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The impact of policy change
announcements by the government on
stock markets
Author: F. Yusibov (6043836)
Supervisor: Prof Dr A. W. A. Boot
University of Amsterdam
Spring 2013
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1. Introduction
Governments play an important role in the financial markets. For example, by
setting the rules that affect the business environment and the market
participants, but also by pursuing a particular monetary policy. Various papers
provide evidence on the effects that political factors have on the financial world.
For example, Bialkowski, Gottschalk and Wisniewski (2008) show that index
return variances are significantly higher around election dates1. Umstead (1977),
Allvine and O’Neill (1980) and Huang (1985) find evidence on the cyclicality of
stock returns: during the third and the fourth year of a presidency the stock
market returns are usually higher than the returns in the first two years of the
presidency. Furthermore, under Democratic presidents the returns are on
average higher than under Republican presidents. Further examples of political
factors that influence the economic environment are taxes (Hassett and Metcalf,
1999), monetary policies (Patelis, 2012; Rigobon and Sack, 2002; Thorbecke,
2012) and corruption (Edgardo Campos, Lien and Pradhan, 1999). Stock markets
also show a significant reaction to announcements of important macroeconomic
news, such as inflation and interest rates (Savor and Wilson, 2009). However, the
research on the impact of policy change announcements by the government on
the stock markets is limited. Pastor and Veronesi (2012) construct a theoretical
framework to assess this relationship and deduce a number of empirical
predictions. The authors “interpret policy changes broadly as government actions
that change the economic environment” (Pastor and Veronesi, 2012, p. 1219).
A policy change has a number of effects. First, the direct effect has an
impact on the profits firms make. In the analysis by Pastor and Veronesi the
profitability of the firms, which follows a stochastic process, is affected by the
prevailing policy. Dismissing a bad policy in favor of a beneficial one increases
the average of the profits firms make.
Second, the learning process set in motion when the current policy was
enforced, might be reset. In other words, investors base their beliefs on the
current set of policies and altering one of them could lead to a higher degree of
A number of papers also study the relationship between elections and investments in the private
sector, see for example Julio and Yook (2012), Yonce (2009) and Durnev (2011).
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uncertainty concerning future returns. The reason is that, compared to the old
policy, investors know less about the impact of the new one. Because of this
higher uncertainty – to which Pastor and Veronesi refer to as impact uncertainty
- the discount rates increase as well. In the end, a new policy that is beneficial for
the returns might still end up eliciting a negative market reaction, if the increase
in expected returns do not fully compensate for the increased risks.
Third, a policy change might have a signaling/(un)predictability effect.
Investors might perceive such an action as a signal that the government will
keep on changing policies, making the future economic environment more
unpredictable and uncertain. Hence, the markets might react even more negative
to a policy change. Pastor and Veronesi do not take this kind of uncertainty into
account. The authors make the government unpredictable in a different way. In
their analysis, the government can either extract a benefit from (unnecessarily)
changing a policy – for example, because it has been bribed – or the government
can view a change as too costly and refrain from taking action, even if it is
necessary. The investors cannot predict which will be the case, so they make
assumptions. The parameter that indicates this unpredictability follows a normal
distribution and the uncertainty concerning the government actions – to which
Pastor and Veronesi refer to as political uncertainty – depends on the standard
deviation of this distribution. The higher this standard deviation, the more
uncertainty there is about a future action of the government. It should be noted
that the analysis by Pastor and Veronesi does not lend itself for the incorporation
of the signaling effect, since their analysis consists of one stage in a finite horizon
and there is only one policy that the government can change, if it decides to do so.
In other words, the action that the government takes does not affect its
predictability. It’s quite possible that in reality a government might refrain from
changing a policy, because it doesn’t want to signal that more policy changes
could be expected, thereby settling for a second best solution. The Pastor and
Veronesi model thus only captures a part of the (un)predictability of the
government.
Fourth, a policy change can have an informational value. To quote Drazen
(2000, p. 40):
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“First, there is the information that current policy gives about the
likelihood of future policies due to the political process itself – for
example, a contractionary policy that results in high unemployment
today may increase the probability that a more expansionary
government will be elected in the future. Second, there is the
information that current policy gives about the likelihood of future
policies due to technical constraints – for example, tax cut today
implies the need to raise taxes or cut expenditures in the future to
ensure that the government’s budget is intertemporarily balanced.”
In a sense, a policy change might give investors insight into a path the
government will likely follow in the future, therefore making the future economic
environment less uncertain. This might counteract at least a part of the
increased uncertainty explained on the previous page. Recall that a policy change
increases the uncertainty because the investors know less or nothing about the
new policy and also because the government might signal it is willing to change
other policies as well, thereby becoming unpredictable. The informational value
of such a change, as explained by Drazen (2000), to some extent sets the path the
government is likely to follow, thereby reducing some of the uncertainty. For
example, if the government raises taxes to decrease the budget deficit, it might
also decide to lower governmental expenses. The informational value might in
some cases even outweigh the increased uncertainty, thereby leading to a
relatively more certain market. Pastor and Veronesi (2012) do not take this
informational value into account.
There is ample evidence in the literature on the significant effects that
(political) uncertainties have. For example, policy uncertainty can reduce
investments made by firms and elections can make corporate investments less
sensitive to stock prices2. As mentioned above, Pastor and Veronesi (2012)
discern two sorts of uncertainty: impact uncertainty and political uncertainty.
The former indicates to which extent investors are unsure about the impact of a
new policy. The latter concerns the uncertainty about whether a new policy will
be enforced or not. To some extent, this could also be seen as (un)predictability in
2
For a brief review, see Pastor and Veronesi (2012).
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government actions. According to the empirical evidence provided by Rodrik
(1991), even moderate policy uncertainties can impose a hefty tax on
investments. This is in accordance with the literature, which posits that a higher
uncertainty can increase the returns to waiting (in order to gather information),
especially if the investment is irreversible (Bernanke, 1983; Van Wijnbergen,
1985). Hermes and Lensink (2001) demonstrate that capital outflow at country
level increases in policy uncertainty, where the uncertainty concerns budget
deficits, tax payments, consumption by the government and the inflation rate.
Edgardo Campos et al (1999) show that investors appreciate predictability even
in harmful governmental features, such as corruption.
This paper provides empirical evidence on the relationship between policy
change announcements and stock market reactions. It does so by analyzing the
abnormal values of the S&P500 returns, the implied volatility and the implied
correlation indexes. The events in this event study are the votes by the House of
Representatives and the Senate on new bills. The abnormal S&P500 returns are
subsequently regressed on the variables indicating the state of the economy and
the political and impact uncertainties of new bills.
The contribution of this paper is twofold. First, it contributes to the
literature by providing empirical evidence on the analysis by Pastor and Veronesi
(2012). Pastor and Veronesi have put forward a couple of empirical predictions
concerning the relationship between policy changes and the returns, the
volatilities and the correlations of the market. These predictions are listed in the
next section. Second, it provides market participants and government officials
with insight on the reaction that the markets are likely to exhibit when a change
is announced. Investors could use this information to protect themselves from
minor market disruptions around announcement days, whereas the government
might take the reaction of the market into account when timing the
announcements.
The study by Pastor and Veronesi (2012) is a major focal point of this
paper, hence it is described and discussed in the next section, Section 2. Section 3
describes the hypotheses, the methodology and the data that are used for the
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empirical research. Section 4 presents and discusses the results. Section 5
concludes with a brief review of the aim and the results of the paper.
2. Discussion of Pastor and Veronesi (2012) analysis
2.1 Introduction
To analyze the impact of policy change announcements, Pastor and Veronesi
(2012) construct a theoretical model. Through multiple simulations the authors
demonstrate possible investor reactions and shifts in market dynamics as a
result of these policy changes. The analysis, its assumptions and the obtained
results are discussed in this section. The section concludes by linking the analysis
of Pastor and Veronesi to the literature.
Papers that deal with asset pricing and political uncertainty are very
limited, and the ones that discuss this subject mainly focus on fiscal policies. The
paper by Pastor and Veronesi (2012) deals with a broader set of policies. Another
major difference is that the model employed by Pastor and Veronesi involves
Bayesian learning, which is discussed in the next subsection. This learning
process is used when the investors and the government form their beliefs and
make their decisions.
The main findings of Pastor and Veronesi (2012) are:
1. There is a significant decline in stock prices after a policy change.
2. The price decline is larger if the policy change is preceded by a short or
shallow economic downturn.
3. There is a positive relationship between the decline in prices and the
uncertainty about the new government policy.
4. A policy change leads to higher volatilities and correlations between
stocks.
5. The jump risk premium associated with policy changes are on average
positive3.
This paragraph is structured as follows. Section 2.2 describes the analysis, the
assumptions and the results. These assumptions and results are discussed in
Section 2.3.
3
Since this conclusion is not a focal point of this paper, it is not explicated in the next section.
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2.2 The analysis and its assumptions
The economy in this analysis has a finite horizon [0; T] and consists of a
continuum of firms i ϵ [0, 1]. These firms are owned by a continuum of identical
investors. Each firm is completely financed by equity and the initial level of
equity across firms is equal. This capital is invested (and returns are reinvested)
in a linear technology. The return, to which authors also refer to as profitability,
follows a stochastic process and contains two Brownian motions, one for the
whole economy, affecting all the firms and one firm-specific Brownian motion.
The rate of return can be considered as a moving average process, whose mean is
affected by the current policy. Authors call the impact of the prevailing policy gt.
In a special case, where gt = 0, the ‘industry’ is unaffected by the current policy.
This gt is unknown to the market participants, but it can be estimated observing
the realized returns. The investors can see whether a policy is changed or not.
The impact of a particular policy does not change over time. If a policy is
dismissed in favor of a new one, gold is replaced by gnew, which affects the mean
profitability (i.e. return on the investment). The new policies are enforced at the
same time they are announced.
There are two kinds of beliefs concerning a particular policy, the priorbelief and the posterior-belief. The former is the expectation concerning the
impact of a policy that is to be enforced in some future time, and is equal for all
new policies. These prior expectations follow a normal distribution with a mean 0
and variance
. The authors refer to the
as impact uncertainty, the
uncertainty concerning the new policy’s impact on the profitability. The true
value of gt remains unknown to investors and the government at all times. All
market participants share the exact same beliefs.
The posterior belief represents the expectations of the investors on the
impact of the prevailing policy. Since investors and the government cannot
observe gt, they estimate it in a Bayesian fashion – that is, observing the
aggregate profitability of all firms. So, the investors start with a ĝ t equal to 0,
namely the prior belief, and adjust this level taking the realized profitability into
account. As time passes by the investors gain more and more information,
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thereby reducing the uncertainty concerning gt, expressed as ̂ . When a policy is
changed, the beliefs of the investors are reset and the learning process starts
anew. This implies that at the change date τ, ̂ <
, due to the learning process.
In other words, a new policy is accompanied by a higher uncertainty.
The firms are owned by a continuum set of identical investors, who try to
maximize their utility function. This utility function is based on the terminal
wealth of the stocks, where terminal wealth is the redistribution of the equity of
all firms to the investors at time T. This redistribution can be viewed as a
liquidating dividend. The final level of the equity is affected by the returns firms
have made along the way.
