-free elections in which the incumbent
loses, less-free elections in which the incumbent wins, less-free elections in which the outcome is favorable, free elections in which the incumbent loses, and favorable elections
CARjb0b1 1ÿFreejLossjb2 1ÿFreejWinjb3 1ÿFreejFavorablejb4FreejLossjb5Favorablejej: f
In the next section we provide a cross-sectional regression analysis of the CARs in order to provide an alternative veri®cation of our results.
5.5. Cross-sectional determinants of the CARs for the ÿ2;0and ÿ2;4event
windows
Using a regression analysis framework we examine the high-prior-uncer-tainty elections classi®ed by Harrington (1993) and identi®ed by our `Less Free', `Early', and `Incumbent Loss' categories. Using dummy variables to indicate the various categories and their interactions, we test whether these elections are associated with a greater degree of uncertainty resolution and hence higher observed cumulative abnormal returns as hypothesized by Brown et al. (1988). We add an additional dummy variable indicating `Favorable Outcomes' for elections. We classify an outcome to be favorable if prior nomic performance is good and the incumbent is re-elected or if prior eco-nomic performance is poor and the incumbent is defeated. According to the UIH of Brown et al. (1988), CARs should be less positive during uncertainty resolution around favorable events.
Panel A of Table 6 includes regressions of the CARs over the ÿ2;0and
ÿ2;4windows on the four primary factors. The only signi®cant variables in
these initial regressions are the intercepts and the coecients for the `Free' variable. Examination of the intercept in both regressions shows that after controlling for multiple factors, we still observe a signi®cantly positive CAR. The negative coecient for the freedom dummy variable shows that free countries show a markedly smaller election eect.
We continue the cross-sectional analysis by focusing on more re®ned clas-si®cations of the elections. In Panel B of Table 6, we include interaction terms between the freedom variables and incumbent performance. We also add a `Favorable OutcomeÕ variable and its interaction with theÔLess FreeÕ classi®-cation. The regression results for this model are consistent with our ®ndings in Table 5 in that the `Less Free' and `Incumbent Loss' coecients are positive and signi®cant (at the 1% level) for all regression models. The coecients for
the ÔFavorable OutcomeÕ dummy variables are mixed in sign and always
in-signi®cant.
Panel C of Table 6 includes interaction terms between the election timing variables, incumbent performance, and the `Favorable Outcome' variable. The net eect for events classi®ed as `Early Election' and `Incumbent Loss' is a positive cumulative abnormal return. This is demonstrated by the non-negative intercepts combined with a signi®cant positive coecient on the `Early Election' and `Incumbent Loss' interaction term (for the ®rst two regressions signi®cance is only at the 10% level using a one-tailed test). The `Favorable Outcome' variableÕs coecient is negative and consistent with the UIH; in the fourth regression the interaction term `Early Election' and `Favorable
Outcome' is signi®cantly negative at a 10% level using a one-tailed test con-sistent with hypothesis three. Also note, in the second regression we observe negative and signi®cant coecients for the interaction term `Early Election' and `Incumbent Win' and the interaction term `Not Early Election' and `In-cumbent Loss'. Combining these coecients with the positive and signi®cant intercept we can conclude that these two groups have no signi®cant positive CARs around the elections. To summarize, the results in Panel C are similar to those in Panel B in that the elections hypothesized to have the highest level of uncertainty resolution (early elections where the incumbent lost) exhibit the highest abnormal returns.
Overall, the cross-sectional analysisÕresults are consistent with the ®ndings in the previous tables. They con®rm that our results are in agreement with the Harrington (1993) model and the Brown et al. (1988) UIH, in that CARs are signi®cantly aected in cases where elections are characterized by informa-tional asymmetries and by attempts of the incumbent to manipulate the elec-tion outcome (elecelec-tions called early). In elecelec-tions with high degrees of uncertainty (i.e., when an incumbent loses the election despite eorts of ma-nipulation or despite having an informational advantage) market prices rise as uncertainty is resolved. We ®nd weak evidence consistent with the UIH con-tention that uncertainty resolution around unfavorable events is associated with higher CARs.
6. Conclusion
We examine stock market behavior around political election dates for 33 countries in the period 1974±1995 using an event study methodology. We ®nd a positive and signi®cant market reaction in the two weeks preceding political elections. This positive election eect is strongest for elections in less-free countries when incumbents lose. These results are generally in agreement with the models of Brown et al. (1988) and Harrington (1993). HarringtonÕs (1993) model implies that policy sensitive elections are asso-ciated with more uncertainty and are most likely to be manipulated; we proxy manipulation with early-held elections. Brown et al.Õs (1988) uncertain information hypothesis (UIH) postulates that prices should rise as uncer-tainty is resolved. It is for these policy sensitive elections and elections with prior informational asymmetries where the reduction of uncertainty is ex-pected to be highest and thus the corresponding return should be greatest. Indeed, this is what we ®nd. After decomposing elections into `Less Free' and `Early' subsamples we ®nd `Less Free' elections display larger CARs and this eect is magni®ed when the incumbent lost. We also ®nd greater CARs associated with the combined uncertainty of early elections and in-cumbent losses.
Our analysis is the ®rst of its type on an international scale and as such provides important insight into the links between stock market performance and economic and political issues for countries around the globe. We show that when there exists a higher degree of uncertainty before an election process, there is a corresponding increase in equity prices as the uncertainty is resolved.
Acknowledgements
We are grateful for the helpful comments of Linda Allen, Stephen Foerster, participants at the 1998 FMA Conference, participants of the University of Manitoba Faculty of Management Seminar Series, two anonymous referees and the editor, G.P. Szego, of the Journal of Banking and Finance. Financial support was provided by the University of Manitoba Centre for International Business Studies (Pantzalis and Stangeland) and the Social Sciences and Humanities Research Council of Canada (Turtle). Special thanks to Chris Lepholtz, who provided valuable research assistance.
References
Alesina, A., Sachs, J., 1988. Political parties and the business cycle in the United States, 1948±1984. Journal of Money, Credit, and Banking 20, 63±82.
Allen, S., 1986. The Federal Reserve Bank and the electoral cycle. Journal of Money, Credit, and Banking 18, 88±94.
Allivine, F.D., OÕNeil, D.D., 1980. Stock market returns and the presidential election cycle. Financial Analysts Journal 36, 49±56.
Atesoglu, S.H., Congleton, R., 1982. Economic conditions and national elections post-sample forecasts of the Kramer equations. American Political Science Review 76, 873±875.
Bachman, D., 1992. The eect of political risk on the forward exchange bias: The case of elections. Journal of International Money and Finance 11, 208±219.
Brown, K.C., Harlow, W.V., Tinic, S.M., 1988. Risk aversion, uncertain information, and market eciency. Journal of Financial Economics 22, 355±385.
Brown, K.C., Harlow, W.V., Tinic, S.M., 1993. The risk and required return of common stock following major price innovations. Journal of Financial and Quantitative Analysis 28, 101±116. Brown, S.J., Warner, J.B., 1985. Using daily stock returns: The case of event studies. Journal of
Financial Economics 14, 3±31.
Burdekin, R.C.K., 1988. Economic performance and the determination of presidential elections in the US. American Economist 32, 71±75.
Campbell, J.Y., 1987. Stock returns and the term structure. Journal of Financial Economics 18, 373±399.
Chapell, H., Keech, W., 1986. Party dierences in macroeconomic policies and outcomes. American Economic Review 76, 71±74.
Chen, N.-F., 1991. Financial investment opportunities and the macroeconomy. Journal of Finance 46, 529±554.
