It’s hard to get folks who work at HFT firms to open up and talk about what they do, well … perhaps except for Manoj Narang, who quite frequently opens up and talks about HFT, as he did opposite Joe and I Friday morning on CNBC.
This morning please read this piece from The Guardian, where a software salesman who sells product to high frequency trading firms, talks about his secretive customers. The software that his company sells “analyses the news and interprets its impact on market sentiment – within 15 milliseconds.”
Some of this unnamed salesman’s insight:
“Our clients are programmers and engineers who build the algorithms used by HFT computers.
“How does it work? While the markets are open there is a constant stream of events in the real world impacting those markets; from announcements by companies (takeover completed, quarterly profits lower than expected) and agencies (new drug approved by Food and Drug Administration, merger approved by Antitrust authority), to macro economic events and unexpected things like earthquakes, coup d’etats or exploding pipelines.
“All these come to us through reports by wire agencies: Reuters, Bloomberg, Dow Jones. What our software does is recognise the language in those reports and make them machine-readable. It identifies the company involved, and picks up terms such as “increase”, “profits”, “warn”. Ultimately the software converts these into a one-sentence line that appears in your inbox – say, “British Airways profits up” or “BP production down”. It adds a scale of minus five to plus five (red to green), depending on how positive or negative the event is for the company involved.
Whoever gets there first can make millions and millions.
“High-frequency trading is incredibly expensive in the sense that the hardware is very costly, and so is the software. Only a limited number of players can muster those funds. Basically a cartel has cornered the market. If you are rich enough to get a piece of the action, then great for you. If not, well, there it is.
Apart from news wires, we are also analysing social media, as these can be important for market sentiment, too. By now I’d say we are getting that 98% right. You don’t need a literally perfect translation, with a one-on-one correspondence between the languages. You need to get the central message, the ‘product sentiment’. The chances of speculators manipulating social media for their own purposes? Well, this is not our concern, all we do is extract whatever is being said and make it machine-readable.
In high-frequency trading your computer buys a share as its price is moving, then sells it immediately on to the next buyer, all within that same price movement. The algorithm is a middle man, exploiting a tiny advantage that comes from being there faster. I suppose high-frequency trading has no value to society. That’s not our concern. At the end of the day it’s a buy-sell market.
The next frontier is undoubtedly machine readable news trading. Firms already do it. In fact NASDAQ bought a firm that provides this data, so that NASDAQ can sell it to their largest customers: high frequency traders.
Sigh. If NASDAQ only spent as much technological resource that they spend on machine readable news data on their IPO algorithm…