Quant Insights Conference:
Machine Learning in Quant Finance

19th September 2024
14:00 - 20:00 BST
09:00 - 15:00 EDT
Globally Live-Online

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

Days

1 Day

Speakers

8 Talks

Price

Free Tickets

Post Conference

Video

About the Conference

The highly-anticipated Machine Learning in Quant Finance Conference is back in September 2024. Book your free ticket today to gain practical insights into the applications of machine learning in finance from leading industry experts.

Tickets are free and you will earn valuable CPD credits. Plus, you will get exclusive access to video on demand after the conference.

Book your free ticket

Proudly supported by

Platinum Sponsor

Affiliate Partners

Academic Partners

Conference Schedule

19th September 2024

14:00 - 14:25 BST

Virtual conference doors open - Familiarize yourself with the event platform

14:25 - 14:30 BST

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

14:30 - 15:05 BST

Econometrics with Text Data
David Ardia, IVADO Professor, HEC Montréal

15:05 - 15:40 BST

Doing More with Tick Data: A Machine Learning Approach to Intraday Signal Development
Edith Mandel, Principal, Greenwich Sreeet Advisors LLC

15:40 - 16:15 BST

Risk Budgeting and Machine Learning for FX Factor Models
Dr. Anton Antonov, Head of AI and Quantitative Research, dxFeed
Dmitry Zotikov, Lead Quantitative Analyst, dxFeed

16:15 - 16:45 BST

Break and Networking

16:45 - 17:20 BST

Algorithms for Tracking the S&P 500: Good Old-Fashioned Heuristics or Machine Learning - Which Works Better?
Dr. Claus Huber, Head of Quantitative Modelling & Analytics, Helvetia Insurance Group

17:20 - 17:55 BST

Quantum Complementarity and Potential for Advantage in Machine Learning
Dr. Noelle Ibrahim, Consultant, IBM

17:55 - 18:10 BST

Break and Networking

18:10 - 18:45 BST

Guidelines for Building a Realistic Algorithmic Trading Market Simulator for Backtesting While Incorporating Market Impact
Dr. Babak Mahdavi-Damghani, Founder, EQRC

18:45 - 19:20 BST

Predicting Financial Crises with Machine Learning: A Data-Driven Approach
Sebastian Petric, CQF - Head of FX, Gold, Geopolitical Strategy at LGT Bank AG

19:20 - 19:25 BST

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

19:25 - 20:00 BST

End of day networking

Conference Speakers

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

Dr. Babak Mahdavi-Damghani
Founder
EQRC

Abstract Biography

Guidelines for Building a Realistic Algorithmic Trading Market Simulator for Backtesting While Incorporating Market Impact

In this paper, a shorter and more publication focused version of our recent article “A Bottom-Up Approach to the financial Markets” (Mahdavi-Damghani, & Roberts, S. 2019 .) is presented. More specifically we propose a new approach to studying the financial markets using the Bottom-Up approach instead of the traditional Top-Down. We achieve this shift in perspective, by re-introducing the High Frequency Trading Ecosystem (HFTE) model Mahdavi-Damghani, B. 2017 . More specifically we specify an approach in which agents in Neural Network format designed to address the complexity demands of most common financial strategies interact through an Order-Book. We introduce in that context concepts such as the Path of Interaction in order to study our Ecosystem of strategies through time. We show how a Particle Filter methodology can then be used in order to track the market ecosystem through time. Finally, we take this opportunity to explore how to build a realistic market simulator which objective would be to test real market impact without incurring any research costs.

Dr. Babak Mahdavi-Damghani, Founder, EQRC

Dr. Babak Mahdavi-Damghani (BMD) completed his PhD in Machine Learning for Quantitative Finance at the University of Oxford. He has a broad range of work experiences in the financial industry, notably having worked for Citigroup, Socgen, GAM Systematic, Credit Suisse, LSEG, the Oxford Algorithmic Trading Programme and Andurand Capital Management.

He has experience through all the major asset classes in both the buy and sell sides. He is the founder of EQRC and an alumni of the Oxford Man Institute of Quantitative Finance. He is also the author of numerous publications, including cover stories of Wilmott magazine and mathematical models currently taught at the CQF.

David Ardia
IVADO Professor
HEC Montréal

Abstract Biography

Econometrics with text data

The advent of massive amounts of textual data has spurred the development of econometric methodologies to transform qualitative sentiment data into quantitative sentiment variables and use those variables in an econometric analysis of the relationships between sentiment and other variables. This talk will briefly introduce this research field and illustrate possible applications in finance.

