Jamal Bouoiyour
email: jamal.bouoiyour@univ-pau.fr

Refk Selmi

The Price of Political Uncertainty: Evidence from the 2016 U.S. Presidential Election and the U.S. Stock Markets

Keywords: 2016 U.S. presidential elections, Political uncertainty, U.S. Stock markets, Sectoral-level analysis. JEL classification: E60, E65, G10, G13, G14 Event-study methodology

There is bountiful evidence that political uncertainty stemming from presidential elections or doubt about the direction of future policy make financial markets significantly volatile, especially in proximity to close elections or elections that may prompt radical policy changes. Although several studies have examined the association between presidential elections and stock returns, very little attention has been given to the impacts of elections and election induced uncertainty on stock markets. This paper explores, at sectoral level, the uncertain information hypothesis (UIH) as a means of explaining the reaction of markets to the arrival of unanticipated information. This hypothesis postulates that political uncertainty is greater prior to the elections (relative to pre-election period) but is resolved once the outcome of the elections is determined (relative to post-election period). To this end, we adopt an event-study methodology that examines abnormal return behavior around the election date. We show that collapsing stock returns around the election result is reversed by positive abnormal return on the next day, except some cases where we note negative responses following the vote count. Although Trump's win plunges US into uncertain future, positive reactions of abnormal return are found. Therefore, our results do not support the UIH hypothesis. Besides, the effect of political uncertainty is sector-specific. While some sectors emerged winners (healthcare, oil and gas, real estate, defense, financials and consumer goods and services), others took the opposite route (technology and utilities). The winning industries are generally those that will benefit from the new administration's focus on rebuilding infrastructure, renegotiating trade agreements, reforming tax policy and labour laws, increasing defense funding, easing restrictions on energy production, and rolling back Obamacare.

Introduction

The political uncertainty is a prevalent phenomenon which is immanent to the political process. Within the political science literature, the political uncertainty refers to the lack of assuredness. [START_REF] Dahl | Modern Political Analysis[END_REF] indicated that elections, wars, governmental processes and threats are all viewed as uncertain political phenomena. Even though political uncertainty takes various shapes and forms including changes in the government and changes in the domestic and foreign policies, the present research focuses on one kind of political uncertainty, which is associated with elections. The latter constitute a major event for re-distribution of political power, which may have meaningful implications for the future political and economic prospects of a country. The political uncertainty naturally emerges since different candidates running for office, if elected, will undertake different policies, and election outcomes are uncertain. One can assert that political uncertainty is just a reflection of policy uncertainty. These two forms of uncertainty, while heavily associated, have different characteristics. Policy uncertainty is the uncertainty with respect the government policies (macroeconomic, monetary and fiscal policies) and their effects on the economic development and financial markets [START_REF] Pasquariello | Prospect Theory and Market Quality[END_REF]. The political uncertainty, nevertheless, encompasses both uncertainty about the election result and uncertainty regarding the policies that may ensue from that outcome [START_REF] Pasquariello | Political Uncertainty and Financial Market Quality[END_REF].

The existing literature documents that the political uncertainty particularly, may exert a significant effect on both the returns and the risk levels of financial assets (Pantzalis et al., 1999;[START_REF] Nippani | The 2000 presidential election and the stock market[END_REF][START_REF] Li | Presidential election uncertainty and common stock returns in the United States[END_REF][START_REF] He | The 2000 presidential election and the information cost of sensitive versus non-sensitive S&P 500 stocks[END_REF][START_REF] Jones | US elections and monthly stock market returns[END_REF][START_REF] Sy | Resolving the presidential puzzle[END_REF][START_REF] Goodell | Price-earnings changes during US presidential election cycles: voter uncertainty and other determinants[END_REF]. For example, Pantzalis et al. (1999) assessed the responses of stock market indices across 33 countries to political election dates during the sample period from 1974 to 1995. They claimed that political uncertainty falls over the two weeks prior to elections, yielding to a rise of stock market valuations, consistently with the uncertain information hypothesis of [START_REF] Brown | Risk aversion, uncertain information, and market efficiency[END_REF]. [START_REF] Nippani | The 2000 presidential election and the stock market[END_REF], [START_REF] Nippani | US presidential election impact on Canadian and Mexican stock markets[END_REF], [START_REF] Li | Presidential election uncertainty and common stock returns in the United States[END_REF], [START_REF] He | The 2000 presidential election and the information cost of sensitive versus non-sensitive S&P 500 stocks[END_REF], and [START_REF] Goodell | Price-earnings changes during US presidential election cycles: voter uncertainty and other determinants[END_REF] examined the impact of US presidential elections on stock markets, and deduced that the ongoing uncertainty over the elections is reflected in the behavior of stock prices. Using polling data on the US presidential elections for the sample period from 1964 to 2000, [START_REF] Li | Presidential election uncertainty and common stock returns in the United States[END_REF] showed that stock prices climb prior to the presidential elections when the election result is uncertain. [START_REF] Nippani | US presidential election impact on Canadian and Mexican stock markets[END_REF], and [START_REF] He | The 2000 presidential election and the information cost of sensitive versus non-sensitive S&P 500 stocks[END_REF] investigated the reaction of stock markets to the delayed result of the 2000 presidential election, and found that stock markets are negatively influenced by the uncertainty surrounding the election outcome. [START_REF] Goodell | Price-earnings changes during US presidential election cycles: voter uncertainty and other determinants[END_REF] indicated that a collapsing uncertainty around US presidential elections prompts a drop in stock prices. [START_REF] Goodell | US presidential elections and implied volatility: The role of political uncertainty[END_REF] studied the impacts of political uncertainty and the political process on implied stock market volatility during US presidential election cycles. They found that the relationship between implied volatility and the election probability of the eventual winner is positive.

Generally speaking, the financial markets tend to react to new information with respect political events that may exert a significant influence on the country's macroeconomic, fiscal and monetary policies. In fact, the political events are followed by investors who form or revise their expectations based on the results of these events. Informational efficiency hypothesis assumes that markets absorb news and political trends into asset prices in anticipation of election results. Much of the uncertainty surrounding the outcome may be resolved prior to the election date. Such policy changes are typically associated to a decrease of stock prices, particularly if the uncertainty is greater (Pastor and Veronesi 2012; Bouoiyour and Selmi 2016). Once the political uncertainty is mitigated, stock prices would rise again [START_REF] Pantzalis | Political elections and the resolution of uncertainty: the international evidence[END_REF]. [START_REF] Brown | Risk aversion, uncertain information, and market efficiency[END_REF] argued that as uncertainty is reduced, price changes tend to be positive on average. On the contrary, if the election outcome does not permit market participants to immediately and effectively evaluate the effect on the nation's future, then the election result constitutes an uncertainty inducing surprise. In this case, positive price changes should be anticipated after the election, i.e. until uncertainty about the policies to be achieved by the winner is resolved.

Against all odds, polls, and projections, the Republican candidate -Donald Trump-claimed win in the 2016 US presidential election, defeating Democratic nominee Hillary Clinton. Financial markets had widely priced in a victory for Clinton, who they viewed as a better short-run outcome because she represented few unknowns and thus less uncertainty. This study examines, at sectoral level, the US stock market behavior around the 2016 presidential election and addresses the following questions. Do markets anticipate the election outcome? Are US stock markets efficient? To what extent does election outcome resolve uncertainty? Are stock markets resilient in dealing with the uncertainty arising from political shocks? Is there homogeneity in stock market behavior around the US election results between the different sectors? We explore these questions using a standard event study methodology that examines abnormal return behavior around the election date. The study tests also the uncertain information hypothesis (UIH) of [START_REF] Brown | Risk aversion, uncertain information, and market efficiency[END_REF].

By gathering separately the responses of eight of largest firms in the Dow Jones, S&P500 and Nasdaq Composite indices, we document that while the Trump's win in US presidential elections has played a negative role on the abnormal return in the day event, a positive reaction for almost all sectors was found during the post-election period (except technology and utilities). These findings are not consistent with uncertain information hypothesis of [START_REF] Brown | Risk aversion, uncertain information, and market efficiency[END_REF]. Moreover, the effect of political uncertainty around elections is likely to be sector-specific. In particular, the uncertainty surrounding the Trump's win divides the US stock markets into winners (health care, oil and gas, real estate, defense, financials and consumer goods and services) and losers (utilities and technology). Several elements of explanations have been offered to explain the heterogeneous reactions of U.S. companies.

