AI Based Investment Systems
Challenges
Noisy Data
The signal to noise ratio in investment systems is typically low
Non-stationarity Distributions change and so it can be challenging to train models that work well out-of-sample
Small data sets
Even with decades of data, the amount of data available to train models is low compared with other domains e.g. image recognition
Approach
Curated data
We add human expert bias to the system through the selection of appropriate data for a given model
Selection of techniques
Some techniques are more applicable to finance than others, and both multivariate data sets and mixed frequencies are important characteristics
Transparency
Being able to interrogate the system and produce explanations for investors are important considerations. We've designed our AI system to be as explainable as possible
Solution
The Global AI Allocator (GAIA) - our machine learning based prediction engine, which combines unsupervised and supervised learning to create asset predictions
Predictions are made for a time horizon of one week to a few weeks ahead
Diversification in terms of data processing, models, and time horizons
Implementation uses Graphics Processing Units (GPUs) on cloud infrastructure
Example Uses
Asset Predictions
Stock Portfolios
Stock portfolios that depend on indicators or themes e.g. inflation expectations
Custom Indicators
NLP derived indicators such as risk appetite and sentiment based
Systematic Strategies
Long only, long short, and market neutral based strategies
Predictions for global assets together with key drivers / explanations