Trump tweets and the efficient Market Hypothesis

Abstract

Abstract. In a Semi-Strong Form (SSF) Efficient Market, asset prices should respond quickly and completely to the public release of new information. In the period from his election on 11/8/16 to his swearing in ceremony on 1/20/17, President-elect Trump posted numerous statements ('tweets') on his Twitter messaging service account that identified ten publicly traded firms. In the absence of new information, the Efficient Market Hypothesis (EMH) predicts that these announcements should have little or no price impact on the common stocks of these firms. Using standard event study methods, we find that positive (negative) content tweets elicited positive (negative) abnormal returns on the event date and virtually all of this effect is from the opening stock price to the close. Within five trading days, the CARs are no longer statistically significant. President-elect Trump's tweets were associated with increases in trading volume and Google Search activity. Taken as a whole, the price and trading volume response, combined with Google Search activity is consistent with hypothesis that it was small/noise traders who were acting on President-elect Trump's tweets and that their impacts were transitory.

Under his thumb the effect of president Donald Trump’s Twitter messages on the US stock market

“…These results confirm those from Juma'h and Alnsour [14] and Ge et al [15] for a much smaller sample. However, they contrast with those of Born et al [13], who studied a sample of fifteen tweets between the presidential elections and the swearing in of president Trump.…”

“…Other studies more closely align with our research question, namely the relationship with stock market performance. A working paper by Born et al [13] studies 15 company specific tweets regarding ten firms in the period of his election and his swearing in ceremony. They find that both positive and negative tweets elicited abnormal returns on the event date.…”

An empirical approach to the “Trump Effect” on US financial markets with causal-impact Bayesian analysis

“…Although the EMH is controversial with both detractors and followers, it is still far from being ignored, even more so when it comes to hypothesize about the formation of financial bubbles (see, e.g., Chernomas and Hudson, 2017). Born et al (2017) used a single index model to estimate the systematic risk coefficient of 10 companies listed in the S&P500 index to conclude that irrational investors or "noise traders", in the sense of Black (1986), were responsible for the rise in US financial asset prices. In our opinion, the results obtained from the application of the Bayesian causal-impact analysis (Brodersen et al, 2015) to determine the extent of the "Trump Effect" also lead to derive the presence of this kind of investor (irrational or uninformed) influenced by two factors: the uncertainty of the result of the US presidential election in 2016 and, in particular, the future impact of the Trump Administration's economic policies on US stocks exchanges (Wagner et al, 2018a,b).…”

“…The old adage “When America sneezes, the world catches a cold”, so often evidenced in geopolitics and international finance (see, e.g., Suardi, 2010 ), is readily apparent when analyzing the “Trump Effect”. In fact, its impact has been studied both exclusively in US stock exchanges ( Born et al, 2017 , Wagner et al, 2017 , Wagner et al, 2018a , Wagner et al, 2018b , Cox and Griffith, 2019 ) and in other stock exchanges as the European ( Klaus and Koser, 2020 ) as well as the Mexican, Japanese, Australian and Brazilian markets ( de Area Leão Pereira et al, 2018 ).…”

“…Following an approach similar to Born et al (2017) , who employed a single-index regression model without specifically adhering to any of the hypotheses contrary to the EMH, this research work has opted to implement a causal methodology based on structural equations ( Bańbura et al, 2011 ). Specifically, a causal analysis of the so-called “Trump Effect” has been carried out through a procedure which, to the extent of our knowledge, has rarely been employed in the financial field: the causal-impact Bayesian analysis ( Brodersen et al, 2015 ).…”