Effects of Election Results on Stock Price Performance:
Evidence from 1976 to 2008
Andreas Oehler
Chair of Finance, Bamberg University, Germany
andreas.oehler@uni-bamberg.de
Thomas J. Walker
Laurentian Bank Chair in Integrated Risk Management
Concordia University, Montreal, Canada, twalker@jmsb.concordia.ca
Stefan Wendt
Department of Finance, Bamberg University, Germany
stefan.wendt@uni-bamberg.de
Abstract
Election results may influence corporate performance by general changes in government
spending and tax changes. In addition, specific companies or sectors might benefit or suffer from
sector-specific governmental decisions. Stock market participants will incorporate expectations
about political change into stock prices prior to an election and adjust their opinion according to
the actual decision making following the election. To date, we do not know whether both the
Republican and Democratic parties are associated with particular stock price effects for certain
companies or sectors and whether these effects persist over several elections. We analyze
abnormal stock price returns around the U.S. presidential elections from 1976 to 2008 with focus
on party-specific favoritism. The results demonstrate statistically significant (positive or
negative) cumulative abnormal price returns for most industries. Most effects appear to be related
to the individual presidents and changes in political decision making per se irrespective of the
underlying political ideology.
Key Words: Partisanship, Sector Performance, Political Economics
JEL Codes: G11, G18, P16

Corresponding author
1
Effects of Election Results on Stock Price Performance:
Evidence from 1976 to 2008
Abstract
Election results may influence corporate performance by general changes in government
spending and tax changes. In addition, specific companies or sectors might benefit or suffer from
sector-specific governmental decisions. Stock market participants will incorporate expectations
about political change into stock prices prior to an election and adjust their opinion according to
the actual decision making following the election. To date, we do not know whether both the
Republican and Democratic parties are associated with particular stock price effects for certain
companies or sectors and whether these effects persist over several elections. We analyze
abnormal stock price returns around the U.S. presidential elections from 1976 to 2008 with focus
on party-specific favoritism. The results demonstrate statistically significant (positive or
negative) cumulative abnormal price returns for most industries. Most effects appear to be related
to the individual presidents and changes in political decision making per se irrespective of the
underlying political ideology.
Key Words: Partisanship, Sector Performance, Political Economics
JEL Codes: G11, G18, P16
2
1
Introduction
Election results may affect post-election corporate performance either by influencing a country’s
overall economy, e.g. via changes in government spending and/or fiscal changes, or through
company- or sector-specific decisions such as changes in the regulatory environment after the
new administration has been established. The latter effect is typically associated with (electoral)
partisanship, hereafter defined as polarized ideologies of governance and policy-making. Here,
the U.S. political system is dominated by two parties: the Democrats and the Republicans (GOP).
At the risk of oversimplification, Republicans are typically seen as pursuing laisser-faire
capitalism, favoring low taxes and deregulation, whereas Democrats employ a more Keynesian
approach, leveraging government as the catalyst for socioeconomic progress. The allocation of
change, however, is not systematic across the market, and can be either beneficial or harmful for
single companies/sectors. In addition, the causality between election results and economic
performance is not always clearly identifiable since underlying economic conditions (particularly
hardship) heavily influence public opinion and voting results (Fiorina 1991).
Stock market participants will price their expectations about political change into stock prices
prior to an election and adjust their opinion according to the actual political decision making after
the election and inauguration took place. In the U.S., presidential elections are a key inflection
point of change in the political landscape. As such, an increasing likelihood of a candidate’s
victory should be reflected in stock prices. However, expectations about election results are not
always clear-cut. Therefore, futures markets increase in volatility in tight elections due to
uncertainty about election results and their implications (Jones 2008). Leading up to an election,
information asymmetry has been shown to exist between the market and political parties. In
3
addition, He et al. (2009) report difficulties of market participants to distinguish fact from
political soap-boxing due to skepticism caused by political posturing, such as short-term
stimulation of the economy to reduce unemployment and increase the likelihood of re-election
(Alesina and Sachs 1986). However, Wolfers and Zitzewitz (2004) document – based on
evidence from prediction markets – that market mechanisms are likewise efficient at reflecting
the political reality and quite accurate at predicting probabilities.
