Getting algos in order

The sheer number of different algorithms in circulation makes selection, performance measurement, and broker feedback difficult. So how are the buy-side opting the right algos? Asks Sarfraz Thind.

By Sarfraz Thind July 12, 2017 10:48 AM GMT

Equity execution can be a tricky business. Countering predatory traders has been a concern for the buy-side ever since the market became electronic some 15 years ago. Participants have devised many ways of stopping the threat but the problem of information leakage and trade disruption has not disappeared. With MiFID II putting greater emphasis on best execution, algorithms need to be in better shape than ever before.

So what can the industry do to improve this side of the business? Buy-siders have certainly ramped up their algorithmic sophistication since rudimentary execution algorithms came into the market in the early 2000s. And there is no shortage of them out there—indeed, according to estimates, there are some 1600 algorithms currently available to buy-siders globally, incorporating anything from volume prediction analytics, market impact models to liquidity heat maps and venue analytics. The sheer numbers of these different algorithms makes selection, performance measurement, and broker feedback difficult. And this has led to some dissatisfaction. In a report published by Greenwich Associates in January just 7% of US buy-side institutions said they were happy with the standard broker algorithms. Furthermore participants say that the variation in the performance of different algorithms remains small.

At present the industry continues with a heavy use of traditional algorithms to handle its liquid equity execution. The likes of VWAP (volume weighted average price) or participation algorithms are particularly prevalent and have, generally speaking, proven adequate to handle large orders on liquid equities. Michel Kurek, head of quantitative – cash execution at Société Générale, says that VWAP accounts for one-third of all global algorithm orders currently undertaken by Societe Generale with participation algorithms second, accounting for 10% of market volume, part of around 10 standard algorithms the bank runs. These will perform standard execution functions well but may still need to be managed according to conditions.

“You need to randomise your algos—you don’t want to give participants the score of your music,” says Kurek. “If you do some counterparty will be able to game your note.”

Forensic analysis

Greater pressure is likely to occur on algorithms when they are tasked with executing trades in less liquid names.

“We rigorously benchmark different algos and analyse them quite forensically—we don’t have any complaints from the ones we use,” says Michael Horan, head of trading services at BNY Mellon Pershing. “You get potential dumb algos more on the less liquid stocks—but these will be difficult for any algo. It is hard for any algo to adjust where liquidity follows an episodic or erratic pattern.”

Algorithm providers have been working on different techniques to frustrate predators. One is algorithmic switching. So-called intelligent switching engines have been used to move between algorithms and choose the best ones depending on market conditions for a decade now. The next generation of switching algorithms are being developed to incorporate artificial intelligence to harness the vast stores of data from historical trading logs and use this for better execution.