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Practitioners’ Seminar 2020

The seminar takes place in the Spring of 2020, Tuesdays and Thursdays 7:40pm — 8:55 pm.

Location: 312 Mathematics Building. For directions please see Directions to Campus and Morningside Campus Map

Organizer: Lars Tyge Nielsen

Click here for the Schedule of Presentations.

Click here for the Schedule of Past Presentations.

Rules

  • The speaker often will not make copies of the presentation available — to protect intellectual property or comply with company rules.
  • No photos or video of the speaker or presentation allowed except with express permission.
  • Only to document the attendance of MAFN students, the audience may be photographed at the beginning of the seminar, and sign-up sheets may be circulated


 

Schedule of Presentations

Click here for the Schedule of Past Presentations.

Tuesday, January 21, 2020

Speaker: Alexander Fleiss, RebellionResearch.com
RebellionResearch.com’s CEO Alexander Fleiss has spoken about Artificial Intelligence Investing in the Wall Street Journal, New York Times, Fox News, BusinessWeek, Bloomberg News, MIT Technology Review, Yomiuri Shimbun, Wired, Geo Magazine, The Economist and Institutional Investor. Chapter 24 of Wall Street Journal Reporter Scott Patterson’s book Dark Pools is on Mr. Fleiss. Mr. Fleiss teaches at Cornell Financial Engineering, has guest taught at Amherst College for over a decade and Yale School of Management for 4 years. Prior to co-founding RebellionResearch.com, Mr. Fleiss was a Principal at KMF Partners LP, a long-short US equity hedge fund co-managed by John Merriwether of Michael Lewis’ Liars Poker. Mr. Fleiss began his career managing an Amherst College-funded Ai research project, then as an analyst & programmer for Sloate, Weisman, Murray & Co which was acquired by Neuberger Berman. Mr. Fleiss developed investment algorithms with the firm’s CEO, Laura Sloate who is now a partner at Neuberger Berman and one of the investors featured in Peter Tanous’ book Investment Gurus. Mr. Fleiss received a BA Degree from Amherst College

Title: AI Investing: Using Artificial Intelligence as an Investment Strategy

Abstract
Our Machine Learning technology allows us to process an extremely diverse set of information, basing its analysis on many hand-selected, macro, fundamental and technical factors, which correlate with more traditional factors like growth, value, momentum, etc. The A.I. uses its performance predictions along with knowledge of the volatility and interrelationships among stocks to create a portfolio that balances risk and expected return. Bayesian statistics serves as the backbone of our artificial intelligence-based investment software. It provides a flexible framework that enables us to automatically integrate the new data available each day with prior market knowledge in order to predict stock performance. The A.I. analyzes information about how each factor relates to stock performance to create an estimated probability distribution of potential returns for each stock. The A.I. analyzes fundamental and macro data from around the globe that is updated and incorporated into our historical database daily.
Thursday, January 23, 2020

Speaker: David Abergel, FGC
David Abergel is a graduate of the MAFN 2011 class. Since then, he founded a social network startup, and has been an equity derivative broker for 4 and half years. Currently at FGC securities, where he specialized in delta 1 products such as swaps and rev/cons.

Title: Institutional Brokerage in a world of electronic trading, instant and near perfect information

Abstract
Institutional brokerage. A short history of brokerage in the USA. The electronic revolution, and its impact on brokerage. Current state of institutional brokerage. The future of brokerage. Importance of knowing how to sell. Selling as a Quant or a Trader.
Tuesday, January 28, 2020

Speaker: Peter Carr, NYU Tandon School of Engingeering, Finance and Risk Engineering
Dr. Peter Carr has been the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering for the last 3.5 years. Prior to that, he headed various quant groups in the financial industry for twenty years. He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Prior to joining the financial industry, Dr. Carr was a finance professor for 8 years at Cornell University, after obtaining his Ph.D. from UCLA in 1989. He has over 85 publications in academic and industry-oriented journals and serves as an associate editor for 8 journals related to mathematical finance. He was selected as Quant of the Year by Risk Magazine in 2003 and Financial Engineer of the Year by IAQF/Sungard in 2010. From 2011 to 2014, Dr. Carr was included in Institutional Investor’s Tech 50, an annual listing of the 50 most influential people in financial technology.

