Key Points

  • An increasing body of research shows that previously documented relationships in the investment literature are the result of reporting bias, data mining, and selection bias. Using data for five international markets, we test the robustness of US results that show a relationship between US stock returns and presidential politics—stock returns are higher under Democratic presidents and lower under Republican presidents—and find the US results appear to be spurious.
  • Our analysis of the respective stock markets and incumbent political parties of Australia, Canada, France, Germany, and the United Kingdom finds a result opposite that identified in the US, indicating that investors should interpret historical statistical relationships with a healthy skepticism.

The current political environment in many developed nations has caused us to consider the robustness of the findings of Santa-Clara and Valkanov (2003), who present evidence that US stock market returns are much higher under Democratic presidents than under Republican presidents. Pastor and Veronesi (2017) update the work of Santa-Clara and Valkanov, finding that the effect is even stronger when the data are extended through the end of 2015. They report that from 1925 to 2015 the average excess market return under Democratic presidents is 10.7% a year, whereas under Republican presidents it is only ?0.2% a year. The difference, almost 11.0% a year, is highly significant both economically and statistically.

Given the strength of their results, Pastor and Veronesi develop a model based on time-varying risk aversion to explain the pattern. They hypothesize that when risk aversion is high, such as in times of economic crisis, voters are more likely to elect a Democratic (left-leaning) president, and when risk aversion is low, to elect a Republican (right-leaning) president. Because risk aversion is higher under Democrats, the equity risk premium is greater, and therefore, average returns are higher.  The hypothesis advanced by Pastor and Veronesi has intuitive appeal.  Is it an ex post rationalization for an observed relationship or is it a real driver of returns?  International results may help answer this question.

In statistical studies, it is often easy to overlook the details when examining the broad statistics. Two key events appear to be responsible for much of the differential returns under Democratic and Republican presidents. Specifically, a Republican was president during the two great financial and economic crashes that began in 1929 and in 2008, respectively; unsurprisingly, a Democrat held the office of president during the immense subsequent recoveries. This appears to explain a majority of the return difference.  Had the order of incumbencies been reversed, the effect would be reversed, suggesting the finding may be serendipitous.1

To further explore this possibility, we turn to international data in five major countries as an out-of-sample test: Australia, Canada, Germany, France, and the United Kingdom. Consistent with our suspicion that the US results are spurious, we find no systematic relationship between the party in power and stock market returns outside the United States.2

Looking Beyond the United States
A growing body of the financial asset and investment literature has documented that a significant fraction of relationships found and reported in published articles may be spurious due to reporting bias (e.g., Lo and MacKinlay [1990], Black [1993], and MacKinlay [1995]). All too often, after results are identified in US markets, a rationale is then developed to explain the results; this is contrary to scientific method.

Related problems associated with data mining and selection bias are also present. In recent work, Harvey, Liu, and Zhu (2016) argue that because so many researchers are looking for statistical relationships using the same database, the traditional t-statistic of 2.0 to measure statistical significance is no longer an adequate hurdle, and they propose an elevated level of t-statistic should be used instead.

In an effort to remediate data-mining bias in factor and smart beta research, Hsu, Kalesnik, and Viswanathan (2015) suggest that a procedure of perturbing factor definitions and examining factor robustness in multiple geographies can serve as the basis for out-of-sample studies. And last year, Arnott et al. (2016) and Arnott, Beck, and Kalesnik (2016a,b) pointed out that academics have generally failed to adjust performance for changing valuation levels; that is, to disentangle factor performance arising from revaluation from factor performance that is structural, and hence, may be more reliable.

Using international data, we test the robustness of the findings of Santa-Clara and Valkanov and of Pastor and Veronesi by examining the relationship between a nation’s ruling-party political affiliation and its stock market performance. We select Australia, Canada, Germany, France, and the United Kingdom because each has a developed stock market, and each has experienced reversals in political control over the last several decades between left-leaning and right-leaning parties. We do not include, for example, Japan because in the post-WWII period, with the exception of relatively short intervals, the prime minister represented a single party, the Liberal Democratic Party.

The data we use are from a database maintained by Global Financial Data. The stock returns are monthly returns of the most widely reported market indices in each of the five countries: S&P/ASX in Australia, S&P/TSX in Canada, CAC 40 in France, DAX 30 in Germany, and FTSE All Shares in the United Kingdom.  Following Pastor and Veronesi, we introduce a dummy variable equal to zero if the “right” party is in power, and one if the “left” party is in power; for example, in the United States, the dummy variable is set to zero for Republican control of the White House, and one for Democratic control.  Considering the different political systems of the countries in our analysis, we define the ruling party as being the same as the political affiliation of the prime minister (Australia, Canada, and United Kingdom), chancellor (Germany), or president (France and United States).  Table 1 summarizes the parties in each country designated as having a right or left orientation.

We likewise follow Pastor and Veronesi in marking the transition point from party to party at the time the actual transition of governing power occurs: for presidents this is inauguration day, and for prime ministers this is the day they assume office. Using this methodology, the observation of the last partial month in office is fully allocated to the incumbent party.  For example, if the transition occurs in March, the March observation is allocated to the incumbent party, and the April observation to the newly elected party.

From a competitive markets perspective, this seems an odd way to define the transition point in stock returns. Stock prices reflect investor expectations.  Investor expectations do not change on the date a new president takes office.  Expectations don’t even change on the date of the election, except in unusual years like 2016.  We think a better way to study the market impact of politics would be based on the change in electoral expectations—when the outcome was deemed likely rather than waiting until election results are settled, let alone the formal transfer of

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