Industry Talk December 2025
False findings in financial research can lead to costly misallocations, flawed regulation, and misguided strategies. This event explores why these errors occur, focusing on the widespread assumption that financial time series are stationary and a-Holder continuous- an assumption at odds with markets shaped by regime shifts, stochastic volatility, and structural breaks.
Through case studies spanning AI-driven models and advanced quantitative techniques, Bloch argues for a fundamental shift toward methodologies that embrace non-stationary dynamics and recognize the limits of prediction in complex systems.
By watching this video, you will gain:
- Critical insights into why false findings persist in financial research and their real-world impact
- Understanding of flawed assumptions in financial modeling, particularly around stationarity and continuity
- Exposure to case studies demonstrating failures in AI-driven models and quantitative techniques
- Alternative frameworks that embrace non-stationary dynamics and market complexity
- Practical perspective on the limits of prediction in complex financial systems
Speaker Bio
Daniel Bloch
As the founder of Quant Finance Limited, Daniel Bloch is at the forefront of statistical arbitrage, specialising in the relative value of stocks, futures, options, and advanced derivatives pricing and risk management. In addition to his role as an industry leader, Daniel is currently focused on bridging advanced AI research with real-world trading applications as a Distinguished Visiting Professor at VinUniversity. He also teaches Reinforcement Learning at the CQF and Systematic Trading at Paris 1 Sorbone, shaping the next generation of quantitative finance experts.
Industry Talk December 2025
“It’s a difference of opinion that makes a horse race.” Generally attributed to Mark Twain, this quote encapsulates the debate over the usefulness of research amid growing concerns in the scientific community about the replicability, reproducibility and reliability of research findings.
By watching this video, you will:
- Understand the limitations of traditional statistical methods – including p-values and NHST—and explore alternative approaches such as Bayesian inference and effect size estimation.
- Evaluate methodological reforms like open science practices and preregistration, and their impact on improving research transparency and credibility.
- Gain practical insights into computational reproducibility, including how programming tools can support robust and replicable quantitative research.
Speaker Bio
Dr. Stephen Weston
Stephen Weston is a visiting professor of computer science at Imperial College where he supervises research by MSc and PhD students. He received his PhD in robust optimal control theory from City University, London. He is also currently a PhD candidate in AI at University College London where he focuses on issues in human-computer interaction at the intersection of generative AI and decision theory. Prior to becoming a PhD student for the second time he spent over 30 years in a variety of front office roles in investment banking, risk management (with JP Morgan and Deutsche Bank) and technology (with Intel).
Speaker Bio
Peter Simpson
Peter joined OneTick in 2019, and is responsible for the OneTick Product, ensuring that the platform continues to support customer needs. Prior to his work with OneMarketData, Peter held several senior roles including VP of Product at Datawatch Panopticon, Senior Manager of Analytics at Deloitte UK, and 10 years in various roles at HSBC Global Markets. Peter holds a Master of Science in Information Systems Engineering and a Bachelor of Science in Space Science & Technology, from Leicester University.
Alexander Serechenko
Alexander Serechenko is a Senior Python Developer specializing in AI-driven solutions for time-series data analytics. With an expertise in machine learning, MLOps, and natural language processing, he leads the development of AI-powered search and automation tools. He holds a Master’s degree in cryptography and information security from the National Research Nuclear University MEPhI.
Industry Talk November 2025
Financial regulatory agencies conduct periodic stress testing of systemically significant financial institutions to ensure they have the requisite capital to continue functioning as viable businesses during times of economic stress without jeopardizing the stability of the financial system. Manual design of these scenarios using historical data, exclusively or primarily, is hamstrung by the inherent limitations of historical experience, which may be inadequate to model unforeseen economic scenarios. To further compound the problem, correlations between macroeconomic variables may change and evolve in markedly different manner during those periods of economic malaise and a manual design of testing scenarios is likely to overlook those aspects of macroeconomic variable evolution. This talk will showcase the ability of generative AI methods to handle the twin challenges of economic scenario generation – generating realistically evolving scenarios that can capture the breadth of potential but unforeseen periods of stress.
