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 , Black , and MacKinlay ). 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.