In this study we examine the effects of presidential election results on the stock price
performance of U.S. stock corporations and industry sectors. Moreover, we aim to identify and
quantify the perceived Republican and Democrat favoritism towards or biases against specific
industries. To do so, the study measures stock price sensitivity to election results for the
presidential elections from 1976 to 2008. In this context, we assume that pre-election polls are
not able to fully forecast election results and that the election itself will reveal new information
which, in turn, will be incorporated into stock prices. Since voters and the market are historically
myopic in their time frame reference, measurement across a four-year interval between elections
is not representative of voter sentiment (Niskanen 1975). In addition, most significant policy
changes occur in the first 90 days following the inauguration (Drazen 2001). Therefore, we
measure abnormal stock price returns in different industries for a number of time windows after
the respective election day. By doing so, we are able to identify both short-run (two and four
weeks) and longer-run effects until up to around 100 days after the inauguration day – a time
window that corresponds to approximately six months after the election day.
The results show that all U.S. presidents, regardless of party, prompt abnormal company and
sector returns following their elections. These effects, however, tend to be more pronounced in
4
the longer post-election time windows. Overall, our results support the hypothesis that, following
a presidential election, the market corrects and thus reflects changes in the underlying governing
philosophy. This is materialized through abnormal returns, the distribution of which varies in
direction and magnitude. However, despite some identifiable distinctions between the political
profiles of the Republican and Democratic Party it appears that change in presidential personas
rather than any consistent and permanent difference in political decision making between parties
influence stock and industry performance.
The paper contributes to the existing literature and to the public debate in two ways. First, it is to
our knowledge the first analysis of potential partisanship of the two major political parties over a
horizon that spans four decades. Second, we show that companies and/or industries are
influenced by single presidents rather than by fundamental and permanent differences in the
governing philosophy of both parties.
The paper is organized as follows. In Section 2, we provide a brief review of the literature on
market reactions to expected and actual election results. In Section 3, we explain the dataset used
in our analysis. In Section 4, we present both the event study and the regression methodology
applied in the analysis. The event study and regression results are presented in Section 5. In
Section 6, we discuss the results and conclude.
2
Literature Review
Empirical evidence on pre- or post-election effects on sector-specific stock price performance is
scarce. Roberts (1990) finds that the stock price performance of the defense sector was positively
5
affected by an increase in the winning probability of Ronald Reagan prior to the 1980 presidential
election whereas the overall market remained largely unaffected. Herron et al. (1999) document a
significant influence on 15 out of 74 economic sectors as a consequence of a change in the
winning probabilities of the candidates during the campaign period of the 1992 presidential
election. The study of Bechtel and Füss (2010) provides some international evidence. They
analyze the stock price performance and volatility of four economic sectors prior to the elections
of the German parliament (Bundestag) during the 1991 to 2005 period. They find that an
increasing probability of a more conservative government increases both the mean return and the
volatility of the defense and the pharmaceutical sector, whereas the alternative energy sector
exhibits higher returns and the consumer sector higher volatility with an increasing probability of
a left-leaning government.
Much more research has been conducted with regard to the effect of presidential elections on the
performance of the overall U.S. stock market. Earlier studies include Niederhoffer, Gibbs, and
Bullock (1970), Huang (1985), and Gärtner and Wellershoff (1995). More recent studies include
Leblang and Mukherjee (2005) and Snowberg, Wolfers, and Zitzewitz (2007a). International
evidence on the stock market’s reaction to elections or to electoral cycles has been provided by,
e.g., Foerster and Schmitz (1997) for the effect of U.S. elections on international stock returns, by
Herron (2000) and Leblang and Mukherjee (2005) for the British stock market, by Siokis and
Kapopoulos (2007) for the Greek market, by Brunner (2009) for the Dutch market, and by Furió
and Pardo (2010) for the Spanish market. The market reaction to political change is not limited to
presidential elections but is also documented for changes in the composition of Congress.
Adjustments reflect new dominance and are sensitive to apparent benefits to the economy
(Snowberg, Wolfers, and Zitzewitz 2007b). Furthermore, the Republican Party follows a sharper
6
cycle pattern than the Democratic Party, indicating a tendency towards partisan reforms (Wong
and McAleer 2009).