The remainder of the paper is organized as follows. In Section 2, we present our formal hypothesis and describe the methodology and the data sources. Section 3 describes the empirical findings, while section 4 checks their robustness. Finally, in Section 5, the conclusions of the analysis are summarized and policy implications are discussed

Testable hypotheses, methodology

Since Efficient Market Hypothesis (EMH) has arisen in the 1960s [START_REF] Fama | The behavior of stock market prices[END_REF][START_REF] Fama | Efficient capital markets: a review of theory and empirical work[END_REF][START_REF] Samuelson | Proof that properly anticipated prices fluctuate randomly[END_REF], it has been subject to a huge number of researches. Under the assumption of rational investor, this hypothesis postulates that share prices completely reflect information and expectation, and that any new information is incorporated into equity prices very quickly. In contrast, empirical studies showed that stock prices do not often fully reflect all information. This contradiction has yielded to the appearance of new hypotheses in behavioral finance including the Uncertain Information Hypothesis (UIH) of [START_REF] Brown | Risk aversion, uncertain information, and market efficiency[END_REF]. The Uncertain Information Hypothesis assumes that anxiety will rise in financial markets following the occurrence of unexpected event. So that investors cannot appropriately respond to unanticipated new information and thus they could in the early stages set security prices below their fundamental values. Moreover, the UIH asserts that the stock return is likely to be greater than the average return over periods when no event-induced uncertainty happens. When election-induced uncertainty is mitigated, we expect a positive response of the cumulative abnormal returns (CARs) in the time period following the election. The first hypothesis to be tested throughout this study consists, therefore, of two parts:

0 : 1 0 ; 10   CAR H a (1) 0 : 1 1 ; 0   CAR H b (2)
One can expect also that the election results partly resolve the great uncertainty surrounding the unanticipated political event and that the market requires more time to adequately evaluate the elections' consequences following the vote count. If there is a greater uncertainty resolution following the election outcome, we would notice post-election positive abnormal returns. In brief, the UIH assumes that a mitigation of uncertainty prompted positive observed returns and that wider uncertainty reduction typically leads to greater observed returns. In this study, we investigate the ten-day period after the election date to test our second hypothesis:

0 : 2 10 ; 1    CAR H (3)
The purpose of this study is to examine stock market behavior around political election dates. We focus our analysis on the 2016 US presidential election outcome and investigate the impact of the Trump's win on different US industries. The final result of the election was disclosed on Tuesday 08 November 2016, which we subsequently view as the announcement day. Our sample data include eight sectors of three US stock price indices: The Dow Jones Industrial Average tracks the prices of 30 widely-traded stocks on the New York Stock Exchange. This is the most known stock market index in the world but it is not representative of the market as a whole. The Nasdaq Composite is the market capitalization-weighted index of approximately 3,000 common equities listed on the Nasdaq stock exchange. The Standard and Poor's 500 (S&P 500) Composite Stock Price Index covers the performance of 500 largest capitalization stocks. For each index, the selected companies include financials (banks, insurance, reinsurance and financial services), oil and gas (oil and gas producers, oil equipment and services), real estate, consumer goods (household goods, home construction, personal goods and tobacco) and services (retail, media, travel and leisure), defense, pharmaceuticals, technology (software and computer services, and technology hardware and equipment) and Utilities (electricity, gas, power generation and water). Each sector index represents a capitalization-weighted portfolio of the largest S&P 500 companies in this sector. The data of sectoral Dow Jones Industrial Average, S&P 500, Nasdaq stock indices are available at Datastream database.

We employ the standard market model event study methodology as depicted by [START_REF] Dodd | On corporate governance: a study of proxy contests[END_REF] and [START_REF] Brown | Using daily stock returns: the case of event studies[END_REF]. Before presenting the conducted procedure, we should point out that an event studies investigates the average stock market response to a specific stock market event, by averaging among the same event in different companies. The best findings with an event study are revealed when the exact date of the event is known or identified. We define the day "0" as the day of the event for a given equity. Thereafter, the estimation and event windows can be determined (Figure 1). The interval [T1+1, T2] is the event window with length L2=T2-T1-1, whereas the interval [T0+1, T1] is the estimation window with length L1=T1-T0-1. The length of the event window often depends on the ability to accurately date the announcement date. If one is able to date it precisely, the event window will be less lengthy and capturing the abnormal returns will be more proper and effective. We should mention here that the length of the event window including the event announcement days normally ranges between 21 and 121 days (Peterson 1989).

[Insert Figure 1 here] For our case of study, we use for each sector a maximum of 120 daily stock return observations for the period around the ultimate election result, beginning at day -115 and ending at day + 5 relative to the event. The first 105 days (-115 through -10) is denoted as "the estimation period", and the following 21 days (-10 through + 10) is designated as "the event period". The cumulative abnormal return (CAR) for a sector i during the event window [ τ 1 ; τ 2 ] surrounding the event day t = 0, where [ τ 1 ;τ 2 ] = ∈ [ -10 ;+10 ] , is expressed as follows:

) ( , , ] , [ , 2 1 2 1 t M i t i t i i R R CAR            (4) where ] , [ , 2 1   i CAR
is the cumulative abnormal return of share i during the event window [τ 1; τ 2 ], R i, t is the realized return of stock i on day t 3 , R M, t is the return of the benchmark index of sector i, i ˆ and i  ˆare the regression estimates from an ordinary least squares (OLS) regression for 105 trading day estimation period until t = -10. We utilize the Datastream's value-weighted total return stock market index of sector i's country of origin as the benchmark index. We set our event day for the Trump's victory event to Tuesday 08 November 2016. We apply, then, a regression analysis to identify the determinants of the observed cumulative abnormal return for each sector. The OLS regression to be estimated is denoted as:

i i Income Size Trump CAR             3 2 1 0 ] , [ ,
] , [ , 2 1   i CAR
is the dependent variable, Trump is a dummy variable which takes the value of one on the first day of trading after the US election outcome and zero otherwise, size is the logarithm of the total assets of a company in U.S. dollars in the year prior to the event, and the Income is the logarithm of the net income of a company in dollars in the year prior to the event, and i  is the error term. The explanatory variables "size" and "Income" were chosen based on recent event studies showing that the largest companies are more threatened by sudden events or political changes, and the response of stocks to uncertainty surrounding an event may depend on the net income of a firm in the year before the occurrence of the event [START_REF] Kolaric | Are stock markets efficient in the face of fear? Evidence from the terrorist attacks in Paris and Brussels[END_REF]Bouoiyour and Selmi 2016).

Empirical results

In this section we present the event study results. We begin the analysis related to the three hypotheses of [START_REF] Brown | Risk aversion, uncertain information, and market efficiency[END_REF] by depicting the performances of the cumulative abnormal return for different sectors of three US stock price indices (Dow Jones Industrial Average, S&P 500 and Nasdaq) around the day relative to announcement of Trump's victory on 08 November 2016 (t=0) and for different event windows: [-10; 0], [0; +1] and [0+10]. The first hypothesis drawn from the UIH of [START_REF] Brown | Risk aversion, uncertain information, and market efficiency[END_REF] states that the tock returns are higher when no event-induced uncertainty emerges. The second hypothesis assumes that positive abnormal returns when uncertainty is reduced (i.e., when the election result is announced or becomes certain), while the third hypothesis postulates that the stock market requires time to properly evaluate the elections results following the vote count. Precisely, greater positive returns should be associated with greater reductions in uncertainty Figure 2 indicates that US stock markets' responses to the election outcome is not uniform across industries either for the announcement day or the [-10; + 10] event window. In other words, while all companies face increasing uncertainty, the Trump's win had varying sectoral effects. Ten days prior to the election vote (i.e., the event period -10; 0), positive abnormal returns are found for all U.S. companies under study. A sharp decrease in stock values surrounding the election result (i.e., the event day 0; 0) is later reversed by a jump in share prices on the next day (i.e., the event period 0; +1). Potentially, the win of Donald Trump is associated to severe stock prices declines for all the sectors on the day relative to the announcement of US election results (t=0). However, we show that the majority of sectors rebounded. The effect of political uncertainty is positive in the ten days after to the vote count (i.e., the event period 0; +10). This holds true for the three US stock indices under study. Exceptionally, for utilities and technology, we observe positive abnormal returns in the ten days before the release of the event (i.e., the event period -10; 0), negative reactions since the day relative to the announcement of the election result (t=0) and the ten-days post election. The findings of the event study of the CAR performances around the 2016 U.S. presidential election are not in line with the UIH hypothesis. Although previous studies indicated that anxiety around political events might have detrimental effect on stock returns, the impact of Trump's win in U.S. presidential election is surprising. In general, policy changes are followed by collapsing stock prices, especially when the uncertainty is strong. Once the political uncertainty is reduced, positive changes in stock returns are highly expected [START_REF] Pantzalis | Political elections and the resolution of uncertainty: the international evidence[END_REF]. By delving into the case of 2016 US presidential election, a sharp decrease of abnormal returns were seen for all the U.S. companies in the day relative to the announcement of the election outcome (t=0), before surging again after the vote count. While the new administration' policy directions remain unclear, the investors have bet the newly US president will deliver on some of his most basic campaign promises including the improvement of infrastructure spending and cutting corporate taxes.

