Video Description
Artificial Intelligence (AI) and Machine Learning (ML) are having a profound impact on systematic investing and trading, fundamentally transforming quantitative strategy development and execution. This talk highlights recent advances in applying AI and ML to systematic investment management, emphasizing their practical roles in generating alpha, optimizing portfolios, and enhancing trading strategies. We argue that maintaining a human-in-the-loop is essential to effectively address challenges and fully realize the benefits of ML-driven automation. The presentation concludes by highlighting promising directions, opportunities, and challenges ahead.
Speaker Bio
Dr. Petter Kolm
Petter Kolm is a Clinical Professor at NYU’s Courant Institute, where he directs the Mathematics in Finance Master’s program. In 2021, he was honored as “Quant of the Year” by Portfolio Management Research (PMR) and the Journal of Portfolio Management (JPM) for his significant contributions to quantitative portfolio theory. He has co-authored numerous influential articles and books on quantitative finance and financial data science. Petter serves on several company advisory boards (including Aisot, Axyon AI, and GoQuant), editorial boards for leading academic journals, and boards of directors for professional associations. Previously, he worked in Quantitative Strategies at Goldman Sachs Asset Management. As a consultant and expert witness, Petter provides expertise in machine learning, portfolio management, risk management, and systematic trading. He earned his Ph.D. in Mathematics from Yale University, an M.Phil. in Applied Mathematics from the Royal Institute of Technology (KTH), and an M.S. in Mathematics from ETH Zurich.
Video Description
Autoencoders can be used to accurately describe the shape of yield curves by a low number of factors, typically 2-3. We demonstrate this using cross sectional data across 10 different currencies spanning a period of 12 years. Using dynamic arbitrage arguments we show that the risk-neutral dynamics of the yield curves can be deduced from the possible shapes produced by the auto-encoder. We use this come up with a new decomposition of fixed income portfolio returns. We draw connections to Cheyette models and discuss application to other asset classes.
Speaker Bio
Dr. Jesper Andreasen
Jesper Andreasen is head of quantitative analytics at Verition Fund Management. Jesper’s career spans 28 years in the industry and he has previously 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 awards 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.
Video Description
Agentic AI is gaining visibility in finance, but many so-called “agentic systems” are little more than PowerPoint proofs of concept or API wrappers with no real autonomy. This talk goes beyond the hype and explains how real agentic systems are being built and deployed. From tool-augmented reasoning and structured reflection loops to infrastructure-aware orchestration. The session outlines what it takes to move from concept to production – cutting through agent-washing and showing where tangible value is already being created.
Speaker Bio
Nicole Königstein
Nicole is the Co-Founder, CEO, and Co-Chief AI Officer at Quantmate, a deep-tech fintech company developing AI agents for portfolio management and strategy development via natural language. She is a globally recognized thought leader in large language models (LLMs) and agentic architectures, with a particular focus on their transformative applications in quantitative finance.
As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at universities. She is also a frequent speaker at AI and quantitative finance events. Nicole has authored Math for Machine Learning and Transformers in Action with Manning Publications. Her forthcoming book, Transformers: The Definitive Guide – Applications Beyond NLP, will be published by O’Reilly Media.
Video Description
AI and humans perform surprisingly alike in classic behavioral psychology experiments. These experiments show that AI shares many cognitive biases of humans and is prone to human-like errors of logical reasoning and recall. We will examine the reasons for this surprising and perhaps even shocking finding, some superficial and some profound. Our analysis will lead to concrete, practical techniques for avoiding these biases and increasing the reliability of AI in business settings.
Speaker Bio
Dr. Alexander Sokol
Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also a co-founder of Numerix, where he served as CTO from 1996 to 2003.
Alexander won the 2018 Quant of the Year Award together with Leif Andersen and Michael Pykhtin for their joint work revealing the true scale of the settlement gap risk that remains in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin), joint measure models and the local price of risk (with John Hull and Alan White), the use of autoencoder manifolds for interest rate modelling (with Andrei Lyashenko and Fabio Mercurio), and the mean reversion skew.
Alexander graduated from high school at the age of 14 and earned 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.
Video Description
Quantum Machine Learning (QML) is an exciting area of quantum computing research that promises to be the first to deliver tangible quantum advantage and quantum utility since many of its algorithms are resistant to some types of noise and do not require large fault-tolerant quantum computers. QML is ideally suited to conducting experiments on the Noisy Intermediate-Scale Quantum (NISQ) computers. We are investigating what is behind the power of QML models and present the latest generation of QML algorithms with applications in quantitative finance.
Speaker Bio
Dr. Oleksiy Kondratyev
Oleksiy Kondratyev is ADIA Lab Research Fellow and Visiting Professor at the Department of Mathematics, Imperial College London. Prior to joining ADIA in 2021 as Quantitative Research & Development Lead, Oleksiy was Managing Director and Head of Data Science & Innovation at Standard Chartered Bank in London.
Oleksiy has over 25 years of quantitative finance experience in both risk management and front office roles and has been recognized as Quant of the Year (2019) by Risk magazine for his research on the application of machine learning techniques to risk factor analysis and portfolio optimisation.
Oleksiy holds MSc in Theoretical Physics from Taras Shevchenko National University of Kyiv and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine. His research interests are in machine learning and quantum computing.
