Algos: Human After All?
“There is a continuing trend towards algorithm-enhanced and fully automated trading,” says Kahler. “This might not be because algorithms are a better way to make money, but they are easier to handle than real trades. You can get one expert and a bunch of programmers and replace a whole trading desk or even a department full of high-paid traders. Letting machines do the work is always driven by cost-cutting and efficiency. Now, we are carried by highly sophisticated machines from one city to the next; a hundred years ago, specially trained horses would have done the same job.”
Lifespan
The vast range of algorithms available makes it inappropriate to discuss the typical lifespan of an algorithm, as such, but Harrell Smith, head of product strategy at Portware, says an algorithm’s longevity depends in part upon which asset class it serves.
“The lifespan of an algorithm is contingent on a number of factors,” he says. “Proprietary trading strategies tend to have much shorter lifespans, while standard execution algorithms tend to be more resilient. That is not to say that execution algos are static; brokers are continually tweaking and updating their algorithms to deal with market structure changes, shifting trading and volumes patterns and other factors. An algorithm’s lifespan can certainly be tied to the underlying asset class. Certain market structures are evolving more quickly than others, while the maturity of an asset class from an electronic trading perspective differs as well.”
The determining factor of how long an algorithm will last rests ultimately with performance, according to Intalus’ Saunders. “A good algorithm should work across asset classes for many years and need only minor tweaking during that time,” he says.
Object Trading’s Turner says that, first and foremost, it is performance that drives an algorithm’s longevity, but adds that only the simple, vanilla algos have remained unchanged since day one. For Turner, an algorithm’s ability to evolve has become a differentiating factor among sell-side organizations.
“Algorithms must always react to market conditions,” he says. “We’ve seen over the last two or three years a number of algos struggling because they are based on a pre-determined understanding of when news is going to come out. The algos know how to perform, but with market volatility and events such as the Arab Spring, when news comes out unexpectedly, algos have to be tuned and adjusted to be able to react to unexpected news in the market as well.”
Black Boxes
Much has been written about regulatory efforts to restrict the practice of algorithmic trading, with a particular nod toward high-frequency traders and black boxes, but Intalus’ Saunders says regulators would be best advised to keep the status quo.
“It’s all just meddling and it will bite somebody on the bum because somebody else will find another method to do something else,” he says. “Ultimately, it will be the clients with the money who lose out, and then the wider economy. This move to curtail black box trading—does anybody think it’s a good thing? Ultimately, if someone’s got money and they invest it stupidly, it’s buyer beware. If something’s too good to be true, it often is.”
ConvergEx’s Capuzzi takes a different stance, saying that applying more controls to the practice of algorithmic trading will be largely beneficial to the industry. “There have been a series of issues during the past few months in terms of rogue trading, and I think the broker community is going to have to provide more controls around making sure algorithms don’t run off the rails and either trade stocks they shouldn’t be trading or trade them in a way that wasn’t designed by the algorithm,” he says. “It’s going to add a bit more control around how algorithms operate, and largely that’s a good thing. I don’t think it’s necessarily going to impact performance or liquidity access; it’s just going to provide another layer to ensure algorithms are trading properly, looking at the right market data and slicing out orders in the right fashion.”
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