[Insert Figure 2 about here]

Table 1 summarizes the event study of the cumulative abnormal returns of different U.S. companies around the 2016 U.S. presidential election day and the post-election period. We attempt from a comparative analysis between the event day [0; 0] and the event window [+1; +10] to test the UIH hypothesis postulating that the election outcome partially resolve prior uncertainty and the stock market requires time to assess the elections' effects following the vote. The findings reported in Table do not appear consistent with the UIH hypothesis; for the majority of U.S. industries studied, there is a negative market reaction in the event day, and the effect becomes positive through the ten-day period following the election outcome. In particular, we show that the announcement of Trump victory (i.e., the event day [0; 0]) resulted in statistically significant negative CARs, being somewhat stronger for utilities, technology, oil and gas, financials and defense (in this order) than for consumer goods and services, real estate and health care. Overall, it appears that Donald Trump's win had market-wide repercussions, leading to a decline of all the companies for the three considered US stock markets, but the collapse of healthcare share prices (in particular) is not as severe. The same sectors which struggled after the election show positive reactions during the [+ 1; +10] event window, except utilities and technology which appear more damaged during the post-election period (i.e., negative responses).

Financials reacted rapidly and positively to Trump's win; thus, this sector ended higher after starting the session (day event [0; 0]) with sharp losses. One of the major causes for the jump seems to be because Donald Trump is expected to lessen regulation hampering bank profitability. Also, the response of oil and gas market bounced back after the presidential election outcome ([+1; +10] event window) as Trump declared his desire to revive the energy sector. The ultimate US election result comes as good news for both crude oil and natural gas due to President-elect Trump plans to minimize regulatory restrictions on crude and gas exploration. In addition, the new Trump's administration will benefit the fossil fuel business and independent oil and gas drillers, promising few regulations on issues such as methane emissions from oil and gas drilling, ozone rules and renewable fuels, and higher access to federal lands. Furthermore, Trump has expressed displeasure for alternative forms of energy, describing them as expensive and needing largest subsidies to work appropriately. In this context, the Trump administration stated that it would reform all forms of energy while trying to reflect their true costs. However, despite these fruitful promises, the reaction of this sector to Trump's win seems weaker. This may be attributed to Donald Trump's aggressive stance towards Mexico -a main partner in the American energy industry-that could severely harm US oil and gas exports south of the border. We should mention at this stage that US gas imports to Mexico exceeded Mexican domestic production in 2016. Also, the renewable energy industries palpitated at the prospect of less commitment to reforms that unhurried climate change.

Differently, real estate does not react negatively to the announcement of Trump victory as the rest of sectors. The US election outcome exerts a positive influence on the housing sector during the day event and the [+1; +10] window event, even if we note a slight increase after the election results. Not surprisingly, for the first time in history, home builders and real estate businessman see one of their own becoming the elect-US president. They are optimistic about Donald Trump stimulating this sector, in the form of lower tax rates or enhancement of roads, bridges, public transit and wider infrastructure spending. Nonetheless, Trump's eloquence on immigration could concern housing investors in big cities such as New York and San Francisco. With Trump's "America First" approach to alienating partners abroad, America will become more isolated and less open, which could seriously impede the international demand for US luxury housing since the foreign buyers constitute a large part of the real estate market.

The consumer goods and services sector is also one of the winners from Trump victory. While its response to the announcement of Trump's win was negative, it bounced back after the event day. This reflects a rise in the consumer confidence 4 , showing that Americans became more optimistic about their finances and the economy after Trump victory. Nevertheless, some of the Trump's proclamations during his campaign exacerbated doubts about globalization and some trade deals, resulting more expensive imported products due to excise taxes that could unhurt consumer goods.

Further, our findings indicate that defense sector is one of the winners from the Trump's presidential win. While the announcement of the election outcome had first affected negatively (but moderately) this sector, we notice a positive response of defense firms during the post-election period as investors in this sector believe that they would post larger benefits under Trump presidency. We can attribute this result to the new administration promises to increase the size of the Army and the Marine Corps, build newly ships for the Navy and to overhaul the aerial warfare service branch, and modernize the nuclear arsenal.

Our results reveal that the health care is the biggest winner from Trump victory given to its heavier support of the pharmaceutical sector and because the drug pricing reforms, proposed by Hillary Clinton's campaign, seem unlikely to materialize. In brief, during [+1; +10], pharmaceutical shares are likely to bounce upward as investors expected relief from the stronger scrutiny of drug prices. Indeed, the health-care industry would gain from the Affordable Care Act; more people bought insurance and had better access to medical care.

However, utilities and technology seem the most damaged from Trump victory. Utilities, especially those levered to natural gas, solar and other renewables, dropped markedly following the victory for Donald Trump. This may mainly due to the Trump's condemnatory proponent of punishing those firms who move facilities out of America, in particular to Mexico. Regarding the technology sector, the Trump campaign made little outreach to issues influencing the tech industry. This little interest may be contradictory with the Trump's campaign message to spur US economic growth. In fact, the tech industry accounts for 12 percent of all jobs, according to the US Bureau of Labor Statistics, and thus the neglect of effective technology policies will have detrimental impact on America's economic development and competitiveness. Further, the Trump's opposition to H1B visas5 for high-skilled immigrants will harm substantially the capability of US tech firms to hire the engineers, data scientists and the information technology workers they need from other countries.

Moreover, the size of the firm is likely to exert significant and negative influence on all the U.S. stock market sectors and across the [0; 0] and [+1; +10] event windows, highlighting that biggest companies are likely to be more harmed by the uncertainty around the election results. The profits of U.S. firms do not help to consistently explain the Dow Jones, S&P 500 and Nasdaq evolutions, as the net income exerts a weak and positive influence on limited sectors (financials and oil and gas for Dow Jones, oil and gas and real estate for S&P 500, and financials, oil and gas and technology for Nasdaq).

[Insert Table 1 here]

Robustness

There exist different ways to ascertain whether our results are fairly solid. In this study, to check the robustness of our findings, we have tested their sensitivity to the inclusion of further control variables. In general, global financial and economic factors could be channels through which fluctuations in the world's economic and financial conditions are transmitted to the different sectors of US stock markets. These factors include the US volatility index (VIX), and the world gold price (gold). Supplementary control variables have been incorporated including silver and Bitcoin prices. The precious metals (gold and silver) have been largely perceived as a hedge against sudden shocks and also a safe haven over extreme stock market fluctuations. In the present study, we tried to see if US investors still rush to precious metals over the announcement of US presidential election results or if they get scared to seek out gold and silver. According to [START_REF] Baur | Is Gold a Safe Haven? International Evidence[END_REF], we characterize safe havens by their negative and significant correlations with asset markets during financial turmoil or troubled times. Moreover, the literature in finance field has been frequently relied on proxies of uncertainty, most of which have the advantage of being directly observable. Such proxies include the implied volatility of stock returns (i.e., VIX). The interest here is to use an index that reflects more adequately the great anxiety over U.S. presidential election. The volatility index is a sentiment indicator that allows determining when there is too much optimism or pessimism in the market. Also, we should point out that VIX responds sensitively to all events (reflecting both economic and geopolitical issues) that may cause uncertainty, and the Trump's win is no exception. Overall, it helps reaching further insights about how the stock markets react to global market news. The Bitcoin is a relatively new phenomenon created in 2009. It is a peer-to-peer network that allows the transfer of ownership without the need of a third party. Bitcoin is regarded as the best-known digital currency to date. Although some consider Bitcoin to be a major financial innovation in recent years [START_REF] Ciaian | The Economics of BitCoin Price Formation[END_REF]Bouoiyour et al. 2016), others suggest that the excessive volatility observed in this market is a major concern [START_REF] Yermack | Is Bitcoin a Real Currency? An economic appraisal[END_REF]. The Bitcoin's climb alongside the announcement of Trump's victory has led some to proclaim it as a "digital gold" and affirm its validity as a safe haven investment.

