QUANT INSIGHTS CONFERENCE:

Machine Learning in Quant Finance

21st September 2023
13:30 - 20:00 CEST
12:30 - 19:00 BST
07:30 - 14:00 EDT
Globally Live-Online

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

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Days

1 Day

Speakers

7+ Talks

Price

Free Tickets

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About the Conference

The Machine Learning in Quant Finance Conference took place on 21st September 2023. Attendees enjoyed talks from some of the biggest names in quantitative finance including Professor John Hull, Dr. Alexander Sokol, Professor Edith Mandel, and more.

Missed the conference? The conference recordings are available to watch now on the CQF Institute website.

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Conference Schedule

21st September 2023

13:00 - 13:30 CEST

Virtual conference doors open - Familiarize yourself with the event platform

13:30 - 13:35 CEST

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

13:35 - 14:10 CEST

Decoding the Auto Encoder
Dr. Jesper Andreasen, Head of Quantitative Research, Saxo Bank

14:10 - 14:45 CEST

Quantum Solutions in Finance
Dr. Araceli Venegas-Gomez, Founder and CEO, QURECA Ltd.

14:45 - 15:20 CEST

Beating the Markets with HPC+AI: Generating Alpha and Excess Returns in Financial Services
Prabhu Ramamoorthy, Customer/Partner Developer Relationship Manager, NVIDIA

15:20 - 15:50 CEST

Break and Networking

15:50 - 16:25 CEST

Results on Pricing American Options with Reinforcement Learning
Dr. Daniel Bloch, Head of Quant Strategies, Ashley Grove Blu Analytics Ltd.

16:25 - 16:50 CEST

Break and Networking

16:50 - 17:25 CEST

Machine Learning Methods for Market-Making and Execution in Fixed Income
Professor Edith Mandel, Principal, Greenwich Street Advisors, LLC

17:25 - 17:55 CEST

Break and Networking

17:55 - 18:30 CEST

Zero-Knowledge Machine Learning
Aaron Brown, Columnist, Bloomberg

18:30 - 19:05 CEST

Hedging Barrier Options Using Reinforcement Learning
Professor John Hull, Maple Financial Professor of Derivatives and Risk Management, Joseph L. Rotman School of Management

19:05 - 19:40 CEST

Customizing Large Language Models for Quant Finance Applications
Dr. Alexander Sokol, Founder, CompatibL

19:40 - 19:45 CEST

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

Conference Speakers

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

Professor John Hull
Maple Financial Professor of Derivatives and Risk Management
Joseph L. Rotman School of Management

Abstract Biography

Hedging Barrier Options Using Reinforcement Learning

We examine the advantages of using reinforcement learning (RL) to hedge barrier options and other similar exotic options. We find that, when the hedger’s objective is to minimize VaR95 or CVaR95, RL is an attractive alternative to other hedging approaches. RL requires an assumption about the stochastic process followed by the underlying asset during the life of the exotic option, but our tests show that the results from using RL are fairly robust to this assumption. We do not consider transaction costs in this research. However, we show that RL involves less trading than other hedging approaches. As a result, the existence of transaction costs can be expected to increase the attractiveness of RL.

Professor John Hull, Maple Financial Professor of Derivatives and Risk Management, Joseph L. Rotman School of Management

John Hull is the Maple Financial Professor of Derivatives and Risk Management at the Joseph L. Rotman School of Management, University of Toronto. He was in 2016 awarded the title of University Professor (an honor granted to only 2% of faculty at University of Toronto.) He is an internationally recognized authority on derivatives and risk management and has many publications in this area. His work has an applied focus. His areas of research have included the impact of stochastic volatility on the pricing and hedging of options, the valuation of interest rate derivatives and credit derivatives, the calculation of value at risk, the evaluation of model risk, and the regulation of financial institutions. Recently, the application of machine learning to finance has the main focus of his research. He was, with Alan White, one of the winners of the Nikko-LOR research competition for his work on the Hull-White interest rate model, which is widely used by practitioners. In 1999 he was voted Financial Engineer of the Year by the International Association of Financial Engineers. He has acted as consultant to many financial institutions throughout the world and has won many teaching awards, including University of Toronto's prestigious Northrop Frye award.

He is well known for his four books: “Risk Management and Financial Institutions” (now in its 6th edition); "Options, Futures, and Other Derivatives" (now in its 11th edition); "Fundamentals of Futures and Options Markets" (now in its 9th edition); and “Machine Learning in Business: An Introduction to the World of Data Science” (now in its 3rd edition). The books have been translated into many languages and are widely used by practicing managers as well as in the classroom.

