Results indicate that the debates and videotape release were not statistically significant, but that a letter to Congress released by FBI Director James B. Comey on October 28, 2016, substantially decreased Clinton's probability of winning the popular vote and simultaneously increased Trump's probability. Financial market uncertainty is found to have some marginal positive effect on Trump's probability of winning.

The present article uses a popular vote prediction market to test the impact of these factors on the probability of Trump winning the election.

The contentious 2016 U.S. presidential election was marked by acrimonious televised debates between the two major candidates, Hillary Clinton and Donald Trump, federal investigations of Clinton's emails that were sent from a personal server when she held office as Secretary of State, and the release of a videotape of lewd remarks by Trump about his behavior toward women. Financial market uncertainty also played in role in the election campaign. The objective of the article is to examine the impact of these different factors on the election.

The 2016 presidential election was one of the most contentious in recent U.S. history. Donald Trump, running on a populist platform, proposed to abandon or reverse many key policies of previous administrations in the areas of healthcare, financial regulation, taxation, international trade, migration, defense, and foreign policy. His anti‐globalization stance appealed to constituents who felt their standard of living threatened by international competition and deindustrialization forces within the U.S. economy, which Trump promised to roll back. Hillary Clinton, on the other hand, advocated a continuation of more traditional, liberal policies that were perceived as less disruptive to the political status quo within Washington. During the campaign, Clinton was viewed as outperforming Trump in the presidential debates, but Clinton's public image was potentially harmed by announcements of FBI investigations of sensitive emails sent from her personal server when she was Secretary of State. Trump's chances of electoral success were seemingly set back by the release of a videotape by the Washington Post in which Trump boasted of sexually assaulting women. Financial markets appeared to respond quite differently to the prospect of a Clinton victory at the polls than to that of a Trump presidency. In almost every U.S. presidential election since 1880, equity markets have risen on the expectation of a Republican win and fallen when Democrats were expected to win (Snowberg, Wolfers, and Zitzewitz, 2007a). This is no doubt due to the fact that the Republican Party has generally supported lower taxes and less government regulation. The 2016 election, however, appeared to be an anomaly. An October 2016 Bank of America/Merrill Lynch poll of fund managers found that a victory by Donald Trump would represent one of the largest risks facing financial markets (Economist, 2016:64). This could be due to the fact that Clinton had received millions of dollars in speaking fees from the banks, whereas Trump was viewed as an unknown entity (Yoon, 2016). Many of Trump's campaign promises caused concern among market participants. In the area of international trade and migration, Trump's threat to abandon or at least renegotiate the North American Free Trade Agreement and his stated intention to impose punitive tariffs on imports from China and other trading partners and to deport large numbers of undocumented immigrants were viewed as potentially destabilizing and damaging to economic growth. There was also worry that his foreign policy proposals to scale back U.S. participation in international alliances and defense pacts would heighten global tensions. Criticism by Trump of the Federal Reserve's low interest rate policies suggested that he might pressure the central bank to adopt a more restrictive monetary policy, or that he might try to limit the Fed's independence, neither of which was a pleasing prospect to most investors. As a result, whenever Trump gained in the polls, the markets fell (Economist, 2016:64). Immediately following Trump's election‐night victory, the financial markets responded much as they had during the campaign: equity markets declined as did the currencies of major trading partners. The Dow Jones Industrial Average futures contract fell by more than 800 points at one stage, the Japanese stock market fell by 4.6 percent, and other Asian markets also retreated. The Mexican peso fell to a new low of almost 20.8 to the dollar. Prices of safe‐haven assets such as U.S. Treasury bonds and gold increased (Economist, 2016:64). However, such negative responses to Trump's win were short‐lived: markets quickly rebounded. On November 9, 2016, U.S. stock market indexes rose, and those in Europe and Asia soon recouped their losses. There was hope that Trump's proposed tax cuts would have a stimulative effect on growth and that his revisions of the tax rules on foreign profits would encourage U.S.‐based multinationals to repatriate earnings and thus boost the dollar. Trump's campaign promise to increase government spending on defense and infrastructure caused an expectation of larger budget deficits and higher inflation, which drove Treasury bond prices down (Economist, 2016:64). Wagner, Zeckhauser, and Ziegler (2017) examine both the initial stock market reaction to the election and the longer‐term response up to the end of 2016. Their study finds that individual stock price reactions to the election reflected investor expectations regarding economic growth, taxes, and trade policy. The anticipation of lower taxes and scaled‐back regulation, particularly in the banking sector where Trump proposed to repeal elements of the Dodd‐Frank legislation, led to gains in the stock prices of firms in manufacturing and banking. However, Trump's promise to eliminate the Patient Protection and Affordable Care Act of 2010 (Obamacare) caused stock price declines in the healthcare, medical equipment, and pharmaceuticals industries, while expectations of drastic tariffs on imports also generated lower stock prices in the textiles and apparel industries. In general, domestically oriented firms fared better than those with a more international orientation (Wagner, Zeckhauser, and Ziegler, 2017). However, the overall gains in the stock market since the election and in the early months of 2017 appear to reflect optimism on the part of investors that some of the more extreme measures on trade and immigration proposed by Trump during the campaign would be abandoned by the new administration or blocked by Congress and that more traditional Republican policies such as tax cuts and deregulation would be implemented (Economist, 2016:64). In the present article, we examine the effects of the presidential debates, the FBI announcements regarding the investigations of Clinton's emails, the release of the Trump videotape, and financial market uncertainty on the probability of Trump winning the election as measured by an electoral prediction market, specifically the price of the popular vote futures contract in the Iowa Electronic Markets (University of Iowa, 2019). The popular vote futures contract is expressed as the probability of winning a plurality of votes in a “winner‐take‐all” popular vote election. The use of the Iowa Electronic Market and its winner‐take‐all contract is well justified in the literature (e.g., see Erikson and Wlezien, 2012; Rhode and Strumpf, 2004). The specific events chosen for analysis were selected as those that received significant Google Trend scores and that continue to be discussed as key determinants of the 2016 election (e.g., “The 2016 U.S. Presidential Election,” History.com, 2019). It should be noted that while both Trump and Clinton received considerable media coverage in the months leading up to the election, most of the coverage focused on events that had nothing to do with policy positions (Patterson, 2016). We also estimate the effect of financial market uncertainty on Trump's odds of winning. Market uncertainty is captured by the Chicago Board Options Exchange Volatility (VIX) Index (Yahoo Finance, 2019). We find that neither the debates nor the Trump videotape had a statistically significant impact on Trump's probability of winning the election. Market uncertainty as gauged by the VIX index is found to have some association with a higher probability of success for Trump. Our results further indicate that an October 28, 2016 letter to Congress from FBI Director James B. Comey regarding Clinton emails found on former Rep. Anthony Weiner's computer substantially enhanced Trump's electoral chances and quite possibly altered the election result. It is possible that Comey's letter and the release of the videotape, because they occurred closer to the election, might be expected to have a greater impact on voters’ choices compared to events occurring in the more distant past.