Financial markets do not only attempt to forecast equity prices in relation to election results, but
also interest rates, currencies, and commodity prices (see Snowberg, Wolfers and Zitzewitz
2007a, 2007b). Political partisanship, however, also influences the more macroeconomic
variables such as inflation, growth, and unemployment rates as documented for the U.S. and for
Europe by authors such as Hibbs (1977), Alesina, Roubini, and Cohen (1997), and Caporale and
Grier (2000).
Importantly, however, the causality of the influence between the economic and the political
sphere flows both ways (Gerber, Huber and Washington 2009), since the market also influences
policy reform through, e.g., lobbyism. Knight (2007) and Mattozzi (2008) reflect this circular
relationship and analyze firm-specific stock price effects for U.S. companies that made campaign
contributions to presidential candidates.
While Roberts (1990) and Herron et al. (1999) analyze sector-specific effects related to a single
U.S. presidential election, we analyze these effects for a total of nine presidential elections
beginning with the election of 1976.
3
Data
We employ data that is related to the nine elections from 1976 to 2008. The period was
expressedly chosen to include only non-interrupted presidencies, providing greater consistency in
7
voter-sentiment1 leading into the subsequent election (Fiorina 1991). We use Wharton Research
Data Services (WRDS) to retrieve daily stock return data (from CRSP) and the respective
company characteristics (from Compustat). Company characteristics include the four-digit
standard industrial classification code (SIC code), stock market capitalization, book value of debt,
total assets, and net income. All company characteristics are identified based on the last reporting
date prior to the respective election. Based on these data we calculate corporate leverage as the
ratio of book value of debt to market capitalization, and the ratio of net income to total assets to
capture pre-election corporate performance independent of the stock market. Data on the risk-free
rate (the one month Treasury bill rate), and on the Fama-French factors (market premium, SMB,
HML) as well as on the momentum factor are also retrieved from WRDS.
After matching stock return data and company characteristics, we sort our sample of companies
around each election into eight industry groups including mining (SIC codes 1000 to 1499),
construction (1500 to 1999), manufacturing (2000 to 3999), transportation, communications,
electric, gas and sanitary services (4000 to 4999; hereafter: transportation), wholesale trade
(5000 to 5199), retail trade (5200 to 5999), finance, insurance and real estate (6000 to 6999;
hereafter: financials), and services (7000 to 8999). We exclude companies with incomplete data
as well as companies that operate in one of the following industries: agriculture, forestry and
fishing (0100 to 0999), public administration (9000 to 9899), and non-classifiable establishments
(9900 to 9999) since there are too few companies (less than 5 each) to draw meaningful
conclusions. An overview of the elections and the respective number of companies in each of the
industry divisions is provided in Table 1.
1
Use of voter-sentiment does not refer to preference towards either the Republican or Democratic parties, but instead attempts to curtail biases
from the presidencies of John F. Kennedy (national tragedy) and Richard M. Nixon (resignation/impeachment).
8
Please insert Table 1 about here.
4
Methodology
4.1
Event study
In order to determine the stock market reaction to changes in the political landscape we apply
event study methodology. For each election we analyze the cumulative abnormal stock price
returns in each industry for the day following the election day as well as for a 2-week, 4-week,
10-week, 18-week, and 26-week horizon following the election day. The shorter windows are
chosen in order to detect any short-run effects of the new information that is transmitted to the
market via the election result. The 10-week window is chosen since the tenth week is typically
the last week prior to inauguration of the elected president. The 18-week and the 26-week
horizons are chosen in order to detect any longer lasting effects and to determine whether the preinauguration trend changes after the actual inauguration day. Within the four weeks after the
inauguration, the incumbent leader typically outlines his roadmap for the coming months. This
generally includes explicitly outlining major goals and priorities for the new administration – the
first opportunity to reinforce or invalidate current market assumptions (Snowberg, Wolfers, and
Zitzewitz 2007a). The 26-week window after the election day also reflects the fact that the new
president typically tackles the major policy reforms within the first 90-100 days in office which
corresponds to around 14 weeks after inauguration (Drazen 2001). Beyond this point, upcoming
mid-term elections discourage the pursuit of significant controversial changes.