In brief, the equation to be estimated is denoted as:

  i t t t t t i Bitcoin Silver Gold VIX Income Size Event CAR                     8 7 6 5 4 3 1 0 , , 2 1 (6) where
] , [ , 2 1   i CAR
is the cumulative abnormal returns and i  is the error term.

The results are reported in Table 2. We show that the consideration of additional control variable have not fundamentally changed our findings for the three stock price indices studied; We robustly do not support the uncertain information hypothesis of [START_REF] Brown | Risk aversion, uncertain information, and market efficiency[END_REF]. We usually note that a drop of abnormal stock returns around the election day event (t=0) is reversed latter by positive abnormal return responses (i.e., event period +1; +10), except for utilities and technology where we show that the cumulative abnormal return reactions are negative. Also, we unambiguously document that the announcement of the Trump's win in 2016 US election exerted a varying effects across US companies. Specifically, it divided the US markets (in particular, Dow Jones, S&P 500 and Nasdaq) into losers (technology and utilities) and winners (health care, oil and gas, real estate, defense, financials and consumer goods and services). The size of company affects negatively the US stock market sectors, sustaining the evidence that largest firms are more exposed to uncertainty surrounding Trump's presidency than smallest companies. The profits of US firms do not exert strong impact the performance of the companies. The implied volatility index has a negative influence on the different sectors of US stocks, indicating that the stock returns decrease as the VIX increases. In addition, the precious metals (gold and with less extent silver) have a negative influence on the abnormal cumulative returns for almost all the industries. Typically, when the economy witnessed an evolving volatility that may impede shares' valuation, investors may shift their funds from stocks and invest them in the gold and silver markets until the economy rebounds. In this context, precious metals could act as a stabilizer control in investment portfolios, and play as safe haven during turbulent times [START_REF] Baur | Is Gold a Hedge or Safe Haven? An Analysis of Stocks Bonds and Gold[END_REF]. Besides, Bitcoin price is likely to have a negative and significant impact on US companies. Remarkably, the effect of Bitcoin on stocks seems more pronounced than that of gold and silver. Although Bitcoin spikes after the announcement of the US election outcome spotlights a new confidence in Bitcoin as a safe haven, investment professionals have been heavily reluctant to give this nascent cryptocurrency such status. Given the great anxiety over Trump's victory, it is obvious that investors will try to seek an easy and secure alternative. Our results suggest the ability of Bitcoin, gold and silver (in this order) to act as a safe haven during uncertain periods. Nevertheless, dubbing Bitcoin a safe haven obfuscates the fact that bitcoin is a high-risk, volatile and speculative investment.

[Insert Table 2 here]

After the announcement of the presidential election outcome, it was anticipated whose companies were poised to gain. Healthcare, housing builders, oil and gas, defense and financial industries would generally behave well. Trump proposed a tax-free health savings account to allow individuals to save money to pay for healthcare costs in order to subtract the cost of their premiums for tax purposes. Trump also urged the prominence of price transparency from all providers (for instance, clinics, hospitals and other healthcare organizations) and raising competition while attempting to minimize insurance costs and stimulate consumer satisfaction. For oil and gas firms, It is clearer that fossil fuels in the United States pledged to make the United States "energy independent" by allowing access to new areas of the country, including federal land, to oil and gas development, and revising environmental and climate policy standards, and completely removing subsidies to renewables. Moreover, protecting the U.S. borders was one of the main Trump campaign' focus. Therefore, surging defense funding will be top priorities for the administration, which will undoubtedly exert a positive influence on the defense industry. For financial sector, there is an optimism that there will be a lighter regulatory hand, but the behavior of financial and banking companies would still conditional on the state of the economy. At the same time, the biggest firms involved with technology and utilities would see stocks slide. Apple and Amazon have been largely criticized by Donald Trump; the first for making iPhones in China, and the second for disobeying antitrust laws. Also, Silicon Valley has long vigorously defended an expanded H-1B Visa programme to allow access to highly skilled workers. Some technology executives think a Trump administration may impede innovation by opposing these visas. These circumstances will hinder the capability of U.S. tech firms to hire the engineers, data scientists, as well as the information technology workers they need from other countries, and then moisten start-ups and damage projects in both the private sector and in the federal government. With wider utility investments in plants, pipelines and infrastructure, the current investments will build the power generation mix for the next years. Under the Clean Power Plan aimed at fighting against the global warning and the greenhouse gases emissions -the most challenging problems of the world-in accordance with standards set by the Paris climate accord, U.S. utilities are opting to substitute retiring coal plants with wind and solar facilities. However, President-elect Donald Trump has vowed that when he is inaugurated he will kill the Clean Power Plan and pull out of the Paris Agreement, two pillars of the Obama administration's drastic efforts to battle against climate change by mitigating greenhouse gas emissions.

Beyond the political fights, the Trump's promises to Trump has proposed to lessen the corporate tax rate to 15 per cent (from 35 per cent) to stimulate start-ups, develop existing companies, promote fund in corporate infrastructure, and make the country more competitive tax environment for multinational corporations. Immigration was another disputable Trump's campaign issue. Based on the Bureau of Labor Statistics, a fully enforcing immigration law would recoil the labor force by 11.2 million workers. In addition, the president-elect put forward to change the North American Free Trade Agreement (NAFTA) would be economically harmful, interrupting investment continuity for industries. Certainly, if the United States were to unilaterally impose temporary trade restrictions, other countries may react in kind with punitive restrictions of their own, which would put a hindrance on global trade and lead the way on menacing protectionism. In this context, many international organizations (in particular, the International Monetary Fund and the World Trade Organization) are worried that the withdrawal from NAFTA, the renegotiation free-trade agreements resulting more isolated and less open US markets would cause a trade slowdown that would damage the global economy. A repeal of trade agreements would prompt shortages in raw materials climbing the prices and adversely influencing the availability of consumer products. But this remains conditional to the overall congress opinion and the legal challenges from private firms which may play a pivotal role in deterring Trump's administration from implementing these measures. As policy directions clarify over time, U.S. industries can firm up their reactions depending to the resulting changes in the operating environment.

Conclusions

This paper examines the effects political uncertainty on stock market performance around the 2016 US presidential election. Previous studies have documented that political election are heavily associated with periods of considerable public uncertainty, and therefore, it is of interest to empirically assess the effects of election-induced uncertainty on stock market. The study examines a sample of U.S industries for testing the uncertain information hypothesis. It focuses on market reaction to announcements of new political event using the event analysis methodology.