Speaker Bio
Samit Ahlawat
Samit Ahlawat currently works at Meta as a Machine Learning Engineer and has worked as a portfolio manager at QSpark Investment, specializing in US equity and derivative trading. He has extensive experience in quantitative asset management and market risk management, having previously worked at JP Morgan Chase and Bank of America. His research interests include artificial intelligence, risk management, and algorithmic trading strategies. Samit holds a master’s degree in numerical computation from the University of Illinois Urbana-Champaign. Samit has authored several research papers in artificial intelligence, finance, economics and numerical computation in addition to holding a patent for facial recognition technology. His research on using machine learning technologies to improve financial forecasting has enabled finance practitioners to leverage generative AI tools, such as variational auto-encoders (VAE), alongside statistical methodologies to model asset price distribution probabilities. Samit also mentors AI professionals at Kaggle and has delivered industry talks and presentations on artificial intelligence.
Industry Talk September 2025
This study provides a detailed framework for incorporating currencies into multi asset portfolios, emphasizing the diversification benefits of active currency exposure. Our findings demonstrate that adding currencies to a traditional bonds and equities portfolio can materially improve risk adjusted returns. We present three systematic approaches to modeling foreign exchange within asset allocation frameworks, each targeting distinct exposures to the carry, value, and trend factors. Across all three models, active currency strategies enhance drawdown profiles and reduce overall portfolio volatility, while delivering incremental positive returns. Because currencies lack fixed cash flows and typically generate near zero long term passive returns, pure passive investment in FX is impractical. The major contribution of this study is therefore the development of a systematic methodology for estimating and implementing active currency benchmarks—tools that investors can reliably use within portfolio construction and tactical asset allocation processes.
Speaker Bio
Dr. Gueorgui S. Konstantinov
Gueorgui S. Konstantinov, Ph.D, CAIA, FDP is a senior portfolio manager and has over 18+ years of experience. He managed global bond portfolios and currencies for institutional investors and pension funds. He is an editorial board member of The Journal of Portfolio Management, The Journal of Alternative Investments, and The Journal of Financial Data Science. He earned the designations of Chartered Alternative Investments Analyst (CAIA) and Financial Data Professional (FDP). He received his MA in economics in 2005 and received a doctoral degree in 2008 from Vienna University of Economics and Business Administration (WU).
Industry Talk August 2025
Non-Maturing Deposits (NMDs) are an important part of a bank’s balance sheet, traditionally forming a stable source of funding. These products are of relevance for liquidity/funding risk as well as interest rate risk management. The latter, in particular, has become increasingly significant given the recent three and half years of rapid transition from more than a decade of low and broadly flat interest rates, into a new and very different environment of high interest rates. Understanding the impact of drivers such as interest rates on NMD balances is of ever greater importance for effective risk management. We discuss how to design models to predict the behaviour of non-maturing deposits balances and the critical role such models can play. We also touch on key design principles to develop multi use NMD models that can support different business areas across the bank.
Speaker Bio
Priya Balan
Priya is a senior Director in the Quantitative Analytics group in Barclays where she is head of Balance sheet modelling supporting Treasury and Finance departments. She has 24 years of industry experience in a career spanning Quantitative Analytics, Sell Side Research and Front Office Software Development. Prior to working in Quantitative Analytics, Priya was a strategist in the Research team at Barclays where she worked in different areas including European Securitisation Research, Structured Credit Strategy and Credit Macro Strategy. Before joining Barclays in 2007 she worked as a software developer at Deutsche Bank supporting the Credit Derivatives and the Rates trading desks. Priya has a Masters degree in Financial Engineering from Birkbeck, University of London.
Industry Talk July 2025
Traditional asset allocation frameworks require the estimation of expected returns and covariance matrices for optimization of multi-asset portfolios. A particular challenge arises for portfolio optimization with allocations to liquid public assets and illiquid private assets and hedge funds.
We develop a systematic risk-based asset allocation framework that incorporates instruments with varying liquidity profiles. We introduce the Hierarchical Clustering Group Lasso method for the estimation of the asset covariance matrix using a set of risk factors.
We apply risk-budgeting optimization for the strategic asset allocation to a universe of public and private benchmarks without using explicit return forecasts. To implement tactical asset allocation, we introduce price-based signals, including momentum and low beta, for traditional investments. For alternative investments, such as private assets and hedge funds, we incorporate the managers’ alphas to account for their systematic and idiosyncratic risks. We design our optimization engine to incorporate instruments with different rebalancing schedules as well as different turnover requirements and tracking error constraints.
