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Practitioners’ Seminar 2021

The seminar takes place in the Spring of 2021, Tuesdays and Thursdays 7:40pm — 8:55 pm.

Organizer: Lars Tyge Nielsen

In some cases, the speaker will not make copies of the presentation available — to protect intellectual property or comply with company rules.

Registered participants, please join via Zoom in Courseworks.

Schedule of Presentations

Click here for the Schedule of Past Presentations.

Tuesday, January 12, 2021

Speaker: Alexander Fleiss, Rebellion Research
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: Deep Reinforcement Learning and Nowcasting’s Fatal Flaw

Thursday, January 14, 2021

Speaker: Antoine Savine and Brian Norsk Huge, Danske Bank
Antoine Savine is a French mathematician and chief quantitative analyst at Danske Bank in Copenhagen (in-house system of the year 2015 Risk award, excellence in risk management and modeling RiskMinds award 2019). He has held multiple leadership roles in the derivatives industry in the past 25 years, including head of research at BNP-Paribas, and also teaches Volatility, Computational Finance and Machine Learning in Finance at Copenhagen University. Antoine holds a PhD in Mathematics from Copenhagen University and he is the author of the books ‘Modern Computational Finance’ with John Wiley and sons. He published ‘Differential Machine Learning: the shape of things to come’ with Brian Huge in Risk Magazine in October 2020: https://www.risk.net/cutting-edge/banking/7688441/differential-machine-learning-the-shape-of-things-to-come.

Brian Norsk Huge works for Danske Bank, where he has worked since 2001. He is working in the Quant group as Chief Analyst in Copenhagen (in-house system of the year 2015 Risk award, excellence in risk management and modeling RiskMinds award 2019) with focus on FX and equity derivatives. Brian has a Ph.D. in Mathematical Finance from Copenhagen University. The thesis title is “On defaultable claims and credit derivatives”. Brian was awarded Quant of the Year 2012.


Title: Differential Machine Learning: the shape of things to come (Risk, Oct 2020)

Abstract
Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations.Differential ML is a general extension of supervised learning, where ML models are trained on examples of not only inputs and labels but also differentials of labels wrt inputs. It is also applicable in many situations outside finance, where high quality first-order derivatives wrt training inputs are available. Applications in Physics, for example, may leverage differentials known from first principles to learn function approximations more effectively.

In finance, AAD computes pathwise differentials with remarkable efficacy so differential ML algorithms provide extremely effective pricing and risk approximations. We can produce fast analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or regulations like XVA, CCR, FRTB or SIMM-MVA.

Tuesday, January 19, 2021

Speaker: Irene Aldridge, AbleMarkets
Irene Aldridge is a co-author of “Big Data Science in Finance”, (co-authored with Marco Avellaneda, Wiley, 2020), an internationally-recognized quantitative and Big Data Finance researcher, Adjunct Professor at Cornell University and President and Managing Director, Research, of AbleMarkets, a Big Data for Capital Markets company. 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 software engineer in financial services.Aldridge holds a BE in Electrical Engineering from Cooper Union, and MS in Financial Engineering from Columbia University, and an MBA from INSEAD. In addition, Aldridge studied in two PhD programs: Operations Research at Columbia University (ABD) and FInance (ABD). Aldridge is the author of multiple academic papers and several books. Most notable titles include “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading, Flash Crashes” (co-authored with Steve Krawciw, Wiley, 2017), “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (2nd edition, translated into Chinese, Wiley 2013), and “The Quant Investor’s Almanac 2011: A Road Map to Investing” (Wiley, 2010). Her recent academic publications include “Neural Networks in Finance: Design and Performance” (with Marco Avellaneda in the Journal of Financial Data Science, 2019), “Big Data in Portfolio Management” (Journal of Financial Data Science, 2019), “ETFs, High-Frequency Trading and Flash Crashes” (Journal of Portfolio Management, 2016), and “High-Frequency Runs and Flash Crash Predictability” (Journal of Portfolio Management, 2014). Aldridge presently serves on the Editorial Advisory Board for the Journal of Applied Data Science to Finance.

Title: Big Data as a New Paradigm in Finance

Abstract
Big Data shows promise delivering unbiased optimal factorization even in a noisy environment with substantial missing observations. This talk will discuss applications in Finance, including optimal driver identification and future prediction.
Thursday, January 21, 2021

Speaker: Mikhail Smirnov, Columbia University

Title: Leveraged ETFs and Dynamic Portfolio Management of Classic Portfolios.

