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Abstract

Donald Trump’s victory in the 2016 presidential election was a political surprise to almost everyone, domestically and internationally. This paper investigates international investors’ reaction to this apparent global political surprise. Employing an event study methodology, we test the three widely known behavioral hypotheses concerning international investor reaction to this unexpected news, that is, the Overreaction Hypothesis (OH), the Uncertain Information Hypothesis (UIH), and the Efficient Market Hypothesis (EMH). The study results show that most of the indexes initially reacted unfavorably to the announcement of Trump’s win; however, subsequent stock index returns exhibit corrective upward patterns consistent with the predictions of the OH, except for the Latin American and France markets. The reactions of the other indexes appear to be favorable and in line with the prediction of the UIH. These findings suggest that in most countries under study, investors could utilize contrarian strategies to generate abnormal returns, as evidenced by price reversal following the arrival of the surprise.

JEL classification: G14, G15.

Keywords

information market efficiency international investors unexpected news U.S. presidential election stock returns

Introduction

The election of Donald Trump as President of the United States in the 2016 election was a surprise to almost everyone at the national and international levels. One may argue that this astonishment rests on several factors, for example, Trump’s conduct during the campaign, the slogans used in his campaign, the simplicity and impulsivity of his rhetorical style, the frequent mediatization of his messages through Twitter, his populist discourse and the entire management of his campaign (see, e.g., Conway & Zubrod, 2022; Jordan et al., 2018; Kayam, 2018). Added to these factors were his unrealistic promises and an exaggeration of his abilities to solve economic and political problems. All of the above were sufficient reasons to stun many when he was elected, notwithstanding arguments made that he was not a seasoned politician broadly.

In general, the study of presidential election is of interest because its legacy is intricately linked to deeper trends in everything related to society. In particular, the unforeseen U.S. presidential election results of 2016 have some unique elements that seem to have short-circuited the norms, principles and institutions of democracy, putting in place dangerous and undemocratic global trends. Moreover, Trump’s legacy might exhibit a certain continuity that would favor the extension of his influence through the next elections and beyond (see, e.g., de la Torre, 2018; Ivie, 2017; Jacobson, 2021). The political uncertainty caused by the surprises related to the presidential election outcome—with great potential to generate a significant policy shift—is mirrored in the performance of the stock market with repercussions on future economic development. More precisely, such a political change, especially when is not widely anticipated, determines investors in stock markets to refine and adjust their expectations regarding future macroeconomic policy. Therefore, in recent decades, a growing body of literature has investigated the extent to which presidential election outcomes shape the dynamics of the stock market (see, e.g., Białkowski et al., 2008; Leblang & Mukherjee, 2005; Maqsood et al., 2020; Pantzalis et al., 2000; Pereira et al., 2021). Furthermore, considering that the U.S. presidential election is a signal at a global scale, the analysis of the effects of such a weighty political event on the stock market is a matter of debate in empirical research (Abolghasemi & Dimitrov, 2021; Kim & Kim, 2021; Leblang & Mukherjee, 2004; Mnasri & Essaddam, 2021). In other words, given the sensitivity of stock markets to news, a change or a surprise related to the results of the U.S. presidential election crosses the national borders having the potential to influence stock market fluctuations worldwide.

Therefore, given the significant importance of a major global event like the U.S. elections on the dynamics of stock markets and the recent growing literature in this direction, we investigate the reaction of international investors to this supposed global political surprise that can be viewed as an exogenous shock to the stock markets. More specifically, we use daily returns of stock market indexes of different countries to examine whether investors worldwide reacted to this announcement in the same manner. Given Trump’s unorthodox presidential campaign to address U.S. domestic and foreign policy, we wanted to analyze whether the challenges of this rapid and abrupt change determined by the election results—which involve uncertain and unpredictable international relations—were perceived differently by investors in various stock markets, including those of traditional allies and long-standing adversaries. In this regard, an event study approach is appropriate precisely because Trump’s victory was a surprise—Clinton being the favorite in nearly all final polls—and because there was an enormous gap between the policies promoted by the two candidates.

