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