The government has a similar function, only it tries to maximize the equity
of all firms. However, there’s an additional side to the utility function of the
government, the cost/benefit factor (C). This factor is modeled in such a way that
when C>1, it constitutes a cost. Or, as the authors put it, “the government must
exert effort or burn political capital to implement a new policy”4. In case C<1, the
factor becomes a benefit: “the government makes a transfer to a favored
constituency, or it simply wants to be seen doing something”5. Thus, the
government is “quasi-benevolent” – it tries to maximize investors’ welfare, but
can deviate from this optimal path if the costs are too high or if it can act
selfishly, in case the benefits from (unnecessarily) changing a policy are large
enough. The cost/benefit factor C is randomly drawn from a lognormal
distribution, centered at C = 1, having a variance of
. This variance is referred
to as political uncertainty, the uncertainty about whether a new policy will be
enforced or not.
The analysis described above is called ‘benchmark model’ by the authors.
In this benchmark model the government can only change a policy at a predetermined time. In one of the extensions to this model the authors introduce
endogeneity with respect to the timing of a policy change. Pastor and Veronesi
(2012) state that the main results of the benchmark model, which are discussed
in Section 2.4, continue to hold.
4
5
Pastor and Veronesi (2012, p. 1225).
See footnote 3.
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When it comes to whether or not to change the policy, the government first
defines a threshold, the minimum tolerable impact level of the prevailing policy.
This threshold,
, has two right-hand side variables6. First one reflects the
increased risk due to a new policy, second one the cost/benefit incurred by the
government in case a new policy is implemented. In case ĝt <
policy is changed. In investors’ view E(c) = 0, so E[
, the prevailing
] becomes E[
]. The
reason for this expectation is as follows: c ≡ log(C), where C is the cost/benefit
factor that is explained above. Recall that the distribution of C is centered at 1,
thus E(C) = 1 implies that E(c) = log(1) = 0. This expectation reduces the formula
to the first right-hand side variable only, which is in itself negative. Hence, we
get
< 0. In other words, in expectation, a policy is changed when its impact is
regarded as sufficiently negative, since ĝt <
automatically means ĝt < . Here
sufficiently means that the expected gain from changing the policy outweighs the
increased risk. In order for the posterior belief ĝt, to be lower than the prior
belief, the market participants must observe unexpectedly low realized returns.
These low returns occur in an economic downturn, therefore a policy is more
likely to be changed after a downturn. Consider the figure below, where expected
profitability is ĝt of the policy in effect at that time, and threshold is
. The
figure represents the average result of multiple simulations. In panel A, a policy
change occurs at time τ, in each simulation. Therefore the condition ĝt <
must
hold. In the figure this is visible from the expected profitability curve descending
below the threshold after some time. This is only possible if the realized
profitability is negative as well. Once a policy change occurs there is a sudden
jump in both forms of profitability, which stay at a level equal to 0 till T=20,
because there is no more conditioning on ĝt <
and the posterior belief follows
the same normal distribution as the prior belief.
6
The exact definition is
=-
̂
-
, where is the relative risk aversion
coefficient and is larger than 1.
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Figure 1. Profitability dynamics around the policy decision. In panel A the policy decision is conditional on a
change and the expected profitability decreases over time, ending up below the threshold. This expected
profitability is what the investors think the current policy’s impact is. For investors to get this impression
the realized profitability needs to be negative as well, as shown by the dashed line. The threshold is the level
below which a policy is deemed harmful by the government and changed. The government can only change
the policy at a predetermined time (τ=10), hence no action is taken when the expected profitability falls
below the threshold (t 1.6). After the policy change the expected policy equals 0, because the ex-ante
expectation follows the distribution N (0, ). In panel B there is no conditioning on a policy change, and the
expected profitability gradually increases 1, implying a 1% increase in profitability on an annual basis. Both
panels reflect the averages of numerous simulations. Source: Pastor and Veronesi, 2012.
How do stock prices react to these policy decisions? When a new policy is
implemented, the expected returns increase, because a new policy implies that ĝt
must have been below the threshold
, which is negative. This detrimental
policy is replaced by a new one that has a higher prior mean (namely 0), but also
a higher variance, which increases the discount rates. Therefore, higher expected
returns are not always beneficial for the investors. The authors formulate a new
level g*, that takes the variances of the old and the new policy, as well as the risk
aversion of the investors into account. If ĝτ > g* and the government decides to
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enforce a new policy, the stock prices drop because the discount effect outweighs
the higher expected returns. So, in order for the market to react negatively to a
policy change: (1) ĝτ
<
must hold, so that the government is motivated to
Figure 2. Probability of a policy change. In all panels the position of ĝt (perceived profitability) is shown
relative to g* (profitability level above which a policy change leads to a negative reaction) and g(0)
(investors’ perception of the government’s threshold level). The bell-shaped curve is the distribution of g(c)
(actual government threshold level) and the shaded area represents the probability of a policy change. In
panel A the markets should react positively to a policy change, however this does not happen since the
effects of the change, due to the high probability, have already been priced in. In panels C and D markets
do not expect a policy change, an event that would lead to a substantial decrease in the market price level.
Source: Pastor and Veronesi, 2012.
replace the current policy and (2) g*<ĝτ must hold as well, for the reason
mentioned above. Figure 2 shows four possible locations for ĝτ, relative to g*,
and 0. The plotted graphs are the probability distribution of
slightly above
. Investors do not observe c, and
, which is
reflects their perception
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just before time τ. The shaded are is the probability of a policy change just before
a policy decision, as perceived by investors. Recall that in order for the market to
react positively to a policy change, ĝt must be below g* (hence, automatically
below
as well), which is the case in panel A. The size of the shaded indicates
that the probability of a policy change is very high. Therefore, the market is not
surprised by a new policy and prices hardly react, as, due to the high probability,
the effects of the new policy have already been priced in. If the government does
not enforce a new policy, the prices drop substantially. In panel B the current
policy is expected to be replaced, which elicits a negative stock price reaction. In
panel C the impact of the prevailing policy is negative, yet the investors expect it
not to be replaced due to an increase in risk that is not completely compensated
for by higher returns (ĝτ >
) . In panel D the current policy affects the
profitability positively. As can be seen, in panels C and D the shaded area is
smaller with respect to the panels A and B. Thus, any policy change will be
unexpected by the investors, therefore the surprised market will react even more
negatively.
The decline in price corresponds to the increase in risk. In other words, the
higher the uncertainty concerning the new policy, the lower the announcement
returns are, as illustrated in Figure 3.
There is also a relationship between the length and the depth of the
downturn and the price reactions after a policy change. The authors define two
dimensions for the downturn, LENGTH (which is equal to policy change date
minus t0) and DEPTH (equal to the number of standard deviations by which ĝt
drops during the downturn). One would expect that, as the length of the depth of
the downturn increases, the probability of a policy change, as viewed by the
investors, increases as well. The simulations performed by the authors confirm
this expectation, as illustrated in Figure 4.
Panel B shows that, holding LENGTH constant, the probability of a policy
change approaches 1 as DEPTH increases. The higher this probability, the
smaller the element of surprise for the market. Hence, announcement returns
barely react to a policy change, as can be seen in panel A. Also in panel A, for
DEPTH < 0, downturns with a shorter length are accompanied with (negative)
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Figure 3. Expected announcement returns. The figure shows that the negativity of the
announcement returns increases in both 𝝈𝒈 (impact uncertainty) and 𝝈𝒄 (political uncertainty). The
former reflects the uncertainty concerning the impact of the new policy, the latter refers to the
uncertainty about whether a new policy will be implemented or not. Source: Pastor and Veronesi,
2012.
Figure 4. Announcement return and downturn length and depth. Panel A illustrates the return at the
announcement time as a function of the depth of the downturn preceding the policy change, for differing
lengths of that particular downturn. Depth is calculated as the amount of standard deviations by which the
expected profitability decreases up to the announcement time τ. Length is calculated as τ – t0. Source: Pastor
and Veronesi, 2012
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returns that are more pronounced. On the other hand, the longer a policy has
been beneficial (DEPTH > 0), the more evident negative announcement returns
we observe. From a social welfare perspective, there is no need to change a policy
if it is beneficial, hence any change will probably be politically motivated and
result in markets being displeased.
Conditional on a policy change, two opposite processes can be discerned.
First, as time passes by, the impact volatility of the current policy ( ̂ ) decreases
due to the learning. As a result, the (stochastic) discount factor (SDF) decreases,
since future returns become less uncertain. Recall that a firm’s profitability is
affected by the current policy as well as two stochastic processes. A decreasing ̂
also decreases the volatility of the returns, hence also the expected returns due to
the lower risk. However, as time passes by and τ, the announcement date, is
being approached, the probability of the policy change grows towards 1. This
implies that the current policy is becoming less and less relevant, since it is going
to be replaced. Therefore the stocks become less sensitive to the time-varying
beliefs of the current policy. In other words, the learning process becomes less
relevant. Whichever of these two effects outweighs depends on the parameters.
In the figure below (Figure 5) the learning process has a larger impact than
the increasing probability of the policy change. This can be deduced from the
decreasing graphs in panels A, B and C. At time τ we observe a jump in all
graphs, due to the higher uncertainty of the new policy. The jump in correlation
is due to the fact that the policy change makes stock more sensitive to the
common factor ĝt, thereby increasing the correlation.
The model discussed above is called ‘benchmark model’ by the authors, who
extend it into a version where the government can choose its own optimal
moment for a change, instead of acting on a predetermined τ. Yet in another
version the firms can choose to disinvest as a reaction to policy uncertainty. This
is in accordance with empirical literature that finds that political uncertainties
impose a large tax on investments7. In the third extension of the model the
authors introduce heterogeneity across firms. Pastor and Veronesi achieve this
7
For a brief discussion, see Pastor and Veronesi (2012, p. 1222).
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by allowing for different firm exposures to government policies. In all extensions,
the results from the benchmark model continue to hold.
Figure 5. Properties of returns around policy changes. In this figure the stochastic discount factor (SDF)
[Panel A], expected return [Panel B], return volatility [Panel C] and correlation of stocks [Panel D] are
plotted, for differing impact uncertainties, conditional on a policy change. The higher this uncertainty, the
larger the movements in the market dynamics at time τ. Source: Pastor and Veronesi, 2012.
2.3 Discussion of the assumptions and the results of Pastor-Veronesi model
In the model of Pastor and Veronesi the government is quasi-benevolent: it cares
about the investors, yet at the same time has an agenda of its own. This
dichotomy in the motives of the government is widely acknowledged in the
literature. In words of Grossman and Helpman (1992, p. 1), “in representative
democracies, trade policies are shaped by a political process that responds not
only to the concerns of the general electorate, but also to the pressures applied by
special interests”. Politicians yield to such pressures because: 1) special interest
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group contributions enhance their chance of reelection, and 2) they extract
personal financial gains, by means of bribes, for example (Coate and Morris,
1995). Corruption could also be a major determinant in the decisions that the
government
makes
(Shleifer
and
Vishny,
1993;
Rose-Ackerman,
1999).
Furthermore, the predictability of the corruption in itself also might affect the
investments made by firms (Campos et al, 1999).
Pastor and Veronesi employ a reduced-form approach to capture these
political forces, which is reflected in the cost/benefit factor C. The authors argue
that the randomness, modeled into C, accounts for the unpredictability and
opaqueness of these forces. One could think of unpredictability in terms of being
unable to predict the outcome of a battle between multiple interest groups. The
model employs the whole spectrum of possibilities, ranging from a policy that
maximizes investors’ welfare to a harmful policy, simply because the government
derives a benefit from changing the policy.
