As I was writing A peak at Natural Language Processing I excluded the “spoken language” component of NLP to simplify certain arguments surrounding this sub-field in AI. A couple hours later I remembered a discussion I had with a friend many years ago. He had been looking at new services via the web that gave you a Squawk Box (not CNBC’s Squawk Box but the name invokes the same instantaneous information flow) . A Squawk Box is a super high-tech cutting edge device that delivers (get this) live audio from the trading floor. Okay, all kidding aside, a Squawk Box is basically a connection to someone calling out the bids and the asks from a pit or trading floor - it is that simple. Even real-time quotes are not as up-to-date (by seconds, not much more) as the information flowing from a Squawk Box, as the quotes need to be entered into a system before they are distributed.

Listen to this clip from the Crude Oil pits. Better yet, check out this clip from the S&P pits (darn it! The clip is now missing - I will try and find another one…) when the Fed announces a rate cut. Listen to the whole clip (or imagine you are listening to it…) - it is a bit boring at the beginning, but about half way through it gets very exciting. Are you listening to what the announcer is saying? Or to how he is saying it?

From an NLP or speech recognition perspective it would be an interesting exercise to attempt to capture the quotes as they flow from the Squawk Box. All kinds of challenges to overcome, especially the noisy environment that the speech would need to be extracted from. The end results - a stream of quotes that would effectively (should) mirror your real-time quotes (from the pits - obviously action in the electronic markets would not be reflected). A lot of additional effort for (perhaps) a second or two faster real-time quotes.

Real-time quotes is not the main value of the Squawk Box to a trader that is not on the floor. Listen to the emotion in the announcer’s voice - especially after the Feb rate cut had been announced. Better yet, listen to the background noise of the traders in the pit. Back to the discussion I had with my friend. Take one of these web based Squawk Box feeds and use it to get a handle on near-term direction of the market. So, we are again dismissing the speech (and in this case dismissing NLP also - it had just been the key to reminding me of this conversation) recognition field and going for something else. Pulling information from unstructured data - in this case we don’t have the luxury of text or even speech - I am thinking of extracting voice inflection, speed of speech, stutters, volume, intensity - you name it. How to extract the emotions of either the announcer or of the entire pit.

How useful would this information be? Would it be predictive or is it just descriptive (describing the events taking place)? Not sure, but this concept is something that has been in the back of my mind for a while. I didn’t do any research for this article, so there may already be some investigation into this, so I will take another look at it once I am done going through NLP.

FYI - here are some services that provide Squawk Box functionality over the web:

www.realtimefutures.com
www.los.net
www.xsquawk.com - no longer around…