The investigation is conducted within the framework of three behavioral hypotheses concerning investor reaction to the arrival of unexpected information; that is, we examine investors’ behavior following Trump’s victory in the context of the Efficient Market Hypothesis (Fama, 1970), the Overreaction Hypothesis (De Bondt & Thaler, 1985), and the Uncertain Information Hypothesis (Brown et al., 1988). Briefly, the EMH posits that stock prices contain all available information, and prices adjust instantaneously to the arrival of information surprises. The OH states that investors overreact to the arrival of market surprises, and stock prices subsequently converge to their fundamental values through a process of correction. More precisely, investors set prices above their intrinsic value as a reaction to the arrival of unexpected positive events and below their intrinsic value as a reaction to the arrival of unexpected negative events. Finally, the subsequent price movements are corrective downward in the case of positive news and corrective upward in the case of negative news. The UIH argues that investors react to the arrival of unforeseen information cautiously due to increased uncertainty generated by higher market volatility. That is, investors behave rationally and set prices below their intrinsic values, but eventually, prices register an upward corrective pattern, increasingly approaching their intrinsic values. Thus, the OH and UIH, following the arrival of unexpected information, predict a process of corrective price patterns.

Our empirical results demonstrate that the investors initially perceived the Trump election as a negative shock in most countries and witnessed higher volatility. However, the volatility of the markets is generally typical after the result of the presidential is announced, which may be caused by uncertainty surrounding the new president’s fiscal and foreign policies. Our results further show that, in most of the cases, subsequent stock index returns follow corrective upward patterns, implying that the stock prices gradually converge to their fundamental values in response to the negative market reactions.

Thus, our research makes two contributions. First, we investigate the impact of political uncertainty generated by an unexpected presidential outcome on various stock markets around the world, with a major focus on the anatomy of three behavioral hypotheses, the efficient market, overreaction and uncertain information hypotheses. Second, we complement the emerging literature on the effects of the U.S. presidential elections on global stock prices.

The remainder of the paper is presented as follows: Section 2 synthesizes the empirical literature on the effects of Trump’s winning on national and international equity markets. Section 3 explains the data and the methodology used to conduct the study. The empirical results are discussed in Section 4, and Section 5 concludes the paper.

Literature Review

It is a known fact that political events such as general elections influence the dynamics of stock prices. The distinctive features of the 2016 U.S. presidential election, including the electoral pendulum swing and unforeseen outcome of the election, the nonconformist campaign strategy, and the importance of political discourse on social media, have drawn particular attention amongst academicians and practitioners. In this regard, several empirical studies investigated the effect of Donald Trump’s election as the 45th President of the United States of America on financial markets at national and international levels.

In this respect, Shaikh (2019) analyzed the behavior of investor sentiment—measured by the SPX and CBOE VIX indexes—against the background of the 2012 and 2016 U.S. presidential elections. Briefly, the author demonstrated that the U.S. stock market was inefficient during the presidential election period; that is, the market was more turbulent before the Election Day, but in the post-election period, VIX reached its normal level. Furthermore, the parallel between the two political events showed that investors’ fear was much higher in the 2016 election year than its 2012 counterpart, that is, investors’ optimism about the 2012/2016 presidential election remained higher under the Democrats’ beliefs, but on the Republican counterpart it was very low (see also Allvine & O’Neill, 1980; Alvarez-Ramirez et al., 2012; Li & Born, 2006 for empirical evidence on U.S. stock market efficiency scenarios during presidential elections). In the same vein, Cox and Griffith (2019) compared the effects of the 2004, 2012 and 2016 U.S. presidential elections on stock market liquidity and concluded that the uncertainty from the 2016 U.S. presidential election increased information asymmetries among market participants, which reduced liquidity. More precisely, the authors found that post-event liquidity following the 2016 U.S. presidential election worsened with respect to transaction costs, adverse selection costs, and volatility relative to the previously analyzed elections.

In an event study framework, Wagner et al. (2018) found compelling evidence that Trump’s election and the early days of the Administration affected the relative health of companies comprising the Russell 3000 index. Interestingly, the authors emphasized that stock market reactions have been overwhelmingly due to changes in expectations about major corporate tax cut, not to policy changes themselves. More precisely, the study reveals that high tax-paying firms and those with high deferred tax liabilities benefited substantially, while companies with large deferred tax assets, high leverage, high interest expenses and significant foreign exposure underperformed. Additional evidence for the significant market reactions to Trump’s 2016 election victory comes from Child et al. (2021). Specifically, the authors demonstrated that S&P 500 firms with pre-existing presidential ties generated higher abnormal returns over a 21-day post-election period and, in addition, during his presidential term, they received more government contracts and were subject to more favorable regulation than nonconnected counterparts. Along these lines, Selmi and Bouoiyour (2020) using an event study methodology, concluded that the election result and the presidential inauguration affected the U.S. stock market in various ways. More precisely, consumer discretionary, energy, industrials, materials, aerospace and defense and real estate sectors exhibited positive reactions, while consumer staples, financial, health care, information technology, communication, and utility sectors were adversely influenced by the election results. These results are in line with those of Pham et al. (2018), which in addition emphasized that the events around the election led to diamond risk structures (see also Aklin, 2018; Blau et al., 2019; Hachenberg et al., 2017 for sectoral effects of the 2016 U.S. presidential election).