The biggest shortcoming of the model is probably its simplistic assumption
that the investors have the same prior belief about each new policy. First, this
implies homogeneity across different laws, which might not be realistic. Second,
it ignores the fact that investors have a history of previously enforced laws, which
can help them to assess new policies better. For example, over the last century,
personal taxes on dividends have been decreasing steadily in the United States.
A new policy, further reducing those taxes, might not elicit the same reaction as
its predecessor – the investors are to a great deal able to predict the impact of the
new policy. Even in absence of previous laws investors might rely on the history
of similar policies to deduct insights on the specific policy they are facing. In the
end the investors might still be wrong about its impact, however this does not
detract from the fact that their initial reaction to the announcement could have
been completely different had they not had any information at all. For example,
Bennet and Howlett (1992) discuss how the society and the government take
lessons from previous actions and how this eventually can lead to a policy
change. In the Pastor-Veronesi model the actors do not rely on their experiences
with the previous policies. An implication is that the markets might react less
surprised than what the model suggests.
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Simply put, policy changes can be divided into three groups: 1)
Government enforces a new policy where previously none was in effect (such as
limitations on CDSs) 2) Government makes amendments to an existing law 3)
Government throws an existing law overboard and enforces a new one. Although
Pastor and Veronesi (2012) do not state it explicitly, their model focuses only on
the third group, even though amendments might occur more frequently8.
Amendments have a higher informational content – for they concern an existing
policy that the investors are familiar with – and therefore might make prior
beliefs more heterogeneous and announcement returns more dispersed. THOMAS,
the online library of the US Congress, categorizes new bills and amendments
separately.
Furthermore, presumably in order not to complicate the matters
unnecessarily, the authors assume an all-equity financing for all firms. Apart
from the fact that this is not realistic, the capital structures of firms are also
dynamic. In prosperous times, due to the lower cost of debt and growth prospects
firms usually hold more debt, whereas in crises equity becomes more desirable.
Higher level of debt make equity riskier, hence stock reactions to policy changes
might depend on the health of the economy. The financial circumstances also
affect the motives of the government to take an action. One could argue that in
prosperous times the government will be less likely to replace an existing policy,
even if it is considerably harmful to the economy. Notes Drazen (2000, p. 449), “it
is striking how little formal empirical testing there has been of the view that a
crisis is necessary for significant policy change.” The (limited) empirical evidence
supports this view.9 In other words, announcement returns might be subject to
endogeneity: deteriorated financial circumstances are more likely to result in a
policy change, but they also affect announcement returns differently that an
economy in a good shape.
Lastly, Pastor and Veronesi (2012) assume homogeneous investors and
make no distinction between direct and indirect investing. For example, assume
two investors, A and B. Investors A trades in stock whereas investor B holds
For example, from January 2009 till January 2013, the US Senate agreed to 1291 amendments,
whereas the number of bills that became a public law was only 666 (Source: www.thomas.loc.gov).
9 Bruno and Easterly (1996), Drazen and Easterly (2001), Alesina, Ardagna and Trebbi (2006).
8
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stock of a car manufacturer and plans to keep it for a long time. If government
enforces a new policy that harms the liquidity of the stock market, the financing
of that car manufacturer might become more costly. Investor B will be displeased,
but not as much as investor A, who’s affected to a larger degree. Investors with
different strategies (e.g., hedge funds versus pension funds) could react
differently to policy changes, and bearing in mind that over the years market
players have changed significantly in their composition (see Figure 6) as well as
disposition, these differences could have a significant effect on the announcement
returns. If the government raises investor taxes related to stocks, then the prices
will drop less in a market that predominantly consists of tax-exempt pension
funds compared to a market where those funds constitute only a small part.
Another implication of homogeneous investors, also pointed out by the
authors themselves, is that the model ignores wealth redistribution by the
government. The importance of wealth redistribution is underscored by the
literature (Alesina and Rodrik, 1994; Persson and Tabellini, 1994). For example,
Alesina and Rodrik (1994) find a negative correlation between income inequality
(due to a lack of redistribution) and economic growth. In the model employed by
Pastor and Veronesi (2012), the government does not take the aforementioned
aspect into account when making a policy decision. Furthermore, absence of
wealth redistribution might also affect the market reactions to policies. For
example, one way of achieving redistribution is through different tax levels.
Investors with a smaller tax burden (such as pension funds) might react less or
not at all to policy decisions concerning taxes on capital gains or dividends.
18
Figure 6. Share of total U.S. financial assets, year-end. Source: http://www.saylor.org/site/wpcontent/uploads/2012/08/ECON302-1.2.pdf. Accessed April 20, 2013.
In the model employed by Pastor and Veronesi the new policy is
implemented at the same time it is announced and the government is the only
body that has the authority on this matter. In accordance with the tradition, and
the availability of information, this paper relies on data from the United States.
The usual (although not obligatory) path of courses for a passed law in the United
States is as follows. Initially a bill is introduced in the House of Representatives,
whence it might be referred to the Committee for approval and adjustments, if
deemed necessary. Once the Committee gives its approval, the bill is sent back to
the House of Representatives for debate. The next stage is the US Senate, where
further debates take place. During the whole process, and after potential
adjustments, a bill might be resent to the Committee, the House of
Representative and the Senate, since a legislation approved by both Chambers
must be identical. Thence the legislation is sent to the President to be signed. In
other words, a bill passes multiple stages before becoming an enacted law. Pastor
and Veronesi (2012) argue that stock markets should react significantly at each
step. Compared to a single-step legislation process as employed by Pastor and
19
Veronesi (2012), the process described above decreases the element of surprise for
the markets. Abnormal returns might become less pronounced or not significant
at all. Another possibility is that shifts in surprise reactions might be observed.
For example, a bill that is deemed ‘petty’ by the markets might not elicit a
significant reaction until the very last stage of the voting. On the contrary, an
eagerly anticipated bill might ‘surprise’ the markets more at the initial steps of
the process, becoming gradually less significant with respect to abnormal returns
as its probability of enactment increases.
In the benchmark model of Pastor and Veronesi (2012), the policies affect
all the firms in the same way. This assumption does not hold in reality:
Boutchkova, Doshi, Durnev and Molchanov (2012) find that industries react
differently to political events (such as elections), in terms of sensitivity. For
example, the authors show that when local political risks are higher, the
industries that depend more on trade, labor and contract enforcement reveal a
higher return volatility. Pastor and Veronesi (2012) extend their model by
introducing heterogeneity across firms. This heterogeneity is based on different
policy exposures, i.e. firms are divided into N sectors and the policies affect those
sectors differently. The authors find that firms with a higher exposure have
higher expected returns at the announcement of a policy change. This conforms
the finding by Boutchkova et al (2012), in the sense that the higher expected
returns compensate for higher volatilities.
In the benchmark model of Pastor and Veronesi (2012) the government can
only change the policy at predetermined times. In the extension to the model the
government can choose its own optimal policy decision moment. The authors find
that the earlier the policy is changed, the more pronounced the negative market
reaction is. In the United States the policy decision moments (signed by the
President or being overridden by a veto) are not equally distributed over time, as
can be seen from the charts below.
20
Figure 7. Statistics on Congressional activity. First chart shows that new bills and resolutions tend to be
introduce at the start of a Congress. Second chart shows that the enactment (signing by the President or
being overridden by a veto) usually happens at the later stage of the year, with much activity concentrating
on the final months of the second year of the term. Source: http://www.govtrack.us/congress/bills/statistics.
Accessed April 2013.
Every two years one-third of the Congress, consisting of the House of
Representatives and the Senate, is replaced. Hence the 24-month horizon on the
X-axis. Bills are usually introduced at the start of a Congress, whereas the policy
decisions usually take place at the end of the year, with the majority of the
activity centered around the second half of the second year.
Concerning the outcomes of the analysis, the limited empirical seems to
support the findings of Pastor and Veronesi (2012). For example, Belo, Gala and
Li (2012) find that firms with a higher government exposure, as measured by the
portion of the revenues generated by the government spending, experience higher
stock returns. However, there are some notable differences between the study by
Belo et al. (2012) and the Pastor-Veronesi model. First of all, the findings of Belo
21
et al. only hold for Democratic presidencies – under Republican presidencies the
aforementioned firms earn lower returns. The Pastor-Veronesi model does not
make a distinction between the types of government. Furthermore, Belo et al.
focus on governmental spending, whereas Pastor and Veronesi analyze policies in
general. Lastly, Belo et al. measure the dependence by firms on the governmental
spending, whereas in the model of Pastor and Veronesi the exposure is defined in
terms of the impact of a policy on the profitability. One could compare the latter
to the market beta of a stock and its expected return.
The most important, and slightly surprising, outcome of the analysis is
that it posits that policy changes, even though beneficial, on average elicit a
negative reaction. This is due to the increased uncertainty. Models by other
researchers also suggest a decline in stock prices as a result of higher uncertainty
(Dzielinski, 2012; Ozoguz, 2009). Concerning empirical evidence, Sum (2012a)
reports that increases in economic policy uncertainties in the European Union
result in negative stock market reactions in almost all member countries. For the
measurement of the uncertainty, the author uses the Economic Policy
Uncertainty Index.10 Similar results are obtained for the US data (Sum, 2012b).
According to Cutler (1988) the stock price index fell after the Tax Reform
Act of 1986 was passed in the House and after the Senate Finance Committee
approved a similar bill. In real terms, the act increased corporate tax burden by
84 billion dollars over the next five years while at the same time decreasing the
marginal tax rate on dividends by approximately 8.1 percent (Cutler, 1988). The
net result was an increase in shareholder burden by 49 billion. The author argues
that despite the aforementioned decreases in the index, the market did not react
significantly to the Tax Reform Act being passed. Over the ten days
encompassing the House vote, the index fell by 0.65 percent, but in the same
period spanning the Senate Finance Committee vote the index increased by 1.19
percent. Cutler (1988) applies two tests to test the market reaction on
significance. The first test is based on correlations of excess returns of 1985
Fortune 500 firms around the voting days. The author argues that since the votes
This index is obtained from http://www.policyuncertainty.com. The methodology employed in
bringing about this index can be viewed at http://www.policyuncertainty.com/methodology.html
10
22
concerned substantially similar bills, the reactions they would elicit should also
be similar. The first days following the votes the correlation of excess returns is
only 0.036 with a standard error of 0.057, showing no significant common
movement. The correlation only gets significant over a longer horizon (10 days),
but is still rather small, equaling 0.197.
The second test concerns the dispersion of the abnormal returns. The
author argues that if the market movement around the voting days is to a large
extent explained by the bills being passed, then the standard deviation of the
excess returns around these days should also be higher than the standard
deviation of the days with no news. The average standard deviations in the
weeks of the House vote and the Senate Finance Committee vote equal 1.81
percent and 1.74 percent, respectively. This is lower than the standard deviation
of the 30 days prior to May 1986 (when the Senate Committee voted), a period of
‘no consistently large tax or other news’ (Cutler, 1988, p. 1116).
This paper applies an event study to examine the impact of a policy change
on stock markets. The market characteristics that are analyzed in light of policy
alterations are price levels, volatilities and correlations among stocks. The
central question of the paper is: What is the impact of government policy changes
on stock markets?
