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Practitioners’ Seminar 2020

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

Organizer: Lars Tyge Nielsen

The speaker often 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.


Past Presentations

Tuesday, January 21, 2020

Speaker: Alexander Fleiss,’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, 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

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

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

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

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

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

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.
Thursday, February 13, 2020

Speaker: Leon Tatevossian, NYU Courant Institute and NYU Tandon School of Engineering
Leon Tatevossian is an adjunct professor in Mathematics in Finance at NYU Courant and in Finance and Risk Engineering at NYU Tandon. From 2009-16 Leon was a director in Group Risk Management at RBC Capital Markets, LLC, where he covered market risk for securitized products in secondary-trading, origination, and proprietary-trading areas. He has thirty-two years of sell-side experience in the fixed-income markets, including positions as a trader, quantitative strategist, derivatives modeler, and market-risk analyst. (Product background: US Treasury securities, US agency securities, interest-rate derivatives, MBSs, ABSs, and credit derivatives.) Leon graduated from MIT (SB; mathematics) and was a graduate student in mathematics at Brown University.

Title: Market-Risk Coverage of Securitized Products

Securitized products comprise a large and important segment of the fixed-income landscape. The understanding of how these assets perform in different market/economic environments and in relation to other fixed-income domains (such as the rates and corporate-credit sectors) is a continuing area of research on both the sell side and among portfolio managers. The availability, structural flexibility, and pricing of consumer/commercial financing and mortgage funding are deeply connected to the risk/reward decision-making of securitized-product sponsors and investors.

Market-risk coverage of the sector requires the modeling of “collateral outcomes.” These models can be static or mult-scenario in their formulation, and the complexity in this trade-off is one reason why care is needed to properly interpret the derived risk information.

Tuesday, February 18, 2020

Speaker: Ilya Zhokhov, JP Morgan
Ilya Zhokhov is currently Executive Director at Chief Investment Office in JP Morgan focusing on risk management of mortgage servicing rights portfolio. Prior to the current role Ilya spent a number of years at Blackrock and was responsible for managing relationships with banks and financial institutions. His team provided risk management and strategic advisory services to many of the nation’s largest banks and financial institutions.

Title: Introductions to MBS markets and instruments

MBS transform illiquid mortgage loans into liquid marketable securities. Because MBS represents a claim against a pool of mortgages their properties depend on the underlying pool of mortgages and borrowers’ ability to prepay their loans. This embedded prepayment option that borrowers have, makes amount and timing of MBS cash flows uncertain. This translates into unique properties that distinguish MBS from other fixed income securities.
Thursday, February 20, 2020

Speaker: Amal Moussa, Citi and Columb
Amal Moussa is Director, Head of North America Single Stocks Exotic Derivatives and Dispersion Trading at Citi

Title: A Quick Peek into the Equity Vol Market


Tuesday, February 25, 2020

Speaker: Wei Lu, Federal Reserve Bank of New York
Wei Lu is a capital market risk manager and a deputy lead of capital trading team in the Supervision Group at the Federal Reserve Bank of New York. Before joining New York Fed, he had about 10 years of experience in the financial industry with a focus on quantitative risk analytics and management.

Title: Market Risk Regulatory Capital Modeling Framework: A Supervisory Perspective

The presentation will provide an overview of regulatory modeling framework in trading book, which includes historical evolution, advancements since 2008 crisis, key challenges in market risk capture, Basel 2.5 market risk rule, and the latest developments in BCBS Trading Book Fundamental Review and certain implementation perspectives.
Thursday, February 27, 2020

Speaker: Ilya Zhokhov, JP Morgan
Ilya Zhokhov is currently Executive Director at Chief Investment Office in JP Morgan focusing on risk management of mortgage servicing rights portfolio. Prior to the current role Ilya spent a number of years at Blackrock and was responsible for managing relationships with banks and financial institutions. His team provided risk management and strategic advisory services to many of the nation’s largest banks and financial institutions.

Title: Structured MBS and MBS derivatives

Tuesday, March 3, 2020

Speaker: Richard Rothenberg, Global AI Corporation
Richard V. Rothenberg is an Executive Director at Global A.I. Corporation, a Big Data and Artificial Intelligence company that provides quantitative research, data-driven signals and alternative data for Institutional clients, including Hedge Funds and Governments. Previously, Richard worked as a quantitative portfolio manager and researcher at multi-billion dollar hedge funds and global investment banks, including Deutsche Bank, MAN investments and other leading institutions.

Richard holds a bachelor’s degree in Economics and Computational Finance from the Monterrey Institute of Technology, a Certificate of Quantitative Finance from the CQF Institute, and a Master’s Degree from Columbia Business School in New York City.
He has served as an expert at the G20 Sustainable Finance Study Group and he is currently Chair of the Quantitative Investing Group at the CFA Society New York.

Richard is a research affiliate at the Lawrence Berkeley National Laboratory – one of the world’s largest supercomputing laboratories – and an advisor at the Defense Advanced Research Projects Agency (DARPA). Richard is a member of the Task Force on data for the Sustainable Development Goals at the United Nations Conference on Trade and Development (UNCTAD) and member of the United Nations Science, Technology, and Innovation (STI) Expert Group.

