Annual Quant Insights Conference

5th - 6th November 2024
08:30 - 14:30 EST
13:30 - 19:30 GMT
Globally Live-Online

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

Days

2 Days

Speakers

14+ Talks

Price

Free Tickets

Post Conference

Video

About the Conference

The annual Quant Insights Conference will be back this November with a brand new line-up of talks from leading quant finance experts.

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

Stay tuned for more detailed information on our speakers and sessions. In the meantime, secure your free ticket below.

Book your free ticket

Conference Schedule

Day 1 Day 2

Day 1 - 5th November 2024

13:30 - 13:35 GMT

Virtual conference doors open

13:35 - 13:40 GMT

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

13:40 - 14:40 GMT

Panel Discussion: Energy Options, Volatility, and Energy Transition
Dr. Ilia Bouchouev, Managing Partner, Pentathlon Investments, and Adjunct Professor, New York University
Professor Doyne Farmer, Director of Complexity Economics, Oxford Martin School
Professor Espen Gaarder Haug, Professor of Finance, Norwegian University of Life Science

14:40 - 15:15 GMT

Time Changes, Fourier Transforms and the Joint Calibration to the S&P500/VIX Smiles
Professor Laura Ballotta, Professor of Mathematical Finance, Bayes Business School

15:15 - 15:45 GMT

Break and Networking

15:45 - 16:20 GMT

Revisiting Elastic String Models of Forward Interest Rates
Jean-Philippe Bouchaud, Chairman and Head of Research, Capital Fund Management

16:20 - 16:55 GMT

Polymodel Analysis of Hedge Funds, Selection and Portfolio Construction
Dr. Raphael Douady, Research Professor, University of Paris I: Panthéon-Sorbonne

16:55 - 17:15 GMT

Break and Networking

17:15 - 17:50 GMT

The Development and Evolution of Mean-Variance Efficient Portfolios in the US and Japan: 30 Years After the Markowitz and Ziemba Applications
Dr. John Guerard, Co-Chief Investment Engineering and Research, Pacific Spirit Investments

17:50 - 18:25 GMT

Non-Adversarial Training of Neural SDEs with Signature Kernel Scores
Blanka Horvath, Associate Professor, University of Oxford

18:25 - 18:45 GMT

Break and Networking

18:45 - 19:20 GMT

A Market Design to Trade Bundles of Securities and Minimal Exercise of American Options
Bruno Dupire, Head of Quantitative Research, Bloomberg

19:20 - 19:25 GMT

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

Day 2 - 6th November 2024

13:30 - 14:00 GMT

Virtual conference doors open - Enjoy networking and explore the EXPO

14:00 - 14:05 GMT

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

14:05 - 14:40 GMT

Is It Possible To Make Investors Happy?
Dr. Paul Wilmott, Founder, Certificate in Quantitative Finance (CQF)

14:40 - 15:15 GMT

Thought and Black-Scholes-Merton: Concept and Intuition in Probability Theory vs. the Financial Market
Elie Ayache, CEO & Founder, ITO33

15:15 - 15:45 GMT

Break and Networking

15:45 - 16:20 GMT

Green Inflation, Money Printing, and Quantum Computers
Professor Espen Gaarder Haug, Professor of Finance, The Norwegian University of Life Science

16:20 - 16:55 GMT

Machine Learning for Factor-based Commodities Investing
Tony Guida, Quantitative Portfolio Manager

16:55 - 17:15 GMT

Break and Networking

17:15 - 17:50 GMT

Quantum-Inspired Tensor Networks in Quantitative Finance
Dr. Samuel Palmer, Engineering Director, Multiverse Computing

17:50 - 18:25 GMT

Continuity and Risk
Professor Dilip Madan, Professor Emeritus of Mathematical Finance, Robert H. Smith School of Business

18:25 - 18:30 GMT

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.

Dr. Paul Wilmott
Founder
Certificate in Quantitative Finance (CQF)

Abstract Biography

Is It Possible To Make Investors Happy?

