Wall Street stock analysts have a language all of their own.
First, there is the technical language. There are buy upgrades, neutral ratings, and very occasionally an underweight rating, or sell. There are all the financial terms that come with analysing public company financials.
Take this nugget from a BMO Capital Markets report that landed in my inbox on Wednesday for example:
SYF surprised the market negatively this morning with new credit guidance; provisions are expected to be higher (than previously guided), following a decline in late-stage cure rates. Specifically, management now expects NCO rates to rise 20-30 bps over the next 12 months, and consequently, the allowance coverage ratio to jump 20-30 bps starting in 2Q16 (relative to 1Q16 levels).
The stock is Synchrony. I’m going to be honest and say I don’t really know much about Synchrony, and I don’t know what this means, though I hope the money managers who follow Synchrony do.
Then, there is the coded language — the bits where money managers have to read between the lines. That is even more difficult to understand. Unless you’re a computer, that is.
In an interview on computer-driven investing on Goldman Sachs’ regular podcast, Armen Avanessians, chief investment officer of Goldman Sachs Asset Management’s quantitative investment strategies team, said that it’s possible to figure out what analysts really mean from their notes, before they have even made a change to a rating.
Here’s a transcript (emphasis ours):
ARMEN AVANESSIANS: There’s a lot more data out there. I mentioned the analyst reports. Before we stripped out the numbers like you said. Today, we read the entire report. We read for the change in an analyst tone. I mean, we know that analysts, you know, before they change their recommendation, a very formal process, you’ll pick up that they’re just starting to get maybe a little more nervous, they’re just starting to use more hedging terms.
JAKE SIEWERT: So you’re actually looking at the adjectives?
ARMEN AVANESSIANS: You’re looking … exactly, at the tone that the analyst uses. And we can train the computer to pick up on tone in the same way that a human has been trained to pick up on tone. We also pick up on the themes that the analysts talk about. So, for example, when an analyst is asking questions at an earnings call, well, the answers may not be as important as the questions. You know, when they’re asking questions of several different companies, well, those are probably key themes we should take note of.
The background here is that quant funds used to have computers analyse research reports looking for numbers and rating changes like buy, sell or hold recommendations. Now the analysis is going a lot further, with algorithms noting changes in tone that could indicate an analyst getting nervous about a company.
Humans could read the reports and look for the same changes in tone, of course. But there is a limit to how many stocks a human can keep track of. Computers can do this for every stock out there. Here is Avanessians (emphasis ours):
A human has a certain limit to how many companies they’re going to keep track of and the number of themes and connections they can find, they personally can find, is limited by what they personally can go through. The set of computers can look for connections across every company in the universe. It can read every research report on every company, every earnings transcript and find themes that you normally wouldn’t be looking for.
According to Avanessians, we’re still in the very early stages of this shift towards using big data in investing:
I think we’re in the third inning. You know, the game has really just begun. There are players out there, there’s some people behind, some people ahead, but I would say that we’re just getting into the rhythm. The game has got a long way to go. And by the way, I think that we’re in the third inning of what’s going to be a seven-game series.
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