Bquant Spotlight: Asset allocation - Can neural networks shine a light on higher returns? (AMER) | Bloomberg Professional Services
Webinar

Bquant Spotlight: Asset Allocation – Can Neural Networks Shine a Light on Higher Returns? (AMER)

Last month we constructed a linear machine learning model to allocate to different indices to out perform a 60/40 benchmark portfolio.

This month we'll try to go one step further by adding more data, utilizing more sophisticated libraries such as PyTorch, and leveraging the power of cloud computing. These additions should allow us to better predict the forward returns of our selected indices, and out perform our original linear model.

Do these advanced machine learning libraries live up to their hype? Join us to find out.

Speakers

Aidan Wilmott

Enterprise Data

Bloomberg L.P.

Aidan looks after a team of quantitative specialists spanning Greater China to Australia and New Zealand. The team primarily looks after the roll out of BQL and BQuant to our buyside customers. Prior to moving to Hong Kong, Aidan was responsible for the UK and Ireland team, and brings extensive experience of EMEA markets.

Christian Contino

BQuant Solution Analyst

Bloomberg L.P.

Christian is the BQuant solution analyst for Australia and New Zealand. He has a wealth of experience in this area, having previously worked in a family office as a quant researcher and at the United Nations as a Fixed Income portfolio manager. He holds a PhD in Financial Statistics from the University of Sydney.

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