Career advice. From me?!; Investors = Bosses; My experience; Behavioural Finance 101; The simplest model for Happiness; A few results

Dr. Paul Wilmott, Founder, Certificate in Quantitative Finance (CQF)

Paul is the founder of the Certificate in Quantitative Finance and Wilmott.com and he is internationally renowned as a leading expert on quantitative finance. His research work is extensive, with more than 100 articles in leading mathematical and finance journals, as well as several internationally acclaimed books on mathematical modeling and derivatives, including the best-selling Paul Wilmott On Quantitative Finance, published by John Wiley & Sons.

Bruno Dupire
Head of Quantitative Research
Bloomberg

Abstract Biography

A Market Design to Trade Bundles of Securities and Minimal Exercise of American Options

Exchanges have a function of facilitating trades buy allowing for buyers and sellers of a security to meet and trade. However many strategies require the trade of not only one security but simultaneous execution of several legs, involving several securities. Examples include pair trading, equity portfolios, multi–maturities Futures strategies and options combinations. We call these multi-legs trades bundles. Not executing the various legs at the same time creates a risk of adverse price movement before the full completion. It can be eliminated by crossing the spreads and posting market orders but it incurs cost. We present a mechanism that allows for market participants to post one-sided orders on arbitrary bundles and then the matching engine computes (case of buy order) the cheapest super-replication of the posted bundle by a portfolio of other already posted bundles. So the features of the algorithm is that 1) it does not match security by security but rather a bundle with a collection of other bundles and 2) it can be a “super-match” in the sense that the super-replication may give the additional benefit of a positive residual. We detail the algorithm and apply this methodology to show how it can in certain cases improve the price of option combinations. This approach has an economic value as it allows for more trades to occur at mutually desirable prices.

We focus in particular on bundles of options of a same maturity on a stock. The dominance in terms of profile is easy to understand in the case of European options and we show how it can be generalized to American options.

Bruno Dupire, Head of Quantitative Research, Bloomberg

Bruno Dupire is heading the Quantitative Research at Bloomberg L.P. after having lead research teams at several banks. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

Professor Doyne Farmer
Director of Complexity Economics
Oxford Martin School

Panel Abstract Biography

Energy Options, Volatility, and Energy Transition

The panel will discuss the importance of real options in the energy ecosystem in the context of energy transition with the focus on battery storage and other critical new technologies. We will debate their potential implications on prices and volatility across different energy markets.

Professor Doyne Farmer, Director of Complexity Economics, Oxford Martin School

J. Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking and Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment, University of Oxford. He is also External Professor at the Santa Fe Institute and Chief Scientist at Macrocosm.

His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to UBS in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology. His book, Making Sense of Chaos: A Better Economics for a Better World, was published in 2024.

During the 1980s he was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student in the 1970s he built the first wearable digital computer, which was successfully used to predict the game of roulette.

Blanka Horvath
Associate Professor
University of Oxford

Abstract Biography

Non-Adversarial Training of Neural SDEs with Signature Kernel Scores

Market generators are a rapidly evolving class of models that simulate financial market behavior using neural networks. These deep learning models learn the underlying distribution of financial data and generate synthetic market scenarios, offering a powerful alternative to classical stochastic models. The expression ``Market Generator" only entered the vocabulary of financial modelling around 2019, but by today it has already grown into an area of its own right, with a rapidly growing number of research contributions appearing on the matter. This talk aims to unravel the evolution of the rapidly evolving landscape of generative models in Machine Learning in the context of more classical modelling techniques in finance for the evolution of stock prices but also in the context of the most recent trends of Generative AI that include LLMs.

More concretely we will present a very powerful generative model for time-series modelling: State-of-the-art performance for irregular time series generation has been previously obtained by training Neural SDEs adversarially as GANs. However typical for GAN architectures suffer from various instabilities in training. Instead, we introduce a novel class of generative models that can be trained non-adversarially, using scoring rules on pathspace based on signature kernels. Most notably this procedure permits to evaluate a corpus of paths against a single observation. But additionally, our procedure permits conditioning on a rich variety of market conditions and significantly outperforms alternative ways of training Neural SDEs on a variety of tasks including the simulation of rough volatility models, the conditional probabilistic forecasts of real-world forex pairs where the conditioning variable is an observed past trajectory. Moreover, our framework can be easily extended to generate spatiotemporal data, including the mesh-free generation of limit order book dynamics.