Our results reveal that Trump's win had a significant impact on valuation of companies for variety of US stock price indices (Dow Jones Industrial Average, S&P 500 and Nasdaq Composite). However, the findings of the study do not provide a consistent conclusion regarding the existence of uncertain information content hypothesis in the U.S. stock market. While prior research on the effects of changes in government policy showed a negative influence on equity markets, the effects of Donald Trump swept to victory on US stocks is unanticipated. Normally, companies have to make prominent choices based on the expected future economic policy decisions of the new government and the resulting policy circumstances [START_REF] Brogaard | The asset-pricing implications of government economic policy uncertainty[END_REF][START_REF] Schiereck | Brexit: (Not) another Lehman moment for banks?[END_REF]. In this way, the Trump's win can be viewed as a drastic change in government policy. Such policy changes are typically linked to a drop of stock prices, particularly if the uncertainty is greater (Pastor and Veronesi 2012). Once the political uncertainty is mitigated, stock prices would rise again [START_REF] Pantzalis | Political elections and the resolution of uncertainty: the international evidence[END_REF]. In the case of 2016 US presidential election, investors and traders who some days prior to the election saw a Donald Trump victory as the heaviest downside risk to the stock market, are now embracing the outcomes. After an initial notable collapse during the event day (t=0), stocks rallied after the vote count, with investors making quick recalculations on various sectors. While many Trump policy proposals are still vague and illdefined, investors are betting that the Trump's promises will recharge the US economy by cutting taxes, rolling back regulation and boosting infrastructure spending. In other words, the basis for the rally is hopefulness about altering Obamanomics consisting of increasing taxes and improving regulation. Also, the Trump's zero-sum approach or "America is first" -in favour of isolationism-to encouraging investments at home and antagonizing partners abroad exerted a positive effect on stocks.

While all of the U.S. companies face a great political uncertainty around U.S. presidential election, varying responses were found. In particular, the Trump victory divides the U.S. stock markets under study into two main groups: (1) a group of winners which is formed by financials, oil and gas, real estate, consumer goods and services, defense and health care, and (2) a group of losers which contains utilities and technology. Part of this division can be explained by the Trump campaign promises to ensure an economic environment of lowered regulation, reduced global trade, increased infrastructure spending and a cancellation of Obamacare and climate policies.

3

  Daily stock returns are calculated as the first natural logarithmic difference of the underlying stock price.

  where

Figure

  Figure 1. Event study windows

Table 1 . Sectoral impacts of 2016 U.S. presidential election on U.S. stock markets

 1 : All regressions are controlled for heteroskedasticity and the p-values are given in parentheses. * , * * , * * * denote statistical significance at the 10%, 5% and 1% levels, respectively.

		Financials Oil and gas Real estate Consumer	Defense Health	Technology Utilities
					goods &		care
					services
				Dow Jones Industrial Average
				Event day [0 ; 0]
	Constant	0.347377	0.450940*	0.227745*	0.565629	-0.01710	0.391338*	0.451239*	0.290433
		(0.2900)	(0.0465)	(0.0806)	(0.9331)	(0.9819)	(0.0315)	(0.0616)	(0.5893)
	Trump	-0.18336*	-0.196***	0.08427*	-0.104**	-0.0220*	0.09586*	-0.24193**	-0.2797*
		(0.0991)	(0.0008)	(0.0929)	(0.0010)	(0.0314)	(0.0527)	(0.0042)	(0.0298)
	Size	-0.13556*	-0.10943*	-0.08303*	-0.028**	-0.1424*	0.454829	-0.228905*	-0.1495*
		(0.0799)	(0.0634)	(0.0309)	(0.0073)	(0.0497)	(0.2674)	(0.0474)	(0.0598)
	Income	0.011872*	0.009439*	-0.458641	0.296641	0.267590	0.225881	0.111417	-0.18673
		(0.0447)	(0.0573)	(0.3425)	(0.1530)	(0.3456)	(0.6197)	(0.7636)	(0.3569)
	Adjusted R 2	0.76	0.72	0.68	0.73	0.72	0.69	0.71	0.70
				Event window [+1; +10]
	Constant	-0.69819	-0.76422*	-0.17454*	-0.52364	-0.5087*	0.72374	-0.34853*	-0.510**
		(0.6079)	(0.0111)	(0.0597)	(0.1621)	(0.0719	(0.2369)	(0.0145)	(0.0052)
	Trump	0.136414*	0.03571**	0.132134*	0.06822*	0.1143**	0.15123**	-0.432937*	-0.3186*
		(0.0425)	(0.0058)	(0.0326)	(0.0519)	(0.0039)	(0.0032)	(0.0757)	(0.0436)
	Size	-0.11819*	-0.0808**	-0.01213*	-0.092**	-0.1256*	0.486343	-0.15750*	-0.0819*
		(0.0556)	(0.0086)	(0.0538)	(0.0091)	(0.0987	(0.4007)	(0.0833)	(0.0475)
	Income	0.477612	0.30338	0.523564	-0.38591	0.247740	0.224828	-0.112774	-0.28015
		(0.3151)	(0.6116)	(0.2200)	(0.3690)	(0.9263)	(0.7170)	(0.3336)	(0.3597)
	Adjusted R 2	0.69	0.73	0.75	0.66	0.71	0.68	0.72	0.77
					S&P 500
				Event day [0 ; 0]
	Constant	0.123325	0.022084	-0.158571	0.681004	1.902***	-0.14387*	0.768447	1.324402
		(0.7852)	(0.9230)	(0.8568)	(0.9246)	(0.0001)	(0.0627)	(0.5001)	(0.3210)
	Trump	-0.22762*	-0.17314**	0.06725**	-0.107998	-0.04***	-0.09194*	-0.164791*	-0.2198*
		(0.0140)	(0.0060)	(0.0015)	(0.9217)	(0.0003)	(0.0453)	(0.0577)	(0.0705)
	Size	-0.091***	-0.032545*	-0.018***	0.467872	-0.063**	0.077173	-0.02335*	0.501412
		(0.0002)	(0.0858)	(0.0007)	(0.8537)	(0.0067)	(0.8202)	(0.0140)	(0.1683)
	Income	0.8652	0.046024	0.0101***	0.467872	1.37319	0.072543	0.574093	0.49106
		(0.5432)	(0.8430)	(0.0000)	(0.7703)	(0.6280)	(0.8246)	(0.6633)	(0.6697)
	Adjusted R 2	0.80	0.77	0.76	0.74	0.78	0.81	0.79	0.76
				Event window [+1; +10]
	Constant	-0.410881	1.719321**	-1.1352**	0.830045	-0.57009	0.543286	4.9476***	0.28161
		(0.2782)	(0.0053)	(0.0025)	(0.2482)	(0.1130)	(0.5308)	(0.0000)	(0.2524)
	Trump	0.154489*	0.069904**	0.1213*	0.08754**	0.10693*	0.202787*	-0.1938***	-0.2510*
		(0.0696)	(0.0074)	(0.0398)	(0.0015)	(0.0580)	(0.0826)	(0.0003)	(0.0975)
	Size	-0.04763*	0.778487	-0.0415*	0.110998	-0.52455	-0.07939*	-0.04804*	-0.082**
		(0.0364)	(0.4319)	(0.0749)	(0.8754)	(0.2938)	(0.0910)	(0.0156)	(0.0063)
	Income	-0.453015	0.009104*	0.0193*	0.159883	-0.50311	-0.25518	0.6702	0.250096
		(0.1288)	(0.0355)	(0.0670)	(0.8280)	(0.3755)	(0.6731)	(0.3561)	(0.3995)
	Adjusted R 2	0.79	0.78	0.77	0.74	0.80	0.76	0.75	0.71

Notes

Table 2 . Sectoral impacts of 2016 U.S. presidential election on U.S. stock markets: Inclusion of further control variables

 2 : All regressions are controlled for heteroskedasticity and the p-values are given in parentheses. * , * * , * * * denote statistical significance at the 10%, 5% and 1% levels, respectively.

