Tag: Conference

Video Description

A Talk from Annual Conference 2024

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.

Speaker Bio

Dr. Raphael Douady

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.

Video Description

A Talk from Annual Conference 2024

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 back testing 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.

Speaker Bio

Dr. John Guerard

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.

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.