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 Spring 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 EST.
This webinar is free and open to all guests. Registration is required (RSVP). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from
no-reply@zoom.us)
Date: Tuesday, March 9th, 2021
Time: 5:00 pm – 6:00 pm EST
Speaker: Bruno Dupire | Bloomberg L.P.
Title: “Some Applications of Machine Learning in Finance”
Abstract
Finance has always tried to make use of all available information to optimize investment decisions. The advent of efficient Machine Learning algorithms, alternative data, and computational powers has deeply impacted many fields in finance. To mention a few, rotation of factors according to market regimes in factor investing, option pricing and hedging, anomaly detection, covariance matrix cleaning, transaction cost analysis. Alternative data include texts from news and tweets, supply chain data, satellite images, vessel routes, weather data, credit card transactions, geolocation data.
This overflow of information opens the door to endless number crunching and apophenia. The desperate search for a signal leads to overfitting and unstable relationships, so beware. As I like to say, the market is a machine made to destroy the signal!
Program Agenda:
- Bruno Dupire’s Presentation
- Q&A
- “Lightning Talk” – Yumeng Ding
- Discussion
Speaker Bio
Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets, and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk Magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting Edge Research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.
After a Master’s degree in Artificial Intelligence in 1982 and a Ph.D. in Numerical Analysis in 1985, he has conducted in 1987-88 a study to apply Neural Nets to time series forecasting for Caisse des Dépôts et Consignations. He has been applying Machine Learning to a variety of problems in Finance and has given many lectures on the topic in the Americas, Europe, and Asia over the past few years.
“Lightning Talk” Info:
CFEM alumna Yumeng Ding will discuss her team capstone project, which was titled, “Interpreting Machine Learning Models.” By utilizing Machine Learning interpretability models, the Cornell CFEM team, sponsored by Alliance Bernstein, explored how black-box models can be explained and evaluated in finance. The team analyzed S&P 500 constituents and explored the interpretability of some widely-used ML modes.
Yumeng Ding (MFE Cornell ’20, BA Finance Fudan University‘15) is a soon-to-be analyst in Strategic and Analytics at Deutsche Bank.
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 CFEM Events
February 16th, 2021
Speaker: Charles-Albert Lehalle (Capital Fund Management)
Title of Presentation: “An Attempt to Understand Natural Language Processing and Illustration on a Financial Dataset”
Upcoming CFEM Events
April 13th, 2021
Speaker: Peter Carr (NYU)
Title of Presentation: Adding Optionality
May 11th, 2021
Speaker: Raja Velu (Syracuse University)
Title of Presentation: TBD