Will Real-Time Commentary on CNBC Be Outdone by StockTwits?
- Posted on Thursday, 06 February 2014 20:00
- Written by Brian Egger
Yesterday afternoon, shortly after 4:00pm, I watched two CNBC reporters, Julia Boorstin and John Fortt, try to walk the network's television audience through the just-released earnings announcement of Twitter (TWTR). The veteran reporters dutifully attempted to decipher the eight-page press release, while Closing Bell anchor, Kelly Evans, gently prodded them with questions.
I watched the televised segment with a sense of empathy. As a Wall Street analyst, I had often been asked by a panting trader for my "quick take" on earnings numbers, even as the first headline of the announcement was flashing across my computer screen.
Grabbing pages of the earnings release from a nearby printer, I would be pressed for my instantaneous reaction to a document that required at least twenty minutes to read.
These instances presented a choice: did I give my "knee-jerk" reaction to the headline numbers on the press release, knowing full well that some important qualifying information several pages into the document might prove my inferences incorrect? Or did I deflect the caller's questions, and ignore my ringing phone, as I attempted to actually read the entire document, along with its pages of accompanying financial tables.
Knowing how challenging it can be for an analyst with sector expertise to react to questions under these circumstances, I can imagine how difficult it must be for a journalist to meet this challenge, without the aid of their own financial model. One might think that watching two reporters decipher financial news would more revealing than the dissonant stream of messages on social media websites. However, I have found the opposite to be true.
The span between 4:07pm and 4:11pm – when Ms. Boorstin first appeared on air and began to review earnings numbers in real time – revealed a well-intended and experienced reporter trying to distill instant insight from an eight-page document. The exercise in real-time analysis became downright stressful, when Twitter's stock gave up its initial after-market gains, and retreated a full 13% – all during the four-minute interval that CNBC had allotted for Ms. Boorstin's analysis.
In contrast, the comment "stream" for "$TWTR" on StockTwits proved to be surprisingly revealing and coherent. During the three minutes that preceded Ms. Boorstin's on-camera appearance at 4:07pm, sixty messages had already spewed forth from StockTwits. The comment string began with observations of a 4% after-hours jump in Twitter's stock in the wake of a top- and bottom-line "beat," but were quickly followed by jeers about a "roller coaster"-like, "whipsaw" retreat.
When Twitter began sliding in after-market trading, interpretative comments immediately followed. Some StockTwits members suggested that the company's guidance had been good, but not great, and that the reported results had fallen short of some "whisper" number on the Street. The StockTwits messages flowed at the same rapid pace exhibited by CNBC's flashing display of after-hours trading quotes.
During the next four minutes – between 4:07pm to 4:11pm – talk on StockTwits turned to observations about bearish options activity, and disappointment about the number of reported active users and mobile channel trends on the social media website. No single StockTwits message carried the story. However, a chorus of sixty disparate voices, each a single line of text long, coalesced into a surprisingly cohesive narrative.
I'll avoid the cliché of characterizing this episode as evidence of the "wisdom of crowds." The incident was less about "wisdom," and more about coherence. CNBC helped create the urgency that goes along with 24-7 "real time" financial news. That sense of urgency can morph into a sense of awkwardness, when two or three individuals, none of whom have deep sector expertise, attempt to match wits with the ticker tape of a mechanized stock market. Under these circumstances, sixty voices, each sharing a tidbit of insight, might actually prove more useful.