David Ardia, IVADO Professor, HEC Montréal

David is an IVADO professor in the Department of Decision Sciences at HEC Montréal. Trained in quantitative methods for finance, he has a keen interest in ML/NLP methods for asset allocation, risk management, and economic forecasting. In 2018, the Swiss Risk Association awarded him “Swiss risk manager of the year.” He is a regular member at GERAD, Quantact, and Fin-ML, an associate researcher at OBVIA, and an instructor at DataCamp.

Sebastian Petric, CQF
Head of FX, Gold, Geopolitical Strategy
LGT Bank AG

Abstract Biography

Predicting Financial Crises with Machine Learning: A Data-Driven Approach

The ability to anticipate banking crises is vital for ensuring global financial stability. This talk will examine how machine learning methods, such as clustering, sequential feature selection, and ridge regression, can significantly enhance the identification and prediction of banking crises. I will share insights from a recent study that applied these techniques to predict the 2023 regional banking crisis in the United States, highlighting how data-driven approaches can detect patterns and anomalies in financial data to foresee crises. Additionally, the discussion will explore the advantages of machine learning models in minimizing risk and enhancing decision-making for investors.

Sebastian Petric, CQF - Head of FX, Gold, Geopolitical Strategy at LGT Bank AG

Sebastian Petric began his career in emerging market asset management, where he gained a deep understanding of the diverse factors influencing returns and risks. This foundation led him to specialize in multi-asset class investing and capital market research, where he developed a strong passion for data science and machine learning. At the University of Liechtenstein, he focuses on these areas and co-founded the Crisis Research Group, which leverages advanced analytics to identify and predict financial crises. As a future-focused leader set to begin his fellowship at the Oxford Internet Institute in October, he brings extensive expertise in leveraging innovative technologies. Committed to making an impact, he actively contributes through advisory roles and advocacy, aiming to shape the field through his thought leadership and publications.

Dr. Noelle Ibrahim
Consultant
IBM

Abstract Biography

Quantum Complementarity and Potential for Advantage in Machine Learning

Quantum and Classical machine learning methods are based on fundamentally different mathematical paradigms and have been shown to be capable of efficiently predicting different patterns in data. Early results suggest that these complementary patterns can be combined through ensemble methods to increase detection performance in such use cases as fraud detection, customer analytics, intrusion detection and more.

Dr. Noelle Ibrahim, Consultant, IBM

Dr. Noelle Ibrahim is a global IBM Quantum Industry Consultant for the Banking and Financial Markets industries. She has worked across verticals within the financial services sector, leading major transformational risk initiatives including stress testing and IFS9.

She has also worked in derivatives pricing, including vanilla and exotic options and modelling of cash flows from structured investment vehicles. She also has experience in the Fintech industry where she worked for a start up applying AI to art as an asset class. She has a depth and breadth of experience in quantitative finance, including quantitative models for Value at Risk (VaR), CVaR, Black-Scholes, Exotic Options pricing and back-testing, credit risk models for PD,EAD,LGD and more.

Noelle has a Ph.D. in Applied Physics from Columbia University, specializing in Quantum Monte Carlo methods for modelling classical and quantum systems. She also holds an M.Sc. in Quantum Optics and Condensed Matter Physics from the University of Toronto and a Bachelor’s degree in Physics from the University of British Columbia.

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

Abstract Biography

Algorithms for Tracking the S&P 500: Good Old-Fashioned Heuristics or Machine Learning - Which Works Better?

Index tracking is a poster problem of optimisation methods in finance. Assets worth billions of dollars are managed with tracking algorithms. The goal of those algorithms is to track an equity index, like the S&P 500, with a small number of stocks, for example, 50 stocks. A tracking algorithm needs to find the stocks and their weights in order to minimise the tracking portfolio's deviation from the index. This talk presents results of a horse race of several ML methods (PCA, Hierarchical Agglomerative Clustering, t-SNE, Auto-Encoders, the Self-Organising Map) as well as an optimisation method recently suggested in the literature. The ML methods exploit their specific characteristics to accomplish close tracking. It turned out that some also have potential to generate excess returns. Heuristics were used as simple but hard to beat benchmarks.

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

Claus is the Head of Quantitative Modelling & Analytics at Helvetia Insurances 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, and advised clients on risk management and quantitative investment solutions. Claus has extensive experience as entrepreneur, risk manager, credit strategist, hedge fund analyst, and government bond trader and has worked for hedge funds, banks, and insurance companies.

Edith Mandel
Principal
Greenwich Street Advisors LLC

Abstract Biography

Doing More with Tick Data: A Machine Learning Approach to Intraday Signal Development

Raw ticks data is rarely available in the format convenient for signal research, and required transformations are often very complex. We will talk about measuring market & trade imbalance when historical data is collected with different levels of granularity, working with RFQ datasets and predictive models of expected execution costs.

