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

You and your colleagues are invited to attend the UBS & CFEM AI, Data & Analytics Speaker Series. UBS Investment Bank is proud to be partnering with Cornell Financial Engineering Manhattan to provide insights directly from practitioners and academics to the next generation of AI, Data & Analytics talent.

All webinars are from 5:30 pm to 6:30 pm ET.

This seminar is free and open to all. 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).

Abstract: 

Assets on blockchain trade 24×7 with very thin liquidity. This demands new fully automated processes, including Automated Market Making (AMM). We dive into the microstructure of the fully-automated systems, comparing the differences between traditional and modern microstructure implementations.

Speaker Bio:

Irene Aldridge is an internationally-recognized quantitative Finance and AI researcher, Adjunct Professor at Cornell University and Lecturer at Cambridge University (U.K.). In addition, Irene is President and Managing Director, Research, of AbleMarkets, an AI-for-Finance company, as well as President of AbleBlox, a blockchain startup. Prior to AbleMarkets and AbleBlox, she designed and ran high-frequency trading strategies in a $20-million cross-asset portfolio. Still previously, Aldridge was, in reverse order, a quant on a trading floor; in charge of risk quantification of commercial loans; Basel regulation team lead; technology equities researcher; lead systems architect on large integration projects, including web security and trading floor globalization. Aldridge started her career as a software engineer in financial services. Irene is a co-author of “Big Data Science in Finance” (with Marco Avellaneda, Wiley 2021),  “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading, Flash Crashes” (co-authored with Steve Krawciw, Wiley, 2017), and the author of “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (2nd edition, translated into Chinese, Wiley 2013), among other work.

We hope to see you online!

**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

January 24^th, 2023
Speaker: Agostino Capponi (Columbia)
Title of Presentation: Do Private Transaction Pools Mitigate Frontrunning Risk?

February 28^th, 2023
Speaker: Ernest Chan (Predictnow.ai)
Title of Presentation: How to Use Machine Learning for Optimization

 Upcoming Events

April 25^th, 2023
Speaker:Harrison Waldon (UT Austin)
Title of Presentation: TBD

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

You and your colleagues are invited to attend the UBS & CFEM AI, Data & Analytics Speaker Series. UBS Investment Bank is proud to be partnering with Cornell Financial Engineering Manhattan to provide insights directly from practitioners and academics to the next generation of AI, Data & Analytics talent.

All webinars are from 5:30 pm to 6:30 pm ET.

This seminar is free and open to all. 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).

Abstract: 

Conditional Portfolio Optimization is a portfolio optimization technique that adapts to market regimes via machine learning. Traditional portfolio optimization methods take summary statistics of historical constituent returns as input and produce a portfolio that was optimal in the past, but may not be optimal going forward. Machine learning can condition the optimization on a large number of market features and propose a portfolio that is currently optimal. We call this Conditional Portfolio Optimization (CPO). Applications on portfolios in vastly different markets suggest that CPO can outperform traditional optimization methods under varying market regimes.

Speaker Bio:

Ernest Chan (Ernie) is the founder and CEO of Predictnow.ai, a machine learning SaaS. He started his career as a machine learning researcher at IBM’s T.J. Watson Research Center’s Human Language Technologies group, which produced some of the best-known quant fund managers. He later joined Morgan Stanley’s Data Mining and Artificial Intelligence group. He is the founder and non-executive chairman of QTS Capital Management, a quantitative CPO/CTA. He received his Ph.D. in physics from Cornell University and his B.Sc. in physics from the University of Toronto.

We hope to see you online!

**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

January 24^th, 2023
Speaker: Agostino Capponi (Columbia)
Title of Presentation: Do Private Transaction Pools Mitigate Frontrunning Risk?

 Upcoming Events

March 21^st, 2023
Speaker: Irene Aldridge (AbleBlox)
Title of Presentation: TBD

April 25^th, 2023
Speaker: Thaleia Zariphopoulou (UT Austin)
Title of Presentation: TBD

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

You and your colleagues are invited to attend the UBS & CFEM AI, Data & Analytics Speaker Series. UBS Investment Bank is proud to be partnering with Cornell Financial Engineering Manhattan to provide insights directly from practitioners and academics to the next generation of AI, Data & Analytics talent.

All webinars are from 5:30 pm to 6:30 pm ET.

This seminar is free and open to all. 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).

