Video Type: Industry Talk

Industry Talk March 2026

Crypto assets represent a new financial primitive whose behavior cannot be fully explained by classical asset pricing theory. Unlike traditional securities, they operate at the intersection of stochastic processes, network theory, game theory, cryptography, and mechanism design, within an open, continuously evolving market microstructure.

This presentation explores the mathematical foundations of crypto assets, focusing on how value, risk, and dynamics emerge from protocol-level rules rather than centralized issuers.

Speaker Bio

Daniele Bernardi

Born in 1969, Automotive Engineer, Daniele Bernardi is a serial-entrepreneur constantly searching for innovation. Founder of DIAMAN, a Group active in investment management, software development and crypto activities, has developed proprietary quantitative models and indicators since 2002.

Bernardi activity is oriented to mathematic model development, which simplify investors and family offices decision making processes, for risk reduction. He is the author of interesting academic papers about innovation in finance, published by Wilmott Magazine in May 2014 and on Journal of Accounting and Finance in April 2015. Models, indexes, and indicators developed by Bernardi have been presented in international conferences such as the Financial Management Association Conference in Denver and Nashville, the World Financial Conference in Shanghai, Venice and New York. Bernardi is also Chairman of DIAMAN Tech SRL, and the CEO of an Asset Management firm, Diaman Partners. He was recognized as “Inventor” by European Patent office for two European and Russian Patent into mobile payment field.

Diaman Partners has launched the first ever AIF Fund investing in Crypto Assets in Malta in 2020 and the first UCITS fund investing in Crypto Related Securities (ETPs) for retail investors.

Industry Talk April 2026

Environmental, social, and governance (ESG) considerations are playing an increasingly important role in investment decision-making. But for many investors, particularly pension fiduciaries, the central challenge remains the same: how to align responsible investing objectives with the financial interests of beneficiaries.

Dr. John Guerard examines the relationship between ESG screening and portfolio performance through a statistical review of the KLD database. The session explores how robust regression techniques can be applied in stock selection modelling to address the presence of outliers in financial data and improve portfolio construction using Sharpe and Information Ratios.

Speaker Bio

Dr. John Guerard

John Guerard, PhD, is an independent financial researcher living in Bluffton, South Carolina. He currently serves as Co-Chief of Engineering and Research at Pacific Spirit Investments in San Diego, CA, a native asset manager. 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, Computational Finance and Risk Management Program, at the University of Washington in Seattle, WA. He served for 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, recipient of the 1991 Nobel Prize in Economic Sciences, on Fund Academy and The Japan Equity Fund. John was awarded the first Moskowitz Prize for outstanding research in socially responsible investing in 1997. He earned his AB in Economics from Duke University, MA in Economics from the University of Virginia, MSIM from the Georgia Institute of Technology, and PhD in Finance from the University of Texas at Austin. He also taught at the University of Virginia’s McIntire School of Commerce and at Lehigh University.

John has published several monographs, including Quantitative Corporate Finance (Kluwer, now Springer, 2007, with Eli Schwartz; third edition published in 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 (Palgrave Macmillan, 2022). He is also under contract to publish Financial Forecasting in 2025 with 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, The IBM Journal of Research and Development, Interfaces, The Journal of Portfolio Management, and The Journal of Investing. He served for over 30 years as an Associate Editor of The Journal of Investing and The International Journal of Forecasting. He recently joined The Journal of Portfolio Management Advisory Board and edited a special issue of Annals of Operations Research in honor of Harry Markowitz, published in March 2025.

Industry Talk March 2026

For the special, but important, case of American option pricing, practitioners often find that otherwise well-functioning finite difference methods yield results that are slower, less accurate, and less stable than expected. Perhaps as a consequence, simpler special-purpose methods (e.g., the binomial tree) are sometimes preferred for American options, despite various theoretical and practical drawbacks. In this talk we show how to remedy this situation, through a range of minimally invasive “tips and tricks” that significantly improve the speed and stability of the popular theta-method finite difference scheme when applied to American option values and greeks.

Speaker Bio

Leif Andersen

Leif B. G. Andersen is the Global Co-Head of The Quantitative Strategies & Data Group (QSDG) at Bank of America, and is an adjunct professor at NYU’s Courant Institute of Mathematical Sciences and at CMU’s Tepper School of Business. He holds MSc’s in Electrical and Mechanical Engineering from the Technical University of Denmark, an MBA from University of California at Berkeley, and a PhD in Finance from Aarhus Business School. Having started his career as a robotics engineer at Bosch GBMH in Stuttgart, Leif moved to Finance in 1993 and has now worked for more than 30 years as a quantitative researcher in the global markets area. He was IAQF’s Financial Engineer of the Year in 2023, and a recipient of Risk Magazine’s 2001 and 2018 Quant of the Year Awards. Leif has authored influential research papers and books in all areas of quantitative finance, including the popular 3-volume monograph Interest Rate Modeling, co-authored with Vladimir Piterbarg. He is an Associate Editor of Journal of Computational Finance and Mathematical Finance.

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:

1. Critical insights into why false findings persist in financial research and their real-world impact
2. Understanding of flawed assumptions in financial modeling, particularly around stationarity and continuity
3. Exposure to case studies demonstrating failures in AI-driven models and quantitative techniques
4. Alternative frameworks that embrace non-stationary dynamics and market complexity
5. 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:

1. Understand the limitations of traditional statistical methods – including p-values and NHST—and explore alternative approaches such as Bayesian inference and effect size estimation.
2. Evaluate methodological reforms like open science practices and preregistration, and their impact on improving research transparency and credibility.
3. 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.