Five hypotheses are formulated in order to assess the impact of policy
changes, based on the conclusions drawn by Pastor and Veronesi (2012).
Hypothesis 1. On average, policy change announcements result in a significant
stock price decline.
Hypothesis 2. The decline in stock prices increases in both impact and policy
uncertainties. In other words, if the uncertainty concerning the
impact of the new policy or the uncertainty concerning whether or
not the government will actually change the policy is high, the
decline in stock prices is more pronounced.
Hypothesis 3. The price decline is larger if the policy change is preceded by a
short or shallow economic downturn. (Formulated differently, the
price decline is larger if the economic downturn that precedes the
policy change is short or shallow.)
23
Hypothesis 4. A policy change leads to higher volatilities.
Hypothesis 5. A policy change leads to a higher correlation among stocks.
3. Methodology and data
3.1 Methodology
As mentioned above, the approach that this paper implements for the research
question is that of an event study, where the events are the votes by the House of
Representatives and the Senate and the signing by the President of new bills.
This paper deals with bills that affect the large corporations either directly (e.g.
regulations, trade agreements with other countries, corporate taxes) or indirectly
(e.g. policies concerning financial sector, bribery and money laundering, Federal
Reserve, or income taxes for individuals).
In this event study, the abnormal values of the returns, as well as market
volatility and market correlation levels around policy changes are obtained and
tested on significance. The abnormal return for the stock returns is defined as
follows:
(1)
where ARt and Rt are, respectively, the abnormal and actual returns of the
S&P500 at time t. E(Rt|Xt) is the expected return of the S&P index given the
information set X. There are multiple ways of defining the expected return. The
constant mean return model, which is used in this paper, defines the expected
return as follows:
(2)
where μit is the mean of the S&P index at time t and ζit the disturbance term in
the same period, having an expectation of zero and a variance equal to
. The
constant mean return model might seem overly simplistic, nevertheless often
gives results similar to more complicated models (Brown and Warner; 1980;
1985). According to MacKinlay (1997), this might be due to the fact that not a
great deal of abnormal return variance is reduced by the application of a more
sophisticated model.
24
Another possible way to define the expected return is using the market
model, which is formulated as follows:
(3)
where Rmt is the market return at time period t and εt is the zero mean
disturbance term. In event studies concerning individual firms usually the
S&P500 index is used as the market benchmark. However, in this case that
would be inappropriate, since the reaction of the market itself is analyzed. Some
sort of a global index could be used as a benchmark, such as Global Dow Total
Return Index USD, but there is a possibility that this index reacts to
announcements as well.11 Hence the constant mean return model is used in this
paper.
The approach of this paper is similar to the method employed by Cutler
(1988). In his paper Cutler not only analyzes the reaction of individual firms and
industries to the Tax Reform Act of 1986, but also the reaction of the market as a
whole. Cutler shows that individual firms and industries that are affected by this
act react significantly, some positive and some negative. However, these
reactions do not provide any information about how the market as a whole
actually perceives the fact there is a policy change. Since the Tax Reform Act is
the only act Cutler focuses on, the traditional event study approach is
inapplicable. In other words, the abnormal return of the index around the voting
days cannot be tested for significance simply because there aren’t enough
observations. Cutler gets around this problem by analyzing the correlations
between the excess returns of 310 firms, as well as their volatilities around the
relevant days, as explained in Section 2.3. The difference between this paper and
the study by Cutler is that in this paper a more direct approach is employed,
where the abnormal returns of the index are tested on significance using tstatistics. The obtained abnormal returns, based on a t-test incorporating the
standard deviation of those returns, are tested against the null hypothesis that
the mean of the excess returns does not significantly differ from 0 at a confidence
Foersted and Schmitz (1997), for example, show that the US election cycle effects may ‘spill
over’ to international stock returns. The same could apply for other political factors, such as policy
changes, although at the moment there is no evidence that (dis)proves this claim.
11
25
level of 5%. If the predictions of Pastor and Veronesi (2012) hold in practice, then
significant non-zero excess returns should be observed around the days of voting
at both the House and the Senate.
In order to find out what determines the magnitude of the excess returns
around the voting days, Cutler (1988) regresses these returns on firm
characteristics. A similar method is used in this paper. Pastor and Veronesi
(2012) argue that the state of the economy and the uncertainties concerning the
passed bills affect the market reaction. To test this, the abnormal S&P500
returns that are obtained from the constant mean return model are regressed on
the variables indicating the state of the economy as well as a couple of proxies
that are indicative of the uncertainties of the policies. Proxies are used since
those uncertainties are hard to capture in numbers. Another difference between
this paper and the study by Cutler (1988) is that Cutler regresses the aggregate
excess return of both the House and the Senate Finance Committee votes,
whereas this paper regresses them separately, making a distinction between the
different stages of the legislative process. This distinction might be necessary
because the market could react differently at different stages.
The regression model is as follows:
CAR-2;2 = α + β1Damendment + β2LENGTHt + β3DEPTHt + β4IMPACTi +
+ β5POLITICALi + β6CONTROLi + εt ,
(4)
where CAR-2;2 is the cumulative abnormal return over a window of 5 days: 2 days
preceding the vote, the voting day and 2 days following the vote. Damendment is a
dummy variable that is equal to one if the policy change happens through means
of an amendment rather than a new law. The distinction between a new bill and
an amendment is made due to their differing informational value. LENGTH and
DEPTH are variables that indicate the state of the economy at the time of a
policy change, being respectively the length and depth of the economic downturn
that precedes the policy change. Pastor and Veronesi (2012) argue that economic
downturns are necessary for major policy changes.
The proxies that are considered to indicate depth are:
1. Logarithm of the average GDP of the past 3 months, 6 months and one
year (LOG_GDP(3), LOG_GDP(6) and LOG_GDP(12), respectively).
26
2. Difference between the actual GDP and the forecasted GDP of the past 3
months, 6 months and one year (DIFFERENCE(3), DIFFERENCE(6) and
DIFFERENCE(12), respectively). Taking the logarithm is not necessary,
since this value is more or less stationary and independent of t. The
forecast is provided by Congressional Budget Office (CBO).
3. Logarithm of the average S&P500 index of the past 3 months, 6 months
and one year (LOG_SP(3), LOG_SP(6) and LOG_SP(12) respectively).
For LENGTH, the proxy is the number of days with a negative S&P500 return in
the past 3 months, 6 months and one year (NEGATIVE(3), NEGATIVE(6) and
NEGATIVE(12) respectively).
IMPACT is the variable that measures the uncertainty of the investors
concerning the impact of a particular policy change. The proxies that are
considered are:
1. The number of days between the introduction date of the bill and the
voting day, assuming that the longer this period is, the more time the
market has to assess the potential impact of the new policy (DAYS).
2. Cumulative abnormal activity (CAV) in the implied volatility index over a
period of 5 days around the event day.
POLITICAL indicates the uncertainty concerning the policy decision, i.e.
whether the government will change a policy or not. The proxies that are
considered are:
1. A dummy variable indicating whether one of the political parties has a
majority in both Chambers (MAJORITY). The rationale is that a new bill
is more likely to pass both Chambers if the majority in both is constituted
by the same political party.
2. A dummy variable that equals 0 if the Congress is in its first year and 1 if
the Congress is in its second year (TERM). The reason is that, as can be
seen in Figure 2, a bill is more likely to be enacted if the Congress is in the
second year of its term.
3. The enactment percentage of the Congress, which indicates the percentage
of bills that went on to become a law in a particular Congress
(ENACTMENT). This number comprises all of the bills that were
27
introduced during that Congress, not only the bills that are related to the
financial or industrial sector. These numbers are obtained from
www.govtrack.us.
4. The amount of votes in favor of passing the bill as a percentage of the total
number of votes from the previous voting session (PERCENTAGE). This
proxy is only applied in the second stage, where the votes from the first
stage are used.
5. The abnormal change in the implied volatility index after the vote takes
place. The reason is as follows: a significantly abnormal increase is
expected in the volatility index prior to the vote. This higher risk
comprises the political uncertainty of the bill to be voted on, as well as its
impact uncertainty. The political uncertainty – whether or not the bill will
pass – is only relevant prior to the vote and equals 0 after the vote takes
place. Therefore a decrease in the VIX is expected to reflect this decrease
in total risk once the vote has taken place. The assumption here is that
this decrease is proportional (if not equal) to the political uncertainty of a
particular bill. Simply put, imagine two governments. One of these
governments is completely unpredictable for the investors, whereas the
other one is to a great extent predictable. Ignoring the signaling effect of a
new policy12, when each of these governments proposes a new policy that is
exactly the same as the policy of the other government, the impact
uncertainty concerning both bills should be the same, ceteris paribus. Once
the bill has passed, the implied volatility index will be at a higher level,
reflecting this impact uncertainty. However, prior to the vote, the index
will increase more in case of the unpredictable government compared to
the predictable government, to reflect the higher political uncertainty.
Hence, it should be the case that the difference between the pre-vote
volatility index and the post-vote volatility index indicates the political
uncertainty of a particular government concerning a particular bill.
Lastly, CONTROL is the control variable. The reaction of the market to a bill
being passed depends to a great extent on the impact the new policy is expected
12
See ‘Introduction’.
28
to have13. Ideally, predictions by analysts would be used in this case. However,
since those data are only available in a very few cases, the expected impact of the
bill on the federal budget is used instead. These expected values are provided by
the Congressional Budget Office (CBO). The CBO estimates these values on a 5year and a 10-year horizon. Both estimations are incorporated in this research
through variables BUDGET5 and BUDGET10, to be regressed separately. A
value of 1 for BUDGET5 means that the new bill will increase the budget deficit
by 1 billion over the next 5 years.
The regression is applied at each stage of the legislative process a major
action takes place. These major actions are identified as the voting by the House
(usually the first stage), the voting by the Senate (usually the second stage) and,
eventually, signing by the President. In accordance with the predictions by
Pastor and Veronesi (2012), the first two stages are expected to elicit significant
market reactions, whereas the third stage is most likely not. The third stage –
the signing by the President – is included for the sake of completeness, since the
President can veto bills that have passed both Chambers. But since the President
rarely makes use of this right, it is not expected that the market reacts surprised
to the bills being signed14. Hence, no regressions are performed on the excess
returns around the days of signing by the President.
Concerning the hypotheses 4 and 5, a similar approach is used. Based on
the Akaike information criterion (AIC) and Bayesian information criterion (BIC)
AR(I)MA models are developed for both the implied volatilities and the implied
correlations. The implied volatility levels are obtained from the Chicago Board
Options Exchange Market Volatility Index (VIX). The implied correlation levels
are obtained from CBOE S&P500 Implied Correlation Index. The AR(I)MA
models are then used to ‘predict’ an expected value for both indexes at a given
point in time. The differences with the actual values, effectively the residuals of
the models, are considered as abnormal values. The abnormal values around the
This is different from the variable IMPACT that is already included in the regression. One
could think of this control variable as the expectation of the magnitude of the impact the bill is
likely to have, with the variable IMPACT being equal to the standard deviation of this
expectation.
14 In the dataset used for this research, only one out of the 251 bills was vetoed by the President
after passing both the House and the Senate.
13
29
voting days are then tested against the null hypothesis that the means do not
significantly differ from 0 at a confidence level of 5%.
