A.I. Pitches Bond Traders Against Bots
by Alex White
Senior Partner, Talent & Research 9th Jan 2017
Alex White is the founding partner at Avellio. He is an industry experienced former analyst, and has led over 120 retained search assignments for data science leaders, with mandates entrusted by leading global investment banks, major retailers and analytics consultancies.
Participants in the global capital markets have been experimenting with Artificial Intelligence (AI) for well over a decade. Computer systems have evolved to the point that nanosecond-quick 'super trades' can now be executed. This is a trading style based on ultra high frequency machine trades, with almost zero latency, well beyond the limitations of man, for less cost.
If Artificial Intelligence is the mother of all invention, then Algorithmic trading is the favourite child, at least in the eyes of canny traders. Big data is essentially the driving force behind modern day investment analytics and AI has propelled data engineering to new heights. This has been made possible through the invention of new 'super tools' that can perform deep data analysis and price modelling, producing optimal outcomes with both speculative trades and long term, value investment decisions.
One bank that has positioned itself at the forefront of Artificial Intelligence is ING. They have just revealed a proprietary new tool called 'Katana', due for implementation this year. ING Bank's Global Head of Credit Trading, Santiago Braje was quoted as saying the new tool works to forecast pricing trends while reducing the workloads placed upon research teams. Since implementation in late 2017 the bank has seen trading costs reduce by 25%, with reduced latency, and human participation on trades down to 10%.
ING bank is not alone in the development of AI engineered trading. Similarly both Goldman Sachs has been active with machine driven trading in the corporate bond space with its 'GSA' [Goldman Sachs Algorithm] corporate bond trading programme, which has resulted in a 3x increase in the number of securities that it quotes on. These advanced trading methods enable optimised trading that is both systematic and fully automated, enabling their people to concentrate on bigger, more strategic trading situations, primarily within the more variable and faster based equities markets.
The sheer quantity of market data on trades, combined with the exponential development in methods for efficient predictive analytics have been widely transformative for pioneers amongst the asset management community, especially those with a ready logic and respect for ‘big data’, its provided them with a real competitive edge and in the process has increased returns.
In the future, with the evolution of algorithmic trading it is almost certainly likely that computers will displace people, in the pursuit of increased efficiency, speed, and the pursuit of ever higher returns.
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