At the aggregate level, the study of Sun et al. (2021) examined the Trump election’s impacts on the S&P500 index’s expected return and high-low range. The authors showed that the uncertainty of the election’s outcome was gradually reduced as time approached the election day. However, the impact of Trump’s victory was significantly positive and durable, fact that implies a positive influence on the fundamental value of aggregated U.S. stock market. In addition, the study pointed out that intra-day volatility decreased, which suggests that the market efficiency has improved. Wolfers and Zitzewitz (2018) explained that the post-election rally could be due to the 3 a.m. victory speech, the failure of investors to anticipate their reaction to Trump’s election—“the expectations about expectations”–, the sidelined investors and the existence of multiple equilibria between political and financial outcomes.

At the international level, Bouoiyour and Selmi (2018) used an event study approach and concluded that the BRICS equities were not equally exposed to the announcement of Trump’s victory, that is, China, Brazil, India and South Africa—in this order—came out losers, while Russia emerged the winner. Using the same methodology, Shaikh (2017) found the same short-run market inefficiency. Specifically, the authors showed that the 2016 U.S. presidential election has disrupted the world stock market, to wit, China, U.K., Mexico, Europe and South Africa have experienced significant sell-off in the equity markets, while Japan, India, the U.S. and Australia have adjusted the market with positive sentiment. Our study complements the last two mentioned works. The main difference is that we have incorporated a much larger number of stock markets around the world into our analysis. Another difference refers to the fact that we employed the constant mean return model, while Bouoiyour and Selmi (2018) used the standard market model and Shaikh (2017) used a dummy regression model, thus providing a robustness check for these previous results.

Summing up, it seems that the conclusions regarding the impact of Donald Trump’s victory on the U.S. and international financial markets tend to point—although not in unison—toward market inefficiency. Also, given the recent increasing interest in the body of literature related to the short-run effects of the presidential elections on stock market (in)efficiency, our work seeks to contribute to the fact-finding on this topic by examining investors’ reactions in global stock markets to the surprising outcome of the 2016 U.S. presidential election.

Data and Methodology

We employ a data set that consists of daily closing values of the most important stock market indexes belonging to nine developed and seven emerging markets, and one regional index, namely: U.S. (S&P500), Canada (S&P/TSX Composite Index), France (CAC 40), Germany (DAX Performance-Index), United Kingdom (FTSE 100), Australia (S&P/ASX 200), Hong Kong (Hang Seng Index), Japan (Nikkei 225), New Zealand (S&P/NZX 50 INDEX GROSS), Argentina (MERVAL), Brazil (Ibovespa), Chile (SP IPSA Index), Mexico (IPC MEXICO), Russia (MOEX Russia Index), China (SSE Composite Index), India (NIFTY 50) and the European index (SX5E). The indexes are expressed in domestic currency to restrict their changes solely to the reaction of investors to stock price movements, thus avoiding potential distortions induced by exchange rate variations—apart from the Argentinian index, which is expressed in USD.

We calculate daily logarithmic stock index returns for each stock market formally, as follows:

R_{it} = \ln (P_{it} / P_{it - 1}) * 100

Where

R_it refers to the daily return of index i on day t, with i = {1, 2, 3, …, 17};

P_it is the closing value of index i on day t;

P_it-1 is the closing value of index i on day t-1;

The unit root tests confirm that all return series are stationary. To assess for the stationarity of the return series, we perform the Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski, Phillips, Schmidt, and Shin tests on each variable. The results of these tests are provided in Appendix 1, along with several descriptive statistics of the log-transformed data.

The methodology adopted in our paper is different from the traditional event study approach. Specifically, we employed a method proposed by Brown et al. (1988) and used by Mehdian et al. (2008) to compute post-surprises abnormal and cumulative abnormal returns.