Empirical Election Studies Empirical studies by economists of U.S. election campaigns have generally focused on the relationship between candidates’ popularity or poll performance and resulting financial market fluctuations. A methodology commonly used in the empirical elections literature is the event study, which gauges the likely impact of a political event using asset price movements (Wolfers and Zitzewitz, 2016:2). In electoral event studies, prediction markets are often utilized to calibrate the news content of an event (Wolfers and Zitzewitz, 2016:2; Snowberg, Wolfers, and Zitzewitz, 2007a, 2007b, 2011, 2013). Once a significant event window has been identified, its impact on financial markets is estimated. One of the earliest of such studies was that of Slemrod and Greimel (1999), who focused on the 1996 contest between Steve Forbes and Robert Dole for the Republican nomination. Forbes had proposed a flat tax, which would eliminate the tax deductibility of municipal bond interest payments. Slemrod and Greimel (1999) found significant declines in municipal bond prices when Forbes’s probability of winning the nomination increased. A similar approach was used by Leigh, Wolfers, and Zitzewitz (2003) to estimate the effect on oil and equity prices of an increase in the probability of war in Iraq, as measured by the price of a future traded on an online betting exchange. Halcoussis, Lowenberg, and Phillips (2009) studied the effects of public opinion data on stock prices in the 2008 presidential election. Their findings showed that improvements in Barack Obama's electoral prospects led stock price declines, while gains by Obama were more likely to be followed by falling stock prices than by rising prices. More recently, Wolfers and Zitzewitz (2016) have examined the impacts of political events during the 2016 presidential race between Hillary Clinton and Donald Trump. Their focus is primarily on the first presidential debate on September 26, 2016, in which Clinton was generally regarded to have substantially outperformed Trump.1 They find that a prediction market security traded on the Betfair prediction market that would have been worth $1.00 if Clinton had won the election rose by 6 cents, from 63 to 69 cents, during the debate window (defined as the duration of the debate plus the first 20 minutes of postdebate analysis). Other prediction markets also indicated an increase of similar magnitude in Clinton's odds of winning the election (Wolfers and Zitzewitz, 2016:11). Also, during the debate window, the S&P 500 futures index rose by 0.71 percent, “implying that the value of the S&P 500 is about 12 percent higher under a President Clinton than under a President Trump” (Wolfers and Zitzewitz, 2016:13). The Nasdaq‐100 and the Dow Jones Industrial Average experienced similar gains, while the Russell 2000 and the S&P Midcap index rose slightly more. Overseas, the FTSE 100 and the Hong Kong and other Asian markets also rose significantly in the wake of the debate. The VIX futures, which track market expectations of future volatility, declined during the debate window, suggesting that markets expected much lower volatility under a Clinton presidency than under a Trump presidency. U.S. Treasury bond prices at all maturities fell during the debate, energy prices increased, and most foreign currencies, especially the Canadian dollar and Mexican peso, rose dramatically, leading Wolfers and Zitzewitz to conclude that “the Peso would be worth nearly 30 percent more under a Clinton presidency, and the Canadian dollar would be worth 10 percent more” (Wolfers and Zitzewitz, 2016:17). Wolfers and Zitzewitz (2016) also study the effects of the release of a videotape on October 7, 2016, 11 days after the first debate, in which Trump made disparaging remarks about women. Following the release of the Trump tape, prediction markets, including the Iowa Electronic Markets, registered a decrease in the probability of a Trump victory. Stocks rose, volatility fell, and the Mexican peso appreciated by 1.62 percent in response to the tape's release (Wolfers and Zitzewitz, 2016:21, 23). Wolfers and Zitzewitz's conclusion from their findings on both the first debate and the videotape release is that “these movements suggest that financial markets expect a generally healthier domestic and international economy under a President Clinton than under a President Trump” (Wolfers and Zitzewitz, 2016:2).