3.2 Data
The data concerning the bills are manually collected from
library of the US Congress. The data on
THOMAS
THOMAS,
the online
go as back as 1973 (93rd
Congress). It mentions the major actions (i.e. voting by the Chambers) and
whether it has become a law.
THOMAS
categorizes bills and resolutions based on
their subject, amongst others. As mentioned above, this paper deals with policies
that affect the large corporations either directly (e.g. regulations, trade
agreements with other countries, corporate taxes) or indirectly (e.g. policies
concerning financial sector, bribery and money laundering, Federal Reserve, or
income taxes for individuals). Although there are different kinds of policies that
the market might react to, such as war declarations on other countries, the
approach of this paper is only limited to “policies that change the economic
environment”, to quote Pastor and Veronesi (2012, p. 1219). The bills analyzed in
this paper mainly come from the categories ‘Finance and financial sector’,
‘Foreign trade and international finance’ and ‘Taxation’. The full list of the
categories (as of 2013) can be found in the appendix. The data on the impact of
the new policies on the governmental budget are extracted from the online
database of the Congressional Budget Office (CBO). This paper uses
Congressional data from January 1973 to June 2013, thereby encompassing 40
years of policymaking over 20 different Congresses. The dataset of this paper
contains 251 individual bills.
The data on the market characteristics, namely the S&P500 returns, the
implied volatilities and the implied correlations are all extracted from the
Datastream. The data on the implied volatilities are obtained from the Chicago
Board Options Exchange Market Volatility Index, also known as VIX. The
implied volatility numbers are only available for the trading days starting from
January 1990. The VIX level indicates the market’s expectation of the market
volatility for the next 30 days. The data on the implied correlations are also
provided by the Chicago Board Options Exchange (CBOE). CBOE uses the
30
S&P500 (SPX) option prices and the prices of the options of the 50 largest stocks
in the S&P500 index to calculate these correlations. CBOE disseminates two
implied correlation indexes (ICI), based on the options with maturities of,
respectively, one year and two years. In this paper the average of those two
indexes is taken as the indicator of the current market correlation. The data are
only available from January 2007 till present.
The data on the GDP and the forecasted GDP levels are also obtained from
Datastream, the latter being provided by the Congressional Budget Office.
4. Results and discussion
This section presents and discusses the results. The next subsection gives a
summary of the descriptive statistics. Subsection 4.2 presents the results and
discusses them in light of the predictions of Pastor and Veronesi (2012).
4.1 Descriptive statistics
The bills
The distribution of relevant bills per Congress is charted below. Due to the
incompleteness of the online database THOMAS, less data are available on the
earlier Congresses, hence the lower amount of relevant bills. The total number of
bills is 251. However, the bills that have coinciding voting dates in one of the
stages are ignored for that particular stage.
40
35
30
25
20
15
10
5
0
Figure 8. Number of relevant bills per Congress. The Y-axis is the number of relevant bills. The 93rd
Congress took place from 1973 to 1975. The 113rd Congress will be active from 2013 to 2015. Every two years
one-third of the Congress is replaced. The lower amount of relevant bills for the earlier Congresses is not due
31
to the inactivity of those Congresses, rather to the incompleteness of the online database
number of bills is 251.
THOMAS.
The total
Since this paper is an event study, the bills that have been included in the
database have at least passed one of the Chambers. Of the 251 bills that have
been
analyzed
approximately
90%
are
originated
in
the
House
of
Representatives, whence, after the approval of the House, they’re referred to the
Senate. Approximately 55% of these bills pass end up becoming a law. The
enactment percentage per Congress is charted below. This should not be confused
with the overall enactment percentage of the particular Congresses. The latter
takes into account all categories of bills and also bills that are introduced but
have not passed the Chamber they are introduced in. The overall enactment
percentages are much lower and can be found in Table 1.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Figure 9. The enactment percentage of the relevant bills per Congress. The X-axis is the Congress number,
the Y-axis the enactment percentage, averaging 55% over the 20 Congresses. Dark areas represent the
percentage of enacted laws.
Congress
93
94
95
Overall
enactment
percentage
3%
3%
4%
Congress
100
101
102
Overall
enactment
percentage
7%
6%
5%
Congress
107
108
109
Overall
enactment
percentage
4%
5%
4%
32
96
97
98
99
5%
4%
6%
6%
103
104
105
106
5%
4%
4%
6%
110
111
112
113
3%
3%
2%
0%
Table 1. The overal enactment percentage per Congress. This number takes into account all introduced
bills in a particular Congress. The percentages are obtained from
http://www.govtrack.us/congress/bills/statistics (accessed March 2013).
Of the bills that do not pass the second stage, only a minority is actually
rejected in that stage. The final major action for the majority of those bills, as
reported by THOMAS, is being read for the first or second time, after which it’s
referred to a committee or placed on the Calendar for action in some future time.
In this dataset, of all the bills that pass both Chambers, only one bill is vetoed by
the President.
Concerning the estimations of the impact of the new bills on the
governmental budget deficit, only data on approximately 103 of the relevant bills
are available. As can be seen from the charts below, the majority of the new bills
has a small negative impact or no impact at all on the budget deficit. The values
on the right-hand side of the X-axes are generally attributable to bills related to
taxes. For example, the highest value for both the 5-year and the 10-year horizon,
equaling 552 and 1,352 billion dollars respectively, belongs to the Economic
Growth and Tax Relief Reconciliation Act of 2001, also referred to as ‘one of the
two Bush tax cuts’.
Figure 10. The histograms of the impact of the new bills on the governmental budget deficit, over a period of
5 and 10 years. A value of, for example, 200 means that the bill is likely to increase the deficit by 200 billion
33
dollars in that particular timeframe. The 5-year horizon impact has a mean of 15.56 and standard deviation
of 68.59. The 10-year horizon impact has a mean of 29.12 and a standard deviation of 160.97.
Market characteristics
The figure below shows the actual one-day S&P500 returns. These returns, with
a standard deviation of 0.01, range from -20% to 12%. The outliers are accounted
for by a winsorisation of 90%. This means that the negative outliers are ‘pushed
back’ to the lower boundary of the 5th percentile, whereas the positive outliers are
set equal to the upper boundary of the 95th percentile.
Figure 11. One-day S&P500 returns, not winsorized.
The figures below depict the progress of the implied volatility index (VIX) and the
implied correlation index (ICI). None of these is stationary. Since stationarity is
required for time series, the first difference of these indexes are taken (also
graphed below), which are random processes. The obtained time series models
are ARIMA(1;1;1) for the VIX and ARIMA(1;1;2) for the ICI. The obtention of
these models is explained in the appendix.
34
Figure 12. The graphs of the implied volatility index (above) and its first difference (below).
35
Figure 13. The graphs of the implied correlation index (above) and its first difference (below).
36
4.2 Results and discussion
First stage
Table 1, depicting the abnormal returns around the voting days, yields some
interesting results. Of the five abnormal returns, four observations are negative.
Two days prior to the vote the index falls by 0,2043% (significant for α=1%) and
on the second day after the vote the decline is 0,1085% (significant for α=10%).
However, the day after the vote the index increases by 0,2014% (significant at 1%
level), slightly lower than the amount by which it had decreased two days prior to
the vote. On the voting day the return, albeit negative, is not significant. The
cumulative return over these two days (the voting day and the following day),
with a coefficient of 0,001768 and t-value of 1,60, is also not significant at a 5%
level.
Abnormal return S&P500
t=-2
t=-1
t=0
t=1
AR
-0,002043***
-0,000215
-0,000246 0,002014***
CAR (-2; t)
-0,002043***
-0,002258** -0,002504* -0,00049
t-value
-2,60722
-2,03734
-1,84505
-0,3125
Observations 223
223
223
223
* Significant at 10% ** significant at 5% ***significant at 1%
t=2
-0,001085*
-0,001574
-0,89863
223
Table 2. The abnormal returns around the voting days during the first stage (usually the House of the
Representatives). The starting point of the cumulative abnormal returns is the two days prior to the vote. T=0 is the
event day. The third row indicates the t-values of the corresponding CAR value.
The pattern above could indicate that in the days prior to the vote the
market underperforms relative to non-event days because of the higher
uncertainty. Once the bill is passed and this uncertainty is eliminated, the
market reacts positively. The cumulative abnormal return, starting from the two
days prior to the vote, is significantly negative for the first two consecutive days
of the window. An investor intending to sell S&P500 stock prior to a major policy
vote might do better to defer this action at least until the bill has been voted on,
in order to avoid a potential loss.
There is no strong evidence in favor of the prediction by Pastor and
Veronesi (2012) that the markets tend to dislike policy changes, however it is
notable the cumulative abnormal return for all t ’s is negative. The results seem
to imply that the movements in the price level index are primarily caused by the
37
political uncertainty – i.e. whether or not a bill will be approved – rather than the
impact uncertainty – i.e. how the new bill will affect the profits.
However, the movements in the implied volatility index seem to suggest
otherwise. As can be seen from the table below, two days prior to the voting the
abnormal value (AV) of the change in the volatility index equals approximately
0,3118 (significant at 1% level). On the day after the vote the index falls by
0,0253 points (significant at 1% level). In contrast to the S&P500 index, the VIX
does not ‘recover’ completely – the decrease in the index is only 1/12th of the
increase of 3 days ago. Recall that the VIX indicates the perception of the
investors about the volatility of the market for the next 30 days. At t=-2 investors
perceive market volatility to be higher than usual by 0,3118 points. They adjust
this level downwards the day after the vote, but the net result is still 0,2865
points higher than usual. This implies that a policy change leads to a higher
(expected) market volatility for, at least, the next 30 days. This is supportive of
Hypothesis 4.
t=-2
AV
0,31181***
Observations
177
*** significant at 1%
Abnormal values ΔVIX
t=-1
t=0
t=1
t=2
0,065608 0,023946 -0,025332*** 0,090691
177
177
177
177
Table 3. The abnormal values of changes in the VIX around the voting days during the first stage. T=0 is the
event day. The number of observations differs from the number of observations in Table 1 because the data
on the volatility index are only available since January 1990.
The pre-event increase in volatility comprises the political and the impact
uncertainty. After the vote the political uncertainty is 0, so the residual increase
is completely attributable to the impact uncertainty. The decrease in the index
after the vote is only 1/12th of the increase at t=-2, suggesting that approximately
90% (i.e. 11/12) of the extra risk can be attributed to impact uncertainty. This
seems contradictory to the conclusion that we’ve drawn from the results in Table
1, namely that the price movements are predominantly caused by the political
uncertainty. On each of these five days, an increase (decrease) in the VIX is
accompanied by a decrease (increase) in the S&P500 index, although not all of
them significant. It is therefore interesting that a policy change approval has a
‘lasting’ impact on the volatility index, whereas in the S&P500 index the price
38
movements cancel each other out. In other words, the impact uncertainty that
can be seen in the increased VIX level is not visible in the S&P500 index. A
possible explanation is that the expected returns for the S&P500 are not correctly
specified, hence the obtained abnormal results do not fully capture the changing
market dynamics. Following the tradition this paper makes use of the constant
mean return model, which posits that the expected return on a particular day is
approximately equal to the average of the returns of the past 120 days. Since
daily stock returns are (assumed to be) jointly multivariate normal and
independently and identically distributed over time, the model generally does not
produce incorrect results (MacKinlay, 1997). MacKinlay (1997, p. 17) also states
that “while this assumption is strong, in practice it generally does not lead to
problems because the assumption is empirically reasonable and inferences using
the normal return models tend to be robust to deviations from the assumption.”