Several key steps are to be implemented to investigate investors’ reactions to the election outcome. Firstly, we set the date of the event as November 9, 2016, which corresponds to the announcement that Trump won the U.S. presidential election. Then, following standard practice in the literature (Lausegger, 2021; MacKinlay, 1997), the estimation window comprises 250 trading days, ending on September 23, 2016. We use this data set to calculate the mean return for each index, and we label this mean as the mean of non-surprise days. We excluded data that ranges between September 26, 2016, to November 8, 2016, as the estimation window is used to predict normal period returns and should not comprise the effects of the event itself. Following Blau et al. (2019), who demonstrated that specific stock prices anticipated the 2016 election outcome, we considered that some sectors may have foreshadowed the possible outcome. Thus, although Hillary Clinton was declared the winner of all three presidential election debates held during September and October—the fact that made Trump’s victory a global surprise—still, we decided not to consider the period that encompassed the election debates precisely having in mind that these events might have influenced the expectations of market participants (see Shaikh, 2019). Finally, we use a post-event window of (1, +10) trading days. This window contains the daily return of each index, which starts on November 10, 2016, the day after the election’s outcome was announced, and ends on November 23, 2016. We believe short-post-event windows of 10 days are more optimal than long windows, especially when the stock market is addressed. We argue that the stock markets may absorb other economic and political-related information such that the data in longer post-event windows become contaminated (see, e.g., Liu et al., 2020; Mnasri & Essaddam, 2021; Zhou & Yin, 2018). We then compute the mean and the variance for the non-event period and 10-day post-surprise window for each stock index. We perform the F-statistic to determine if the variances of the post-surprise window and non-surprise days are significantly different.

We employ the mean-adjusted return model to examine investors’ reactions to market surprises (Lausegger, 2021; Mazur et al., 2021). Specifically, to capture current expectations, we calculate the abnormal return for each day of the post-market surprise window as the difference between the actual ex-post return and the normal index return that would be expected in the absence of the surprise, defined as:

A R_{it} = (R_{it} - \bar{R_{non, i}})

Where

AR_it is the abnormal return for index i on day t following surprise;

t takes the value from 1 to 10 days;

R_it is the return of index i on day t in the surprise window;

$\bar{R_{non, i}}$ is the mean return for the index i over the non-surprise days.

To assess the accumulated effect of surprise during the period of interest, abnormal returns are cumulated for each index i over post-surprise days. The Cumulative Abnormal Return (CAR) is figured out for the window of 10 days as follows:

CA R_{it} = CA R_{i (t - 1)} + A R_{it}

The last step of the methodology is to test whether the ARs and CARs are statistically significantly different from zero. For this purpose, the traditional parametric t-test has been conducted to determine the statistical significance of CARs, along with the nonparametric test of Corrado (1989) to assess the statistical significance of ARs.

Briefly set forth, if the CARs show a statistically significant downward trend, then the stock markets initially perceive the surprise as good news, with a subsequent downward adjustment observed. If the CARs show a statistically significant upward trend, then this may imply that the investors perceive the surprise as initially bad news and then gradually adjust to this surprise. This is a process consistent with the prediction of OH. If the CARs are found to be statistically significant positive or at least non-negative, then according to UIH, the surprise is probably considered good or bad. In contrast, if the CARs do not show any statistically significant trends, investors’ behavior is consistent with the prediction of EMH.

Empirical Results

Table 1 displays the mean and the variance of returns for non-surprise and post-surprise days for the market indexes employed in this study, along with the corresponding F-tests. Firstly, as the figures of this table show, the mean of daily index returns of post-surprise days is higher than that of non-surprise days for nine market indexes. It is interesting to note that the Japanese, Russian, and Chinese stock markets enjoyed the best performance following the election of Trump. Secondly, the market return volatility of the post-event period is higher for six indexes compared to the market return volatility of the non-event period. Furthermore, for four of these indexes, the difference is statistically significant. The rejection of the null hypothesis of equality of the variance of returns for non-surprise and post-surprise days provides evidence that the arrival of the unexpected outcome of the 2016 U.S. presidential election increases the market volatility due to uncertainty. Thus, these four indexes provide some insight into investor reactions, as predicted by the UIH. For the other 11 indexes, the non-surprise period’s market return volatility is higher than the market return volatility of the post-event period, and, for eight of these indexes, the difference is statistically significant. This finding contradicts the prediction of the UIH, which postulates that market volatility rises following the arrival of unexpected information.

Table 1.

Mean and Variance of Returns for Non-Surprise Days, Post-Surprise Days—International Stock Markets.