Abstract
Leveraged ETFs provide a convenient mechanism to dynamically change portfolio exposure and can be successfully used to construct robust portfolios that perform well during equity market drops. Instead of leveraging with borrowing at the portfolio level, we can use a portfolio of leveraged ETFs. We consider several classic balanced stocks/bonds portfolios created with leveraged ETFs but without borrowing money at the portfolio level and show that they present a very attractive risk-adjusted alternative to just stock index and classical stocks/bonds portfolios without leverage. The performance of these types of portfolios first proposed in 2017, in recent years including 2020, was surprisingly robust. A classical portfolio insurance strategy of Black-Jones-Perold can be easily implemented with leveraged ETFs. We consider more complex dynamic portfolio strategies and how they can also be implemented using leveraged ETFs as well as consider a Dynamic Leverage a risk measure extending classical VAR-based risk measures.
Tuesday, January 26, 2021

Speaker: Praveen Kolli

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Thursday, January 28, 2021

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Tuesday, February 2, 2021

Speaker: Natalia Zvereva, JP Morgan Asset Management
Natalia Zvereva, CAIA, is an Executive Director, Investment Risk, at J.P. Morgan Asset Management. Natalia and her team focus on risk management of the Alternative Investment Funds, including Hedge Funds, Private Equity, and Liquid Alternatives. 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. Prior to that, Natalia was a market risk manager in OTC Derivatives Clearing, where she helped to facilitate the launch of the OTC Clearing business. Natalia is an analyst and associates champion for JPM Asset Management Risk. She created a training curriculum and organized global technical training and networking events for analysts, associates, and interns in Asset and Wealth Management. Natalia holds a Master’s Degree in Financial Mathematics from Columbia University (2014), BBA in Finance & Investments from Baruch College (2009), and is a member of Chartered Alternative Investment Analyst Association.

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Thursday, February 4, 2021

Speaker: Graham Giller, Giller Investments (New Jersey), LLC
Graham Giller is one of Wall Street’s original data scientists. Starting his career at Morgan Stanley in the UK, he was an early member of Peter Muller’s famous PDT group and went on to run his own investment firm. He was Bloomberg LP’s original data science hire and set up the data science team in the Global Data division there. He them moved to JP Morgan to take the role of Chief Data Scientist, New Product Development, and was subsequently Head of Data Science Research at JP Morgan and Head of Primary Research at Deutsche Bank.

Title: Financial Data is not Normal

Abstract
The Normal distribution is omnipresent in social sciences and physical sciences due to it’s unique properties as a stable distribution with a finite variance. It is a key part of stochastic calculus and a large amount of research in finance, both empirical and theoretical, is built upon it. However, financial data is not Normal! Examining real data in many markets leads to an unambiguous rejection of the hypothesis of homoskedastic Normal distributions in favour of heteroskedastic and fundamentally leptokurtotic data and the consequences of this for active managers are significant.
Tuesday, February 9, 2021

Speaker: David Fournie

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Thursday, February 11, 2021

Speaker: Samvel P. Gevorkyan, Freepoint Commodities

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Tuesday, February 16, 2021

Speaker: Alberto Botter

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Thursday, February 18, 2021

Speaker: David Skovmand, University of Copenhagen

Title: Roll-Over Risk in Benchmark rates (tentative)

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Tuesday, February 23, 2021

Speaker: Miquel Noguer Alonso

Title: Reinforcement Learning in Finance

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Thursday, February 25, 2021

Speaker: Michael Pykhtin, Federal Reserve Board


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Tuesday, March 2, 2021 — Spring Recess, no seminar
Thursday, March 4, 2021 — Spring Recess, no seminar
Tuesday, March 9, 2021

Speaker: Julien Guyon, Bloomberg L.P. and Columbia University

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Thursday, March 11, 2021

Speaker: Sanne de Boer

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Tuesday, March 16, 2021

Speaker: Samim Ghamami, Financial Services Forum

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Thursday, March 18, 2021

Speaker: Leon Tatevossian

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Tuesday, March 23, 2021

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Thursday, March 25, 2021

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

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Tuesday, March 30, 2021

Speaker: Kelly Ye


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Thursday, April 1, 2021

Speaker: David Lando, Copenhagen Business School

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Tuesday, April 6, 2021

Speaker: Andres Jaime Martinez

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Thursday, April 8, 2021

Speaker: Gordon Ritter, Ritter Alpha, LP, and NYU

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Tuesday, April 13, 2021

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Thursday, April 15, 2021

Speaker: Peter Carr, NYU
Dr. Peter Carr is the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering. He has headed various quant groups in the financial industry for the last 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: Multiplication, Addition and Options

Abstract
Multiplication and addition are commutative and associative binary operations with the former distributing over the latter.We treat optionality as a third binary operation operating between a fixed strike price and the current known price of a risky underlying security.

We show that when all prices are real valued, then demanding commutativity and associativity of the optionality operation implies that the risky underlying security price at maturity must be logistically distributed. In this case, ordinary addition distributes over the optionality operation. We discuss applications and variations to eg non-negative domains.

Past Presentations

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