Watch the recordings from the AI and Machine Learning in Quant Finance Conference 2025.
Video Description
A Talk from the Volatility and Risk Conference 2025
Liar’s Poker was a popular game among Wall Street traders from the mid-1980s to the mid-1990s, made famous by Michael Lewis’ book of the same name. Although the game was played for very high stakes among highly quantitative people, no exact game theory solution has been found. Recent AI approaches seem to dominate human players. Given the close resemblance between the game and financial trading, this could be a bellwether for the impact of AI on trading and risk management.
Speaker Bio
Aaron Brown
Aaron Brown is a columnist for Bloomberg and Wilmott Magazine, and teaches at New York University, University of California at San Diego and New Mexico State University. He worked for 35 years on Wall Street as a trader, portfolio manager, head of mortgage securities and risk manager for firms including Morgan Stanley and AQR Capital Management. He is the author of The Poker Face of Wall Street, Red-Blooded Risk, Financial Risk Management for Dummies and A World of Chance (with Reuven and Gabrielle Brenner). He won the 2011 Risk Manager of the Year award from the Global Association of Risk Professionals. He was a top professional poker player in the 1970s and 80s, and continues active poker, sports betting and other gambling activities. He splits his time between Manhattan, New Mexico and Coronado Island. He has degrees from Harvard (Applied Math) and the University of Chicago (Finance and Statistics). He has been actively involved in crypto trading, venture capital and start-ups since 2011.
Video Description
A Talk from Volatility and Risk Conference 2025
Dr. Mark Rosenberg has a PhD in political science from UC Berkeley and is speaking about using data science and political science to systematically measure geopolitical risk and how it impacts financial markets. – The presentation focuses on “Trump 2.0” and analyzing the risks and market impacts emerging from Trump’s second administration, including political volatility, geopolitical tensions, regime stability, etc. – Mark explains GeoQuant’s methodology for quantifying political risk across 146 countries using machine learning and natural language processing on news reports, social media, and expert analysis. – He discusses the “EMFication” of the US – how US political risk is increasingly resembling that of volatile emerging markets. – The talk examines rising protectionism, nationalism/populism in the US and abroad and its market consequences like impacts on the US dollar, Treasuries, equities, etc. – It shares models GeoQuant has built around assets like gold and US equities that incorporate political risk factors to predict prices and risk-adjusted returns. – Rosenberg also discusses forecasts for increasing geopolitical tensions and bilateral risks in regions like South Asia.
Speaker Bio
Dr. Mark Rosenberg
This video of Dr .Rick Bookstaber giving a quant insights conference presentation related to risk management and how it can be transformed using artificial intelligence covers the following: – Innovations in risk management enabled by AI, specifically large language models (LLMs) and embedding/similarity scoring – Rick argues risk management has not changed much since the 1990s and still relies on historical data and models. AI can enable qualitative improvements. – He outlines 4 key areas of improvement: moving from numbers to narratives; from financial to physical risk; from static to dynamic methods; and from historical to forward-looking risk – LLMs can now process vast amounts of text data and generate narratives and explanations around risk, instead of just generating reports – Embedding company data can enable better analysis of physical/supply chain risk and correlations – Agent-based modeling can capture market dynamics better than static models – LLMs may ultimately be able to serve as “chief risk officers” that can converse with executives, relate risks to past crises, and provide rich explanations – Dr .Rick Bookstaber is actively developing new AI-enabled risk applications and encourages collaboration
Video Description
A Talk from Volatility and Risk Conference 2025
Professor Emanuel Derman, Professor Emeritus of Financial Engineering, Columbia University presents the history of quantitative finance and financial models over the past several decades. Professor Derman outlines key innovations and developments in finance, starting with derivatives and the ideas of Spinoza in the 17th century. He then discusses later advancements like the concepts of risk, volatility, diversification, hedging, and no-arbitrage pricing. Much of the talk focuses on the evolution of models like CAPM, APT, and Black-Scholes for valuing securities and options. Emanuel Derman describes how these models quantify relationships between risk and return. Towards the end of the talk, Derman touches on more recent innovations like machine learning in finance as well as continued challenges with modeling volatility and risk. He speculates briefly on potential future directions like AI and quantum finance. Throughout the talk, the Derman aims to show how modern finance has built upon major conceptual breakthroughs over the past 70+ years. He uses stock examples and diagrams to illustrate key points.
Speaker Bio
Dr. Rick Bookstaber
This video of Dr .Rick Bookstaber giving a quant insights conference presentation related to risk management and how it can be transformed using artificial intelligence covers the following: – Innovations in risk management enabled by AI, specifically large language models (LLMs) and embedding/similarity scoring – Rick argues risk management has not changed much since the 1990s and still relies on historical data and models. AI can enable qualitative improvements. – He outlines 4 key areas of improvement: moving from numbers to narratives; from financial to physical risk; from static to dynamic methods; and from historical to forward-looking risk – LLMs can now process vast amounts of text data and generate narratives and explanations around risk, instead of just generating reports – Embedding company data can enable better analysis of physical/supply chain risk and correlations – Agent-based modeling can capture market dynamics better than static models – LLMs may ultimately be able to serve as “chief risk officers” that can converse with executives, relate risks to past crises, and provide rich explanations – Dr .Rick Bookstaber is actively developing new AI-enabled risk applications and encourages collaboration
