Video Type: Conference

Video Description

A Talk from Annual Conference 2024

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.

Speaker Bio

Elie Ayache

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).

Video Description

A Talk from Machine Learning in Quant Finance Conference 2024

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.

Speaker Bio

Dr. Noelle Ibrahim

Dr. Noelle Ibrahim is a Technical Sales Executive for IBM Quantum. Previously she was Associate Partner and IBM Quantum Industry Consultant for the Banking and Financial Markets industries. She currently works across sectors and industries, bringing an ability to cross-pollinate ideas to benefit investment banks.

She has worked across verticals within the financial services sector, leading major transformational risk initiatives including stress testing and IFS9. In addition to leading major transformational risk initiatives in the risk space, 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.

Video Description

A Talk from Machine Learning in Quant Finance Conference 2024

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.

Speaker Bio

Dr. Claus Huber

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.

Video Description

A Talk from Machine Learning in Quant Finance Conference 2024

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.

Speaker Bio

Dr. Anton Antonov

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).

Dmitry Zotikov

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).

Video Description

A Talk from Machine Learning in Quant Finance Conference 2024

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.

Speaker Bio

Edith Mandel

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).

Video Description

A Talk from Machine Learning in Quant Finance Conference 2024

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.

Speaker Bio

David Ardia

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.

Here’s the Annual Quant Insights Conference 2024 with a brand new line-up of talks from leading quant finance experts.

Video Description

A Talk from Machine Learning in Quant Finance Conference 2024

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.

Speaker Bio

Sebastian Petric

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.

Video Description

A Talk from Machine Learning in Quant Finance Conference 2024

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.

Speaker Bio

Dr. Babak Mahdavi-Damghani

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.