Abstract: 

Blockchain users who submit transactions through private pools are guaranteed pre-trade privacy but face execution risk. We argue that private pools serve the intended purpose of eliminating frontrunning risk, only if such risk is high. Otherwise, some validators may decide to avoid monitoring private pools to preserve rents extracted from frontrunning bots. Private pools intensify the execution arms race for bots, thus decreasing their payoffs and increasing validators’ rents. The private pool option reduces blockspace allocative inefficiencies and raises aggregate welfare.

Speaker Bio:

Agostino Capponi is an Associate Professor in the IEOR Department at Columbia University. His research interests are in financial technology, market microstructure, systemic risk, and economic networks. Agostino’s research has been funded by major agencies such as NSF, DARPA, DOE, IBM, GRI, Ripple, and Ethereum. His research has been recognized with the 2018 NSF CAREER award, and with the inaugural JP Morgan AI Research Faculty award. His research findings have attracted attention from major media outlets, including Bloomberg, Thomson Reuters, Politico, and the Financial Times. Agostino is a fellow of the crypto and blockchain economics research forum, an academic fellow of Alibaba’s Luohan academy, and an external fellow of the FinTech Initiative at Cornell. He serves as an editor of Management Science in the Finance Department, co-editor of Mathematics and Financial Economics, and area editor of Operations Research Letters. Agostino is the former Chair of the SIAG/FME Activity Group and of the INFORMS Finance Section, and the founding director of the Columbia Center for Digital Finance and Technologies.

We hope to see you online!

**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!

 Upcoming Events

February 28th, 2023
Speaker: Ernest Chan (PredictNow.ai)
Title of Presentation: TBD

March 2023 – TBD

April 2023
Speaker: Thaleia Zariphopoulou (UT Austin)
Title of Presentation: TBD

The NYU Tandon Department of Finance and Risk Engineering welcomes Dilip Madan to the Peter Carr Brooklyn Quant Experience (BQE) Seminar Series on
Thursday, December 8th at 6 pm ET on Zoom*.

This will be our final seminar of the Fall 2022 semester. We will return in January 2023 with a brand new list of speakers.
Thank you for attending!

“High Dimensional Markovian Trading of a Single Stock”
Dilip Madan

Attend Virtually >>
Meeting ID: 968 8243 9910
Password: PCBQE128

*NYU Students are highly encouraged to attend in person.
All other non-NYU guests are invited to attend virtually.

The NYU Tandon Department of Finance and Risk Engineering welcomes Bruno Kamdem to the Peter Carr Brooklyn Quant Experience (BQE) Seminar Series on
Thursday, December 1st at 6 pm ET on Zoom*.

“A Reinforcement Learning Mechanism for Trading Wind Power Futures”
Bruno Kamdem

Attend Virtually >>
Meeting ID: 918 5967 7307
Password: PCBQE121

*NYU Students are highly encouraged to attend in person.
All other non-NYU guests are invited to attend virtually.

The NYU Tandon Department of Finance and Risk Engineering welcomes Luyao Zhang to the Peter Carr Brooklyn Quant Experience (BQE) Seminar Series on
Thursday, November 17th at 6 pm ET on Zoom.

“A Systemization of Knowledge (SoK): Blockchain Decentralization and Implications for Tokeneconomy”
Luyao Zhang

Attend Virtually >>
Meeting ID: 920 6370 5213
Password: PCBQE1117

*NYU Students are highly encouraged to attend in person.
All other non-NYU guests are invited to attend virtually.

The NYU Tandon Department of Finance and Risk Engineering welcomes Federico Maglione to the Peter Carr Brooklyn Quant Experience (BQE) Seminar Series on
Thursday, November 3rd at 5 pm ET on Zoom*.

“Compound Option Pricing and the Roll-Geske-Whaley Formula Under the Conjugate-Power Dagum Distribution”
Federico Maglione

Attend Virtually >>
Meeting ID: 994 8852 7239
Password: 937624

*NYU Students are highly encouraged to attend in person.
All other non-NYU guests are invited to attend virtually.

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

You and your colleagues are invited to attend the UBS & CFEM AI, Data & Analytics Speaker Series. UBS Investment Bank is proud to be partnering with Cornell Financial Engineering Manhattan to provide insights directly from practitioners and academics to the next generation of AI, Data & Analytics talent.

All webinars are from 5:30 pm to 6:30 pm ET.

This seminar is free and open to all, but please note that we are restricting the remaining talks to online attendance only. 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).

