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 online talks in Fall 2021, we are excited to collaborate with Citi in highlighting machine learning applications in finance. Our upcoming talk builds on the neural network modeling topic discussed in the seminar with Zihao Zhang (Oxford-Man Institute), which you can watch online.
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, November 16th, 2021 |
| Time | 5:00pm – 6:00pm ET |
| Speaker | Laura Leal | Princeton University |
| Title | Learning a Functional Control for High-Frequency Finance |
Abstract: We use a deep neural network to generate controllers for optimal trading on high-frequency data. For the first time, a neural network learns the mapping between the preferences of the trader, i.e. risk aversion parameters, and the optimal controls. An important challenge in learning this mapping is that, in intra-day trading, traders’ actions influence price dynamics in closed-loop via the market impact. The issue of scarcity of financial data is solved by transfer learning: the neural network is first trained on trajectories generated thanks to a Monte-Carlo scheme, leading to a good initialization before training on historical trajectories. Moreover, to answer genuine requests of financial regulators on the explainability of machine learning generated controls, we project the obtained “blackbox controls” on the space usually spanned by the closed-form solution of the stylized optimal trading problem, leading to a transparent structure. For more realistic loss functions that have no closed-form solution, we show that the average distance between the generated controls and their explainable version remains small. This opens the door to the acceptance of ML-generated controls by financial regulators.
Speaker Bio: Laura Leal is a final-year Ph.D. student in the Operations Research and Financial Engineering department at Princeton University. Her research interests are centered in high-frequency finance, using machine learning, deep neural networks, optimization, statistical and econometric methods to study high-frequency trading data. The main topics she has worked on include optimal execution, market making, identification of institutional activity, and tail risk.
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)
Oct. 26th, 2021
Speaker: Zihao Zhang
Title of Presentation: Deep Learning for Market by Order Data