Recent announcements from some of the largest banks show artificial intelligence (AI) working its way further into financial markets.
Credit Suisse has announced it is to deploy 150 new ‘robots’ over the course of the year, with an overall aim of cutting CHF 4.8 billion (GBP 3.7 billion).
UBS has unveiled a new AI system which uses machine learning to develop strategies for trading volatility on behalf of clients. The bank claims that this is the first ‘adaptive strategy’ product offered by an investment bank.
J.P. Morgan is developing a machine learning technology called LOXM which aims to improve execution quality in the bank’s European equities business. As the buy-side increasingly focuses on execution quality, this is driving ever greater adoption of algorithmic trading across asset classes. LOXM is programmed to learn from historical trading patterns and tweak its algorithmic strategies accordingly, using a technique J.P. Morgan calls ‘deep reinforcement learning’.
The ability to adapt and learn without human intervention allows LOXM to optimising the execution gains of algo trading.
Mosaic Smart Data is looking at how AI can improve trading across asset classes, taking on the challenge of providing machine learning capabilities to the FICC markets, which have far less standardised data and a greater portion of voice trading.
Mosaic provides both real time and predictive analytics insights for sell-side FICC traders, giving them a view of their market in a way that takes in far more data than a human being is able to comprehend. This augments the human trader’s capabilities and could lead to significant performance gains for sell-side FICC departments.
While initial uses of AI focused on process improvements, it is significant that the technology has reached a level where its insights are now helping to influence trading itself.
Although we are still some way from a fully automated robo-trader, this represents a significant increase in confidence in AI technology.