Dr. Hull is academic director of FinHub (Rotman’s Financial Innovation Lab). He teaches on Rotman’s MBA program and has been actively involved in the development of Rotman’s Master of Finance and Master of Financial Risk Management programs. In addition to the University of Toronto, Dr. Hull has taught at York University, University of British Columbia, New York University, Cranfield University, and London Business School.

Professor Edith Mandel
Principal
Greenwich Street Advisors, LLC

Abstract Biography

Machine Learning Methods for Market-Making and Execution in Fixed Income

Abstract coming soon.

Professor Edith Mandel, Principal, Greenwich Street 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).

Dr. Alexander Sokol
Founder
CompatibL

Abstract Biography

Customizing Large Language Models for Quant Finance Applications

Due to the highly specialized subject matter, the leading commercial (GPT-4) and open source (Llama 2, Code Llama) LLMs are unable to provide specialized comprehension and generation suitable for quant finance applications out of the box.

In this presentation, I will describe fine-tuning and prompt engineering techniques that convert stock LLMs into specialized tools that can assist with the following model governance functions, subject to final sign-off by human analysts:

1) Validation of trade capture using LLM comprehension of trade confirmations
2) Validation of model documentation using LLM comprehension of source code
3) LLM generation of model documentation and release note drafts

Dr. Alexander Sokol, Founder, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Prabhu Ramamoorthy
Customer/Partner Developer Relationship Manager
NVIDIA

Abstract Biography

Beating the Markets with HPC+AI: Generating Alpha and Excess Returns in Financial Services

The Holy Grail of Investing is to stay ahead of markets with real-time calculations as well as tapping into new organizational sources such as alternative data. Learn how financial firms are leveraging NVIDIA to accelerate price discovery/risk for real-time exposures as well as unstructured data such as Gen AI NLP/LLM Models to serve their customers. You can also accelerate your data processing workloads for a platform that provides one stop shop for Quant Finance + Data Processing + ML/AI needs

Prabhu Ramamoorthy, Customer/Partner Developer Relationship Manager, NVIDIA

Prabhu Ramamoorthy is the financial ecosystem partner manager at NVIDIA, where he focuses on HPC/ML/AI acceleration for financial services. Previously, he held leadership roles as the head of technology of a margin software firm Dash Regtech, catering to leading investment banks and broker-dealers and served in leadership roles at Big Four firm KPMG/EY as a director in financial services practice, where he helped 80+ financial institutions over the last 10 years. Mr. Ramamoorthy holds an MBA from the University of Wisconsin-Madison and an undergraduate engineering degree from BITS-Pilani, one of the top engineering institutes in India. He is a CFA charterholder, Financial Risk Manager (FRM), and Chartered Alternative Investment Analyst (CAIA), specializing in financial use cases.

Aaron Brown
Columnist
Bloomberg

Abstract Biography

Zero-Knowledge Machine Learning

In many ML applications either or both of the input data and model code may require shielding from public view. For example, an ML credit authorization algorithm might accept confidential data from applicants, and its internal parameters might be proprietary. How can downstream customers of the credit scores be confident that the input data were accurate and that the ML algorithm processed them correctly, without seeing either the input data or algorithm details? Or suppose a smart contract on the Ethereum blockchain requires ML processing that cannot be done on-chain both for efficiency and privacy reasons? How can an on-chain smart contract rely on an off-chain ML operation? This talk will only provide high-level answers to the mathematical questions—sketching out the approach rather than demonstrating finished tools. The main emphasis will be on how zero-knowledge machine learning can be useful in important financial applications.

Aaron Brown, Columnist, Bloomberg

Aaron Brown spent 35 years on Wall Street as a trader, portfolio manager, head of mortgage securities and risk manager for some of the largest global financial institutions, the last ten as risk manager for AQR Capital Management. He has written a few books, and a lot of articles, most recently including regular columns for Bloomberg and Wilmott magazine. He is a long-time crypto investor and participant. He was named Global Association of Risk Professionals Risk Manager of the Year and Wilmott Magazine Financial Educator of the Year. Aaron holds an B.S. degree in applied mathematics from Harvard, and an MBA in finance and statistics from the University of Chicago.

Dr. Araceli Venegas-Gomez
Founder and CEO
QUERCA Ltd

Abstract Biography

Quantum Solutions in Finance

This talk will provide an overview of the quantum ecosystem, investments worldwide, startups overview, and use cases for different business sectors, with a focus on financial applications, who is involved and what the results have been so far.

Dr. Araceli Venegas-Gomez, Founder and CEO, QURECA Ltd.