Blanka Horvath, Associate Professor, University of Oxford

Blanka Horvath is an Associate Professor of the University of Oxford, researcher at the Oxford Man Institute and a Member of the DataSig group affiliated with the Alan Turing Institute and Emmy Noether Fellow of the London Mathematical Society. Prior to her current role, she held faculty positions at the Technical University of Munich and at King's College London and postdoctoral positions at Imperial College London and ETH Zurich.

Blanka's research interests in mathematical finance include stochastic volatility modelling with a particular focus on rough volatility models, and more recently, generative models with a special interest for data representation via rough path signatures. Her research has a pronounced emphasis on practical applicability, which was recognised by the 2020 Rising Star Award of Risk Magazine. Several of her research contributions have been implemented by various entities in the financial industry including solutions to pricing and calibration of rough volatility models and to the development of appropriate market generators in the deep hedging paradigm.

Dr. Ilia Bouchouev
Managing Partner
Pentathlon Investments

Panel Abstract Biography

Energy Options, Volatility, and Energy Transition

Dr. Ilia Bouchouev, Managing Partner, Pentathlon Investments

Dr. Ilia Bouchouev is the former President of Koch Global Partners where he launched and managed global derivatives trading business for over 20 years. Over the years, he introduced several energy derivatives products and was recognized as one of the pioneers in energy options trading. He is currently a managing partner at Pentathlon Investments and an adjunct professor at New York University, where he teaches energy trading at Courant Institute of Mathematical Sciences. He is also a senior research fellow with Oxford Institute for Energy Studies.

Dr. John Guerard
Co-Chief Investment Engineering and Research
Pacific Spirit Investments

Abstract Biography

The Development and Evolution of Mean-Variance Efficient Portfolios in the US and Japan: 30 Years After the Markowitz and Ziemba Applications

In 1993, John Mulvey co-edited a Special Issue, entitled “Financial Engineering”, in the Annals of Operations Research. In that issue, Guerard, Takano, and Yamane (1992) reported mean-variance efficient portfolios for the Japanese and U.S. equity markets and showed that the use of a regression-weighted composite model of earnings, book value, cash flow, sales, and their relative variables and forecasted earnings, outperformed their respective equity benchmarks by approximately 400 basis points annually. William T. (Bill) Ziemba was the referee of the Guerard et al. (1993) paper. Markowitz and Xu (1994) tested the composite model strategy and found that its excess returns were statistically significant from a variety of models tested, and the composite model strategy was not the result of data mining. Thirty years after the issue, we report factor backtesting results and robust regression modeling in creating optimized US and Japanese portfolio results for the 1995-2022 period, a combination of methods and the latest commercially available multi-factor models for portfolio selection. Recent publications by Markowitz, Guerard, and Xu report additional support for the absence of data mining. Furthermore, the weighted latent root regression modeling is still relevant. Our results suggest that stock selection models can be effectively employed to deliver excess returns. The authors believe that financial anomalies exist, persist, and most likely will exist and can be profitably exploited.

Dr. John Guerard, Co-Chief Investment Engineering and Research, Pacific Spirit Investments

John Guerard, PhD, is Co-Chief Investment Engineering and Research, Pacific Spirit Investments, living in Bluffton, South Carolina.

John was a member of the McKinley Capital Management Scientific Advisory Board and has served as an Affiliate Instructor in the Department of Applied Mathematics, the Computational Finance and Risk Management Program, The University of Washington, Seattle, WA. He served almost 15 years as Director of Quantitative Research at McKinley Capital Management, in Anchorage, Alaska. John previously worked at Drexel, Burnham Lambert, and Daiwa Securities, where he was co-Portfolio Manager, with Dr. Harry Markowitz, who was awarded The Nobel Prize in Economic Sciences in 1991, on Fund Academy and The Japan Equity Fund. John was awarded the first Moskowitz Prize for outstanding research in socially responsible investing in 1997. John earned his AB in Economics from Duke University, MA in Economics from the University of Virginia, MSIM from the Georgia Institute of Technology, and Ph.D. in Finance from the University of Texas, Austin. John taught at The University of Virginia, in its McIntire School of Commerce, and at Lehigh University.