Our extensive empirical analysis clearly demonstrates that the proposed methodology provides substantial improvements in terms of risk-adjusted performance metrics, including superior Sharpe ratios and reduced drawdown risks relative to static weight benchmarks.
This presentation is based on the joint work with Ivan Ossa and Mika Kastenholz.
Speaker Bio
Dr. Artur Sepp
Artur Sepp is the Global Head of Investment Services Quant Group at LGT bank in Zurich focusing on quantitative asset allocation and systematic investment strategies. Artur has almost 20 years of experience in financial markets, including heading quant research and portfolio management at a systematic hedge fund and a family office, as well as leading development of front-office quant strategies and derivatives at private (Julius Baer) and investment banks (Merrill Lynch/BofA). Artur has a PhD in Mathematical Statistics from the University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. His expertise covers quantitative investing and asset allocation, quantitative modelling of derivative securities, machine learning and data science, and blockchain applications within decentralized finance. He is the author and coauthor of several research articles on quantitative finance published in key journals. Artur won the Quant of the Year Award from Risk Magazine (2024). He is an active martial arts practitioner in his free time.
Video Description
Industry Talk June 2025
Large Language Models (LLMs) are increasingly being integrated with reinforcement learning (RL) to push the boundaries of generalist AI agents. In finance, where real-time decision-making is critical, test-time compute efficiency plays a pivotal role in ensuring models can adapt dynamically to evolving market conditions. In-context reinforcement learning (ICRL) is emerging as a transformative approach, enabling LLMs to learn and refine on the fly without explicit fine-tuning. ICRL enhances adaptability in trading, risk assessment, and portfolio optimization. This paradigm shift moves us closer to AI agents capable of robust decision-making, paving the way for more autonomous and generalizable systems in high-stakes applications.
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
Industry Talk May 2025
We develop a joint model for the S&P500 and the VIX indices with the aim of extracting forward looking market consistent information on the correlation between the two markets. We achieve this by building the model on time changed Lévy processes, deriving closed analytical expressions for relevant quantities directly from the joint characteristic function, and exploiting the market quotes of options on both indices. We perform a piece-wise joint calibration to the option prices to ensure the highest level of precision within the limits of the availability of quotes in the dataset and their liquidity. Using the calibrated parameters, we are able to quantify the‘leverage/volatility feedback’effect along the term structure of the VIX options and corresponding VIX futures. We illustrate the model using market data on SPX options and both futures and options on the VIX.
This is joint work with Ernst Eberlein and Gregory Rayée.
Speaker Bio
Professor Laura Ballotta
Laura Ballotta is a Professor of Mathematical Finance at Bayes Business School (formerly Cass). Prof. 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.
She serves as associate editor and referee for several international journals in the field.
Prof. Ballotta holds a BSc in Economics from Universita’ Cattolica del Sacro Cuore, a MSc in Financial Mathematics from the University of Edinburgh, and a PhD in Mathematical and computational methods for economics and finance, from Universita’ degli Studi Bergamo.
She is the course director of the Quants cluster of MSc programmes at Bayes Business School.
Video Description
Industry Talk April 2025
This talk illustrates the enormous model risk that is present in the quantitative finance field and other domains. Various models calibrated to the same data can lead to significantly different results. Even within a single model, particular choices, like the objective function of a calibration, the numerical scheme of a Monte-Carlo simulation, the instruments to include or exclude from the calibration exercises can again lead to a variety of different outcomes. As a result, we must conclude that there is quite a bit of uncertainty around various pricing exercises. This issue is also present in other domains of science, like climate modelling, and hence one has to be cautious by using the outcome of a single model for policy making. Finally, we connect model risk with conic finance. Speakers: Wim Schoutens is a quantitative finance professor at the University of Leuven, Belgium. He has extensive practical experience of model implementation and validation. He is well known for his consulting work to the banking industry and national and supra-national institutions. He is an independent expert advisor to the European Commission, has worked for the IMF and is the author of several books on quantitative finance. He is also member of different editorial Boards of international finance journals.
Speaker Bio
Wim Schoutens
Wim Schoutens is a quantitative finance professor at the University of Leuven, Belgium. He has extensive practical experience of model implementation and validation. He is well known for his consulting work to the banking industry and national and supra-national institutions. He is an independent expert advisor to the European Commission, has worked for the IMF and is the author of several books on quantitative finance.
He is also member of different editorial Boards of international finance journals. He likes arbitrages, political incorrect statements and making jam.
