We are a boutique asset management firm, specialising in macro and machine learning. The two founders previously worked in Credit Suisse as the Chief Strategist and Head of Data Science, respectively. Our firms philosophy on AI is that human and machine combined is more powerful than machine on its own, and providing both transparency and trust in AI systems is essential in the financial services domain. This presentation details our research on AI, including the design and development of our prediction engine, called GAIA (the Global AI Allocator), which has been running in production since January 2018. Some specific topics include: how to combine fundamental and quantitative skills effectively (a challenge for many incumbent firms), how to approach the problem of generating meaningful predictions with highly noisy, non-stationary data sets (financial time series), transparency and ‘explainability’ in the context of investment management (why this is important from an investor and regulatory perspective, and how we achieve it), and the use of heterogeneous compute architectures, specifically GPUs and FPGAs. In addition, the topic of creating macro relevant indicators from alternative data (newspapers and social media) will also be covered, such as real time unemployment and geopolitical risk indicators.
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), for which he runs several research programs with UCL students on machine learning topics.
He focuses on the combination of neuroscience, artificial intelligence (A.I.), and investing, with a particular emphasis on developing investment systems that are transparent (enabling trust in investment decisions) and that operate on longer timescales than has historically been the case with algorithmic systems (typically months).
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.
He holds a PhD in Electronic Engineering from UCL (2006).
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