		Financials Oil and gas Real estate Consumer	Defense Health	Technology Utilities
					goods &		care
					services
				Dow Jones Industrial Average
				Event day [0 ; 0]
	Constant	1.6223***	-1.613535*	-1.0468*	-0.5408*	0.9785**	0.663966	0.796386*	1.108502
		(0.0000)	(0.0164)	(0.0154)	(0.0439)	(0.0041)	(0.1700)	(0.0304)	(0.1989)
	Trump	-0.162***	-0.171225*	0.063***	-0.10543	-0.048**	0.07307*	-0.18619**	-0.2359*
		(0.0000)	(0.0200)	(0.0007)	(0.7025)	(0.0037)	(0.0174)	(0.0074)	(0.0474)
	Size	-0.085***	0.100477	-0.046***	-0.27732	-0.07***	-0.0530**	-0.08249**	-0.0919*
		(0.0000)	(0.5297)	(0.0003)	(0.5309)	(0.0008)	(0.0040)	(0.0012)	(0.0377)
	Income	0.0157***	0.478110	-1.3087	-0.812805	0.510256	0.006439*	0.002378**	0.10406
		(0.0000)	(0.2239)	(0.5076)	(0.7197)	(0.2346)	(0.0233)	(0.0028)	(0.5851)
	VIX	-0.226***	-0.122072	-0.111313	-0.09861*	-0.1158*	-0.09687*	-0.14102**	-0.0984*
		(0.0000)	(0.1927)	(0.2324)	(0.0865)	(0.0137)	(0.0672)	(0.0059)	(0.0126)
	Gold	-0.138***	-0.134937*	-0.081748	-0.05799*	-0.1217*	-0.1078*	-0.09369**	-0.0931*
		(0.0000)	(0.0550)	(0.4473)	(0.0153)	(0.0173)	(0.0306)	(0.0016)	(0.0460)
	Silver	-0.026***	-0.049471*	-0.236187	-0.0246**	-0.0221*	0.092213	-0.00369**	0.016201
		(0.0000)	(0.0279)	(0.2954)	(0.0050)	(0.0235)	(0.2164)	(0.0036)	(0.2761)
	Bitcoin	-0.249***	-0.153349*	-0.18407*	-0.141***	-0.129**	0.543518	-0.144249*	-0.1102*
		(0.0002)	(0.0591)	(0.0885)	(0.0007)	(0.0095)	(0.4610)	(0.0131)	(0.0202)
	Adjusted R 2	0.93	0.92	0.88	0.90	0.87	0.86	0.91	0.92
				Event window [+1; +10]
	Constant	0.526160	1.162812	1.1717***	-0.27057*	-1.059**	-0.32362	0.255759*	0.235810
		(0.4906)	(0.5185)	(0.0000)	(0.0426)	(0.0071)	(0.7345)	(0.0855)	(0.6221)
	Trump	0.160213*	0.08235**	0.1442***	0.08889*	0.1239**	0.1755**	-0.35759*	-0.262**
		(0.0941)	(0.0080)	(0.0000)	(0.0261)	(0.0010)	(0.0038)	(0.0705)	(0.0059)
	Size	0.028896*	0.157778	0.0218***	0.0032**	0.0104**	0.30766	-0.498615	0.0064*
		(0.0137)	(0.5319)	(0.0000)	(0.0030)	(0.0022)	(0.3396)	(0.8096)	(0.0152)
	Income	0.00154*	0.149428	0.0068***	0.4612	-0.879	-0.06164	-0.005152	0.6190
		(0.0257)	(0.5187)	(0.0000)	(0.2353)	(0.1168)	(0.2067)	(0.9976)	(0.3028)
	VIX	-0.0973**	-0.12142**	-0.110***	-3.3430	-0.0588*	-0.1569*	-0.46367**	0.3756*
		(0.0052)	(0.0038)	(0.0000)	(0.2693)	(0.0316)	(0.0341)	(0.0032)	(0.0168)
	Gold	-0.0688**	0.301423	-0.111***	-0.1017*	-0.086**	-0.0667**	-0.09479*	-0.0764*
		(0.0015)	(0.2356)	(0.0000)	(0.0933)	(0.0046)	(0.0087)	(0.0943)	(0.0230)
	Silver	-0.0107**	-0.00310*	-0.001***	-0.00361*	-0.59934	-0.0060**	-0.00266*	-0.0068*
		(0.0011)	(0.0372)	(0.0000)	(0.0302)	(0.3012)	(0.0079)	(0.0780)	(0.0943)
	Bitcoin	-0.1782**	-0.092282*	-0.131***	-0.1093**	-0.1049*	-0.152***	-0.98847	-0.14***
		(0.0012)	(0.0441)	(0.0000)	(0.0087)	(0.0743)	(0.0005)	(0.1298)	(0.0002)
	Adjusted R 2	0.88	0.86	0.89	0.90	0.87	0.88	0.91	0.86
					S&P 500
				Event day [0 ; 0]
	Constant	0.580116	0.748055	0.402721	0.338153	-1.18***	-0.6286*	0.565019	0.847395
		(0.5071)	(0.3617)	(0.7487)	(0.3371)	(0.0000)	(0.0109)	(0.2963)	(0.6548)
	Trump	-0.15359*	-0.1713**	0.04688*	-0.1181**	-0.03***	0.05231*	-0.2684**	-0.3048*
		(0.0739)	(0.0080)	(0.0327)	(0.0026)	(0.0000)	(0.0218)	(0.0077)	(0.0465)

Notes

The University of Michigan claimed that the index of consumer sentiment increased from 87.2 in October to 93.8 in the post-election period.

H1B visas are designed to allow US employers to recruit foreign professionals in specialty occupations within the America for well specified period of time.

Consumer goods and services

Technology Utilities


V Guipont

R Molins

Evry / F M Jeandin

Wholen, CH G Barbezat

Plasma-sprayed Ti-6Al-4V coatings in a reactive nitrogen atmosphere up to 250 kPa

Two spherical Ti-6Al-4V (25-45 µm and 45-75 µm) powders, were sprayed in a CAPS system ("Controlled Atmosphere Plasma Spraying") operating in a coupled mode: High-Pressure Plasma Spraying (HPPS) and Reactive Plasma Spraying (RPS). Four different pressure settings up to 250 kPa with reactive nitrogen atmosphere were tested in order to assess the influence of chamber pressure and chamber atmosphere on the deposition of Ti-6Al-4V coatings. The microstructures and phase compositions of the plasma sprayed Ti-6Al-4V coatings were studied using standard X-ray diffraction (XRD) and electron probe microanalysis (EPMA) with help of electron microscopy techniques (SEM and TEM). These established the pressure-assisted nitriding of the Ti-6Al-4V in the CAPS chamber with fine and coarse TiN precipitates embedded in a α-Ti matrix. A High-Pressure coupled with RPS enhanced the nitriding of the Ti-6Al-4V powder with a content of nitrogen which was all the higher because particle size was low.

Introduction

"In-situ" chemical reactions between the melted material and its gaseous environment are generally inherent to the thermal spray process. If these chemical reactions are promoted and controlled, this characterizes the reactive plasma spraying (RPS) mode. This very powerful spraying technique allows to manufacture composite materials, intermetallic alloys and reinforced or toughened ceramics [START_REF] Smith | Reactive plasma spray forming for advanced materials synthesis[END_REF]. Already with conventional APS (atmospheric or air plasma spraying) mode, oxide compounds of metallic materials are synthetized and located at the lamella boundary. If low oxide contents are generally targeted, a rather high oxide content might be interesting to enhance tribological properties for example. Thus, the APS mode can be considered as the most common "reactive" plasma spraying mode to achieve multi-phased or composite oxidized coatings. In case of titanium coatings sprayed in the APS mode (with or without nitrogen in the plasma gas), titanium particles react to form an oxinitride of titanium and a solid solution of α-Ti that contains both nitrogen and oxygen [START_REF] Ponticaud | Plasma spraying of Ti particlesin flight reactivity -coating properties[END_REF]. More generally, RPS coatings can be achieved when bringing liquid, gaseous or solid precursors into contact with the sprayed material at a high temperature.

To promote the chemical reaction, the reactants can be the dissociated species or elements that form or interact with the plasma itself or the gaseous species of the surrounding atmosphere of the process. Moreover, if the reactive atmosphere is in contact with the deposit that is held at a high temperature during spraying, further reactions can occur after impact and solidification of the droplet. "In-situ" synthetized carbides or nitrides are the two types of RPS coatings that are always studied and corresponds to these various RPS operating routes [START_REF] Eckardt | Reactive Plasma Spraying of Silicon in Controlled Nitrogen Atmosphere[END_REF][START_REF] Tsunekawa | Synthesis of chromium nitride in situ composites by reactive plasma spraying with transferred arc[END_REF][START_REF] Smith | Reactive plasma spraying of wear-resistant coatings[END_REF][START_REF] Ananthapadmanabhan | Titanium carbide-iron composite coatings by reactive plasma spraying of ilmenite[END_REF][START_REF] Al-Sabouni | Reactive plasma spraying of 80/20 nickel-chromium powders[END_REF].In some works, pure titanium powder have been sprayed using RF plasma equipment with nitrogen content in the plasma gas [START_REF] Fukumuto | Fabrication of functionally gradient TiN Coating by reactive plasma spraying[END_REF]. In other works, titanium coatings containing titanium nitrides were synthetized in a surrounding nitrogen atmosphere. The latter mode is one of the most convenient potential industrial process for wear and corro-sion-resistant coatings [START_REF] Lugscheider | Reactive plasma spraying of coatings containing in situ synthesized titanium hard phases[END_REF][START_REF] Valente | Corrosion resistance properties of reactive plasma-sprayed titanium composite coatings[END_REF]. This mode could operate with a DC plasma gun operating with injected nitrogen reactive gas in a shrouding reactor [START_REF] Lugscheider | Reactive plasma spraying of titanium[END_REF] or with a controlled nitrogen atmosphere at different gas pressures in a chamber [START_REF] Valente | High pressure reactive plasma spray synthesis of titanium nitride based coatings[END_REF][START_REF] Bacci | Reactive plasma spraying of titanium in nitrogen containing plasma gas[END_REF].

