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Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

The use of recommender systems in the chief investment office: A case study

Gaurav Chakravorty (qplum)
16:00–16:40 Wednesday, 10 October 2018
Implementing AI
 Location: Windsor Suite
Secondary topics:  Deep Learning models, Financial Services, Temporal data and time-series
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Who is this presentation for?

  • Data scientists, quant traders, portfolio managers, machine learning engineers, and developers

Prerequisite knowledge

  • A basic understanding of data science, machine learning, neural networks, matrix algorithms, and deep learning

What you'll learn

  • Understand how to use deep learning to determine ideal asset allocation

Description

Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today.

Gaurav begins by diving into chief investment offices, which are growing their in-house machine learning teams to fine-tune their allocation, using both traditional and alternative strategies. Gaurav shares a novel approach to deciding asset and strategy allocations, inspired by research in recommender systems.

Gaurav then explores the application of deep learning in trading, discussing useful techniques for AI-driven asset managers as well as the blind alleys they’ve gone down. With these cases as context, Gaurav addresses some of the technical and operational aspects of AI, such as key bottlenecks in training and inference, the software frameworks and hardware platforms that are most useful for those workloads, deployments, the scaling challenges, and the key drivers of the cost.

Photo of Gaurav Chakravorty

Gaurav Chakravorty

qplum

Gaurav Chakravorty is the cofounder and head of strategy development at qplum, a digital wealth management firm that specializes in family offices and HNWI. Qplum also works with endowment funds and pension funds to provide outsourced asset management services that integrate with the company’s in-house technology. Gaurav has been one of the early pioneers in machine learning-based high-frequency trading. He built the most profitable algo-trading group at Tower Research and was the youngest partner in the firm. Gaurav has been a guest speaker on a few popular podcasts.