Title: Adding Optionality

Abstract
We present a radically simplified way to think about both European and Bermudan style optionality, based on applying ideas from non-classical arithmetic. The approach leads to closed form formulas for both types of options which are simple elementary functions of the inputs. The underlying dynamics lie closer to market reality than the benchmark models based on normality.
Thursday, January 30, 2020

Speaker: Jonathan Schachter, Independent Consultant at Nataxis
Jonathan Schachter is an independent consultant at Natixis (Group BPCE), the second largest French bank. He specializes in vetting models in the Federal Reserve’s framework, SR 11-7. His current emphasis is transitioning from USD LIBOR to SOFR.

Dr. Schachter has worked in finance since 2000 at banks, a derivative software company, and a big four consulting firm. He is a 2002 graduate of the MAFN program. Prior to finance, Dr. Schachter was a staff scientist in the Department of Astronomy at Harvard (1990-2000) and part of the team to launch the Chandra X-ray Observatory on the Space Shuttle. He holds a PhD in physics from Berkeley, and a BA in physics from Princeton. He is a native Manhattanite, currently residing in Brooklyn. He has a son who is a freshman at Beloit College in Wisconsin, a middle-school student son crazy about math, and a cuddly cat.


Title: Bicurve Models for LatAm Trading

Abstract
Traded Latin-American financial products often are collateralized in USD, rather than in the local currency. A smaller number use EUR, while others are uncollateralized.

To value complex instruments, we need to model the currency of the collateral in such a way that liquid market instruments are correctly priced (“bicurve” models). I will present two separate approaches currently in use, which theoretically should give the same result. One is based on interest-rate parity, and the other on a calibration procedure.

I will then evaluate the performance of the models to create a framework for ongoing monitoring. The work is an outgrowth of digital transformation, replacing legacy Excel spreadsheets used by traders with more robust C# code. But it also provides a window into model risk generally. This has been a concern of the Federal Reserve since 2000, and rose in stature after the financial crisis.

Tuesday, February 4, 2020

Speaker: Leon Xin, JP Morgan Asset and Wealth Management.
Leon Xin is the Head of Risk and Portfolio Construction and Hedge Fund Strategist for the CIO team of the Endowments and Foundations Group at JP Morgan. Mr. Xin joined J.P. Morgan in 2016 and has 13 years of investment industry experience. Prior to J.P. Morgan, Mr. Xin worked for over 10 years as the Head of Alternative Investment Risk at UBS Asset Management, where he covered UBS O’Connor, an internal multi-strategy hedge fund. Prior to UBS, Mr. Xin worked as an associate in Ping An Insurance of China for two years on strategic planning projects. Mr. Xin receives a M.S. degree on Applied Math from the University of Illinois at Chicago and is a CFA charter holder.

Title: A Pragmatic Way of Portfolio Optimization — Expected Returns with Leverage Constraints and Target Return

Abstract
Classic mean-variance optimization is very sensitive to expected returns. An alternative and more robust approach is to calculate the implied returns given the current portfolio allocation and risk profile. Managers can then do a reality check on the implied returns and find opportunities for better allocations. The most common implied return calculation assumes normal distribution and unlimited leverage, and use volatility as risk measure and covariance matrix as model input. However, practitioners usually have leverage constraints, often use non-parametric risk models, and care about portfolio tail risk. This paper presents a new approach to calculate expected returns with leverage constraints. This approach is flexible enough to alleviate normal distribution assumption, connect with non-parametric risk models, and use tail risk measures, such as conditional VaR.
Thursday, February 6, 2020