However, Savor and Wilson (2009) show that investors holding risky assets (such
as longer term Treasury bills) require a higher expected return on the days that
macroeconomic news are scheduled to be announced. If the actual expected
returns prior to a House/Senate vote are also higher than the constant mean
return model suggests, then this implies that the model used in this paper
underestimates negative abnormal returns while overestimating the positive
abnormal returns. Further researches on this matter could try to employ a model
that allows for higher expected returns, for example by incorporating the
(expected) returns of the Treasury bills.
For the implied correlation index the same pattern is observed as in the
case of the implied volatility index. In the two days prior to the vote, the implied
correlation index increases by 0.7291 (significant at 1% level) and 0.3727
(significant at 10% level) points respectively. The day following the vote the index
decreases by 0.4041 (significance level 5%) points. The net result is an increase of
at least 0,7291 – (-0,4041) = 0.325 points. This is supportive of Hypothesis 5.
Abnormal values ΔICI
t=-2
t=-1
t=0
AV
0,729054*** 0,372741* -0,12477
Observations
79
79
79
t=1
t=2
-0,40414** -0,05185
79
79
39
*significant at 10% **significant at 5% ***significant at 1%
Table 4. The abnormal values of changes in the ICI around the voting days during the first stage. T=0 is the
event day. The number of observations differs from the number of observations in Table 1 because the data
on the volatility index are only available since January 2007.
Second stage
Interestingly enough, the second stage does not seem to elicit any significant
market reaction at all. Abnormal returns and cumulative abnormal returns for
the days prior to the vote are negative (with exception t=-2), and positive for the
days following the vote, none of them being significant.
t=-2
AR
0,001055
CAR (-2; t)
0,001055
t-value
1,108198
Observations 111
Abnormal returns S&P500
t=-1
t=0
-0,001415 -0,000144
-0,00036
-0,0005
-0,26757
-0,30602
111
111
t=1
0,000851
0,000347
0,182265
111
t=2
0,000775
0,001122
0,527416
111
Table 5. The abnormal returns around the voting days during the first stage (usually the Senate). The
starting point of the cumulative abnormal returns is the two days prior to the vote. T=0 is the event day. The
third row indicates the t-values of the corresponding CAR value.
A reason for the absence of significant results might be the possibility that
the second stage voting does not come as a surprise to the market. Usually the
second stage voting happens in the Senate. If, as a rule, the Senate follows the
advice given by the Senate Finance Committee, then it is possible that the
Senate vote itself becomes more predictable. Cutler (1988), for example, considers
in the case of the Tax Reform Act of 1986 the vote by the House and by the
Senate Finance Committee as surprising to the market.
The fact that the second stage vote does not come as a surprise can also be
seen in the inactivity of the implied volatility index. Although the abnormal
values have the right signs – i.e. positive before the vote and negative afterwards
– none of them is significant at a 5% significance level.
t=-2
AV
0,050353
Observations
79
* significant at 10%
Abnormal values ΔVIX
t=-1
t=0
t=1
0,180418
-0,05501
-0,11133
79
79
79
t=2
-0,26944*
79
40
Table 6. The abnormal values of changes in the VIX around the voting days during the
second stage. T=0 is the event day.
A similar pattern is observed for the changes in the implied correlation index.
The abnormal values of the change in the index are positive before the vote and
negative afterwards, except for t=1. However, none of these abnormal values is
significant at a 5% significance level.
t=-2
AV
0,555749
Observations
30
* significant at 10%
Abnormal values ΔICI
t=-1
t=0
0,772125*
-0,37286
30
30
t=1
0,095419
30
t=2
-0,61776
30
Table 7.The abnormal values of changes in the ICI around the voting days during the second stage. T=0 is
the event day.
Third stage
As was expected, the signing by the President does not elicit any significant
market reaction. The volatility and the correlation indexes show no activity
either, except for the correlation index at t=-2, which shows an increase of 0,55
points (α=10%). However, the number of observations is too low to infer any
statistically justified conclusions.
t=-2
Coefficient
0,000511
CAR (-2; t)
0,000511
t-values
0,442863
Observations 98
* significant at 10%
Abnormal returns S&P500
t=-1
t=0
t=1
0,002044* -0,000317 0,000594
0,002555
0,002239
0,002832
1,566001
1,120068
1,227189
98
98
98
t=2
0,000263
0,003095
1,199487
98
Table 8. The abnormal returns around the days of signing by the President. T=0 is the event day. The
starting point of the cumulative abnormal returns is the two days prior to the vote. T=0 is the event day. The
third row indicates the t-values of the corresponding CAR value.
AV
Observations
t=-2
-0,09102
69
Abnormal values ΔVIX
t=-1
t=0
t=1
-0,06724
0,050296
0,274502
69
69
69
t=2
-0,04555
69
Table 9. The abnormal values of changes in the VIX around the days of signing by the President. T=0 is the
event day.
41
AV
Observations
t=-2
0,553215
27
Abnormal values ΔICI
t=-1
t=0
-0,28245
-1,0143
27
27
t=1
0,531965
27
t=2
-0,36917
27
Table 10. The abnormal values of changes in the ICI around the days of signing by the President. T=0 is the
event day.
Regressions
Regressions are performed on the abnormal S&P500 returns in order to examine
the impact of the variables LENGTH, DEPTH, POLITICAL and IMPACT on
those excess returns. Pastor and Veronesi (2012) argue that an economic
downturn is needed in order for a policy change to take place. If this downturn
hasn’t been long enough (indicated by LENGTH) or severe enough (indicated by
DEPTH), then the market reacts negatively to a policy change. This negative
reaction becomes more pronounced if the uncertainty about whether or not a new
policy will be accepted (indicated by POLITICAL) or the uncertainty about the
impact of the new policy (indicated by IMPACT) is high.
However, before the regressions are performed, it is necessary to
determine to what extent the proxies for IMPACT and POLITICAL account for
the variances in the implied volatility index. Doing so helps to assess whether or
not the considered proxies are actually usable for the main regressions. Plus, one
could see these regressions as a dissection of the abnormal VIX activity in order
to determine what aspects of the bill and the voting process contribute to it. The
cumulative abnormal values (CAV) of the changes in the VIX around the five
days encompassing the vote are regressed on the IMPACT proxy. The absolute
value of the abnormal decrease in the VIX after the vote is regressed on the
POLITICAL proxies15.
The results are tabulated below. Although none of the coefficients is
significant (for an α of 10% or lower), they all have the right sign. Recall that the
VIX index usually decreases once the vote has taken place. The magnitude of this
decrease is considered political uncertainty. Since this abnormal change, being
(usually) a decrease, is negative, any factors that abate the political uncertainty
15
For the rationale, see section ‘Methodology’.
42
should have a positive sign. The F-test that none of these variables is
significantly different from 0 is not rejected in the second stage, with the
corresponding p-value of 0.1141. The F-test for the first stage, having a p-value of
0.0352, states that at least one of the variables is significantly different from 0.
MAJORITY
0.2630
(0.5754)
0.8270
(1.0131)
TERM
0.5250
(0.3220)
0.7054
(0.5776)
ENACTMENT PERCENTAGE Constant Observations
162
0.2219
-1.4237
(0.1583)
(0.5001)
0.4236
1.2308
-3.8831
42
(0.2923)
(1.4581)
(1.1069)
Table 11. Regression of the abnormal post-vote decrease in the VIX on the proxies for political uncertainty.
The dependent variable is the cumulative abnormal value of the post-vote change in the implied volatility
index over the two days following a vote. The first row is the regression of the first-stage abnormal changes
in the VIX, the second row is the second-stage regression. MAJORITY is a dummy variable equal to 1 if one
of the parties constitutes a majority in both Chambers. TERM is equal to 0 if the Congress is in its first year,
1 if the Congress is in its second year. ENACTMENT refers to the overall percentage of bills that were
enacted by a particular Congress. PERCENTAGE is the number of ‘Yeas’ as a percentage of total votes cast
on a bill in the previous stage. The numbers between the parentheses are the standard errors. None of the
coefficients are significant at a 10% level.
Therefore, the variables MAJORITY, TERM and ENACTMENT are
separately regressed on the cumulative abnormal change in the VIX over the two
days following the first-stage vote. The results can be found in the table below.
MAJORITY
0.7991
(0.077)
-
TERM
-
ENACTMENT
-
0.4705
(0.160)
-
-
** significant at 5%
0.2918**
(0.027)
Constant
-0.8167
(0.051)
-0.3700
(0.096)
-1.2575
(0.016)
R2
0.0194
Observations
162
0.0113
177
0.0275
177
Table 12. Regression of the cumulative abnormal changes in the VIX over the two days following the firststage vote on the proxies for political uncertainty. MAJORITY is a dummy variable equal to 1 if one of the
parties constitutes a majority in both Chambers. TERM is equal to 0 if the Congress is in its first year, 1 if
the Congress is in its second year. ENACTMENT refers to the overall percentage of bills that were enacted
by a particular Congress. The numbers between the parentheses are the standard errors.
The variable ENACTMENT, indicating the enactment percentage of a
particular Congress, seems to be significant at a 5% level. The positive coefficient
of 0.2918 implies that for an extra percentage of enacted laws the abnormal
decrease in VIX is smaller by 0.2918 points after a bill has been voted on. In
other words, the more laws a particular Congress enacts, the smaller the political
uncertainty concerning a new bill. This means that, in this case the market is
43
less surprised that a new bill has been approved, compared to a Congress with a
lower overall enactment percentage. However, the low R2 values in Table 11
imply that a great deal of the political uncertainty is unexplained by the
considered proxies.
Concerning the impact uncertainty, the abnormal volatility activity over
the 5 days encompassing the vote seems to be unaffected by the number of days
between the voting date and the introduction date of the bill, as can be seen from
the table below. Hence, instead of these proxies, the actual abnormal changes in
the VIX are used to account for the political and the impact uncertainties.
Unfortunately, the changes in the VIX alone do not convey any information about
the individual aspects of a particular bill. In other words, no explanation can be
provided about how and why the abnormal changes differ per bill.
DAYS
0.000240
(0.0018)
0.000259
(0.0030)
Constant
0.215092
(0.3152)
-0.164145
(0.5824)
Observations
177
78
Table 13. Regression of the cumulative abnormal changes in the VIX over the 5 days encompassing the vote
on the proxy for impact uncertainty. The first row is the regression of the first-stage abnormal changes in
the VIX, the second row is the second-stage regression. DAYS is the number of days between the voting date
and the date the bill was introduced. The numbers between the parentheses are the standard errors.
Main regressions
The cumulative abnormal returns of the S&P500 index over the 5 days
encompassing the vote are regressed on the proxy for LENGTH and the proxies
for DEPTH, in order to determine which proxies have the highest explanatory
power. The results of these regressions can be found in the appendix. Concerning
the proxy for LENGTH, NEGATIVE(3) seems to give the best results. Concerning
the proxies for DEPTH, DIFFERENCE seems to give the best results, being the
only significant variable.