Daily abnormal stock price returns, ARi ,t , for each company i are calculated as
9
 
ARi ,t  Ri ,t  E Ri ,t ,
 
where Ri , t represents the actual stock price return on day t, while E Ri ,t denotes the expected
 
stock price return on that day. E Ri ,t is calculated using the four-factor model established by
Carhart (1997),
 
E Ri ,t  R f ,t  i ,1 * Rm,t  R f ,t   i , 2 * SMBt  i ,3 * HMLt  i , 4 * MOMt ,
where R f , t represents the one month Treasury bill rate on day t, which is used as a proxy for the
risk-free rate, and Rm ,t denotes the respective stock market return. SMBt and HMLt are the
Fama-French size and book-to-market factor returns, and MOM t represents the momentum
factor return.  i ,1 ,  i , 2 ,  i ,3 , and  i , 4 are estimated by regressing the excess stock price returns
of company i on the market excess return, size, book-to-market, and momentum factor returns for
the estimation period, which is chosen as the one-year period that ends two weeks prior to the
election day. The two weeks leading up to the election are excluded since expectations about the
election outcome might already be clear-cut and reflected by adjustments in stock prices.
However, we still assume that pre-election polls are not able to fully forecast election results and
that the election itself will reveal new information which, in turn, will be incorporated into stock
prices in the post-election period. We do not use long-term pre-election betas since we need an
estimate that only reflects the previous presidency in order to increase the likelihood of capturing
the policy bias.
10
For the above described windows, we calculate the cumulative abnormal stock price returns as
t2
CARi ,t1 ,t 2   ARi ,t
t  t1
where t1 denotes the day following the election day and t2 denotes the end of the 2-week, 4week, 10-week, 18-week, and 26-week event windows. Since the election day is always
scheduled on the Tuesday after the first Monday in November, all relevant dates t1 and t2
represent a Wednesday.
For each of the above described industry groups, we calculate industry cumulative abnormal
returns, CARtindustry
, as the mean value of the equally weighted cumulative abnormal stock price
1 ,t 2
returns of the single companies in the respective industry:
industry
t1 , t 2
CAR

1
N industry
Nindustry
i 1
 CAR
i , t1 , t 2
,
where Nindustry is the number of companies in the respective industry (as presented in Table 1).
The results are tested for significance by calculating t-values.
11
4.2
Regression analysis
We run a regression analysis in order to determine whether the market perceives any biases of the
Democratic and/or Republican parties towards certain industries. We thus form two sub-samples:
one sub-sample contains all cumulative abnormal stock price returns following elections that
were won by a Democratic candidate, i.e. the elections of 1976, 1992, 1996, and 2008; the other
sub-sample contains all cumulative abnormal stock price returns following the elections that were
won by a Republican candidate, i.e. the elections of 1980, 1984, 1988, 2000 and 2004. For each
sub-sample we then run a series of regressions with the cumulative abnormal stock price return
over each window (i.e., the election day and the 2-week, 4-week, 10-week, 18-week, and 26week event windows) as a dependent variable, respectively. As explanatory variables we include
dummy variables for each industry group which are assigned a value of 1 if the respective
company belongs to that industry and 0 otherwise; the dummy variable for the industry devision
retail trade is not included in the regression and serves as a reference group since this industry
has turned out in the event study as being the least influenced by the election results. The
regression model is set up as follows:
CARi ,t1 ,t 2    β * D   * ln(mcapi )   * levi   * incomei  
where  denotes the constant, D is a vector of dummy variables for seven of our eight industry
groups (mining; construction; manufacturing; transportation; wholesale trade; financials;
services) and β denotes the respective vector of coefficients. We include the logarithm of the
stock market capitalization for each company i, ln(mcapi), leverage, levi, and the ratio of net
12
income to total assets, incomei, in order to control for possible size, risk, and performance effects,
respectively, that are independent of the stock market.
5
Results
5.1
Event study
The results of the event study for the nine presidential elections from 1976 to 2008 are presented
in Table 2. From each election there appears to be some – at least temporary – effect on stock
price performance in a number of industries, with more statistically significant results for the
elections since the 1990s which might be affected by the larger number of included companies
for the more recent elections.