John has published several monographs, including Quantitative Corporate Finance (Kluwer, now Springer, 2007, with Eli Schwartz; the third edition is recently published, 2022), The Handbook of Applied Investment Research (World Scientific Publishing, 2020, with William T. Ziemba), and The Leading Economic Indicators and Business Cycles in the United States (Switzerland: Palgrave Macmillan, 2022). John is under contract to publish Financial Forecasting in 2025, Oxford University Press. Dr. Guerard has published in peer-reviewed and practitioner journals, including Management Science, Annals of Operations Research, The International Journal of Forecasting (IJF), The IBM Journal of Research and Development, Interfaces, The Journal of Portfolio Management, and The Journal of Investing (JOI). He served for over 30 years as an Associate Editor of the JOI and IJF. John is currently editing a Special Issue of Annals of Operations Research to honor Harry Markowitz, to be published in late 2024.

Jean-Philippe Bouchaud
Chairman and Head of Research
Capital Fund Management

Abstract Biography

Revisiting Elastic String Models of Forward Interest Rates

Twenty five years ago, several authors proposed to model the forward interest rate curve as an elastic string along which idiosyncratic shocks propagate, accounting for the peculiar structure of the return correlation across different maturities. In this paper, we revisit the specific ``stiff'' elastic string field theory of Baaquie-Bouchaud 2004 in a way that makes its micro-foundation more transparent. Our model can be interpreted as capturing the effect of market forces that set the rates of nearby tenors in a self-referential fashion. The model is parsimonious and accurately reproduces the whole correlation structure of the FRC over the time period 1994-2023, with an error below 2 \%$. The dependence of correlation on time resolution (also called the Epps effect) is also faithfully reproduced within the model and leads to a cross-tenor information propagation time of a few tens of minutes. Finally, we confirm that the perceived time in interest rate markets is a strongly sub-linear function of real time. In fact, our results are fully compatible with hyperbolic discounting, in line with the recent behavioural Finance literature.

Jean-Philippe Bouchaud, Chairman and Head of Research, Capital Fund Management

Jean-Philippe Bouchaud is a pioneer in Econophysics. He co-founded the company Science & Finance in 1994, which merged with Capital Fund Management (CFM) in 2000. In 2018 he was appointed as an adjunct Professor at Ecole Normale Supérieure, where he teaches a course on complex systems. He is also a member of the French Académie des Sciences. His work includes the physics of disordered and glassy systems, granular materials, the statistics of price formation, stock market fluctuations, and agent based models for financial markets and for macroeconomics. He was awarded the CNRS Silver Medal in 1995, the Risk Quant of the Year Award in 2017, the Lars Onsager prize in 2024. He is the author of several books in physics and finance, including “Trades, Quotes & Prices” (CUP, 2018).

Dr. Samuel Palmer
Engineering Director
Multiverse Computing

Abstract Biography

Quantum-Inspired Tensor Networks in Quantitative Finance

This presentation introduces the application of quantum-inspired Tensor Networks in quantitative finance, focusing on their ability to handle high-dimensional data efficiently. We explore the relationship between quantum computing and Tensor Networks, and how these methods can enhance machine learning models. Specific attention is given to the use of Tensor Network machine learning in derivatives pricing, where they can offer improved accuracy and computational efficiency. By leveraging these techniques, Tensor Networks have the potential to transform both machine learning and financial modeling.

Dr. Samuel Palmer, Engineering Director, Multiverse Computing

Dr. Samuel Palmer is a seasoned expert in machine learning and quantum computing, with extensive experience leading cutting-edge projects in the financial technology space. As Engineering Director at Multiverse Computing, Samuel spearheads the development of advanced machine learning solutions and quantum computing applications for options pricing and other financial applications. With a PhD in Computational Finance from University College London, Samuel is also deeply involved in research, holding multiple patents and publishing on quantum-inspired methods for derivatives pricing and portfolio optimization.

Elie Ayache
CEO & Founder
ITO33

Abstract Biography

Thought and Black-Scholes-Merton: Concept and intuition in probability theory vs. the financial market

We consider thought under its two indissociable aspects, concept and intuition. You can only expand the concept by analysis, and fail to synthesize objects, in Kant's sense of objects of possible experience. For that purpose, something must be added to the concept, which is sensibility, or intuition. Concepts without intuition are empty, according to Kant.