Data and Empirical Results A nested maximum likelihood autoregressive (AR(1)) model is used to test the relationship between the probability of Trump or Clinton winning a plurality of the popular vote and the debates, videotape release, and FBI announcements preceding the election.2 While the Iowa Electronic Markets also publishes data for a market regarding the vote shares won by the two major party candidates in the 2016 election, we chose to use the “winner‐take‐all” market based on the popular vote plurality winner of the 2016 U.S. presidential election 〈https://iemweb.biz.uiowa.edu/markets/PRES16.html〉. We chose to focus on the popular vote on the grounds that it is a more intuitive measure of the public's forecast of the election outcome. While the president is chosen through Electoral College voting rather than popular vote plurality, Electoral College futures data were not available when performing this research; however, the determinants of the popular vote are well correlated with the determinants of Electoral College outcomes.3 The regression results are presented in Tables 2 and 3 and will be discussed next. The overall equation that contains the other specifications as subsets can be expressed as: Data are daily, Monday through Friday, starting January 1, 2016, and ending Election Day, November 8, 2016. TRUMP is the daily closing price of a futures contract that would have paid $1.00 if Donald Trump had won a plurality of the popular vote in the 2016 presidential election. The contract paid 0, since Hillary Clinton won the popular vote. Since winning the popular vote is highly correlated with winning the election, the price of this contract is a proxy for the market's evaluation of the probability that Trump would win the election. These data are from the Iowa Electronic Markets 〈https://tippie.biz.uiowa.edu/iem/markets/data_Pres16.html〉. Similarly, CLINTON is the daily closing price of a futures contract that would have paid $1.00 if Hillary Clinton had won a plurality of the popular vote in the 2016 presidential election. The contract actually paid $1.00, since she won the popular vote. Prior to the election, similar to the TRUMP variable, CLINTON reflected the market's probability assessment that Clinton would win the election. OTHER is a constructed variable reflecting the sum of the market assessment of third‐party candidates’ probabilities of winning a plurality. This variable is a residual variable, defined as OTHER = 1 – TRUMP – CLINTON, and is best interpreted as a measure of general dissatisfaction with the two major party candidates since it reflects the cumulative performance of all third‐party candidates collectively. Various binary impact variables are constructed, equaling 0 before the event and equaling 1 until the subsequent similar event occurs. We are viewing the periods between potential impact events as separate regimes, which we test following standard financial regime‐change event study methodology (Jeng, 2015). The Iowa Electronic Markets are generally semistrong efficient (Oliven and Rietz, 2004). In such a market, information is quickly evaluated and incorporated into asset prices and remains present until updated information, for example, the next debate, is provided. PRES DEBATE1 = 1 from the date of the first presidential debate, September 26, 2016, through October 3, 2016, the day before the vice‐presidential debate. Before September 26, 2016, and starting with October 4, 2016, the date of the vice‐presidential debate, PRES DEBATE1 = 0. VP DEBATE = 1 from the date of the vice‐presidential debate on October 4, 2016 through October 8, 2016, the day before the second presidential debate. Before October 4, 2016 and starting with the second presidential debate on October 9, 2016, VP DEBATE = 0. PRES DEBATE2 = 1 from the date of the second presidential debate on October 9, 2016 through October 18, 2016, the day before the third presidential debate. Before October 9, 2016 and starting with the third presidential debate on October 19, 2016, PRES DEBATE2 = 0. PRES DEBATE3 = 1 from the date of the third presidential debate on October 19, 2016 until the election. Before October 19, 2016, PRES DEBATE3 = 0. FBI1 = 1 starting on July 5, 2016 through September 1, 2016, the day before the next major FBI announcement. Before July 5, 2016 and after September 1, 2016, FBI1= 0. On July 5, 2016, FBI Director James B. Comey announced to the press that the FBI had examined Clinton's emails and found that some of the emails on her private server were confidential and some were top secret (U.S. Department of Justice, Federal Bureau