Country/Index	Sample	Mean	Variance	F-value
US-S&P500	Non-Surprise Days	0.0560	0.8385	6.5139*
US-S&P500	Post-Surprise Days	0.1898	0.1287	6.5139*
CA-S&P/TSX	Non-Surprise Days	0.0376	0.8279	1.6566
CA-S&P/TSX	Post-Surprise Days	0.2151	0.4997	1.6566
FR-CAC 40	Non-Surprise Days	0.0027	2.0424	5.4418*
FR-CAC 40	Post-Surprise Days	−0.0315	0.3753	5.4418*
DE-DAX	Non-Surprise Days	0.0381	2.0724	15.31425*
DE-DAX	Post-Surprise Days	0.0154	0.1353	15.31425*
UK-FTSE 100	Non-Surprise Days	0.0625	1.2754	2.2376***
UK-FTSE 100	Post-Surprise Days	−0.1371	0.5699	2.2376***
AU-S&P/ASX 200	Non-Surprise Days	0.0397	0.9482	2.5276***
AU-S&P/ASX 200	Post-Surprise Days	0.2891	0.3751	2.5276***
HK-HIS	Non-Surprise Days	0.0322	1.5746	1.4859
HK-HIS	Post-Surprise Days	0.1160	1.0597	1.4859
JP-Nikkei 225	Non-Surprise Days	−0.0345	2.9274	1.2846
JP-Nikkei 225	Post-Surprise Days	1.2054	3.7607	1.2846
NZ-S&P/NZX 50	Non-Surprise Days	0.1032	0.3214	1.3339
NZ-S&P/NZX 50	Post-Surprise Days	0.2196	0.2410	1.3339
EUR-SX5E	Non-Surprise Days	−0.0162	2.4193	9.3254*
EUR-SX5E	Post-Surprise Days	−0.0793	0.2594	9.3254*
AR-MERVAL	Non-Surprise Days	0.1718	4.7260	1.7546***
AR-MERVAL	Post-Surprise Days	0.1986	8.2920	1.7546***
BR-Ibovespa	Non-Surprise Days	0.0924	2.9000	1.3173
BR-Ibovespa	Post-Surprise Days	−0.2988	3.8202	1.3173
CL-SP IPSA	Non-Surprise Days	0.0456	0.4282	2.6657*
CL-SP IPSA	Post-Surprise Days	−0.1724	1.1416	2.6657*
MX-IPC MEXICO	Non-Surprise Days	0.0526	0.6712	4.0363*
MX-IPC MEXICO	Post-Surprise Days	−0.4587	2.7093	4.0363*
RU-MOEX	Non-Surprise Days	0.0818	1.1859	2.2357***
RU-MOEX	Post-Surprise Days	0.3621	0.5304	2.2357***
CN-SSE	Non-Surprise Days	−0.0153	2.6877	7.5233*
CN-SSE	Post-Surprise Days	0.3541	0.3572	7.5233*
IN-NIFTY50	Non-Surprise Days	0.0515	0.8132	2.1625**
IN-NIFTY50	Post-Surprise Days	−0.5691	1.7586	2.1625**

Note. The F-statistic tests the null hypothesis that the variance of returns for non-surprise days is equal to the variance of returns for post-surprise days.

,**,*** indicates statistical significance at the 1%, 5% and, respectively, 10% levels.

Table 2 exhibits the post-surprise ARs and CARs and corresponding t-statistics. The ARs show that the impact effect of the announcement of Trump’s victory is significant and positive on the first day in the case of seven indexes. On the other hand, an initial negative impact effect is registered in the remaining 10 stock markets, albeit it is statistically significant only in four cases. Specifically, the results suggest that, initially, the investors within 10 stock markets (Canada, France, Germany, U.K., New Zealand, SX5E, Argentina, Brazil, Chile, and Mexico) perceived the announcement of the Trump election as unfavorable news. Out of these indexes, as expected, the Latin American indexes experienced the most negative outcomes due to the surprise in the immediate run. However, as shown by the figures in Table 2 and Figure A1 from the Appendix, in the first 4 days, the ARs gap between the analyzed index is quite wide but starting with the fifth day it begins to narrow. Within the post-event window, the t-Corrado statistics show mixed results, as in the case of t-test statistics. However, it became well-known that when parametric and nonparametric tests are employed, they frequently lead to different inferences (Obradović & Tomić, 2017).

Table 2.

Post-Surprise Abnormal Returns (AR) Cumulative Abnormal Returns (CAR).