Probability theory is a theory of the concrete world, as such indissociable from possible experience. For this reason, there is something more in probability theory than in real analysis. It is the concrete situation, or trial of the world, or random sample ω, without which probability theory would lack the two characteristic notions of independence and repetition. The trial ω is the Kantian sensibility or intuition of probability theory. The event A, which alone is subject to analysis and is assigned probability, thus admits the concrete situation ω as content ( ω ϵ A).

Under the concepts of probability theory, the Black-Scholes-Merton (BSM) analysis leads only to the dynamic replication of contingent payoffs, and to no market thereof. To change constant volatility into stochastic volatility does not help. In order to exist, the derivatives market requires a new form of Kantian sensibility, or intuition, or concrete situation, which we denote by ϖ. The situation ϖ of the market is radically different from the situation ω of probability theory or statistical analysis. Implied volatility is logically (even temporally) incompatible with historical volatility.

The specific sensibility of the market , ϖ, is the "exchange". As Helyette Geman says: "BSM is risky by definition because it amounts to exchanging volatility." As a result, BSM must be thought differently. It becomes the first instance of the "market models", whose conceptual analysis relies on the "trading decision" and the "pricing function", or the very denial of probability and stochastic structure.

Elie Ayache, CEO & Founder, ITO33

Elie Ayache is a former option market-maker on MATIF (1987-1990) and LIFFE (1990-1995). He is the co-founder (1999) and currently the CEO of ITO 33, a software company specializing in convertible bond pricing, and more generally equity index and equity-to-credit single name derivative pricing. Elie is the author of The Blank Swan: The End of Probability (Wiley 2010) and The Medium of Contingency: An Inverse View of the Market (Palgrave 2015).

Professor Laura Ballotta
Professor of Mathematical Finance
Bayes Business School

Abstract Biography

Time Changes, Fourier Transforms and the Joint Calibration to the S&P500/VIX Smiles

We develop a model based on time changed Lévy processes and study its ability of reproducing the joint S&P500/VIX implied volatility smiles and the VIX futures prices - a problem known in the literature as the ‘joint calibration problem’. The model admits semi-analytical characteristic functions for the key quantities, and therefore efficient Fourier based pricing schemes can be deployed. We focus on a specification of the proposed general setting which uses purely discontinuous processes. Results from the application to market data show satisfactory performances in solving the joint calibration problem, and therefore demonstrate that the class of affine processes can provide a workable fit. (Joint work with Ernst Eberlein and Grégory Rayée).

Professor Laura Ballotta, Professor of Mathematical Finance, Bayes Business School

Professor Ballotta works in the areas of quantitative finance and risk management. She has written on topics including stochastic modelling for financial valuation and risk management, numerical methods aimed at supporting financial applications, and the interplay between finance and insurance. Recent major contributions have appeared in Journal of Financial and Quantitative Analysis, European Journal of Operational Research and Quantitative Finance among others. A former pole-vaulter, she is passionate of track and field.

Dr. Raphael Douady
Research Professor
University of Paris I: Panthéon-Sorbonne

Abstract Biography

Polymodel Analysis of Hedge Funds, Selection and Portfolio Construction

Polymodels are a statistical analysis technique for dynamic objects evolving within an environment, which is dynamic as well. Both the object and the environment are described by time series. In a financial context, the object is an asset, a fund, a portfolio, anything that can represent an investment, while the environment is made of variables or "factors" that describe the state of the market. Polymodels provide the individual response of the object to every single variable of the environment, together with a reliability score. These response functions being nonlinear, the fragility/antifragility properties of the object can be evaluated.

We will show how to estimate polymodels and to use them for asset selection, risk assessment and portfolio construction, as well as recent research on the risk of hedge funds. Our focus is the fragilities of those prone to blow up at the time of crisis, despite impeccable track record.

