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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 1 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). 1 2 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): 3 “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). 4 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 5 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. 6 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, 7 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. 8 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. 9 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 10 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 11 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) 12 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 13 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). 14 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 15 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. 16 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 17 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. 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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