Title: Applying Machine Learning and SDG Risk Factors to Global Macro & FX Strategies

An overview of big data, machine learning, natural language processing and risk factors based on alternative data applied to Global Macro and FX Strategies. Topics will include the practical application of machine learning techniques for FX investment strategies. A use case will be provided to illustrate the use of Natural Language Processing and taxonomies based on the Sustainable Development Goals (SDGs) for risk management and alpha generation. The analytical process involves the extraction, processing, geo-tagging and analysis of unstructured data from tens of thousands of sources in dozens of languages, including news, blogs, company and NGO reports, social media, and Google trends. We discuss the challenges and implications of this approach.
Thursday, March 5, 2020

Speaker: Mikhail Smirnov, Columbia University

Title: Leveraged ETFs and Their Use in Portfolio Construction and Portfolio Protection

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. We start with classical asset allocation portfolios.

We consider several balanced portfolios created with leveraged ETFs but without borrowing money at portfolio level and show that they present an attractive risk-adjusted alternative to classical portfolios without leverage.

A classical portfolio insurance strategy of Black-Jones-Perold can be easily implemented with leveraged ETFs. More complex dynamic portfolio strategies can also be implemented using leveraged ETFs and we consider some of these strategies and analyze them.

Tuesday, March 10, 2020

Thursday, March 12, 2020

Speaker: Boris Lerner
Boris Lerner is the Global Head of Quantitative Equity Research at Morgan Stanley, based in New York. Boris joined Morgan Stanley in 2003 and has worked on a broad range of projects over the years, including derivative research, quantitative modeling, data analytics, and structuring. In his current role, Boris is focused on the quantitative analysis of the equity markets, developing alpha models, evaluating alternative data sources, and applying quantitative techniques to traditional fundamental research. Prior to joining the Morgan Stanley Research department, Boris co-headed the Morgan Stanley Quantitative Investment Strategies (QIS) structuring team in North America, where he developed cross-asset, rules-based hedging and risk premia investment strategies for use in diversified institutional portfolios. Boris holds a Master’s degree in Financial Mathematics from Columbia University, and a Bachelor’s degree in Finance and Information Technology from the New York University Stern School of Business.

Title: Equity Factor Investing


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

Thursday, March 26, 2020

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

Title: CORONA CRISIS: Why aren’t the Dealers taking the Fed’s Money

Why the Market has become dysfunctional.
What the Fed is trying to do.
Where we go from here.
Tuesday, March 31, 2020

Speaker: Irene Aldridge, AbleMarkets
Irene Aldridge is Managing Director, Research, of AbleMarkets, a Big Data for Capital Markets company. She is also Adjunct Professor at Cornell University. Prior to AbleMarkets, Aldridge 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 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 “Big Data Science in Finance” (co-authored with Marco Avellaneda, Wiley, 2020), “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading, Flash Crashes” (co-authored with Steve Krawciw, Wiley, 2017), and “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (2nd edition, translated into Chinese, Wiley 2013). 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: Predicting Analysts’ Buy/Hold/Sell Recommendations Using Semi-Supervised Learning (SSL)

Quality financial analysis is an expensive skill that involves years of training and experience. Top-notch financial analysis may be inaccessible to the majority of investors due to the costs involved. Semi-supervised learning (SSL) is a technique that can democratize access to financial analysis, as this research shows.
Thursday, April 2, 2020

Speaker: Natalia Zvereva, JP Morgan Asset Management
Natalia Zvereva is Executive Director, Investment Risk, at J.P. 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).

Title: Derivatives Use and Risk Management

In this talk, we will discuss the use of derivatives across asset classes and client types, and will go over various derivatives risk management techniques used in the industry
Tuesday, April 7, 2020


Thursday, April 9, 2020

Speaker: Miquel Noguer i Alonso, Artificial Intelligence Finance Institute
Dr. Miquel Noguer I Alonso is Founder of the Artificial Intelligence Finance Institute. He is a financial markets practitioner with more than 20 years of experience in asset management. He is Head of Development at Global AI and co-Editor of the Journal of Machine Learning in Finance. He serves in the Advisory board of FDI and CFA Quant Investing Group.

Dr. Alonso worked for UBS AG (Switzerland) as Executive Director. He is member of European Investment Committee for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006. He started his career at KPMG.

Dr. Alonso is Visiting Professor at NYU Courant Institute of Mathematical Sciences and the CQF institute. He has been Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He is also Professor at ESADE teaching Hedge Fund, Big Data in Finance and Fintech. He taught the first Fintech and Big Data course at the London Business School in 2017. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He completed a Postdoc in Columbia Business School in 2012. He collaborated with the Mathematics department of Fribourg during his PhD. He also holds the Certified European Financial Analyst (CEFA) 2000 and the ARPM certificate.