Dr. Raphael Douady, Research Professor, University of Paris I: Panthéon-Sorbonne

Raphael Douady is a French mathematician and economist specializing in data science, financial mathematics and chaos theory at the University of Paris I-Panthéon-Sorbonne. He formerly held the Frey Chair of quantitative finance at Stony Brook University and was academic director of the French Laboratory of Excellence on Financial Regulation. He earned his PhD in Hamiltonian dynamics and has more than 25 years of experience in the financial industry. He has particular interest in researching portfolio risks, for which he has developed especially suited powerful nonlinear statistical and data science models, as well as macroeconomics and systemic risk. He founded fin tech firms Riskdata (risk management for the buy-side) and Datacore Innovations (quantitative portfolio of ETFs). He also advises several quantitative hedge funds and family offices on their risks and trading strategies. With Nassim Taleb and Robert Frey, he founded the Real World Risk Institute, aimed at educating the industry on extreme risks. Douady is a founding member of the Club Praxis, a New York-based think tank advising the French government on its economic policy and sits on the board and, previously on the investment committee, of Friends of IHES, a foundation supporting the Institut des Hautes Etudes Scientifiques (the French brother of Princeton IAS). He is an alumni of Ecole Normale Supérieure in Paris and was awarded a gold medal at the International Mathematical Olympiads.

Professor Dilip Madan
Professor Emeritus of Mathematical Finance
Robert H. Smith School of Business

Abstract Biography

Continuity and Risk

It is argued that in a variety of ways randomness plus path continuity is a strange and almost contradictory request. Attention is then turned to discontinuous continuity approximations with financially relevant results being delivered by finite activity processes of finite variation. The CGMY model is employed to decompose stochastic volatility into stochasticity in speed, scale and the degree of continuity. The bilateral gamma model is used to relate risk premia to the degree of continuity as measured by the speed and scale coefficients. Rescaling mechanisms are introduced to recover return distributions from option prices that are useful for investment analysis.

Professor Dilip Madan, Professor Emeritus of Mathematical Finance, Robert H. Smith School of Business

Dilip Madan is Professor Emeritus of Mathematical Finance at the Robert H. Smith School of Business. Currently he serves as a consultant to Morgan Stanley, and Norges Bank Investment Management. He is a founding member and past President of the Bachelier Finance Society. He received the 2006 von Humboldt award in applied mathematics, was the 2007 Risk Magazine Quant of the year, received the 2008 Medal for Science from the University of Bologna, held the 2010 Eurandom Chair and is the IAQF Financial Engineer of the Year 2021. He has published over 200 papers and serves on the Advisory Board of Frontiers of Mathematical Finance and as a Director of the Scientific Association of Mathematical Finance.

Professor Espen Gaarder Haug
Professor of Finance
The Norwegian University of Life Science

Abstract Panel Abstract Biography

Green Inflation, Money Printing, and Quantum Computers

This talk will explore how certain aspects of the green shift may potentially lead to "green inflation," driven by money printing and investment in less effective energy production methods, especially in an environment where populism is prevalent.

Additionally, we will briefly discuss our views on quantum computers, including quantum gravity computers, and their potential implications for finance and the society.

Energy Options, Volatility, and Energy Transition

Professor Espen Gaarder Haug, Professor of Finance, The Norwegian University of Life Science

Espen Gaarder Haug has accumulated over 30 years of experience in derivatives trading and research. He served as a proprietary option trader at J.P. Morgan Chase in New York and as a trader for the hedge fund Paloma Partners. Dr. Haug's prolific publications span journals such as Quantitative Finance, the International Journal of Theoretical and Applied Finance, and Wilmott Magazine.

He currently holds the position of Professor of finance at the Norwegian University of Life Science. Despite being professor in finance he has over the past decade primarily dedicated his efforts to research in the field of physics, particularly focusing on developing a full quantum gravity theory. Professor Haug has also authored numerous articles at the intersection of finance and physics, highlighting the connection between these two disciplines, his talk will be along some of these lines.

Tony Guida
Quantitative Portfolio Manager

Abstract Biography

Machine Learning for Factor-based Commodities Investing

In the past decade, machine learning techniques have been widely adopted in the equities space, driving innovation both in academia and among practitioners. Leveraging the rich data environment of single-name equities, these advancements have unlocked new insights in quantitative finance. However, the application of machine learning in commodities futures markets remains underexplored, primarily due to the "data-poor" nature of this asset class compared to equities.

This research investigates the use of machine learning in systematic commodities futures trading, expanding beyond the traditional price-based strategies that have dominated the field. We integrate well-established factor-based signals, commonly used in commodities investing, as inputs into our machine learning models. Our findings demonstrate that machine learning enhances the evolution of systematic commodities strategies, offering a more adaptable and comprehensive framework for forecasting market behavior and constructing long-short portfolios.