of Investigation, 2016a). Comey characterized Clinton's behavior as “extremely careless” but recommended against any criminal charges (Collinson and Kopan, 2016). FBI2 = 1 starting on September 2, 2016 and through October 27, 2016, the day before the next major FBI announcement. Before September 2, 2016 and after October 27, 2016, FBI2 = 0. On September 2, 2016, the FBI released a summary of an interview with Clinton concerning her emails and server (U.S. Department of Justice, Federal Bureau of Investigation, 2016b). FBI3 = 1 from October 28, 2016 until the election. Before October 28, 2016, FBI3 = 0. On October 28, 2016, FBI Director Comey informed Congress in a letter that the FBI had found emails on former Rep. Anthony Weiner's computer that were said to be relevant to the FBI’s investigation of Clinton's emails.4 TRUMPTAPE = 1 from October 7, 2016 to the election. Before October 7, 2016, TRUMPTAPE = 0. On October 7, 2016, the media first reported the story about Trump's videotaped remarks concerning his behavior toward women (see Burns, Haberman, and Martin, 2016). VIX is the Chicago Board Options Exchange Volatility Index. These data can be found at 〈http://finance.yahoo.com/quote/%5EVIX/history?ltr=1〉. The VIX, sometimes called the market's fear index, is the implicit volatility of the S&P 500 market index and has been shown to impact popular perceptions of elected officials (Chong, Halcoussis, and Phillips, 2011). Consider the two key variables, TRUMP, the election market price for Trump winning a plurality of the popular vote, and the so‐called fear index, VIX. For context, Table 1 shows descriptive statistics for these two variables. At one point, on October 20, 2016, before Comey's letter to Congress concerning Anthony Weiner's computer and its possible relevance to the FBI’s investigation of Clinton's emails (FBI3), TRUMP reached a minimum of 0.087, meaning that a bet on Trump to win the popular vote would have paid off more than 10 times the initial investment, had Trump actually won the popular vote. The evaluation of the market at this time was that it was very unlikely that Trump would be the next president. On October 28, 2016, the day of the announcement concerning Anthony Weiner's computer, the value of TRUMP rose to 0.395 (it had been 0.290 the day before). The maximum value of 0.75 occurred the day of the election. This is a tribute to how uncertain the outcome was—even on the day of the election neither Trump nor Clinton had a 90 percent chance or greater of winning as evaluated by the market. Table 1. Descriptive Statistics for TRUMP and VIX, n = 221 Variable Mean Median Standard Deviation Minimum Maximum TRUMP 0.31 0.31 0.074 0.087 0.75 VIX 16.33 14.85 4.08 11.34 28.14 In the regression results discussed below, the daily election market price for the subject (TRUMP, CLINTON, or OTHER) will be related to the indicator variables previously explained. This is similar to a statistical event study in which the coefficient indicates the unexpected contribution to the observed variable (see Jeng, 2015). In the regressions, if one of the explanatory variables is found to be statistically insignificant, in an event study context this means that there was no unexpected information associated with the event that was not previously incorporated into the market price. Table 2 shows the results of maximum likelihood autoregressive (AR(1)) regressions in which TRUMP serves as the dependent variable and the other variables listed above are the independent variables. AR(1) is an appropriate estimation method in a time‐series model of this nature (see Halcoussis, 2005:152–54). Table 2. AR(1) Results Using Weekday Data (Dependent Variable Is TRUMP) (1) (2) (3) Coefficients Coefficients Coefficients Variable (p‐Value) (p‐Value) (p‐Value) Constant 0.278** 0.342** 0.342** (less than 0.001) (less than 0.001) (less than 0.001) PRES DEBATE1 −0.000912 0.00436 −0.000593 (0.988) (0.961) (0.993) VP DEBATE −0.0258 −0.0162 −0.0322 (0.982) (0.970) (0.734) PRES DEBATE2 −0.0806 −0.0637 −0.0927 (0.805) (0.877) (0.172) PRES DEBATE3 −0.041 −0.0244 −0.0588 (0.901) (0.953) (0.343) FBI1 −0.0535* −0.0689* −0.0693* (0.024) (0.030) (0.035) FBI2 −0.0238 −0.0442 −0.0474 (0.482) (0.269) (0.260) FBI3 0.182** 0.155** 0.147** (less than 0.001) (0.001) (0.004) TRUMPTAPE −0.0525 −0.0439 (0.872) (0.915) VIX 0.00356 (0.122) Adjusted R2 0.701 0.697 0.696 N 221 221 221 AR(1) ρ 0.638 0.706 0.715 Regression (1) in Table 2 shows results for a model containing all of the independent variables. Although the adjusted R2 is 70 percent, only two variables have coefficients that are