Dr. Araceli Venegas-Gomez spent several years working for Airbus in Germany and France as an aerospace engineer, before falling in love with quantum mechanics. She then decided to follow her passion for physics, and moved to Scotland to pursue a PhD in quantum simulation at the University of Strathclyde. Following discussions with the different quantum stakeholders she had over the last years, Araceli identified the need to bridge the gap between businesses and academia, as well as to raise the quantum awareness to the general public. Continuing her work on outreach advocating quantum technologies, she was named the “quantum ambassador”, after winning the Optical Society Milton and Rosalind Chang Pivoting fellowship in 2019. Araceli founded her own company called QURECA (Quantum Resources and Careers) to create a link between the different stakeholders in the quantum community through a common language, and to generate global opportunities with quantum technologies. QURECA provides a range of professional services, business development, and the solution to the quantum workforce skills bottleneck: the first online platform for quantum training and resourcing, to support individuals and businesses to be part of the quantum revolution.

Dr. Daniel Bloch
Head of Quant Strategies
Ashley Grove Blu Analytics Ltd.

Abstract Biography

Results on Pricing American Options with Reinforcement Learning

Some of the main difficulties when pricing an American put option with reinforcement learning is to make sure that the continuation value one step before maturity is equal to the European put option, and that the Snell envelope is equal to the strike at maturity and decreases exponentially with time-to-maturity. We chose to assess the performance of existing RL networks based on those two criteria and propose some modifications to improve results. We benchmark our model with the continuous BAW on non-dividend paying stock and obtain a satisfactory outcome.

Dr. Daniel Bloch, Head of Quant Strategies, Ashley Grove Blu Analytics Ltd.

Dr. Daniel Bloch is Head of Quantitative Strategies at Blu, a systematic event trading fund using NLP to anticipate large market moves based on news. Prior to working at Blu, Daniel managed teams of quant researchers in top tier banks, developing and implementing option pricing and risk models. He was also a Portfolio Manager on multi-strategies systematic trading across continents, using multifractal analysis and machine learning. Daniel conducts research on mathematical finance and AI, focusing on dynamical models applied to forecasting the stock and option market in order to maximise return and minimise risk.

Dr. Jesper Andreasen
Head of Quantitative Research
Saxo Bank

Abstract Biography

Decoding the Auto Encoder

We fit auto-encoder neural networks to swap yield curves in 10 different currencies and over a period of 15 years. We show that the different yield curve shapes can be captured well with only two to three factors. The same neural network can be used across all currencies. We further show that the resulting yield curves are close to dynamically arbitrage free and we identify the stochastic process of the short rate under the risk neutral measure that is consistent with the neural network.

Dr. Jesper Andreasen, Head of Quantitative Research, Saxo Bank

Jesper Andreasen’s career spans 26 years in the industry and he has headed quant teams at Saxo Bank, Danske Bank, Bank of America, Nordea and General Re Financial Products. Jesper co-received Risk Magazine’s quant of the year award in 2001 and 2012 and their in-house risk system of the year in 2015.

He is an honorary professor of mathematical finance at the University of Copenhagen and he holds a phd in the same subject from Aarhus University.

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 Recordings

Conference recordings are available for all CQF Institute members.
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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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QUANT INSIGHTS CONFERENCE:

Machine Learning in Quant Finance

About the Conference

Conference Schedule

Conference Speakers

Hedging Barrier Options Using Reinforcement Learning

Professor John Hull, Maple Financial Professor of Derivatives and Risk Management, Joseph L. Rotman School of Management

Machine Learning Methods for Market-Making and Execution in Fixed Income

Professor Edith Mandel, Principal, Greenwich Street Advisors, LLC

Customizing Large Language Models for Quant Finance Applications

Dr. Alexander Sokol, Founder, CompatibL

Beating the Markets with HPC+AI: Generating Alpha and Excess Returns in Financial Services

Prabhu Ramamoorthy, Customer/Partner Developer Relationship Manager, NVIDIA

Zero-Knowledge Machine Learning

Aaron Brown, Columnist, Bloomberg

Quantum Solutions in Finance

Dr. Araceli Venegas-Gomez, Founder and CEO, QURECA Ltd.

Results on Pricing American Options with Reinforcement Learning

Dr. Daniel Bloch, Head of Quant Strategies, Ashley Grove Blu Analytics Ltd.

Decoding the Auto Encoder

Dr. Jesper Andreasen, Head of Quantitative Research, Saxo Bank

Dr. Randeep Gug, CQF Institute, Managing Director

Conference Recordings

Conference Organizers

Explore more Quant Insights Conferences

Portfolio Management in Quant Finance

Black-Scholes 50th Anniversary

Annual Quant Insights Conference

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