The aim of the present work was to achieve Ti-6Al-4V nitrided coatings using high-pressure plasma spraying (HPPS) coupled with reactive plasma spraying in a reactive nitrogen atmosphere, namely HPRPS (High Pressure Reactive Plasma Spraying). The influence of chamber pressure and the influence of chamber atmosphere with some experiments using air atmosphere were studied. In addition, two spherical Ti-6Al-4V (25-45 µm and 45-75 µm) powders were sprayed in order to assess the influence of particle size on the resulting nitriding process.The formation of nitrided compounds was studied using standard X-ray diffraction (XRD) and electron probe microanalysis (EPMA) with help of scanning and transmission electron microscopy techniques (SEM and TEM) in order to assess the pressure-assisted nitriding of the Ti-6Al-4V.

Materials and Processes

Powders

Two different spherical, pre-alloyed and low-oxygen content Ti-6Al-4V powders (PyroGenesis Inc., Canada) were used corresponding with two different particle size range: 25-45 µm and 45-75 µm. A SEM crosssection view of the powder (chemical etching with "Kroll" reagent) showed the martensitic structure of the Ti-6Al-4V alloy, Fig. 1.

High-Pressure Reactive Plasma Spraying (HPRPS)

Plasma spraying experiments were carried out using a multi-process plasma equipment (CAPS, Sulzer-Metco, Switzerland) with a F4-MB plasma gun. The CAPS system has an 18 m 3 chamber, which could operate in a controlled atmosphere of air, argon or nitrogen from 2 kPa to 350 kPa. The plasma spraying parameters are given in Table 1. The same Ar/He plasma mixture was used for all HPRPS pressure settings. Some experiments with air instead of nitrogen were performed and APS coating with Ar/H 2 plasma was sprayed as a reference. The addition of N 2 in the plasma was studied in the HPRPS mode. Further experiments with a shorter spraying distance (75 mm) were done at 250kPa with a nitrogen atmosphere.

Sample analysis

As-sprayed samples were placed in a dry chamber under vacuum to prevent any further oxidation. Phase analysis were performed using XRD D-500 goniometer (Siemens, Germany) with Co Kα radiation (800W) on the surface of the as-sprayed coatings (irradiated area = 10 mm²). EPMA quantitative analyses of crosssections (with fittings for the Ti and N elements) were carried out using a SX 50 microprobe (Cameca, France) in the WDS. Cross-section observations of as -sprayed or etched coatings ("Kroll" reagent) were performed using a DSM 982 Gemini SEM (Zeiss, Germany) with back-scattered electron (BSE) detector. Parallel foils of the coating were ion-thinned and analysed with a 300kV EM430-T TEM (FEI, the Netherlands) coupled with EDS analysis.

Results and discussion

HPRPS process in nitrogen atmosphere

In a previous work [START_REF] Guipont | High-Pressure Plasma Spraying of Hydroxyapatite (HA) Powders[END_REF] using Ar/He in argon atmosphere up to 250 kPa, it was shown that the pressure concentrated the energy density within the HPPS plasma and improved the heat transfer between the plasma and the particle. When comparing an argon atmosphere with a nitrogen atmosphere, it was calculated that the temperature of the plasma decreased more quickly along the plasma axis in a nitrogen atmosphere [START_REF] Leylavergne | Comparison of plasma-sprayed coatings produced in argon or in nitrogen atmosphere[END_REF]. Nevertheless, Ar/He plasma rather than Ar/H 2 plasma (used in the conventional APS mode) showed the advantage to operate up to 250 kPa without overheating and damaging of the plasma nozzle for a nearly similar plasma effective electric power, Fig. 2.

Fig. 2. Plasma effective electric power (F4-MB gun)

The same plasma gas mixtures were kept whatever the applied pressure. However, when adding a low content of N 2 gas, it was necessary to reduce the arc current down to 650A to avoid any damage to the nozzle due to the high enthalpy of N 2 plasma gas (nearly similar to that of H 2 ). For this ternary plasma gas mixture, a rather slight decreasing of the plasma effective electric power was obtained when pressure increased. Note that HPRPS selected conditions were all with a lower plasma power than that for APS. Nevertheless, this selected parameters with the cooling of the substrate allowed the explicit influence of surrounding reactive gas pressure to be assessed. This aspect was a key issue of this study.

3.2

Influence of atmosphere on plasmasprayed Ti-6Al-4V

Typical phase composition

Typical XRD diagrams of as-sprayed Ti-6Al-4V coatings were selected to bring to the fore the titanium compounds (with help of the JCPDS cards) that could crystallize during the process, Fig. 3. If TiO and TiN peaks overlap, this could correspond to a titanium oxi-nitride phase. However, if Ti-6Al-4V coatings in air and nitrogen atmosphere are compared (A and D diagrams), a difference between these two compounds was clearly exhibited, i.e. TiO for APS and TiN for HPRPS. Spraying Ti-6Al-4V coatings in HPPS (Profile B) led to a titanium oxi-nitride. A high TiN content and a low α-Ti content were obtained in the HPRPS mode. In this case Ti 2 N peak was also identified. These results are in agreement with those obtained for pure titanium [START_REF] Valente | High pressure reactive plasma spray synthesis of titanium nitride based coatings[END_REF]

Pressure-assisted nitriding of Ti-6Al-4V

On the basis of the value of the (200) peak area of the TiN phase (2θ=50.1°), a qualitative assessment of the nitriding level was obtained as a function of nitrogen pressure in the CAPS chamber, Fig. 4. This diagram featured the influence of the particle size and that of nitrogen in the plasma gas. The higher content of TiN was qualitatively determined for the HPRPS Ti-6Al-4V coating using the smaller particle size. In this case, pressure effect can be said to be linear. This established clearly the gas pressure effect on the nitriding process in the HPRPS mode using nitrogen atmosphere. This effect was promoted by the use of a rather fine Ti-6Al-4V powder. Further nitriding could be obtained, provided that 25-45µm Ti-6Al-4V powder is sprayed in the HPRPS mode using an Ar/He/N 2 plasma. Unfortunately, only one result in the RPS mode (100 kPa, N 2 atm.) was available. Furthermore, pressure settings higher than 250 kPa could be interesting too. But, further investigation is needed to define suitable plasma parameters at 350 kPa for example.

3.3

Nitrided Ti-6Al-4V coating microstructure

Nitrogen distribution within coatings

Using EPMA of HPRPS cross-sections (200 kPa, Ar/He plasma, 25-45 µm specimen), the quantitative distribution of the main constitutive elements (Ti, Al, V, N, O) was measured along a direction perpendicular to the coating surface. Typical distribution of the nitrogen element within the lamellar HPRPS coatings could be obtained, Fig. 5. The diagram could be divided in three different regions. These regions corresponded to three different lamellae. Two of these lamellae were rich in nitrogen as well shown in the diagram (from 0 to 10 µm and from 18 to 24 µm) by mean of the nitrogen weight percentage which was rather high and constant in the range of 11-15%. The third lamella (from 10 to 18 µm) was a low-nitrogen content lamella (0-3%) that could be considered as a Ti-6Al-4V lamella.