statistically significant at 5 percent or better, namely, FBI1 and FBI3. Despite the hype in the media, and the widespread belief that Clinton had dominated in the debates, none of the debates seem to have had a statistically significant effect on the value of TRUMP. According to Wolfers and Zitzewitz (2016:21n), this is the historical norm for most presidential and vice‐presidential debates. FBI1, the first announcement by the FBI that Hillary Clinton's server did contain some confidential emails, had a relatively small but statistically significant negative association, at 5 percent, with the market's evaluation of Trump's chances. Since Comey recommended against criminal charges, perhaps the market felt that the announcement was not as damaging to Clinton as expected, causing Clinton's futures contract price to rise and Trump's to fall (by about 5 cents). The next FBI announcement, the release of the FBI’s interview with Hillary Clinton, did not have a statistically significant impact on the futures market. FBI3, the announcement that material found on Anthony Weiner's computer was relevant to the FBI’s investigation, had a dramatic and positive effect on Trump's chances, statistically significant at the 1 percent level, increasing the price of his futures contract by 18 cents for a dollar contract. After the election, both Bill and Hillary Clinton commented that Comey's announcement (FBI3), coming so close to the election, cost Hillary Clinton the election.5 The results shown in Table 2 support this assertion. Comey himself has acknowledged that the release of his letter may have influenced the election outcome (Dennis and Strohm, 2017). TRUMPTAPE, representing the release of the videotape showing Trump making crude remarks about his behavior toward women, did not have a statistically significant impact on TRUMP. VIX has a p‐value of 0.122 and a positive coefficient. Higher volatility in the stock market is associated with support for Trump at the margin. Regression (2) shows results for a regression equation in which VIX is omitted and regression (3) shows results for a model in which both VIX and TRUMPTAPE are omitted. The coefficient for FBI1 remains significant at 5 percent and the coefficient for FBI3 remains significant at 1 percent in both regressions. The slope estimates for FBI1 and FBI3 are roughly similar across the three regressions and the goodness of fit of the regressions, as measured by the adjusted R2, are similar. In a fourth regression (not shown), VIX is included, TRUMPTAPE is omitted, and the results are similar to those in the other three regressions. Finally, consider the results reported in Table 3 with the computed dependent variable OTHER. Unlike the TRUMP or CLINTON variables, which relate to single candidates, the OTHER variable is the difference between $1 and the sum of the prices for TRUMP and CLINTON. For example, if TRUMP = $0.35 and CLINTON = $0.45, then OTHER = $0.20. However, OTHER is not an election market price for a single candidate but a total valuation across all third‐party candidates. Therefore, the example $0.20 is not to be interpreted as the probability of a plurality of the vote for a third‐party candidate because there may be many third‐party candidates who together would be valued at $0.20. The importance of the regression in Table 3 is to look at the factors that would tend to cause voters to move away from the major party candidates and toward some third‐party candidate. Table 3. AR(1) Results Using Weekday Data (Dependent Variable is OTHER where OTHER = 1‐TRUMP‐CLINTON) (1) Coefficients Variable (p‐Value) Constant 0.013 (0.10) PRES DEBATE1 −0.00403 (0.800) VP DEBATE −0.00316 (0.957) PRES DEBATE2 0.0231 (0.989) PRES DEBATE3 0.0218 (0.990) FBI1 −0.00813 (0.053) FBI2 −0.00417 (0.639) FBI3 −0.0242 (0.686) TRUMPTAPE −0.0159 (0.993) VIX −0.0012** (0.005) Adjusted R2 0.031 N 221 AR(1) ρ 0.044 We find that the FBI announcements had a negligible impact on voters choosing to support third‐party candidates but increased market volatility was strongly associated with voters opting away from a third‐party choice (see Table 3). This is itself an interesting result, suggesting that voters view market turbulence as a reason to more strongly support a major party candidate rather than a third‐party candidate; it seems that voting for a major party candidate is the voting equivalent of a “flight to safety,” like a move toward blue chip dividend paying stocks away from start‐up investments in financial markets. This perceived safety of major parties and riskiness of minor parties is a topic for future research.

