Trump vs. the ruble

During his tenure as U.S. president, Donald Trump tweeted angry tweets about Russia on many occasions (to be fair, he tweeted angry tweets against lots of targets). It would be fair to wonder whether those tweets conveyed any useful information about the U.S.'s stance towards Russia, in relation to economic or other sanctions. In a recent article published in the Journal of Economic Behavior and Organization (ungated version here), Dmitriy Afanasyev (JSC Greenatom), Elena Fedorova (Financial University Under the Government of Russian Federation), and Svetlana Ledyaeva (Aalto University) look at the impact of President Trump's tweets on the Russian ruble exchange rate, over the period from October 2016 to August 2018.

Over that period, Trump tweeted 5548 times, of which 296 tweets related to Russia. Afanasyev et al. used five different lexicons to code each tweet as positive, negative, or neutral, and tested seven different decay schemes (in terms of how the effect of a tweet fades over time). To overcome the problem of having lots of variables to test and determining which ones to include in their final model, they use an elastic net (which, for those of you who are pointy-headed, is a mix of LASSO regression and ridge regression - essentially a type of machine learning approach). Having established which variables to include in their model, they then move to a Markov regime-switching model, which is increasingly used in modelling time series where the researcher believes that there are multiple different relationships between the variables (different regimes) that occur at different points in time.

Overall, Afanasyev et al. find that:

...oil price (the only economic variable (fundamental) chosen by elastic net) remains the main long-term determinant of ruble exchange rate... The impact of Trump’s tweets’ sentiment tends to be episodic and short-term. Significant toughening of Trump’s Twitter Russia-related negative rhetoric can lead to short-term (around 3 days) "abruptions" in the process of ruble exchange rate’s formation based on oil price causing significant depreciation of the Russian ruble.

In other words, the Russian ruble exchange rate is mostly determined by the oil price (since oil is such a large component of the Russian economy). However, Trump's negative tweets caused short-term deviations in the exchange rate. However, I wasn't entirely convinced by this paper. The throw-everything-at-the-wall-and-see-what-sticks approach is dodgy, regardless of whether you use a fancy machine learning approach to help with variable selection or not. The results were only statistically significant for the variables that were chosen in the final model, and we don't know if the other measures of sentiment (as noted above, there were five measures) or different decay periods (there were seven) would have led to similar results. Those sorts of robustness checks are important to include, if you want readers to find your results convincing.

Of what we do know from the paper, Trump's negative tweets only affected the ruble some of the time, and there isn't a good explanation for why they affected the ruble those times, and not others (maybe the time of day mattered?). The research question is interesting and potentially important - I hope other researchers are looking at this as well.