Note that the specimen was free of oxygen except the regions wherein pores were filled with mounting organic resin.

wt%(N ) wt%(O ) wt%(Al) wt%(V ) wt%(Ti)

Nitrogen rich lamellae Nitrogen rich lamellae

Fig.5. EPMA profiles on HPRPS specimen

This typical distribution of nitrogen within HPRPS coating showed that nitrogen was rather homogeneously distributed on a micrometric scale within a nitrided lamella. However, nitrided lamellae were randomly located near non-or poorly-nitrided lamellae. The resulting material can be considered as a Ti-6Al-4V based matrix composite with TiN-rich lamellar addition (the size of which is the size of a splat). Despite the pressure-assisted nitriding, this quantitative analysis showed that the nitriding process did not affect homogeneously all the sprayed particles. Further work is needed to better control the TiN distribution within the HPRPS coatings. Argon cooling should probably be no longer used to allow a better atmosphere homogenisation. Furthermore, it was observed in nitrogen-rich regions that the weight percentages of all the Ti-6Al-4V constitutive elements decreased, with a steep gradient for Al, i.e. from 6% down to 2-3%. No particular Ti, Al or V increase in the rest of the coating that could rebalance this chemical removal was detected using EPMA. This fact exhibited one distinctive characteristic of Ti-6Al-4V reactive spraying compared to pure Ti. One may assume (but need confirmation) that intermediate Ti-Al-V compounds could form then be evaporated during the HPRPS process.

Coating microstructure

SEM views of etched cross-section were achieved for RPS and HPRPS coatings (25-45µm particle size) using Ar/He/N 2 or Ar/He plasma gas mixture respectively, Fig. 6. These coatings were selected according to the different TiN contents obtained by XRD qualitative (see section 3.2.2) and because of the very different coating features. Using nitrogen in the plasma gas in the RPS mode (100 kPa, N 2 ) resulted in a wellflattened and dense typical lamella structure, Fig 6-a-1. Nitrogen-rich regions were in dark grey while light grey regions corresponded to non-nitrided (or lessnitrided) lamellae using BSE SEM. Nitrided dark regions were homogeneously distributed and were for around 50% of the coating. Using a high and reactive pressure at 250 kPa (Ar/He plasma), large dark nitrided regions were observed, Fig 6-b-1. In this case, a coating morphology with round particles was observed. The high nitrogen content within HPRPS coatings showed that the particles were melted, nitrided and probably partially solidified before impinging. Nevertheless, the flattening ratio was sufficient to form a plasma-sprayed coating. At higher magnification, very different nitrided microstructures were exhibited, Figs. 6-a-2 and 6-b-2. The well-flattened RPS lamellae were with a small-sized and homogeneous microstructure in the nitrided region. Some smooth and glassy regions were attributed to the Ti-6Al-4V material (not represented in the figure). This feature corresponded to an homogeneous removal of Ti alloy or N-rich Ti alloy. This was confirmed by TEM analysis, Fig. 7-a. Microcrystalline TiN particles were embedded and homogeneously distributed in a pure or N-rich α-Ti matrix. On the other hand, the highly nitrided agglomerated HPRPS particles exhibited an heterogeneous microstructure, as shown by selective chemical etching, Fig 6-b-2. Ti-N phases (TiN and/or Ti 2 N phases) with a typical inner dendritic microstructure and some dense layer at the particle periphery were observed [START_REF] Bell | Energy beams in second generation surface engineering of aluminium and titanium alloys[END_REF]. The Ti-N compound was actually a corrosion resistant phase and could not be etched by "Kroll" reagent. This revealed large areas with both fine and coarse Ti-N dendrites. TEM view of this coating allowed to observe the corresponding micro-scaled TiN particles and submicro-scaled TiN precipitates, Fig. 7-b. These microcrystalline TiN particles were surrounded by an α-Ti alloy. Fine TiN precipitates corresponded to a high solidification rate while coarser TiN particles were nitrided and partially solidified before impacted. As a confirmation of this, it was observed that the dense peripherical layer was identified as sequence of coarse TiN particles. TiN or Ti 2 N could not be easily detected using SEM of etched coatings and Ti 2 N was not yet detected by TEM. Decreasing the spraying distance to 75 mm led to HPRPS coatings with a well-flattened lamella structure and with a TiN content similar to that of the RPS coating sprayed at 100kPa using Ar/He/N 2 plasma. This means that the nitriding process occurred mainly during the particle flight and could easily form highnitrided titanium compounds in the pressure-assisted process. Further investigation is needed to determine a spraying distance suitable for building-up wellflattened coatings with the highest content of nitrided titanium compounds. Pressure-assisted nitriding of plasma-sprayed Ti-6Al-4V was obtained using CAPS up to 250kPa. Pressure effect is a key parameter to carry out highly-nitrided Tibased coatings because the nitrogen reacts mainly during the particle flight to form TiN or Ti 2 N. With an increased pressure, it was possible to form nitrided lamellae within the coating even with a short spraying distance. The use of nitrogen in the plasma gas was not necessary to obtain a high nitrogen content. Using a rather low enthalpy Ar-He plasma gas mixture coupled with a high-pressure of nitrogen in the chamber, HPRPS Ti-6Al-4V coatings at 250 kPa with small particle size powder exhibited the highest TiN content with some Ti 2 N phase. Pressure improved the heat transfer between the plasma and the particles and allowed the use of a low enthalpy plasma to melt the particles. In these conditions, the "in-situ" nitriding process was so efficient that a typical dendritic growth of fine and coarse TiN particulates was observed within sprayed lamella as confirmed by TEM. A lower nitrogen content within HPRPS or RPS Ti-6Al-4V coatings led to a lower TiN content without typical TiN dendrites but with very fine microcrystallized TiN and/or N-rich titanium solid solution and residual Ti-6Al-4V martensitic phase. A promising application could be the tailoring of Ti-6Al-4V-based matrix composites with TiN-rich lamellae containing titanium hard phases of different micro-scaled sizes that could improve wear properties. Work is in progress to characterize the TiN hard phase within HPRPS coatings using nano-indentation coupled with atomic force microscopy. Some significant removal of Ti-Al-V was detected in the N-rich lamellae of Ti-6Al-4V using EPMA and TEM analyses. Further work is needed to explain this particular phenomenon and to discuss the role of Al and V on the nitriding process. The particle size is another key parameter to manufacture nitrided Ti-based coatings. If fragmentation into fine particles occurred during the spraying process, it led to fine TiN round particulates. The direct use of ultra-fine or nanoscaled Ti based powder could be very interesting, especially in case of co-spraying for the achievement of nitrides reinforced composite coatings for example.

Fig. 1 .

 1 Fig. 1. Cross-section SEM image of a typical Ti-6Al-4V particle (45-75µ µ µ µm size range). Table1. Plasma spray parameters Reactive Plasma Spraying Atmosphere Nitrogen Pressure (kPa) 100 150 200 250 Plasma (l/min) Ar:50/He:30, I=700A Ar:50/He:30/N 2 :2, I=650A Ti-6Al-4V 45-75 µm or 25-45 µm Distance 130 mm Cooling gas argon Atmospheric Plasma Spraying Atmosphere Air Pressure (kPa) 100 100 250 Plasma (l/min) Ar:47/H 2 :10 I=650A Ar:50/He:30 I=700A Ti-6Al-4V 45-75 µm Distance 130 mm Cooling gas air

Fig. 3 .

 3 Fig. 3. XRD Diagrams: A: APS (100kPa, air atm., Ar/H 2 plasma), B: HPPS (250 kPa, air, Ar/He plasma), C: RPS (100 kPa, N 2 atm., Ar/He/N 2 plasma), D: HPRPS (250kPa, N 2 atm., Ar/He plasma)

Fig. 4 .

 4 Fig.4. TiN (200) XRD peak area for different HPRPS conditions.

Fig. 6 .Fig. 7 .

 67 Fig.6. SEM views, a: RPS (100 kPa, N2 atm., Ar/He/N 2 plasma), b: HPRPS (250kPa, N2 atm., Ar/He plasma)

Acknowledgements

This study was done in the frame of the C2P (Center for Plasma Processing/France) Club activity. Industrial "members" of the Club are acknowledged for support and helpful discussions. The authors thank Pyro-Genesis Inc. for kindly providing the Ti-6Al-4V powders. Mrs N. De Dave, Mr G.Frot and Mr F.Borit are also gratefully acknowledged for their work for samples spraying and microstrutural analyses.