Utilizing a 30-year dataset of 44 commodities futures contracts, we train machine learning models to predict future performance and build long-short portfolios. The results indicate that these models not only rival the performance of traditional systematic approaches but also provide deeper insights into market dynamics and critical factors influencing commodities asset pricing.

Tony Guida, Quantitative Portfolio Manager

Tony Guida is a Quantitative Portfolio Manager and researcher. He began his career at Unigestion in 2006, joining the quantitative equity team as a research analyst. He later became a member of the Research and Investment Committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients. In 2015, Tony moved to Edhec Risk Scientific Beta as a Senior Consultant, specializing in risk allocation and factor strategies. In 2016, he joined a major UK pension fund to build an in-house systematic equity strategy, co-managing £8 billion as a Senior Quantitative Portfolio Manager.

In January 2019, Tony joined RAM Active Investments as a Senior Quantitative Researcher in equities, eventually co-heading the systematic macro hedge fund offering. In 2023, he co-founded a hedge fund, serving as Co-Head of Research and Chief Data Scientist, focusing on systematic cross-asset strategies.

Tony holds Bachelor's and Master's degrees in Econometrics, Economics, and Finance from the University of Savoy, France. He has co-authored and edited several books on the application of machine learning in finance, including:
• Big Data and Machine Learning in Quantitative Investment (Wiley, 2018)
• Machine Learning for Factor Investing, R Version (CRC, 2020)
• Machine Learning for Factor Investing, Python Version (CRC, 2023)

He is also an advisory board member for the Financial Data Professional Institute and serves as a reviewer for machine learning papers in several academic journals.

Dr. Randeep Gug
Managing Director
CQF and CQF Institute

Biography

Dr. Randeep Gug, CQF Institute, Managing Director

Dr. Randeep Gug is the Managing Director of the CQF Institute and 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

5th - 6th November 2024

Time

08:30 - 14:30 EST / 13:30 - 19:30 GMT

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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Conference Schedule

Conference Speakers

Is It Possible To Make Investors Happy?

Dr. Paul Wilmott, Founder, Certificate in Quantitative Finance (CQF)

A Market Design to Trade Bundles of Securities and Minimal Exercise of American Options

Bruno Dupire, Head of Quantitative Research, Bloomberg

Energy Options, Volatility, and Energy Transition

Professor Doyne Farmer, Director of Complexity Economics, Oxford Martin School

Non-Adversarial Training of Neural SDEs with Signature Kernel Scores

Blanka Horvath, Associate Professor, University of Oxford

Energy Options, Volatility, and Energy Transition

Dr. Ilia Bouchouev, Managing Partner, Pentathlon Investments

The Development and Evolution of Mean-Variance Efficient Portfolios in the US and Japan: 30 Years After the Markowitz and Ziemba Applications

Dr. John Guerard, Co-Chief Investment Engineering and Research, Pacific Spirit Investments

Revisiting Elastic String Models of Forward Interest Rates

Jean-Philippe Bouchaud, Chairman and Head of Research, Capital Fund Management

Quantum-Inspired Tensor Networks in Quantitative Finance

Dr. Samuel Palmer, Engineering Director, Multiverse Computing

Thought and Black-Scholes-Merton: Concept and intuition in probability theory vs. the financial market

Elie Ayache, CEO & Founder, ITO33

Time Changes, Fourier Transforms and the Joint Calibration to the S&P500/VIX Smiles

Professor Laura Ballotta, Professor of Mathematical Finance, Bayes Business School

Polymodel Analysis of Hedge Funds, Selection and Portfolio Construction

Dr. Raphael Douady, Research Professor, University of Paris I: Panthéon-Sorbonne

Continuity and Risk

Professor Dilip Madan, Professor Emeritus of Mathematical Finance, Robert H. Smith School of Business

Green Inflation, Money Printing, and Quantum Computers

Energy Options, Volatility, and Energy Transition

Professor Espen Gaarder Haug, Professor of Finance, The Norwegian University of Life Science

Machine Learning for Factor-based Commodities Investing

Tony Guida, Quantitative Portfolio Manager

Dr. Randeep Gug, CQF Institute, Managing Director

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