While the index's "utility in scoring any given tweet is somewhat limited", the bank said, it added that it works "as a supervised machine learning exercise - specifically we can train a classifier to infer how likely each tweet is to move markets".
"This makes rough sense as much of the president's tweets have been focused on the Federal Reserve, and as trade tensions are broadly seen as, first and foremost, impactful on near-term economic performance and, likewise, the Fed's reaction to such developments", JPMorgan wrote in a report.
Trump's tweets, which have in recent months often focused on market-sensitive topics such as trade and monetary policy, have increasingly moved United States rates markets, analyst Munier Salem said in a note on Friday.
To be even more precise, "a 1pm tweet is roughly three times as likely as one arriving at any hour of the afternoon or evening", the analysts found.
"Market-moving tweets exhibit distinct statistical properties, primarily addressing trade and, more recently, monetary policy".
But analysts at JP Morgan have now quantified the impact of his tweets on the U.S. interest rates market.
It showed both share indices dropping sharply when he tweeted on December 5: "We are either going to have a REAL DEAL with China, or no deal at all".
"Through the course of the year, weak global growth, trade policy uncertainty and muted inflation have prompted the [Federal Open Market Committee] to adjust its assessment of the appropriate path of interest rates".
The index could also be applied to currency and equity markets, JPMorgan said. The bank said: "Market moving tweets' have ballooned in frequency this August". Eastern Time announcing he'd extend tariffs across virtually all Chinese imports. Consider August 1, when Trump sent out a tweet at 1:26 p.m.
That tweet was posted one week before the G20 summit in Japan, when investors' excitement was at fever-pitch that the economically-damaging trade war would be over soon.
In the report, which runs well over 4,000 words, J.P. Morgan analysts use machine learning techniques and their own volatility model to show how the president's 280-character missives served to raise investors' expectations for future moves in the US rates market.
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