In the collective mind, the trading floor is often depicted as a chaotic and noisy place where traders shout out orders at the top of their lungs. The reality is much much quieter. Where humans used to shout, bits and bytes now move silently.
The process started when trading shifted from manual, voice-based actions to computers. Then it developed further when automated order routing was added, saving time and resources. Then, automated trading systems rose in popularity, responsible for about 75 percent of today’s market volume.
In recent years, with the advent of cheap computational power, the next step of automation is trading automation driven by AI or machine learning. It is here that popular media propagates fears about the ultimate takeover of machines.
“It feels like some of the buzz and the hype has died down, which is an interesting stage in the lifecycle,” said Tom Doris, CEO of OTAS Technologies, a market analytics and trader intelligence company. Now, he adds, we can get back to looking at the problems that technologies like these are supposed to solve.
A typical trader, explains Doris, has too many orders in his queue. Faced with this, he will start processing them from the top and work his way down to the bottom. He might linger a little bit more on those that are more volatile or require more attention, but it might be hard to spot those among the noise.
“The market is generally pretty boring,” Doris said. “If you have 100 orders, 95 of them are perfectly ordinary. It is all very predictable. Your task as a trader is to find those 5 percent where something unusual is happening.” OTAS’ technology is able to create a predictive model of how a stock should behave, and alerts the trader when unexpected information changes a stock’s behavior. The technology can be plugged directly into an Execution Management System, so that the trader can act on those alerts instantly.
The trend over the last couple of years was towards increased automation, attempting to take humans out of the loop. Now, it’s understood that there is a limit to what a trader can do systematically.
“You want humans looking at situations where there is a human story going on,” Doris explained. For example, when a stock starts to rally because of aggressive buying, only a human with a good understanding of the risk landscape and the company’s story can discern if this is informed flow or a trend that may revert.
There have been several hedge funds priding themselves in the use of AI software to guide their decision making, including Bridgewater Associates, Renaissance Technologies, D.E. Shaw, and Two Sigma. Many more firms describe themselves as “systematic”, meaning they base their decision making on computer models, which might not be driven by AI.
Perhaps the most common approach to AI in investing is the use of natural language processing to be able to make sense of unstructured market data and the use of neural networks to identify patterns, relationships and hidden trends.
AI is seen now more as another tool in the toolbox of traders, rather than a magic bullet, Doris concluded.