FX: Machine learning use grows, but lags in HFT
Machine learning has gained influence in FX in the last year, although many observers doubt whether the technology has completely mastered the demands of high frequency trading.
In a paper publisher earlier this year, Saeed Amen, founder of macro research firm Cuemacro, outlined how machine-readable news from Bloomberg could be used to create systematic FX trading strategies.
Unstructured text data was converted into structured data, which was then aggregated into sentiment indicators for currencies. According to Amen, the news-based FX trading strategy considerably outperformed a generic FX trend-following strategy over a similar period.
Bank of America Merrill Lynch recently made its first foray into FX research based on machine learning, using a combination of supervised and unsupervised learning (the latter providing no guidance to the algorithms on how to process the information) to analyse fundamental and survey data around EUR/USD.
Celent senior analyst, Josephine de Chazournes, refers to increased use of machine learning for tasks such as routing logic across disparate venues, analysis of technology and connectivity infrastructure and various analytics around risk and P&L as well as client and counterparty analysis.
According to Yaron Golgher, co-founder and CEO of algorithmic forecasting solutions provider I Know First, demand for his firm’s artificial intelligence-based FX predictive models has increased threefold over the past 12 months.