Speaker: Graham Giller, Giller Investments (New Jersey), LLC
Graham is Chief Executive Officer at Giller Investments (New Jersey), LLC. He has a doctorate from Oxford University in Experimental Elementary Particle Physics, where his field of research was statistical cosmic ray astronomy which featured large scale computer based data analytics. He joined Morgan Stanley in London in 1994 and was an early member of the now famous Process Driven Trading group run by Peter Muller (now “PDT Partners”). Subsequent to Morgan Stanley, he ran a small “friends and family” investment fund that specialized in systematic trading of financial futures. He was recruited to Bloomberg LP to run the Data Science within Bloomberg’s Global Data division and joined JP Morgan as Chief Data Scientist, New Product Development, in 2015, ultimately becoming Head of Data Science Research. He joined Deutsche Bank’s new Data Innovation Group (“dbDIG”) in March, 2018. In July 2019 he created a new venture to provide clients with innovative quantitative and primary research for clients.

Title: Trading from Predictive Models of Macroeconomic Data – Machine Learning Meets Survey Research

Abstract
The expectations of consumers are a significant predictor of market returns. This talk will demonstrate how consumer expectations can be measured and processed to produce a time-series that is predictive of major market returns and how this can be used as the input to a trading strategy.
Tuesday, February 11, 2020

Speaker: Mikhail Smirnov, Columbia University

Title: Dynamic Portfolio Management and Market Anomalies
We discuss some known market anomalies and their utilization through dynamic risk allocation. We will introduce the notion of Dynamic Leverage as a VAR extending risk measure taking into account the investment time horizon. We introduce a modification of Black-Jones-Perold portfolio insurance. For an investment fund with dynamically controlled risk exposure and certain risk inertia, we demonstrate the existence of a critical NAV level below which the efficacy of de-leveraging is compromised.

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Thursday, February 13, 2020

Speaker: Ilya Zhokhov

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Tuesday, February 18, 2020

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Thursday, February 20, 2020

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Tuesday, February 25, 2020

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Thursday, February 27, 2020

Speaker: Ilya Zhokhov (Tentative)

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Tuesday, March 3, 2020

Speaker: Richard Rothenberg, Global AI

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Thursday, March 5, 2020

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Tuesday, March 10, 2020

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Thursday, March 12, 2020

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Tuesday, March 17, 2020 — Spring Recess, no seminar
Thursday, March 19, 2020 — Spring Recess, no seminar
Tuesday, March 24, 2020

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Thursday, March 26, 2020

Speaker: Rosanna Pezzo-Brizio, New York Life Investment Management and Columbia University

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Tuesday, March 31, 2020

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Thursday, April 2, 2020

Speaker: Natalia Zvereva, JP Morgan Asset Management
Natalia Zvereva is a Vice President in the Investment Risk team at JP Morgan Asset Management. Natalia and her team focus on risk management and oversight of the Liquid Alternatives Funds, Hedge Funds, and Beta strategies, including ETF business. Natalia has been working in risk management since 2009, and held a number of roles in JP Morgan within market risk and credit risk space. Prior to her current position, she covered counterparty risk, credit and funding pricing of cross-asset derivatives portfolios across Rates, FX, Equities, Credit and Commodities. Prior to that, Natalia was a market risk manager in OTC Derivatives Clearing for 4 years, where she helped to facilitate the launch of the OTC Clearing business. Before joining JP Morgan is 2011, Natalia was a market risk manager at MF Global. Natalia is an analyst and associates champion for JPM Asset Management Risk in North America. She created a training curriculum and organized global technical training and networking events for analysts, associates, and interns in Asset and Wealth Management throughout the year. Natalia holds a Master’s Degree in Financial Mathematics from Columbia University (2014) and BBA in Finance & Investments from Baruch College (2009).

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Tuesday, April 7, 2020

Speaker: Petter Kolm, NYU

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Thursday, April 9, 2020

Speaker: Asset Tarabayev

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Tuesday, April 14, 2020

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Thursday, April 16, 2020

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Tuesday, April 21, 2020

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Thursday, April 23, 2020

Speaker: Karen Pham Van, Davidson Kempner Capital Management

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Tuesday, April 28, 2020

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Thursday, April 30, 2020

Speaker: Kelly Ye, Index IQ

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Past Presentations

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