Quant Insights Conference:
Portfolio Management in Quant Finance

23rd March 2023
10:30 - 17:35 GMT
Globally Live-Online

Brought to you by
CQF Institute, Fitch Learning, and Wilmott

Days

1 Day

Speakers

7+ Talks

Price

Free Tickets

Post Conference

Video

About the Conference

The Portfolio Management in Quant Finance Conference returned in March 2023 to focus exclusively on the latest industry innovations. Attendees enjoyed talks from some of the biggest names in quantitative finance including Dr. Alexandre Antonov, Professor Natalie Packham, Dr. Claus Huber, and more.

The Portfolio Management Conference will be back in 2024. In the meantime, catch up with the conference recordings on demand.

Watch conference recordings

Conference Schedule

23rd March 2023

10:30 - 11:30 GMT

Virtual conference doors open - explore the event platform and connect with your peers

11:30 - 11:35 GMT

Welcome Remarks
Dr. Randeep Gug, Managing Director, CQF Institute

11:35 - 12:10 GMT

Generative Models and Predictive Machines with Uncertainty Quantification for Financial Applications with Kernels
Dr. Jean-Marc Mercier, Head of Research & Development, MPG Partners

12:10 - 12:45 GMT

Overcoming Markowitz’s Instability with the help of the Hierarchical Risk Parity (HRP): Theoretical Evidence
Dr. Alexandre Antonov, Quantitative Research & Development Lead, ADIA

12:45 - 13:20 GMT

Portfolio Management for People
Dr. Rick Bookstaber, Head of Risk & Co-Founder, Fabric

13:20 - 13:40 GMT

Break

13:40 - 14:15 GMT

Risk Factor Detection with Methods from Explainable ML
Dr. Natalie Packham, Professor of Mathematics and Statistics, Berlin School of Economics and Law

14:15 - 14:50 GMT

Building a Tool for Strategic Asset Allocation at a Swiss Insurance Company
Dr. Claus Huber, Head of Quantitative Modelling & Analytics, Helvetia Insurance

14:50 - 15:20 GMT

Break - CQF Information Session

15:20 - 16:20 GMT

Developments and Applications in (explainable) ML in Portfolio Management
Dr. Natalie Packham, Professor of Mathematics and Statistics, Berlin School of Economics and Law (Panel Chair)
Tony Guida, Co-Head - Systematic Macro, RAM AI
Renee Yao, Founder, Neo Ivy Capital Management
Dr. Jan Rosenzweig, Portfolio Manager, Pine Tree Market Neutral
Dr. Dario Villani, CEO & Co-Founder, Duality Group

16:20 - 16:55 GMT

Predicting Stock Market Drawdowns using Polymodels
Dr. Thomas Barrau, Head of Intraday Research, AXA Investment Managers Chorus Ltd

16:55 - 17:30 GMT

The Hidden Cost in Costless Put-Spread Collars: Rebalance Timing Luck
Dr. Roni Israelov, Chief Investment Officer & President, NDVR

17:30 - 17:35 GMT

Closing Remarks
Dr. Randeep Gug, Managing Director, CQF Institute

17:35 - 18:15 GMT

End of day networking

Day 2 - 28th October 2021

07.30 - 08.30 EDT

Virtual conference doors open - Familiarize yourself with the Hopin platform

Conference Speakers

Confirmed speakers and panelists. Click on the images below to explore abstracts and biographies.

Dr. Alexandre Antonov
Quantitative Research & Development Lead
ADIA

Abstract Biography

Overcoming Markowitz’s Instability with the help of the Hierarchical Risk Parity (HRP): Theoretical Evidence

In this talk we compare two methods of portfolio allocation, the classical Markowitz one and the hierarchical risk parity (HRP) based on a clustered optimization. We drive theoretical values of a noise of allocation weights coming from the variance estimation. We demonstrate that the HRP is indeed less noisy (and thus more robust) w.r.t. the classical Markowitz.

Dr. Alexandre Antonov, Quantitative Research & Development Lead, ADIA

Dr. Alexandre Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997. He worked for Numerix, Standard Chartered and Danske Bank. Currently, Alexandre is a Quantitative Research & Development Lead at ADIA in Abu-Dhabi.

His activity is concentrated on modeling and numerical methods for portfolio optimization, interest rates, cross currency, credit and XVA, as well as Machine Learning and its applications. AA is an author for multiple publications in mathematical finance and a frequent speaker at financial conferences.

He has received a Quant of Year Award of Risk magazine in 2016.