Please insert Table 2 around here.
The election of Democratic candidate James Carter in 1976 is associated with hardly any
statistically significant cumulative abnormal industry returns. While the mining, transportation,
financial, and service sectors appear to be somewhat positively influenced, we observe a negative
effect on the manufacturing, construction, and retail sectors – for the latter two sectors at least in
the longer run. The first election of Republican candidate Ronald Reagan in 1980 results in a
somewhat reverse picture compared to Carter’s 1976 victory. The mining and financial sectors
suffer significant (longer run) negative abnormal returns, whereas there is positive performance
in the manufacturing sector. The effects on the other sectors are rather ambiguous. The reelection of Ronald Reagan in 1984 again results in negative industry returns in the mining sector.
13
This time, however, the financial sector shows significant positive performance, while all other
sectors do not reveal a clear trend.
The electoral victory of George H.W. Bush (Republican Party) in 1988 results in negative longerrun overall stock performance in the construction and financial sectors. For the other sectors, the
picture is much less clear-cut. The first election of William J. Clinton (Democratic Party) in 1992
has a strong positive influence on nearly all industries particularly in the longer run. We
document the strongest effects (more than 20 percent cumulative abnormal stock returns) for the
mining, construction, transportation, and the service sectors. The other sectors (manufacturing,
wholesale trade, retail trade, and financials) are also positively affected but to a lesser degree.
The re-election of Clinton in 1996 leads to much more moderate stock market reaction. However,
the mining, manufacturing, retail trade, and financial sectors are again positively influenced,
while all other sectors are hardly affected – at least not at statistically significant levels.
Republican candidate George W. Bush’s first election in 2000 results in statistically significant
short-run negative returns in the mining, manufacturing, retail trade, and service sectors – a trend
that is largely reversed after inauguration. The manufacturing, transportation, wholesale,
financial, and service sectors exhibit significant positive long-run abnormal returns. The stock
price effects following George W. Bush’s re-election in 2004 are much weaker in most
industries. However, positive stock price effects can still be documented for the manufacturing,
transportation, and wholesale trade sectors, as well as for the construction industry. Most
industries exhibit very volatile cumulative abnormal stock returns after the election of Barack H.
Obama in 2008. The performance of the mining, construction, and service sectors is negative in
the first weeks after the election day followed by strong upward trends directly before and also
14
after the inauguration. For all other sectors we cannot detect a clear trend in industry
performance.
While we do not see consistent patterns in industry performance across all candidates of one of
the two political parties, it becomes obvious that changes in the governing party result in much
larger cumulative abnormal industry returns than re-elections. Interestingly, when compared
across all elections, the cumulative abnormal returns tend to be more frequently on the positive
than on the negative side, suggesting that stock market participants expect positive effects from
changes in political decision making irrespective of the direction of these changes – an effect that
is much in line with the fact that most political reform takes place at the beginning of a
presidential term.
5.2
Regression analysis
The results of our regression analysis are presented in Table 3. For the four elections won by a
Democratic candidate, as presented in Panel A, the cumulative abnormal stock price returns
hardly appear to be influenced by the industry the company belongs to. Only companies from the
mining sector exhibit a short-run and statistically significant negative effect which, however, is
reversed in the long-run. For all other industry dummies the coefficients are statistically largely
insignificant. This means that stock market participants do not appear to expect an industryspecific bias in political decision making after the election and inauguration of a Democratic
candidate per se. When combining these results with our event study results, potential industry
biases are rather associated with single Democratic presidents than with the party.
15
Please insert Table 3 around here.
The results for the elections won by a Republican candidate, presented in Panel B of Table 3,
paint a somewhat clearer picture than the results for the Democratic candidates did. In particular,
the affiliation of a company with the manufacturing, transportation, wholesale trade, and financial
sectors appears to have a significant positive influence on the stock price performance after the
victory of a Republican candidate. From this, we can conclude that stock market participants
expect a positive influence of political decision making by a Republican candidate on these
sectors. This effect, however, is largely reversed in the longer run, in particular after the
inauguration date. Again, when comparing the regression results to the event study results we see
that the stock market effects appear to be primarily driven by expectations with regard to single
candidates than to the Republican party per se. In addition, the overall explanatory power of the
regressions for both sub-samples is relatively weak.