Abstract: 

Causal regularization solves several practical problems in live trading applications: estimating price impact when alpha is unknown and estimating alpha when price impact is unknown. In addition, causal regularization increases the value of small A/B tests: one draws more robust conclusions from smaller live trading experiments than traditional econometric methods. Requiring less A/B test data, trading teams can run more live trading experiments and improve the performance of more trading algorithms. Using a realistic order simulator, we quantify these benefits for a canonical A/B trading experiment.

Speaker Bio:

Dr. Kevin Webster graduated with a Ph.D. from Princeton University Operations Research and Financial Engineering Department (ORFE). At ORFE, he studied mathematical models applied to high-frequency trading, with a significant emphasis on price impact and market making. He previously worked at Deutsche Bank and Citadel and is currently a visiting assistant professor at Imperial College, London.

Dr. Kevin Webster created and taught a course, ORF 474 High-Frequency Markets: Models and Data Analysis, as a visiting lecturer at Princeton in the 2015 school year. His publications include “The self-financing equation in high frequency markets,” “Information and inventories in high frequency trading,” “A portfolio manager’s guidebook to trade execution,” and “High frequency market making.”

We hope to see you online!

**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

September 13, 2022
Speaker: Ciamac Moallemi (Columbia)
Title of Presentation: Liquidity Provision and Automated Market Making

September 16, 2022
Future of Finance Conference

October 25, 2022
Speaker: Yuyu Fan (Alliance Bernstein)
Title of Presentation: Leveraging Text Mining to Extract Insights from Earnings Call Transcripts

 Upcoming Events

November 29, 2022
Speaker: Chakri Cherukuri (Bloomberg)
Title of Presentation: TBD

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

You and your colleagues are invited to attend the UBS & CFEM AI, Data & Analytics Speaker Series. UBS Investment Bank is proud to be partnering with Cornell Financial Engineering Manhattan to provide insights directly from practitioners and academics to the next generation of AI, Data & Analytics talent.

All webinars are from 5:30 pm to 6:30 pm ET.

This seminar is free and open to all, but please note that we are restricting the remaining talks to online attendance only. 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).

Abstract: 

We apply text-mining techniques in earnings call transcripts to extract meaningful features that capture management and investment community signals. Using a corpus of transcripts of earnings calls for global companies from 2010 to 2021, we create fundamentally driven features spanning document attributes, readability, and sentiment on different sections of the transcripts. We test the efficacy of these features in predicting the future stock returns of companies and find that there are opportunities for investors to use these signals in stock selection. Specifically, we find that readability and sentiment-based techniques can enhance an investor’s ability to differentiate amongst outperformers and underperformers and these results are robust across market capitalization as well as investment universes (US Large Cap, US Small Cap, World ex-US, and Emerging Markets). We also introduce methods to create more robust sentiment features for active and systematic investors. By analyzing the performance patterns of the various call participants, we find evidence that the analyst questions may contain more information than the executive sections. Finally, we observe that sentiment features derived from context-driven deep learning language models like BERT are promising and may have more efficacy than bag-of-words approaches.

Speaker Bio:

Yuyu Fan is a Senior Data Scientist at AllianceBernstein. She leverages statistical, machine learning, and deep learning models to distill insights from financial data. Yuyu leads projects leveraging the latest NLP techniques to generate investment signals for AB’s portfolio management teams using text data. She also engages with AB’s client teams to develop models and actionable insights to improve client engagements and the sales process. Prior to joining the firm in 2018, Yuyu worked at College Board as a psychometrician intern for two years, using machine learning models to monitor test validity, reliability, and security. Yuyu holds a BA in sociology from Zhejiang University (Hangzhou, China), MAs in sociology and psychology from Fordham University, and a Ph.D. in psychometrics and quantitative psychology from Fordham University.

We hope to see you online!

**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

September 13, 2022
Speaker: Ciamac Moallemi (Columbia)
Title of Presentation: Liquidity Provision and Automated Market Making

September 16, 2022
Future of Finance Conference

 Upcoming Events

November 8, 2022
Speaker: Kevin Webster
Title of Presentation: TBD

November 29, 2022
Speaker: Chakri Cherukuri (Bloomberg)
Title of Presentation: TBD

The NYU Tandon Department of Finance and Risk Engineering welcomes Sebastien Bossu to the Peter Carr Brooklyn Quant Experience (BQE) Seminar Series on
Thursday, October 20th at 6 pm ET on Zoom*.

“(In Memoriam) Generalizations of the Carr-Madan Spanning Formula”

Sebastien Bossu

Attend Virtually >>

*NYU Students are highly encouraged to attend in person.
All other non-NYU guests are invited to attend virtually.