Title: Latest developments of Deep learning in Finance

We will discuss the mathematical and practical considerations of using deep learning in finance applications like time series, factor models, reinforcement learning and natural language processing. Financial mathematicians should incorporate in deep learning in their modeling to contrast with benchmark models sometimes replacing econometric/stochastic modeling benchmark models and in some other using in paralel models with different properties. We will discuss the merits of universal approximation properties and stochastic modeling and the limitations of interpretation and potential overfitting.
Tuesday, April 14, 2020

Speaker: Alberto Botter, AQR

Title: Fundamentals of Active Investing
Alberto Botter is an Executive Director of Portfolio Management at AQR Capital Management. In this role, he oversees the construction, optimization and management of AQR’s Equities Long-Short products. Previously, Mr. Botter was a Quant in the Wealth Strategies group at Morgan Stanley. Mr. Botter earned a B.S. and an M.S. in economics from the University of Bologna, as well as an M.A. in financial mathematics from Columbia University.


Thursday, April 16, 2020

Speaker: Bryan Jianfeng Liang, Bloomberg L.P. and Columbia University
Bryan Liang is a senior quant researcher at Bloomberg L.P. and adjunct assistant professor at Columbia University. He joined the Bloomberg quant research team in 2011 and has been working extensively on various aspects of derivatives modelling, including pricing, hedging, risk management, structuring, market making, trading strategies, parallel computing and applications of machine learning techniques in derivatives. He is also an adjunct professor at the Courant Institute NYU. Prior to joining Bloomberg, He worked for derivatives analysis group at Goldman Sachs, as an interest rate derivatives quant. Bryan received his Ph.D. in mathematics from University of Michigan and was a faculty member at Northwestern University and UC Davis before he moved to finance.

Title: Multilinear Principal Component Analysis (MPCA) and Its Application to Finance

Multi-dimensional data are very common in finance. In recent years, there has been an increasing interest in dimensionality reduction technique for multi-dimensional data. Multilinear principal component analysis (MPCA), as a natural extension of classical principal component analysis (PCA), offers a simple yet effective and robust solution to find lower-dimensional representation of data. In this talk, we present an in-depth study of MPCA, including theoretical development, practical issues and its applications to financial data such as multiple yield curves and volatility surfaces. We shall pay particular attention to the pros and cons between PCA and MPCA, and what one can benefit from exploiting the information naturally embedded in the tensor structure of data.
Tuesday, April 21, 2020

Speaker: Sanne de Boer, Voya Investment Management
Sanne de Boer is director of Quantitative Equity Research at Voya Investment Management responsible for overseeing the firm’s quantitative equity research agenda. Prior to joining the firm, he was a senior research analyst for quantitative strategies for Invesco. Previously, he was a research analyst for global quantitative equities at QS Investors as well as ING Investment Management, Voya’s predecessor firm. Sanne’s research has been published in the Journal of Asset Management, the Journal of Index Investing, and the Journal of Investing. He received a Ph.D. in Operations Research from the Massachusetts Institute of Technology and an M.S. in Mathematics and an M.A. in Econometrics cum laude from the Vrije Universiteit Amsterdam. He holds the Chartered Financial Analyst designation.

Title: Nonlinear Factor Attribution

Factor attribution based on linear regression often fails to satisfactorily explain the performance of systematic investment strategies. Sizeable attribution residuals that do not average out to zero over time suggest latent exposures to nonlinearities in factor returns. Our proposed adjustment takes a portfolio manager’s perspective in attributing the impact thereof, identifying which factor tilts were most responsible for the unexplained performance. The resulting nonlinear attribution better reconciles realized returns with the investment process and is testable for statistical significance.
Thursday, April 23, 2020

Speaker: David Fournie, Bank of America

Title: Backwards pricing – dynamic programming on a Grid

Will go through examples on how to use PDE grids for strongly path-dependent derivatives.
Tuesday, April 28, 2020

Speaker: Cristian Homescu, Bank of America
Cristian Homescu is Director, Chief Investment Office, Investment Solutions Group, at Bank of America.

Title: Machine learning for quantitative investment and wealth management: opportunities and challenges

Machine learning for quantitative investment and wealth management (QWIM): What is hype and what is reality? What differences are observed for ML applications in this area compared to ML applications in other areas? Within this context this presentation aims to provide an overview of ML applications (classification, network analysis and clustering, forecasting and prediction, etc.) in QWIM, while also discussing the practical challenges
Thursday, April 30, 2020

Speaker: Kelly Ye, Index IQ
Kelly Ye, CFA, Director of Research, leads investment research and development at IndexIQ, the ETF platform for New York Life Investments. She has 10+ years of quantitative investment management experience from New York Life Investments and Goldman Sachs. She is a board member of CFA Society New York and a graduate from MIT.

Title: Demystify Private Asset Class Returns – How much is the illiquidity Premium?

Private asset classes (Private Equity, Private Debt and Real Estate) have gained a lot of interests from both retail and institutional clients as investors are seeking high return and income in this prolonged bull market. Unlike public market where data on stocks and companies are readily available, data on private assets are still scarce, which present challenges on how to benchmark, attribute and forecast private asset class returns. Investors attribute the superior return of private equity over public equity to the illiquidity premium. Does illiquidity premium really exist, or it’s just a term that covers what we cannot explain? This talk seeks demystify private asset class returns by comparing it to its public market peers on a more apples-to-apples bases in a factor framework.
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