6
Discussion and Conclusions
In this study, we document that the elections of all recent U.S. presidents, regardless of their
political affiliation, have prompted abnormal company and sector returns. These effects,
however, are particularly pronounced when examining longer post-election time windows. We
propose two potential interpretations: (1) the market remains uncertain (and does not adjust) until
the president’s political priorities are clear; (2) the market struggles to reconcile the effects of
political changes. This leaves us with a rather ambiguous view of market efficiency with regard
to political effects. There appears to be some rationality and efficiency with regard to information
16
about actual political changes (after inauguration), but there also seem to be behavioral effects, in
particular with regard to the slowness of the market reaction.
Overall, our results support the hypothesis that, following a presidential election, the market
corrects, and thus reflects changes in the underlying governing philosophy. This is materialized
through abnormal returns, the distribution of which varies in direction and magnitude. However,
despite some identifiable distinctions between the political profiles of the Republican and
Democratic parties, changes in stock returns and industry performance appear to be driven by
market expectations related to individual presidents rather than any consistent and permanent
differences in the political decision making between the parties.
17
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21
Table 1
Summary statistics
President
Party
Election Day
Mining
Construction
Manufacturing Transportation
Wholesale
Retail
Financials
Services
James E. Carter
Democrat
02.11.1976
18
5
212
12
6
3
34
17
Ronald W. Reagan
Republican
04.11.1980
21
3
311
23
13
7
57
24
Ronald W. Reagan
Republican
06.11.1984
49
6
554
29
36
19
110
77
George H. W. Bush
Republican
08.11.1988
75
9
781
55
58
41
186
130
William J. Clinton
Democrat
03.11.1992
88
13
899
76
73
41
222
131
William J. Clinton
Democrat
05.11.1996
95
16
1012
91
82
42
346
148
George W. Bush
Republican
07.11.2000
101
26
1107
107
73
48
352
227
George W. Bush
Republican
02.11.2004
82
21
848
90
62
20
347
119
Barack H. Obama
Democrat
04.11.2008
92
25
673
54
40
19
220
97
Note: This table provides summary statistics for each election covered by our analysis. In the first column, we list the U.S. presidents that were elected during our 1976 - 2008
sample period. For each president we document the respective political party (Republican or Democrat) and the election day. In addition, we document the number of companies in
each of the outlined industry divisions included in the analysis of cumulative abnormal stock price returns following the election day.
22
Table 2
Industry cumulative abnormal returns following the election day
Election
Mining
Construct.
Manufact.
0.3
1.7
1.4
0.5
6.5
15.1
0.4
1.4
3.2
-0.5
-9.5
-12.7
-0.1
-0.8
-0.6
-0.9
-2.0
-1.6
0.6
0.8
0.1
0.9
2.7
3.7
0.2
0.4
-0.2
1.0
2.0
1.1
Transport.
