Author: Level E Research – leveleresearch.com
Investment managers rely on technology to support decision-making, administration, and governance. It is an industry that spends billions annually on computational power and terabytes of data. Sophisticated quantitative funds rely on huge data sets and statistical models for trading, risk management and asset allocation.
Machine learning takes this further, interrogating the data to discover patterns and behaviours continuously learning and adapting without the need for human intervention, removing foibles, heuristics, and biases.
Three trends are emerging where AI us being used to augment or determine investment decisions and processes.
- Share selection and portfolio composition controlled by AI
- ETF’s investing in AI businesses e.g. robotics and automation
- Autonomous funds where the investment lifecycle of front, middle and back office processes is fully enabled by AI technology
Working continuously 365 days a year, machine learning investment should both out-perform traditional asset managers and cost less. This theory is now being tested in the front office
- ACATIS launched two equity funds that invest globally in individual stocks where the selection and portfolio composition process is entirely controlled by artificial intelligence
- ETFMG launched the first managed ETF harnessing the power of IBM Watson to mimic a team of 1,000 research analysts working 24/7
- The Japanese Government Pension Investment Fund commissioned Sony Computer Science Laboratories to investigate how they could use AI to improve performance and better understand alpha generation
Meanwhile, AI fintechs are focused more holistically on developing technologies and solutions across the investment management value chain to improve efficiency and reduce cost
- Investment management – front and middle office activities
- RegTech – risk, compliance and governance
- Administration – custody, accounting, transfer agency, audit and legal services