14.15 – PANEL DISCUSSION: Deep Learning vs Reinforcement Learning vs Causality in Finance
Moderator: Dr. Michael Recce, Founder Alpha ROC, Fmr Head of Data Science Point72, Neuberger Berman & GIC
Professor Matthew Dixon, Quant of The Year 2021, Finance Professor Sudip Gupta, Johns Hopkins University, Gordon Ritter, Quant of the Year 2019, Adjunct Professor, Cornell Financial Engineering Manhattan, Adjunct Professor, Baruch College, NYU Courant Institute, Trevor Mottl, Managing Director Lazard Asset Management
Cornell Financial Engineering Manhattan 2022 Future of Finance Conference Agenda
Gordon Ritter completed his PhD in mathematical physics at Harvard University in 2007. His publications while at Harvard were in quantum field theory, differential geometry, quantum computation and abstract algebra, including a well-known simplicity theorem for Kac-Moody groups and a mathematically rigorous treatment of Euclidean QFT on Riemannian manifolds. Prior to Harvard he earned his Bachelor’s degree with honors in Mathematics from the University of Chicago.
Dr. Ritter currently teaches at Columbia, NYU, University of Chicago and the Baruch MFE program. His academic research is on portfolio optimization and statistical machine learning; his finance publications can be found in journals including Risk, the Journal of Portfolio Management. Journal of Financial Data Science, European Journal of Operations Research, and others.
He was named Buy-Side Quant of the Year in 2019!
(Researchers awarded the same honor in other years included Marcos Lopez de Prado, Alex Lipton, and Jean-Phillipe Bouchaud). In parallel with his teaching responsibilities, Dr. Ritter works full time in the industry. In 2019 he founded Ritter Alpha LP. A registered investment adviser running systematic absolute-return trading strategies across multiple asset classes and geographies, based on cutting-edge technology and rigorous applications of statistics and the scientific method to investment problems.
Prior to founding Ritter Alpha, he built a successful trading system from scratch at GSA Capital, a firm which won the Equity Market Neutral & Quantitative Strategies category at the Eurohedge awards four times. Prior to GSA, Dr. Ritter was a Vice President of Highbridge Capital Management (HCM) and a core member of the HCM statistical arbitrage group. Moreover, a small team sharing full discretion over systematic trading models that generated billions in profit for investors and directed trillions of dollars of trades across global equities, futures and options. Several senior members of the HCM statistical arbitrage group later joined Gordon at Ritter Alpha. Lastly, in his spare time Gordon enjoys scuba diving in Hawaii. In addition, is passionate about preservation of Native Hawaiian culture and marine ecosystems.
Full Conference Agenda
Professor Sudip Gupta is an associate professor of finance at Johns Hopkins Carey Business School.
His current research and teaching interests are in the areas of Auctions, Big Data-Machine Learning, Corporate Finance, ESG and Fintech. He is an award-winning teacher, and his research has appeared in top academic journals. He has written papers in the areas of credit derivatives, treasury auctions, IPOs, nowcasting with alternative data, alternative credit rating, ESG ratings and portfolio formation with alternative data etc.
Prof Gupta is a data hackathon champion and consults various multinational financial corporations and government committees. Moreover, his consulting areas and expertise include auctions, credit derivatives, ESG, financial class action litigations, fintech, machine learning and textual analysis, merger simulations etc.
Furthermore, he has served as an expert in multiple high profile financial class action litigations.
Prior to joining Carey, Prof Gupta was a faculty and director of the top ranked MSQF program of the Gabelli School of Business (GSB), Fordham University. Where he introduced and taught big data-machine learning in finance into the MS curriculum.
Previously, Dr. Gupta was a full-time faculty and taught at Indiana University’s Kelley School of Business, Indian School of Business (ISB), New York University’s Stern School of Business, and the University of Maryland’s Smith School of Business, in the areas of corporate finance, econometrics, fintech, investments, and machine learning at both undergraduate and graduate levels. Lastly, he was the recipient of the Dean’s Impact award for faculty excellence and high impact in research, teaching, and service at GSB. He has a PhD in economics from the University of Wisconsin, Madison.
Michael Recce previously served as Chief Data Scientist at Neuberger Berman and prior he was Chief Data Scientist at both GIC, the Singapore Sovereign Wealth Fund, and Point72 Asset Management.
Before transitioning into the investment industry, he was a university professor for over twenty years, and helped set up and manage two successful start-up companies. Recce holds a PhD in Neuroscience from University College London and a BS in Physics from the University of California in Santa Cruz.
Matthew Dixon is a British applied mathematician working in the area of algorithmic finance.
His research focuses on applying concepts in computational and applied mathematics to financial modeling. Especially in the area of algorithmic trading and derivatives. Matthew’s research is currently funded by Intel Corporation and he develops codes for high performance architectures. His work in deep learning with Diego Klabjan (NWU) has brought wide recognition. And he is a frequently invited speaker at quant and fintech events around the world. In addition to becoming referenced as a computational finance expert in multiple reputed media outlets including the Financial Times and Bloomberg Markets.
Trevor Mottl is the Managing Director at Lazard Asset Management.
He runs an artificial intelligence team that uses machine learning to identify patterns in markets that are too complex for the human mind, but which can still generate alpha. He has spent his career making markets and pricing derivatives.
Trevor brings tremendous knowledge in bridging the gap between legacy business processes and disruptive technology. Particularly in enterprise, AI, NLP, and financial applications. Moreover, he also has over 20 years of experience in asset management. Portfolio theory. In addition capital markets at Goldman Sachs, Merrill Lynch, and global hedge funds. Lastly, Trevor has a Bachelor of Science in Biochemistry from Brown University.