Dr. Natalie Packham
Professor of Mathematics and Statistics
Berlin School of Economics and Law

Abstract Panel Abstract Biography

Risk Factor Detection with Methods from Explainable ML

The importance of risk management in the financial industry has increased rapidly since the financial crisis, in particular with regard to financial market stability. A particular focus is on stress testing methods, which captures portfolio risk under adverse conditions. Advances in statistical learning and the availability of large, granular data sets offer new methodological possibilities for stress testing. Financial risk management applications such as hedging, scenario analysis and stress testing rely on portfolio models based on risk factors. In addition to observable risk factors, factor models with non-observable, data-based factors offer interesting alternatives. However, the lack of interpretability of the output is limiting. We develop time-dynamic methods for the interpretability of principal components (PCA), which allow to generate aggregated risk factors from existing risk factors. This aggregation makes it possible to plausibly implement less granular and even global stress scenarios.

Developments and Applications in (explainable) ML in Portfolio Management

As Machine Learning algorithms are transitioning from cutting edge to state-of-the-art, a panel of experts in the portfolio management will share some insights into their experience and best practices. A particular focus will be on explainable Machine Learning (XML) in the context of portfolio management. XML refers to the ability of a machine learning model to provide clear and transparent explanations of its predictions and decisions. This is becoming increasingly important as more and more financial institutions are using machine learning algorithms to make investment decisions. The discussion will explore the current state of XML in portfolio management, especially trust in the decision-making process, as well as its limitations.

Dr. Natalie Packham, Professor of Mathematics and Statistics, Berlin School of Economics and Law

Dr. Natalie Packham is Professor of Mathematics and Statistics at Berlin School of Economics and Law and Principal Researcher within the International Research Training Group “High Dimensional Nonstationary Time Series” (IRTG 1792) at Humboldt University Berlin. Natalie has several years of industry experience as a front office software engineer at an investment bank, and is frequently involved in industry-related research and consulting projects. Her research expertise includes Mathematical Finance, Financial Risk Management and Computational Finance, and her academic work has been published in Mathematical Finance, Finance & Stochastics, Quantitative Finance, Journal of Applied Probability and many other academic journals. She is associate editor of “Methodology and Computing in Applied Probability” and “Digital Finance” and co-chair of the GARP Research Fellowship Advisory Board. Natalie holds an M.Sc. in Computer Science from the University of Bonn, a Master’s degree in Banking & Finance from Frankfurt School, and a Ph.D. in Quantitative Finance from Frankfurt School.

Dr. Claus Huber
Head of Quantitative Modelling & Analytics
Helvetia Insurance

Abstract Biography

Building a Tool for Strategic Asset Allocation at a Swiss Insurance Company

One aspect of Helvetia Asset Management currently revamping its processes is to build a quantitative tool to support Strategic Asset Allocation. We have implemented an SAA model suggested by Black/Litterman (1992) and tailored it to our needs. The implementation path was not clear from the outset and it took a few detours to arrive at a productive tool. This includes, for example, specifying a range of parameters of the Black/Litterman model, modelling illiquid asset classes, like Real Estate, Private Equity, or incorporating insurance-specific constraints, like Solvency II and Swiss Solvency Test. Typical Use Cases are balance sheet optimisation, adding new Asset Classes (e.g., High Yield or Swiss Mortgages) to an existing portfolio or suggesting an allocation for a new investment strategy. This talk summarises our experience building an SAA tool and shares a few insights of our journey from the beginnings to a production-ready tool.

Dr. Claus Huber, Head of Quantitative Modelling & Analytics, Helvetia Insurance

Claus is the Head of Quantitative Modelling & Analytics at Helvetia Insurance in Basel, Switzerland, where his team develops digital tools for Strategic and Tactical Asset Allocation, risk budgeting, overlay management, manager selection, visual representation of complex data structures, and a few more. In previous roles he developed new investment products for Quantitative Multi Asset Funds and, as Head of Digital Transformation, drove the development of new tools and data products that allow smart data usage and sharing. As the founder of Rodex Risk Advisers LLC, based in Altendorf (Switzerland), he advised clients on risk management and quantitative investment solutions, for example, a Machine Learning approach to select hedge fund managers. He has extensive experience as entrepreneur, risk manager, credit strategist, hedge fund analyst, and government bond trader and worked for hedge funds, banks, and insurance companies.