Day	US-S&P500		CA-S&P/TSX		FR-CAC 40		DE-DAX		UK-FTSE 100
Day	AR	CAR	AR	CAR	AR	CAR	AR	CAR	AR	CAR
1	0.139*	0.139	−0.143*	−0.143	−0.279*	−0.279	−0.188*	−0.188	−1.283	−1.283*
2	−0.196	−0.057	−1.327	−1.470	−0.927	−1.206*	0.317*	0.130	−1.501	−2.785*
3	−0.068*	−0.125	0.258*	−1.212	0.426*	−0.780*	0.203*	0.332	0.275*	−2.510*
4	0.689*	0.565	1.036*	−0.176	0.616*	−0.164	0.349*	0.681*	0.521*	−1.988*
5	−0.214	0.350	−0.193	−0.369	−0.786	−0.950*	−0.704	−0.023	−0.698	−2.686*
6	0.411*	0.761	0.591*	0.222	0.587*	−0.362	0.165*	0.142	0.602*	−2.084*
7	−0.295	0.466	0.218*	0.439	−0.521	−0.884*	−0.235	−0.093	−0.342*	−2.426*
8	0.687*	1.153*	1.139*	1.578	0.556*	−0.328	0.155*	0.062	−0.030*	−2.456*
9	0.160*	1.313*	0.364*	1.942*	0.411*	0.083	0.230*	0.292	0.552*	−1.905*
10	0.025*	1.338*	−0.167	1.775*	−0.424*	−0.341	−0.519	−0.227	−0.092*	−1.997*

Day	AU-S&P/ASX 200		HK-HIS		JP-Nikkei 225		NZ-S&P/NZX 50
Day	AR	CAR	AR	CAR	AR	CAR	AR	CAR
1	0.744*	0.744	1.841*	1.841*	6.543*	6.543*	−0.638	−0.638
2	−0.506	0.237	−1.390	0.451	0.209	6.752*	0.492*	−0.146
3	−0.405	−0.169	−1.413	−0.961	1.734*	8.486*	0.380*	0.234
4	−0.012*	−0.179	0.424*	−0.537	0.009	8.496*	0.693*	0.927*
5	0.163*	−0.017	−0.227	−0.764	1.127*	9.623*	−0.249	0.679
6	0.351*	0.335	−0.111*	−0.875	0.037	9.659*	0.529*	1.207*
7	−0.191	0.144	0.332*	−0.543	0.619*	10.279*	−0.233*	0.974*
8	1.112*	1.256	0.029*	−0.514	0.803*	11.081*	−0.580	0.394
9	1.265*	2.521*	1.390*	0.876	0.348*	11.430*	0.410*	0.804
10	−0.027*	2.494*	−0.038*	0.838	0.969*	12.399*	0.360*	1.164*

Day	EUR-SX5E		AR-MERVAL		BR-Ibovespa		CL-SP IPSA
Day	AR	CAR	AR	CAR	AR	CAR	AR	CAR
1	−0.302*	−0.302	−4.949	−4.949*	−3.398	−3.398*	−1.956	−1.956*
2	−0.529	−0.831*	−3.753	−8.702*	−3.444	−6.842*	−1.568	−3.524*
3	0.338*	−0.493*	0.045*	−8.657*	0.704*	−6.138*	−1.160	−4.683*
4	0.342*	−0.151	4.707*	−3.950	1.738*	−4.400*	1.132*	−3.551*
5	−0.753	−0.903*	0.086*	−3.864	−1.734	−6.134*	0.661*	−2.890*
6	0.525*	−0.379	−0.470*	−4.334*	0.228*	−5.905*	0.032*	−2.859*
7	−0.675	−1.054*	−0.699	−5.033*	1.739*	−4.167*	−0.044*	−2.903*
8	0.417*	−0.637*	2.592*	−2.441	1.345*	−2.822*	1.092*	−1.811*
9	0.390*	−0.247	2.537*	0.096	−0.041*	−2.863*	−0.449*	−2.260*
10	−0.385*	−0.631*	0.172*	0.269	−1.049*	−3.912*	0.079*	−2.180*