44
Constant
AMENDMENT
NEGATIVE(3)
DIFF.(3)
1.8152
0.1075
-0.0795**
-0.0012***
(1.28)
(0.38)
(0.04)
(0.00)
1.7773
0.1107
-0.0781**
(1.29)
(0.38)
(0.04)
1.8602
0.0972
-0.0792**
(1.30)
(0.38)
(0.04)
**significant at 5% ***significant at 1%
DIFF.(6)
DIFF.(12)
-0.0012***
(0.00)
-0.0011***
(0.00)
R2
Obs.
0.0756
220
0.0749
220
0.0667
220
Table 14. Regression of CAR(-2; 2) on the proxies for LENGTH and DEPTH, upscaled by 100. CAR(-2;
2) is the cumulative abnormal return of the S&P500 index over a period of 5 days encompassing the
vote. AMENDMENT is a dummy variable equal to 1 if the proposed policy is an amendment to an
existing one. NEGATIVE(3) is the number of days the S&P500 index decreased in the past 3 months
prior to the vote. DIFFERENCE(3), DIFFERENCE(6) and DIFFERENCE(12) are the differences
between the actual GDP and the forecasted GDP in the, respectively, past 3 months, 6 months and 12
months prior to the vote. The numbers between the parentheses are the standard errors. A coefficient
equal to 1 means a 1% higher cumulative abnormal return.
Interestingly enough, the variable NEGATIVE(3) has a negative sign, implying
that for every day that the S&P500 index incurred a loss in the previous three
months, the index fell by additional 0.08% over the 5 days encompassing the
approval of a bill. This contradicts Hypothesis 3. The DIFFERENCE variables,
on the other hand, have the sign predicted by Hypothesis 3, albeit with small
coefficients. Taking 2005 as the index year, a difference of 1 billion dollars
between the actual GDP and the forecasted GDP leads to a drop in the S&P500
index equaling approximately 0.0012%, if the bill is approved. This implies that
the better the state of the economy is prior to the vote, the bigger the price
decline is in the S&P500 as a result of the vote.
The next step is the regression of the 5-day cumulative abnormal returns
at the first and the second stage on the variables AMENDMENT, NEGATIVE(3),
DIFFERENCE(3), CAV(-2;2), -CAV(1;2) and the control variables. CAV(-2;2) is
the cumulative abnormal change in the implied volatility index in the same
period. –CAV(1;2) is the negative of the cumulative abnormal change in the VIX
on the two days following the vote. As explained before, this value is considered
as an indicator of the political uncertainty concerning a particular bill. The
negative of this value is taken for the sake of convenience. The results are
tabulated below. For the regressions for the first stage (Models 1.1, 1.2 and 1.3),
the variable CAV(-2;2) is significant at a 1% level and is equal to approximately 0.46. This means that, for each point the VIX increases due to the impact
uncertainty concerning a bill, the S&P500 index falls by 0.46% in case the bill is
45
approved. This is supportive of Hypothesis 2, insofar the hypothesis concerns the
impact uncertainty.
In the first stage, -CAV(1;2), which is the indicator of the political
uncertainty, is only significant in Model 1.1, having a positive sign and a
coefficient equal to 0.1562. This implies that for an extra point decrease in the
VIX after the vote, the S&P500 index increased by 0.1562% over the five days
encompassing the vote. This is contradictory of the political uncertainty
hypothesis. Although it may seem logical – for a decrease in the volatility index is
beneficial for the returns – recall that this decrease only concerns the two days
after the vote, whereas the dependent variable is the abnormal return over 5
days. In the models 1.2 and 1.3, which contain control variables, this –CAV(1;2)
is not significant.
The other variable that is significant in models 1.2 and 1.3, at a 5% level,
is NEGATIVE(3), similar to the results in Table 12. The coefficient is
approximately -0.10, implying that for every day that the S&P500 index incurred
a loss in the previous three months, the index fell by additional 0.1% over the 5
days encompassing the approval of a bill. This is contrary to the predictions. One
could argue that this might be due to the reverse causality, namely that a new
bill is more likely to be approved if the voting is preceded by significant number
of negative return days. Thus, the negative return over the 5 days would not be
caused by the bill being approved, rather by the trend of negative returns
preceding the bill. However, this argument is easily refuted taking into
consideration the fact that significant, abnormal shifts are observed in the
implied volatility index around a vote.
Concerning the two other variables, AMENDMENT and DIFFERENCE(3),
the latter has the right sign while the former does not. A negative AMENDMENT
coefficient implies that an amendment to a bill would cause a more pronounced
negative return, had it been significantly different from 0. The negative sign of
DIFFERENCE(3) would mean that the better the economy fares in the 3 months
preceding a vote, compared to the forecast in terms of GDP, the more negative
the market reaction is to a new bill. But again, this variable is not significant
either.
46
Independent variables
Model
1.1
1.2
1.3
2.1
2.2
2.3
Constant
1.3696
(1.0420)
2.9895
(1.3918)
3.5509
(1.3584)
-0.6281
(1.7475)
-2.8614
(2.1371)
-3.2636
(2.3154)
Control variables
-
R2
0.6130
Obs.
175
-
0.6704
94
0.0012
(0.0008)
-
0.6685
95
0.6476
78
-
0.7408
48
-0.0017
(0.0010)
0.7321
47
AMENDMENT
NEGATIVE(3)
DIFFERENCE(3)
CAV(-2;2)
-CAV(1;2)
BUDGET5
BUDGET10
-0.3536
(0.2863)
-0.3002
(0.3589)
-0.3515
(0.3669)
-0.8250
(0.5294)
-0.1965
(0.5882)
-0.1856
(0.6422)
-0.0420
(0.0327)
-0.0932**
(0.0442)
-0.1091**
(0.0427)
0.0383
(0.0540)
0.0978
(0.0662)
0.1095
(0.0705)
-0.0004
(0.00)
-0.0005
(0.0004)
-0.0005
(0.0004)
-0.0006
(0.0004)
-0.0007
(0.0006)
-0.0007
(0.0006)
-0.4396***
(0.0412)
-0.4612***
(0.0617)
-0.4632***
(0.0617)
-0.4686***
(0.0640)
-0.5982***
(0.0782)
-0.5922***
(0.0810)
0.1562**
(0.0642)
0.0876
(0.0866)
0.0855
(0.0871)
0.1632
(0.1202)
0.4028**
(0.1513)
0.4129**
(0.1595)
0.0030
(0.0021)
-
** significant at 5% ***significant at 1%
-0.0048
(0.0023)
-
Table 15. Regression of CAR(-2;2) on the variables of interest. CAR(-2;2) is the cumulative abnormal return of the S&P500 index over the
5 days around a vote. AMENDMENT is a dummy variable equal to 1 if the proposed policy is an amendment to an existing one.
NEGATIVE(3) is the number of days the S&P500 index decreased in the past 3 months prior to the vote. DIFFERENCE(3), is the
difference between the actual GDP and the forecasted GDP in the past 3 months prior to the vote. CAV(-2;2) is the cumulative abnormal
change in the VIX over the two days prior to the vote to two days after the vote. –CAV(1;2) is the negative of the cumulative abnormal
return in the VIX over the two days following the vote. BUDGET5 and BUDGET10 are the estimations of the impact of the new policy on
the governmental budget, for the next 5 and 10 years respectively. Models 1.1 to 1.3 are regressions of the first-stage votes, models 2.1 to
2.3 of the second-stage votes.
47
In the second stage the only variable that is significant in all models is
CAV(-2;2) having the same significance level and approximately the same
magnitude as in the models of the first stage. A major difference between the two
stages is that NEGATIVE(3), albeit not significant, has a positive sign in models
2.1 to 2.3, thereby conforming the predictions. Another major difference is that
including control variables leads to –CAV(1;2) being significant, as opposed to the
first stage, where the same variable becomes insignificant when the control
variables are added. The significant coefficients of 0.40 and 0.41, respectively, are
also four times higher than the coefficients in models 1.1 to 1.3. However, it the
reader is reminded that none of the abnormal returns and cumulative abnormal
returns are significantly different from 0 in the second stage, as shown on page
34.
Conclusion
This paper analyzes the stock market reaction to policy changes by the
government. It does so by empirically testing the predictions put forward by
Pastor and Veronesi (2012). According to Pastor and Veronesi, on average policy
changes lead to a negative market reaction. This negative reaction is more
pronounced if the political uncertainty – whether or not the policy will change –
and the impact uncertainty – how the new policy will affect the economy – are
high. Pastor and Veronesi also argue that usually an economic downturn is
needed for a policy change and that if this downturn is not long or deep enough,
the market reaction will be even more negative. Additionally, Pastor and
Veronesi claim that a policy change leads to higher market volatilities and
correlations.
This paper applies an event study approach to test the predictions by
Pastor and Veronesi (2012). Pastor and Veronesi consider the whole spectrum of
policy changes: advantageous and disadvantageous policies; policies that bring
about big changes and petty policies. Therefore, this paper analyzes a wide array
of bills that have been introduced and voted on in the United States Congress
from 1973 to 2013. The bills that are examined either directly affect large
corporations (i.e. corporate taxes, regulations) or indirectly (i.e. through the
48
financial sector, or income taxes). The events in this event study are the votes by
the House of Representatives and by the Senate and the eventual signing of a bill
by the President.
This paper finds that a policy change – i.e. a new bill passing a Chamber –
only elicits significant market reactions in the first stage, which is usually the
vote by the House of the Representatives. Two days prior to the vote S&P500
drops significantly by 0,20%. However, the day following the vote the index
‘bounces’ back by approximately the same amount. The cumulative abnormal
return over the five days encompassing the vote is not significantly different from
0. The conclusion is that although the prices fluctuate around the voting days, a
bill being approved does not lead to a significantly lower S&P500 index.
In the first-stage regressions, the variable indicating the political
uncertainty, measured by the number of points by which the VIX falls back after
the vote, is either insignificant or has the wrong sign. For example, in the model
with no control variables, the coefficient of this variable is equal to 0.1562. This
implies that for each point the VIX falls in the two days following a vote, the
cumulative abnormal return over the five days encompassing the vote increases
by 0.1562%, contrary to the prediction by Pastor and Veronesi (2012). However,
this paper does find evidence for the impact uncertainty hypothesis. The variable
indicating this uncertainty, measured by the significant abnormal change in the
VIX over the five days around a vote, is significantly different from 0 in all
models, having an average coefficient of 0.50. This implies that each point of
abnormal increase in the VIX due to the vote leads to a decrease in the
cumulative S&P500 returns by 0.50%.
This paper also finds that the approval of a new bill does lead to a higher
market volatility and a higher market correlation. The implied volatility index
(VIX), measuring the investors’ expectation of the market volatility for the next
30 days, increases by 0.2865 points as a result of an approval of a bill. At the
same time, the implied correlation index increases by 0.325 points.
All of the obtained results are only significant in the first stage. The second
stage and the eventual signing by the President do not lead to significant changes
in the S&P500 index, the VIX or the implied correlation index.
49
In short, this paper finds that out of the three major stages of
policymaking, being the vote by the House of Representatives, the vote by the
Senate and the signing by the President respectively, only the first one elicits
significant market reactions. S&P 500 returns fluctuate around the voting days,
but the post-vote index level does not significantly differ from the pre-vote level.
The index tends to fall prior to the vote and increase back to the pre-vote level
after the vote. This gives rise to the question whether arbitrage is possible here,
which includes buying shares in S&P 500 index just before the vote and selling
them afterwards. Further research, taking into account the transaction costs,
might shed some light on this matter. Lastly, the market volatility and
correlation levels do increase significantly as a result of policy changes. The
government, for example, might do better to avoid changing many policies in a
market that is already too volatile.