Dr. Rick Bookstaber
Head of Risk & Co-Founder
Fabric

Abstract Biography

Portfolio Management for People

Portfolio risk is an insufficient measure for wealth management. The individual also has risk coming from their own life circumstances. When we look at portfolio risk, we need to do so in the context of the individual on the other side of that portfolio. Risk is dynamic and personal. The individual’s risk changes with their life’s circumstances. So the static measures of risk that work for the portfolio manager, whose objective is measured in months and is sitting in mean-variance space, need to be expanded when we recognize there are people on the other side of the portfolio.

Dr. Rick Bookstaber, Head of Risk & Co-Founder, Fabric

Dr. Rick Bookstaber is the Head of Risk and Co-Founder of Fabric (www.fabricrisk.com). Fabric provides risk management and portfolio design applications to the wealth management and financial advisor community.

Rick is a noted expert in financial risk management, and is the author of The End of Theory (Princeton, 2017), and A Demon of Our Own Design (Wiley, 2007).

He has served in chief risk officer roles at Salomon, Morgan Stanley, Moore Capital, and Bridgewater Associates. From 2009 to 2015 he served at the SEC and the U.S. Treasury, drafting the Volcker Rule and modeling risk for the Financial Stability Oversight Council. Most recently he was the chief risk officer in the University of California Office of the CIO for the university’s $170 billion pension and endowment funds.

His roles have placed him at the center of financial crises of the last three decades – working with portfolio insurance during the 1987 Crash while at Morgan Stanley, overseeing risk at Salomon during the 1998 failure of Long-Term Capital Management (dubbed “Salomon North”), and with the aftermath of the 2008 Crisis while in the regulatory sphere.

A black belt in Brazilian jiu-jitsu, Rick can be found training at the Renzo Gracie Academy in his free time.

Rick received a Ph.D. in economics from MIT.

Dr. Thomas Barrau
Head of Intraday Research
AXA Investment Managers Chorus Ltd

Abstract Biography

Predicting Stock Market Drawdowns using Polymodels

We propose a Systematic Risk Indicator derived from a polymodel estimation. Polymodels allow us to measure the strength of the links that a stock market maintains with its economic environment. We show that these links tend to be more extreme before a market crisis, confirming the well-known increase of correlations while proposing a more subtle perspective. A fully automated and successful trading strategy is implemented to assess the interest of the signal, which is shown to be strongly significant, both from an economic and statistical point of view. Results are robust across different time-periods, for various sets of explanatory variables, and among 12 different stock markets.

Dr. Thomas Barrau, Head of Intraday Research, AXA Investment Managers Chorus Ltd

Dr. Thomas Barrau is Head of Intraday Research for the hedge fund AXA Investment Managers Chorus Ltd. He is leading the development of a portfolio of quantitative intraday trading strategies invested in various asset classes.

With Raphael Douady, he co-authored the book "Artificial Intelligence for Financial Markets: The Polymodel Approach", published by Springer.

He has been a Senior Quantitative Researcher at AXA IM Chorus, a role during which he developed a team of 4 researchers working on an Equity Market Neutral portfolio. Prior to this, he worked at Societe Generale as banker and financial advisor to small businesses, and as CFO in an aerospace company. He holds a PhD in Applied Mathematics from Paris 1 Pantheon-Sorbonne University. Previously, he validated with honors three different Masters of Science from Aix-Marseille School of Economics, Ca'Foscari University of Venice and Poitiers IAE.

Dr. Jean-Marc Mercier
Head of Research & Development
MPG Partners

Abstract Biography

Generative Models and Predictive Machines with Uncertainty Quantification for Financial Applications with Kernels

We consider generative and predictive methods based on kernels (RKHS theory). This approach allowed us to consider various Finance applications, ranging from time series prediction with model-free models to intradays, real time pricing / hedging methods with predictive machines, or valuation algorithms of large portfolios depending on many risk sources for XVA or pricing purposes. The proposed approach is not only fast and efficient, but also reliable and explainable, because it is sheltered by solid error estimates, allowing us to fully understand the results. We illustrate our approach with several reproducible numerical examples, relying on an open source project. This led us to a modern and competitive approach of Portfolio Management, coupling machine learning and quantitative analysis, through an original approach mixing RKHS and optimal transport theory.