Conclusion Our study has demonstrated that, despite the fact that the presidential debates were regarded as significant victories for Hillary Clinton, and despite the release of an apparently damaging videotape of Donald Trump's remarks about women, neither candidates’ electoral chances were significantly affected by these events. Announcements by the FBI regarding investigations of Clinton's emails, however, did appear to have an effect. In particular, regardless of one's political views, the data suggest that Trump's probability of winning the election received a substantial boost from FBI Director James B. Comey's “last‐minute” announcement on October 28, 2016. The models presented here suggest that, but for the Comey announcement, Clinton, who won the popular vote, would have had approximately an 18 percent greater probability of doing so. To the extent that a higher probability of winning implies greater votes cast for the candidate, then this potentially could have changed the election outcome. Presumably, future elections may not feature candidate emails and regular announcements about possible illegality. Nevertheless, while neither the debates, scandals, nor FBI revelations seemed to impact the popularity of third‐party candidates, strategists should be aware of the subtle impact of market volatility tilting voter choice toward, or away from, major party candidates.

1 Polls taken immediately after the debate found that voters thought Clinton had won the debate by a clear margin (Wolfers and Zitzewitz, 2016

2 Huber‐White consistent standard errors were used for regression testing as appropriate.

3 An analysis of popular vote percentage and Electoral College vote percentage for 49 presidential winners from 1824, when popular vote was first counted, through 2016 reveals a Pearson correlation of 0.736 and a Spearman rank correlation of 0.732. There were five elections in which the Electoral College winner lost the popular vote (data from U.S. National Archives and Records Administration, 2019

4 For the letter, see New York Times, October 28, 2016 2016

5 For Hillary Clinton's comments, see Chozick ( 2016 2016