Day	MX-IPC MEXICO		RU-MOEX		CN-SSE		IN-NIFTY50
Day	AR	CAR	AR	CAR	AR	CAR	AR	CAR
1	−4.731	−4.731*	1.518*	1.518*	1.378*	1.378*	1.054*	1.054
2	−0.598*	−5.330*	−0.676	0.842	0.793*	2.171*	−2.780	−1.726
3	0.675*	−4.655*	−0.572	0.271	0.463*	2.633*	−2.342	−4.067*
4	−0.679	−5.334*	−0.326	−0.055	−0.090	2.543*	−0.013*	−4.080*
5	−0.324*	−5.658*	0.504*	0.449	−0.045*	2.498*	−0.442*	−4.523*
6	−0.012*	−5.670*	0.598*	1.047	0.121*	2.620*	−0.124*	−4.646*
7	−1.297	−6.967*	−0.301*	0.746	−0.472	2.148*	−1.864	−6.510*
8	1.012*	−5.956*	0.918*	1.664*	0.804*	2.952*	0.867*	−5.643*
9	0.715*	−5.241*	0.277*	1.942*	0.949*	3.901*	0.335*	−5.308*
10	0.128*	−5.113*	0.861*	2.803*	−0.207	3.694*	−0.899*	−6.207*

Note. This table presents the Abnormal Returns and Cumulative Abnormal Returns of each stock index computed using the mean-adjusted return model, along with their t-statistics following the 2016 U.S. election announcement. The Corrado test is performed for the significance of AR and t-test is performed for the significance of CAR.

Denotes statistical significance at the 10% level or higher.

To further explore the reactions of global market investors to the unexpected result of the 2016 U.S. presidential election, we consider the CARs results and the corresponding Figure A2 (Appendix 1). The trend of the CARs suggests that investors in the UK, China, Hong Kong, Japan, India and Europe markets rather behave somehow in line with the OH prediction in most of the analyzed cases. This means that investors overreact to the surprise and set the prices less than their fundamental values; albeit, as time passes, further clarification regarding the surprise results in an upward corrective pattern such that the value of indexes converges to their fundamental values. In the cases of the U.S., Canada, Germany, Australia, New Zealand and Russia markets, it seems that the pattern of the graphs can be mostly in line with the prediction of UIH; that is, investors react rationally to the election surprise announcement, and due to increased uncertainty they set the prices below their fundamental values; however, as uncertainly subsides, fundamental stock values and market prices converge. Nevertheless, the results in the remaining stock markets fails to support either of the three tested hypotheses. It seems that in the Argentinian, Brazilian, Chilean, France and Mexican markets, the CARs following a perceived negative event are significantly negative for several trading days.

Therefore, as expected, our results indicate that while the reaction of the majority of indexes to Trump’s election is initially unfavorable, these indexes’ trend afterward shows an upward movement within the 10 days following the unexpected 2016 U.S. election outcome—with the exception of the Mexican, Chilean, Brazilian, Indian and British indexes which showed a relatively downward trend along the analyzed window. These negative overreaction patterns in global stock markets were to be expected, given the uncertainty surrounding a possible radical change in international policy induced by Trump’s pre-election declarations. However, there are several markets, namely those belonging to U.S., Japan, Russia and China, which registered a relative upward trend throughout the post-event window. These quite odd positive reactions are also documented in Bouoiyour and Selmi (2018) and Hoe and Nippani (2017). In this regard, it seems that the Russian stock market benefited from this surprising election outcome against Trump’s suggestions to improve relations between the two countries. Also, Hoe and Nippani (2017) argue that the election of Trump as president of the U.S. did not have the expected effect on the Chinese market due to the presence of supposed unsophisticated investors and country’s censorship of the media.

Furthermore, it seems that the aggregated U.S. stock market generally tends to have a historical tendency to rise after elections (Sun et al., 2021). In summary, regardless of the positive or negative impact of Trump’s victory on stock markets, our results point toward a short-run inefficiency of international stock markets following the U.S. presidential election. Overall, these mixed responses and lack of efficiency are in line with the findings of Shaikh (2017), which emphasized similar heterogeneous effects of Trump’s victory on international financial markets.

Conclusions

Considering Donald Trump’s unconventional campaign rhetoric, his victory was perceived as a surprise and unexpected information at the global level. Therefore, in this paper, we examine and test the reaction of international investors to the 2016 U.S. presidential election result within the frameworks of the EMH, OH, and UIH, which attempt to predict the behavior of investors in response to the arrival of unexpected information.

The empirical results of this paper provide evidence to suggest that, in general, the investors in international stock markets responded negatively to this unexpected news in the immediate run, but, eventually, they adjusted their sentiment, with several exceptions to this picture. Somehow, some exception to this result is found primarly on Latin American markets, which recorded significant losses over several trading days, and did not show a substantial upward pattern over the analyzed window. However, in all cases, we find that the markets are inefficient in the short run. In addition, in accordance with the previous literature on the impact of presidential election results at the international level, these findings reveal asymmetrical reactions of investors following the announcement of Trump’s winning the presidential election.