Dr. Jean-Marc Mercier, Head of Research & Development, MPG Partners

Dr. Jean-Marc Mercier is head of Research & Development at MPG Partners, a consulting firm specializing in Risk Management. His main task is to develop an open-source kernel-based IA platform, named codpy, focusing on Finance applications for either prospective researches or clients projects. Jean-Marc is a former public researcher, PDE (Partial Differential Equation) specialized. He then turned to industry, and has several years of industry experience as a quantitative engineer and a business analyst. His research is involved in machine learning, artificial intelligence, computer sciences, with 30+ published papers. Jean-Marc holds a Ph-D in Applied Mathematics from Bordeaux University.

Dr. Roni Israelov
Chief Investment Officer & President
NDVR

Abstract Biography

The Hidden Cost in Costless Put-Spread Collars: Rebalance Timing Luck

Prior research and empirical investment results demonstrate that strategy performance can be highly sensitive to rebalance schedules, an effect called rebalance timing luck (“RTL”). We extend the empirical analysis to option-based strategies. As a case study, we replicate a popular strategy – the self-financing, three-month put-spread collar – with three implementations that vary only in their rebalance schedule. We find that the annualized tracking error between any two implementations is in excess of 400 basis points. We also decompose the empirically-derived rebalance timing luck for this strategy into its linear and non-linear components. Finally, we provide intuition for the driving causes of rebalance timing luck in option-based strategies.

Dr. Roni Israelov, Chief Investment Officer & President, NDVR

Dr. Roni Israelov serves as Chief Investment Officer and President at NDVR. He is responsible for managing the quantitative research team and overall corporate strategy. Previously, Roni served as Principal at AQR Capital Management, a global quantitative investment management firm serving both institutional and retail clients. Over 11 years at AQR, Roni's responsibilities included building and overseeing AQR's options-oriented strategies and overseeing the portfolio implementation research of AQR's equity strategies. His research has been published in leading journals, including the Financial Analysts Journal and the Journal of Portfolio Management. He is a member of the Editorial Board for The Journal of Alternative Investments. Roni has a B.S. in Mechanical Engineering from the Georgia Institute of Technology, a M.S. in Mathematical Risk Management from Georgia State University, and a Ph.D. in Financial Economics from Carnegie Mellon University.

Tony Guida
Co-Head - Systematic Macro
RAM AI

Panel Abstract Biography

Developments and Applications in (explainable) ML in Portfolio Management

Tony Guida, Co-Head - Systematic Macro, RAM AI

Tony Guida joined RAM AI in 2019 as a Senior Quantitative Researcher and became Co-Head of Systematic Macro. His work focuses primarily on extracting market inefficiencies from different sources from traditional fundamentals, market signals, alternative data and machine learning. Tony started his career at Unigestion in the Quantitative Equity Low Volatility Team and later became a member of the Research and Investment Committee In 2015, he moved to Edhec Risk Scientific Beta as a Senior Consultant for Risk allocation and Factor Strategies before going to a major UK pension fund in 2016 to build the in-house systematic equity, co-managing 8 billion GBP as a Senior Quantitative Portfolio Manager.

Tony is a Lecturer and researcher in Quantitative Finance and Machine Learning. He is the co-author and editor of 'Big Data and Machine Learning in Quantitative Investments and Machine Learning for Factor Investing' (R and Python versions).

Renee Yao
Founder
Neo Ivy Capital Management

Panel Abstract Biography

Developments and Applications in (explainable) ML in Portfolio Management

Renee Yao, Founder, Neo Ivy Capital Management

Renee Yao began her career on Wall Street at Citadel, LLC. Later she left Citadel to join WorldQuant, a Millennium Management Portfolio Manager, as a Trader with discretionary trading authority for her own designated account. Subsequently she joined a prop trading firm where she managed multiple statistical arbitrage portfolios totaling several hundred millions USD in gross positions. In early 2015, Renee set up her own firm, Neo Ivy Capital Management, a quantitative hedge fund manager that focuses on trading liquid, publicly traded equity securities via artificial intelligence strategies.

Renee was named as “Tomorrow’s Titan” by The Hedge Fund Journal in 2019 and “100 People Transforming Business” by Business Insider in 2020. Renee holds a M.A in Statistics from Columbia University.

Dr. Jan Rosenzweig
Portfolio Manager
Pine Tree Market Neutral

Panel Abstract Biography

Developments and Applications in (explainable) ML in Portfolio Management

Dr. Jan Rosenzweig, Portfolio Manager, Pine Tree Market Neutral

Jan has been working in the financial markets since 2005, for Credit Suisse, Rabobank, HSH Nordbank, IV Capital, Brancherose and Pine Tree. He worked as a Quant, Structurer, Trader and Portfolio Manager.