Wholesale
Retail
Financials
Services
-0.3
1.0
1.7
4.8
2.1
3.4
0.8
2.1
8.5
9.2
15.2
17.7
J.E. Carter
0
+2w
+4w
+10w
+18w
+26w
**
**
**
-0.1
0.3
2.3
3.1
2.1
8.1
*
-0.2
0.7
1.1
6.0
0.0
1.2
-0.2
3.9
4.6
0.7
-4.4
-11.5
1.2
1.1
1.9
1.0
3.1
7.9
-0.1
3.4
3.0
-2.5
-1.2
1.7
*
***
**
**
*
R.W. Reagan (1)
0
+2w
+4w
+10w
+18w
+26w
-0.5
-2.3
-3.2
-6.2
-6.9
-13.2
***
*
***
-0.3
1.0
-2.9
-4.2
-8.1
2.0
-0.2
-0.8
-2.4
-9.4
-5.5
-9.1
*
***
*
**
0.7
0.6
0.6
-1.1
9.5
9.0
***
*
***
***
0.6
4.8
3.7
1.7
0.8
-2.4
**
*
0.0
-0.1
-0.3
-2.4
-4.4
-5.5
**
***
-1.1
-0.6
0.9
2.9
4.5
7.4
0.1
0.8
1.3
2.3
4.1
6.5
**
**
***
***
-0.5
-0.5
0.1
-3.5
4.4
-0.1
*
0.0
-0.3
-1.1
-2.6
-3.0
-2.9
**
***
***
***
0.5
-0.1
0.0
0.5
-0.7
-1.7
**
**
**
0.2
1.7
2.2
5.9
10.2
9.5
***
***
***
***
***
-0.4
2.1
5.5
14.0
17.6
25.4
0.2
0.7
0.9
2.7
8.0
6.7
**
**
***
***
***
-0.9
-0.9
-1.1
1.1
1.7
1.4
R.W. Reagan (2)
0
+2w
+4w
+10w
+18w
+26w
*
*
**
-0.2
-1.6
-1.5
-0.2
-1.5
-2.9
-0.3
-0.4
-1.6
0.4
3.3
2.5
-0.3
0.2
-0.4
4.4
4.3
0.4
-0.4
-2.0
1.6
1.7
4.6
4.3
0.1
-4.5
-3.0
-1.4
2.9
0.6
*
G.H.W. Bush
0
+2w
+4w
+10w
+18w
+26w
-0.3
0.2
0.0
2.7
4.8
2.9
*
1.3
3.7
-0.1
-8.0
-8.4
-6.7
**
*
3.0
5.8
10.9
15.7
22.7
17.9
**
**
**
-0.1
-0.1
-0.5
0.9
0.5
0.8
*
***
*
**
*
0.0
0.1
1.0
-0.7
3.4
5.9
W.J. Clinton (1)
0
+2w
+4w
+10w
+18w
+26w
-0.3
-0.8
1.3
3.1
11.9
25.7
***
***
**
**
**
0.1
2.2
3.0
5.2
5.0
6.8
***
***
***
***
***
1.0
4.7
9.2
13.9
16.6
22.0
***
***
***
0.5
0.3
0.0
3.0
2.3
4.2
*
***
***
***
***
***
-0.3
0.4
1.3
3.7
5.1
6.7
**
*
-0.1
0.8
2.4
7.9
10.9
9.3
*
*
*
**
W.J. Clinton (2)
0
+2w
+4w
+10w
+18w
+26w
0.3
2.2
1.3
9.3
5.6
7.9
**
***
**
*
0.2
-1.0
-2.9
11.2
12.0
10.7
*
-0.1
0.2
0.6
3.5
5.9
6.3
-0.4
0.9
-0.1
1.0
2.1
1.2
0.8
3.8
2.4
8.3
12.2
11.3
1.0
-2.1
-3.2
4.2
11.1
15.2
-1.2
-8.0
-10.5
-5.4
3.4
-1.3
*
*
*
**
G.W. Bush (1)
0
+2w
+4w
+10w
+18w
+26w
0.4
-5.5
-8.3
4.9
1.4
0.2
***
***
0.4
-1.5
-3.8
1.4
6.7
12.4
-0.4
-1.7
-2.4
0.1
5.3
7.4
0.3
-2.9
-1.1
13.2
12.4
5.5
**
***
***
***
***
1.0
0.8
-0.5
4.6
6.0
11.2
-0.1
1.7
2.4
4.9
4.4
2.8
***
***
***
***
***
0.0
0.7
2.5
1.0
4.0
3.5
0.1
-5.4
-4.3
3.1
-5.0
16.8
***
***
**
***
***
-0.3
-2.1
-3.7
2.5
-10.0
27.3
**
*
*
***
***
***
**
***
***
0.4
0.0
2.1
4.6
12.0
12.4
**
***
***
***
***
0.8
-1.4
-4.0
5.3
9.0
10.5
***
***
***
***
G.W. Bush (2)
0
+2w
+4w
+10w
+18w
+26w
0.5
0.5
0.8
-0.6
5.8
0.5
0
+2w
+4w
+10w
+18w
+26w
1.3
-20.3
-17.2
11.1
15.7
66.0
*
***
*
*
**
**
*
-0.1
1.5
2.7
5.2
7.4
4.8
*
**
**
***
0.2
1.7
1.8
7.2
5.6
0.6
*
-0.1
-0.1
1.1
1.2
-0.2
-0.5
**
-0.4
-0.5
-0.6
1.4
1.2
1.3
B.H. Obama
*
***
***
**
**
***
1.5
-8.4
4.2
16.1
11.7
26.9
**
***
*
***
0.5
-7.9
-9.7
1.5
-7.7
14.8
**
**
**
-0.5
-0.6
0.8
-2.8
-14.0
-5.8
-0.8
2.6
4.4
4.3
1.5
-1.8
*
***
**
0.1
-4.9
-3.6
5.4
3.8
31.9
**
*
***
Note: We report mean cumulative abnormal stock price returns for eight industries following the election day of the U.S.