This paper has two shortcomings. First, it considers the vote by the Senate
in the second stage as a major event. However, it is possible that the element of
surprise trickles away before the Senate actually gets to vote, which might
explain the insignificant market reaction. Further researches might analyze bills
more carefully and identify the major events in each case separately.
Second, this paper uses the constant mean return model to calculate the
expected returns for the S&P500 index around the event days. The assumption
hereby is that these expected returns are not significantly different from the
expected returns in days outside the event window. This assumption might be
incorrect, for Savor and Wilson (2009) show that expected returns for Treasury
bills are higher around days with macroeconomic news. If this is also the case for
stock markets around the days of Congress votes, then this implies that the
constant mean return model underestimates the negative stock reactions while at
the same time overestimating the positive reactions. A solution could be
developing a new model that allows for higher expected returns by, for example,
taking into account the higher expected returns for the Treasury bills.
50
References
Alesina, A., Ardagna, S., & Trebbi, F. (2006). Who adjusts and when? On the
political economy of reforms (No. w12049). National Bureau of Economic
Research.
Alesina, A., & Rodrik, D. (1994). Distributive politics and economic growth. The
Quarterly Journal of Economics, 109(2), 465-490.
Allvine, F. C., & O'Neill, D. E. (1980). Stock market returns and the presidential
election cycle: Implications for market efficiency. Financial Analysts
Journal, 49-56.
Becker, R., Clements, A. E., & McClelland, A. (2009). The jump component of
S&P
500
volatility
and
the
VIX
index. Journal
of
Banking
&
Finance, 33(6), 1033-1038.
Becker, R., Clements, A. E., & White, S. I. (2007). Does implied volatility provide
any
information
beyond
that
captured
in
model-based
volatility
forecasts?. Journal of Banking & Finance, 31(8), 2535-2549.
Belo, F., Gala, V. D., & Li, J. (2012). Government spending, political cycles, and
the cross section of stock returns. Journal of Financial Economics.
Bennett, C. J., & Howlett, M. (1992). The lessons of learning: Reconciling theories
of policy learning and policy change. Policy sciences, 25(3), 275-294.
Bernanke, B. S. (1983). Irreversibility, uncertainty, and cyclical investment. The
Quarterly Journal of Economics, 98(1), 85-106.
Białkowski, J., Gottschalk, K., & Wisniewski, T. P. (2008). Stock market
volatility around national elections. Journal of Banking & Finance, 32(9),
1941-1953.
Boutchkova, M., Doshi, H., Durnev, A., & Molchanov, A. (2012). Precarious
politics and return volatility. Review of Financial Studies, 25(4), 11111154.
Brown, S. J., & Warner, J. B. (1980). Measuring security price performance.
Journal of financial Economics, 8(3), 205-258.
Brown, S. J., & Warner, J. B. (1985). Using daily stock returns: The case of event
studies. Journal of financial economics, 14(1), 3-31.
51
Bruno, M., & Easterly, W. (1996). Inflation's children: tales of crises that beget
reforms (No. w5452). National Bureau of Economic Research.
Coate, S., & Morris, S. (1995). On the form of transfers to special
interests.Journal of Political Economy, 1210-1235.
Cutler, D. M. (1988). Tax reform and the stock market: An asset price
approach. The American Economic Review, 78(5), 1107-1117.
Drazen, Allan (2000). Political economy in macreeconomics. Princeton University
Press, Princeton.
Drazen, A., & Easterly, W. (2001). Do crises induce reform? Simple empirical
tests of conventional wisdom. Economics & Politics, 13(2), 129-157.
Dzielinski, M. (2012). Measuring economic uncertainty and its impact on the
stock market. Finance Research Letters, 9(3), 167-175.
Durnev, A. (2010, December). The Real Effects of Political Uncertainty: Elections
and Investment Sensitivity to Stock Prices. In Paris December 2010
Finance Meeting EUROFIDAI-AFFI.
Edgardo Campos, J., Lien, D., & Pradhan, S. (1999). The impact of corruption on
investment: Predictability matters. World Development, 27(6), 1059-1067.
Engle, R. F., & Ng, V. K. (2012). Measuring and testing the impact of news on
volatility. The Journal of Finance, 48(5), 1749-1778.
Foerster, S. R., & Schmitz, J. J. (1997). The transmission of US election cycles to
international stock returns. Journal of International Business Studies, 127.
Grossman, G. M., & Helpman, E. (1992). Protection for sale (No. w4149).
National Bureau of Economic Research.
Hassett, K. A., & Metcalf, G. E. (1999). Investment with uncertain tax policy:
Does
random
tax
policy
discourage
investment. The
Economic
Journal,109(457), 372-393.
Hermes, N., & Lensink, R. (2001). Capital flight and the uncertainty of
government policies. Economics letters, 71(3), 377-381.
Huang, R. D. (1985). Common stock returns and presidential elections. Financial
Analysts Journal, 58-61.
52
Julio, B., & Yook, Y. (2012). Political uncertainty and corporate investment
cycles. The Journal of Finance, 67(1), 45-84.
MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of
economic literature, 35(1), 13-39.
Ozoguz, A. (2009). Good times or bad times? Investors' uncertainty and stock
returns. Review of Financial Studies, 22(11), 4377-4422.
Pastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock
prices. The Journal of Finance, 67(4), 1219-1264.
Patelis, A. D. (2012). Stock return predictability and the role of monetary policy.
The Journal of Finance, 52(5), 1951-1972.
Persson, T., & Tabellini, G. (1991). Is inequality harmful for growth? Theory and
evidence (No. w3599). National Bureau of Economic Research.
Rigobon, R., & Sack, B. P. (2002). The impact of monetary policy on asset
prices (No. w8794). National Bureau of Economic Research.
Rodrik, D. (1991). Policy uncertainty and private investment in developing
countries. Journal of Development Economics, 36(2), 229-242.
Rose-Ackerman, S. (1999). Corruption and government: Causes, consequences,
and reform. Cambridge University Press.
Savor, P., & Wilson, M. (2009). How much do investors care about macroeconomic
risk?: evidence from scheduled economic announcements. Rodney L. White
Center for Financial Research, The Wharton School, University of
Pennsylvania.
Shleifer, A., & Vishny, R. W. (1993). Corruption. The Quarterly Journal of
Economics, 108(3), 599-617.
Sum, V. (2012a). Economic policy uncertainty and stock market performance:
Evidence from the European Union, Croatia, Norway, Russia, Switzerland,
Turkey and Ukraine. Croatia, Norway, Russia, Switzerland, Turkey and
Ukraine (June 27, 2012).
Sum, V. (2012b). Economic policy uncertainty and stock market returns.
Available at SSRN 2073184.
Thorbecke, W. (2012). On stock market returns and monetary policy. The Journal
of Finance, 52(2), 635-654.
53
Umstead, D. A. (2012). Forecasting stock market prices. The Journal of
Finance, 32(2), 427-441.
Van Wijnbergen, S. (1985). Trade reform, aggregate investment and capital
flight: on credibility and the value of information. Economics Letters, 19(4),
369-372.
Yonce, A. (2009). Political cycles and corporate investment. University of
California, Berkeley.
54
APPENDIX
A. The complete list of the categories
THOMAS
applies in the classification of the
bills, as of 2013:
Agriculture and food
Government operations and politics
Animals
Health
Armed forces and national security
Housing and community
Arts, culture, religion
development
Civil rights and liberties, minority
Immigration
issues
International affairs
Commerce
Labor and employment
Congress
Law
Crime and law enforcement
Native Americans
Economics and public finance
Private legislation
Education
Public lands and natural resources
Emergency management
Science, technology, communications
Energy
Social sciences and history
Environmental protection
Social welfare
Families
Sports and recreation
Finance and financial sector
Taxation
Foreign trade and international
Transportation and public works
finance
Water resources development
55
B. The obtention of the ARIMA models for the VIX and the ICI.
The tables below show the AIC and BIC values for the different values of p and q
for the ARIMA(p;1;q) models. The lowest AIC and BIC values indicate the best
model.
AR/MA
1
2
3
1
22274,26
22274,93
22276,72
2
22274,96
22276,84
22278,75
3
22276,72
22278,76
22277,12
AR/MA
1
2
3
1
22301,13
22308,51
22317,03
2
22308,54
22317,14
22325,77
3
22317,03
22325,78
22330,86
Table 16. The AIC (above) and BIC (below) values for the VIX. In both tables p=1 (ARcolumn) and q=1 (MA-row) give the best model.
AR/MA
1
2
3
1
7261,547
7253,624
7255,266
2
7253,292
7255,182
7257
3
7255,205
7257,147
7256,493
AR/MA
1
2
3
1
7283,095
7280,559
7287,588
2
7280,227
7287,504
7294,709
3
7287,528
7294,857
7299,59
Table 17. The AIC (above) and BIC (below) values for the ICI. In both tables p=1 (ARcolumn) and q=2 (MA-row) give the best model.
56
C. Regression of the CAR(-2;2) on the variables indicating the state of the
economy.
Model 1
0.000371
(0.004)
-0.001057***
(0.000)
AMENDMENT
NEGATIVE(3)
NEGATIVE(6)
Model 2
0.001827
(0.004)
Model 3
0.001766
(0.004)
0.000004
(0.000)
NEGATIVE(12)
Constant
R2
Observations
*** significant at 1%
0.029489
(0.012)
0.0359
222
-0.003404
(0.015)
0.0010
222
-0.000069
(0.001)
-0.001197
(0.019)
0.0010
222
Table 18. The regression of CAR(-2;2) on the variables NEGATIVE. The numbers
between the parentheses are the standard errors.
Model 1
0.000265
(0.0039)
-0.001004**
(0.0004)
0.002755
(0.0051)
AMENDMENT
NEGATIVE(3)
LOG_GDP(3)
LOG_GDP(6)
Model 2
0.000263
(0.0039)
-0.001004**
(0.0004)
Model 3
0.000256
(0.0039)
-0.001000**
(0.0004)
0.002769
(0.0051)
LOG_GDP(12)
Constant
R2
Observations
** significant at 5%
0.002527
(0.0513)
0.0352
220
0.002403
(0.0512)
0.0352
220
0.002979
(0.0050)
0.000363
(0.0509)
0.0354
220
Table 19. The regression of CAR(-2;2) on the variables NEGATIVE(3) and LOG_GDP.
The numbers between the parentheses are the standard errors.
AMENDMENT
NEGATIVE(3)
Model 1
0.000435
(0.0039)
-0.001081***
Model 2
0.000438
(0.0039)
-0.001080***
Model 3
0.000395
(0.0039)
-0.001065***
57
LOG_SP(3)
(0.0004)
-0.000480
(0.0017)
LOG_SP(6)
(0.0004)
-0.000501
(0.0017)
LOG_SP(12)
Constant
R2
Observations
*** significant at 1%
(0.0004)
0.033288
(0.0184)
0.0362
222
0.033392
(0.0183)
0.0363
222
-0.000202
(0.0017)
0.031034
(0.0181)
0.0360
222
Table 20. The regression of CAR(-2;2) on the variables NEGATIVE(3) and LOG_SP. The
numbers between the parentheses are the standard errors.
58