Edith Mandel, Principal, Greenwich Sreeet Advisors LLC

Edith's research focuses on machine learning in quantitative trading, big data, and automation in Fixed Income markets.

As a principal at Greenwich Street Advisors, LLC, Edith provides her clients with Fixed Income quant trading know-how and signal research tools, advises them on applications of machine learning and data science in finance, and develops innovative analytics infrastructure solutions.

Edith is CEO and Co-Founder of INFIO (https://www.inf.io) and an advisor to Proximilar LLC (https://proximilar.com)

Edith Mandel is a seasoned professional with over 20 years of experience in quantitative finance. Prior to starting her own firm in 2015, Edith Mandel was a Managing Director at Goldman Sachs, Citadel and the head of Fixed Income Mid-Frequency Trading at KCG (formerly GETCO).

Dmitry Zotikov
Lead Quantitative Analyst
dxFeed

Abstract Biography

Risk Budgeting and Machine Learning for FX Factor Models

We introduce PARIS, a benchmark for an FX three-factor model developed at dxFeed. We consider carry, value and momentum style factors, and study their contribution to time-series and cross-sectional returns in a broad selection of currencies. For asset allocation, we select and compare several approaches based on the risk budgeting framework. Additionally, we show how risk budgets may be inferred from machine learning models and what benefits are associated with such designs.

Dmitry Zotikov, Lead Quantitative Analyst, dxFeed

Dmitry Zotikov is a Lead Quantitative Analyst at dxFeed. He holds a BSc in Applied Maths and Statistics from St. Petersburg State University and a master's degree from Yeongnam University, where he did work on biologically realistic neural networks. Dmitry is a CQF alumni (2021). His research interests are focused on applying methods of stochastic dynamics, as well as artificial neural networks for modelling market microstructure. Currently, he leads a working group that seeks to utilise advanced data analytical methods to construct market indices.

Dr. Anton Antonov
Head of AI and Quantitative Research
dxFeed

Abstract Biography

Risk Budgeting and Machine Learning for FX Factor Models

We introduce PARIS, a benchmark for an FX three-factor model developed at dxFeed. We consider carry, value and momentum style factors, and study their contribution to time-series and cross-sectional returns in a broad selection of currencies. For asset allocation, we select and compare several approaches based on the risk budgeting framework. Additionally, we show how risk budgets may be inferred from machine learning models and what benefits are associated with such designs.

Dr. Anton Antonov, Head of AI and Quantitative Research, dxFeed

Anton Antonov is a head of AI and Quantitative Research at dxFeed. He holds a Ph.D. from St. Petersburg State University (Russia) with a specialization in numeric methods and statistical modeling. With more than ten years of financial IT experience, Anton achieved expertise in financial markets, software engineering, and applied research. At dxFeed, he and his team work with AI/ML technologies and quantitative methods. Anton is a CQF alumni (2018).

Dr. Randeep Gug
Managing Director
CQF and CQF Institute

Biography

Dr. Randeep Gug, CQF and CQF Institute, Managing Director

Dr. Randeep Gug is the Managing Director of the Certificate in Quantitative Finance (CQF) and the CQF Institute. 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

19th September 2024

Time

14:00 - 20:00 BST

Book your free ticket

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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Quant Insights Conference:Machine Learning in Quant Finance

About the Conference

Proudly supported by

Conference Schedule

Conference Speakers

Guidelines for Building a Realistic Algorithmic Trading Market Simulator for Backtesting While Incorporating Market Impact

Dr. Babak Mahdavi-Damghani, Founder, EQRC

Econometrics with text data

David Ardia, IVADO Professor, HEC Montréal

Predicting Financial Crises with Machine Learning: A Data-Driven Approach

Sebastian Petric, CQF - Head of FX, Gold, Geopolitical Strategy at LGT Bank AG

Quantum Complementarity and Potential for Advantage in Machine Learning

Dr. Noelle Ibrahim, Consultant, IBM

Algorithms for Tracking the S&P 500: Good Old-Fashioned Heuristics or Machine Learning - Which Works Better?

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

Doing More with Tick Data: A Machine Learning Approach to Intraday Signal Development

Edith Mandel, Principal, Greenwich Sreeet Advisors LLC

Risk Budgeting and Machine Learning for FX Factor Models

Dmitry Zotikov, Lead Quantitative Analyst, dxFeed

Risk Budgeting and Machine Learning for FX Factor Models

Dr. Anton Antonov, Head of AI and Quantitative Research, dxFeed

Dr. Randeep Gug, CQF and CQF Institute, Managing Director

Conference Tickets

Conference Organizers

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Quant Insights Conference:
Machine Learning in Quant Finance