He has a PhD from Cambridge University and a BSc from Zagreb University. Jan is based in London.

Dr. Dario Villani
CEO & Co-Founder
Duality Group

Panel Abstract Biography

Developments and Applications in (explainable) ML in Portfolio Management

Dr. Dario Villani, CEO & Co-Founder, Duality Group

Dr. Dario Villani is CEO and Co-Founder of Duality Group. Dario has managed multi-billion dollar portfolios within credit, interest rates, and commodities. Previously, he served as Global Head of Portfolio Strategy and Risk at Tudor Investment Corporation. He shared the 2016 Risk.net Buy-Side Quant of the Year Award, and has authored research papers in finance, theoretical physics, statistics and portfolio management. Dario holds a Ph.D. in Theoretical Physics from Salerno University and a Master in Finance from Princeton University where he also taught a popular course in trading and risk management. Dario is currently a visiting professor in the Department of Mathematics at King’s College London and serves on the Board of Trustees of MSRI.

Dr. Randeep Gug
Managing Director
CQF Institute

Biography

Dr. Randeep Gug, CQF Institute, Managing Director

Dr. Randeep Gug is the Managing Director of the CQF Institute and a lecturer on the Certificate in Quantitative Finance (CQF). Prior to joining Fitch Learning, Randeep worked in a variety of roles. He spent five years working in the Equities division at Salomon Smith Barney and later traded futures and options on the Indian National Stock Exchange (NSE). More recently he has spent time teaching mathematics at all levels. He is a qualified teacher, holds a 1st class honours degree and a PhD for research in semiconductor physics. He is a CQF Alumnus, achieving a distinction on the programme and his current interests are based around improving and promoting the teaching and learning of Quant Finance.

Conference Tickets

Date

23rd March 2023

Time

10:30 - 17:35 GMT

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Conference Organizers

Quant Insights is presented by the CQF Institute, Fitch Learning, & Wilmott

Promoting the highest standard in practical financial engineering, the CQF Institute, part of Fitch Learning, is a global membership organization dedicated to educating and building the quantitative finance community. The CQF Institute is also the awarding body for the Certificate in Quantitative Finance (CQF) the world’s largest professional qualification in quantitative finance.

Part of the Fitch Group, Fitch Learning partners with businesses to help develop the future leaders of the financial services industry. Alongside centers in established financial hubs, Fitch Learning utilizes a best-in-class technology platform to deliver blended learning solutions that maintain the personal element of development.

Wilmott is the leading resource for the quant finance community, comprised a website and discussion forum and Wilmott magazine.

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Conference Schedule

Conference Speakers

Overcoming Markowitz’s Instability with the help of the Hierarchical Risk Parity (HRP): Theoretical Evidence

Dr. Alexandre Antonov, Quantitative Research & Development Lead, ADIA

Risk Factor Detection with Methods from Explainable ML

Developments and Applications in (explainable) ML in Portfolio Management

Dr. Natalie Packham, Professor of Mathematics and Statistics, Berlin School of Economics and Law

Building a Tool for Strategic Asset Allocation at a Swiss Insurance Company

Dr. Claus Huber, Head of Quantitative Modelling & Analytics, Helvetia Insurance

Portfolio Management for People

Dr. Rick Bookstaber, Head of Risk & Co-Founder, Fabric

Predicting Stock Market Drawdowns using Polymodels

Dr. Thomas Barrau, Head of Intraday Research, AXA Investment Managers Chorus Ltd

Generative Models and Predictive Machines with Uncertainty Quantification for Financial Applications with Kernels

Dr. Jean-Marc Mercier, Head of Research & Development, MPG Partners

The Hidden Cost in Costless Put-Spread Collars: Rebalance Timing Luck

Dr. Roni Israelov, Chief Investment Officer & President, NDVR

Developments and Applications in (explainable) ML in Portfolio Management

Tony Guida, Co-Head - Systematic Macro, RAM AI

Developments and Applications in (explainable) ML in Portfolio Management

Renee Yao, Founder, Neo Ivy Capital Management

Developments and Applications in (explainable) ML in Portfolio Management

Dr. Jan Rosenzweig, Portfolio Manager, Pine Tree Market Neutral

Developments and Applications in (explainable) ML in Portfolio Management

Dr. Dario Villani, CEO & Co-Founder, Duality Group

Dr. Randeep Gug, CQF Institute, Managing Director

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Quant Insights Conference:
Portfolio Management in Quant Finance