Data Science

Webinar: Deep Learning for Sequences in Quantitative Finance

Two Sigma’s David Kriegman explains how researchers apply deep learning to sequences in quantitative investing.

Quantitative investing seems mystifying to many–so much so that observers often refer to the investment process itself as a “black box.” Moreover, quantitative investing continues to grow in complexity, and today Two Sigma and others often employ powerful deep learning techniques in various parts of the process to make decisions informed by vast pools of data.

Even so, the process isn’t necessarily as mysterious as it’s sometimes made out to be. Two Sigma’s David Kriegman–who’s also a professor of computer science and engineering at the University of California San Diego–recently hosted a webinar to shed light on how Two Sigma researchers apply deep learning to sequences in quantitative investing.

As Kriegman explains, the process naturally decomposes into steps of feature extraction, forecasting the returns of individual instruments, portfolio allocation to decide quantities to trade, and trading execution. Many of the steps in this process are readily expressed as machine learning problems that can be addressed using deep learning sequence methods.

Curious how deep learning fits into the quantitative investment process? Watch the webinar here:

This article is not an endorsement by Two Sigma of the papers discussed, their viewpoints or the companies discussed. The views expressed above reflect those of the authors and are not necessarily the views of Two Sigma Investments, LP or any of its affiliates (collectively, “Two Sigma”). The information presented above is only for informational and educational purposes and is not an offer to sell or the solicitation of an offer to buy any securities or other instruments. Additionally, the above information is not intended to provide, and should not be relied upon for investment, accounting, legal or tax advice. Two Sigma makes no representations, express or implied, regarding the accuracy or completeness of this information, and the reader accepts all risks in relying on the above information for any purpose whatsoever. Click here for other important disclaimers and disclosures.