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Cornell – Citi Financial Data Science Webinars

October 26, 2021 @ 5:00 pm - 6:00 pm UTC+0

Cornell Engineering. Operations Research and Information Engineering. Financial Engineering Manhattan

Featuring Machine Learning experts from Cornell, Citi, and more…

You and your colleagues are invited to attend the Cornell – Citi Financial Data Science Webinars. Through the online talks in Fall 2021, we are excited to collaborate with Citi in highlighting machine learning applications in finance.

All webinars are from 5:00 pm to 6:00 pm ET.

This webinar is free and open to all guests. Registration is required (Please RSVP here). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from
no-reply@zoom.us)

Date: Tuesday, October 26th, 2021
Time: 5:00pm – 6:00pm ET
Speaker: Zihao Zhang | Oxford-Man Institute
Title: Deep Learning for Market by Order Data

Abstract:  Market by order (MBO) data – a detailed feed of individual trade instructions for a given stock on an exchange – is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots and expanding the universe of alpha discovery. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy – indicating that MBO data is additive to LOB-based features.

Speaker Bio: Dr. Zihao Zhang is a postdoctoral researcher at the Oxford-Man Institute and Machine Learning Research Group at the University of Oxford. Zihao’s research focuses on quantitative finance with a special emphasis on applying deep learning models to financial time series modelling. Zihao’s current projects include portfolio optimization and reinforcement learning. Zihao holds a Ph.D. and MSc in Applied Statistics from the University of Oxford and a BSc in Economics and Statistics from University College London.

We hope to see you online!

The Cornell-Citi Team

**Please excuse any duplication of this announcement


If you are interested in our past seminars, you are welcome to subscribe to our YouTube Channel and watch our videos!

Past Events

Oct. 1-3, 2021

5th Eastern Conference on Mathematical Finance (ECMF)

Oct. 5th, 2021

Speaker: Alok Dutt (Citigroup)
Title of Presentation: The Top Challenges for a Financial Data Scientist (And How to Overcome Them)

Upcoming CFEM Events

Nov. 16th, 2021
Speaker: Laura Leal (Princeton)
Title of Presentation: TBD

Details

Date:
October 26, 2021
Time:
5:00 pm - 6:00 pm UTC+0

Organizer

Cornell-Citi Financial Data Science Seminars

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Collaborative events organized by Bloomberg LP, Global Risk Institute, Cornell Financial Engineering Manhattan, International Association of Quantitative Finance (IAQF), NYU Courant Institute of Mathematical Sciences, and NYU Tandon School of Engineering.