Our findings add to the empirical literature on political events, emphasizing the sensitivity of international stock markets to significant political new information, such as the U.S. presidential elections. Also, our results provide evidence related to the speed of adjustment of market prices to presidential election outcomes. Furthermore, the paper presents insights for academics, investors, and policymakers about the stock markets’ reactions to the potential policy changes initiated by the new president. Finally, on the practical side, our findings imply that in most countries included in the sample, following the unexpected outcome of an election, equity markets participants may be able to utilize market sentiment to establish contrarian investment strategies to generate abnormal returns, in the short run, before market digest the surprises.

Footnotes

Appendix 1

Table A1.

Summary Statistics of Log-Transformed Variables.

Descriptive statistics	Panel A
Descriptive statistics	Mean	Median	Min.	Max.	Std.dev.
US-S&P500	0.054057	0.017291	−3.658079	2.445865	0.876121
CA-S&P/TSX	0.041018	0.062404	−2.835328	2.896350	0.870952
FR-CAC 40	0.005341	−0.044754	−8.384365	3.482588	1.353384
DE-DAX	0.033797	0.132558	−7.067272	3.445679	1.368307
UK-FTSE 100	0.048808	0.044728	−3.519209	3.514914	1.085240
AU-S&P/ASX 200	0.037093	0.065476	−3.223323	3.284857	0.957702
HK-HIS	0.012689	−0.010980	−3.924856	3.215085	1.221596
JP-Nikkei 225	0.001299	0.094445	−8.252933	6.911309	1.693323
NZ-S&P/NZX 50	0.069900	0.157882	−3.395226	2.412354	0.632429
EUR-SX5E	−0.013804	−0.065012	−9.010986	4.874356	1.466765
AR-MERVAL	0.165811	0.147340	−6.250828	5.949367	2.109277
BR-Ibovespa	0.094831	0.111289	−4.988023	6.388665	1.672477
CL-SP IPSA	0.049924	0.056137	−2.025106	3.094401	0.667961
MX-IPC MEXICO	0.026515	0.055459	−4.678887	2.877891	0.876600
RU-MOEX	0.082415	0.023577	−4.403700	2.830052	1.043506
CN-SSE	0.009654	0.102912	−7.305356	4.219568	1.546712
IN-NIFTY50	0.008962	0.021127	−3.373336	3.311503	0.921600
	Panel B
Unit root test	ADF		PP		KPSS
US-S&P500	−18.13936*		−18.15250*		0.739000
CA-S&P/TSX	−16.09131*		−16.08412*		0.739000
FR-CAC 40	−16.64467*		−17.58085*		0.073886
DE-DAX	−16.15854*		−16.30794*		0.739000
UK-FTSE 100	−15.80336*		−16.35715*		0.739000
AU-S&P/ASX 200	−16.27885*		−16.26670*		0.739000
HK-HIS	−16.85480*		−16.86081*		0.739000
JP-Nikkei 225	−18.83672*		−19.11173*		0.739000
NZ-S&P/NZX 50	−15.10632*		−15.16948*		0.739000
EUR-SX5E	−17.19694*		−17.92816*		0.739000
AR-MERVAL	−15.62222*		−15.62071*		0.739000
BR-Ibovespa	−15.96300*		−15.96300*		0.739000
CL-SP IPSA	−12.27058*		−12.80282*		0.739000
MX-IPC MEXICO	−15.11349*		−15.01295*		0.739000
RU-MOEX	−16.37649*		−16.73049*		0.739000
CN-SSE	−18.73656*		−18.73656*		0.739000
IN-NIFTY50	−15.60274*		−15.55634*		0.739000

Indicates statistical significance at the 1% level.

Appendix 2 Acknowledgements

Authors are thankful to Romanian Ministry of Research, Innovation and Digitization, within Program 1—Development of the national RD system, Subprogram 1.2—Institutional Performance—RDI excellence funding projects, Contract no.11PFE/30.12.2021, for financial support.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Romanian Ministry of Research, Innovation and Digitization, within Program 1—Development of the national RD system, Subprogram 1.2—Institutional Performance—RDI excellence funding projects, Contract no.11PFE/30.12.2021

Ethics Statement

Not applicable

ORCID iDs

Delia Diaconaşu

Ovidiu Stoica

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