presidential elections between 1976 and 2008. The event windows are listed below each president and denoted as “0” (the day
following the election), +2w (two weeks following the election), etc. All values represent percentage returns. The symbols ***,
**, and * denote statistical significance at the one, five, and ten percent level, respectively.
23
Table 3
Sector performance based on the winning political party (all elections)
Panel A: Elections won by a Democratic candidate
Period
0
+2w
+4w
+10w
Constant
0.005
0.033
**
0.037
**
0.061
Mining
0.003
-0.076
***
-0.066
***
0.015
Contruction
0.013
-0.041
0.017
0.073
-0.001
-0.022
-0.019
Manufacturing
Transportation
+18w
**
0.087
**
+26w
0.043
0.045
0.248
0.064
0.098
-0.023
-0.037
0.011
*
0.003
-0.005
0.003
Wholesale
-0.004
-0.029
-0.037
Financials
-0.002
-0.001
Services
-0.006
-0.025
ln(mcap)
-0.001
-0.003
-0.003
leverage
0.000
0.000
0.000
net income / total assets
0.001
0.059
0.080
Adjusted R-squared
0.001
0.013
0.008
0.002
0.004
0.016
Prob(F-statistic)
0.195
<0.001
<0.001
0.038
<0.001
<0.001
**
***
0.005
-0.020
0.082
-0.037
-0.053
-0.010
0.003
-0.019
0.004
-0.022
-0.012
0.009
0.018
0.107
**
0.000
-0.005
*
0.005
**
0.000
0.000
***
0.000
*
0.076
0.044
*
***
**
**
0.043
Observations: 4,902
Panel B: Elections won by a Republican candidate
Period
Constant
0
+2w
+4w
+18w
+26w
-0.004
-0.036
-0.023
0.044
Mining
0.005
0.011
0.003
0.014
-0.018
-0.030
Contruction
0.009
0.019
0.014
0.046
0.023
0.048
Manufacturing
0.003
0.028
**
0.030
**
0.028
-0.002
0.019
Transportation
0.007
0.035
***
0.038
**
0.039
*
0.005
0.030
Wholesale
0.006
0.022
*
0.034
**
0.044
**
0.034
0.059
Financials
0.005
0.028
**
0.042
***
0.027
0.001
0.022
Services
0.006
0.022
*
0.018
ln(mcap)
0.000
0.001
**
0.002
leverage
0.000
0.000
net income / total assets
0.012
0.083
Adjusted R-squared
0.000
Prob(F-statistic)
0.355
*
**
*
***
-0.043
+10w
***
**
*
0.026
0.035
*
0.009
0.026
0.002
*
-0.002
-0.002
0.000
0.000
0.040
0.091
0.005
0.005
0.001
0.001
0.001
<0.001
<0.001
0.072
0.044
0.044
***
0.000
*
0.197
*
0.000
***
0.154
*
Observations: 6,305
Note: We report regression results with mean cumulative abnormal stock price returns following the election days of the U.S.
presidential elections between 1976 and 2008 as dependent variables. The event windows are denoted as “0” (the day following
the election), +2w (two weeks following the election), etc. The explanatory variables are as explained in the text. Panel A contains
the sub-sample for the elections in 1976, 1992, 1996, and 2008 which were won by a Democratic candidate. Panel B contains the
sub-sample for the elections in 1980, 1984, 1988, 2000 and 2004 which were won by a Republican candidate. The symbols ***,
**, and * denote statistical significance at the one, five, and ten percent level, respectively.
24