Explainable machine learning for investment decisions

Speaker: Aric Whitewood (WilmotML, UK)

Date: 9 October 2017

Time: 14:00 – 15:00

Venue: The Alan Turing Institute

Email: Turing Events to register your place or watch live online.



This presentation will cover the philosophy, approach, and results of WilmotML in the Machine Learning (ML) field, as applied to investment decisions and, in particular, global asset allocation. Broadly speaking, our philosophy is one of using smaller, more focused (parsimonious) data sets, together with prior/expert knowledge, to achieve the maximum benefit with ML. Our approach combines recent research from the topics of human brain development and knowledge abstraction, time series ML techniques, and signal processing. We are of the firm belief that ML is best used, at least in the foreseeable future, in augmenting human skills rather than replacing them. Our machine learning platform is combined with our proprietary macro framework to provide both discretionary and systematic outputs. We are therefore providing investment recommendations as well as systematic strategies, all of which are relatively transparent and explainable. In so doing, we aim to bridge the gap between the discretionary and quantitative sides of the asset management industry.

This talk is suitable for anyone with an interest in machine learning, finance, asset management, investing, signal processing and does not require deep technical understanding.

Biography

Aric Whitewood is co-founder of WilmotML, a machine learning and macroeconomics focused investment and advisory firm. He is also an Honorary Senior Lecturer in the Computer Science Department of University College London (UCL).

At WilmotML, he focuses on the combination of neuroscience, artificial intelligence (A.I.), and investing, with a particular emphasis on developing investment systems which are transparent (enabling trust in investment decisions) and that operate on timescales of several weeks. These ideas will be implemented as a machine learning based fund to be launched later in 2017.

Previous to his current position, he was Head of Data Science in Credit Suisse Zurich, where he ran A.I. projects across a number of businesses and geographic locations. He also served as the Banks subject matter expert in machine learning, regularly presenting to both the Banks management as well as its major clients.

Aric holds a PhD in Electronic Engineering from UCL (2006).