Inquire UK 3rd Feb 2026: In-Person Practitioner Seminar, London

We examine the pricing of carbon risk in the European equity market between 2013 and 2024 using two complementary proxies based on the EU Emissions Trading System (EU ETS). Our first measure, a market-based carbon convenience yield from futures prices, captures investor expectations of future tightening in carbon markets. We document a significant carbon risk premium associated with this forward-looking measure; a portfolio buying high-risk stocks and selling low-risk stocks yields a monthly return of 0.89% and a multi-factor alpha of 1.09%. Our second measure, an accounting based carbon expense ratio, gauges the realized financial impact of compliance. We find this impact is substantial, with carbon expenses for the top quartile of firms reaching 12.4% of operating income in 2023. However, portfolio sorts on this expense ratio yield no significant return premium. This distinction suggests that while investors price the expectation of future carbon costs, the realized financial impact has yet to translate into a discernible risk premium.

Dr. Harald Lohre
Head of Quant Equity Research
Robeco

Harald Lohre is Head of Quant Equity Research at Robeco. Prior to joining Robeco in 2022, Harald served on the global management team of Invesco Quantitative Strategies and was Head of Quantitative Research and Portfolio Manager at Deka Investment. He is also an Honorary Researcher at Lancaster University Management School as well as a Research Fellow at the Hamburg Financial Research Center. He has published in the Journal of Empirical Finance, the Financial Analysts Journal, and the Journal of Portfolio Management, among others. Harald has been awarded several research grants, and he is the winner of The Sir Clive Granger Memorial Best Paper Prize and the Bernstein Fabozzi / Jacobs Levy Award. Harald holds a Doctorate in Finance summa cum laude from the University of Zurich and a Diploma in Mathematical Finance from the University of Konstanz. He is a past Fellow of the Centre for Endowment Asset Management at Cambridge Judge Business School and serves on the Research Committee of Inquire Europe.

In Systematic Fixed Income we have a range of strategies to time duration, curve, credit, etc. Over this session we will give an overview of the various buckets and signals used to time duration and global interest rates markets. Thematic signals range from fundamentals, sentiment, valuations, cross-asset and global macro.

Filipe Sodagar
Senior Systematic Fixed Income Portfolio Manager
BlackRock

Filipe Sodagar, Director, is a Portfolio Manager for the Systematic Fixed Income team within BlackRock’s Systematic group, focusing in particular on Macro, RV and Interest Rate strategies. Mr. Sodagar began his career as an analyst at Millennium BCP Investment Bank in 2011, where he worked as a fixed income trader. Prior to joining BlackRock in 2012, Mr. Sodagar was a teaching and research assistant at Nova School of Business and Economics in 2012. Mr. Sodagar earned a BSc degree in Business Economics and a double MSc degree in Finance and International Management from Nova School of Business and Economics, in 2009 and 2011 respectively.

We show that relevance-based prediction captures complex relationships, like a neural network, but with the added benefit of transparency. Many prediction tasks in economics and finance lie beyond the reach of linear regression analysis. Researchers, therefore, often turn to machine learning techniques, such as neural networks, to address these complex dynamics. A neural network has the potential to extract nearly all the useful information from a dataset, however it is difficult to implement and notoriously opaque. Alternatively, relevance-based prediction is a model free and theoretically-grounded approach that forms a prediction as a relevance-weighted average of past outcomes. In a sample application to predicting stock market volatility, we show that relevance-based prediction captures complex relationships like a neural network. However, unlike a neural network, it is remarkably transparent, revealing how each observation and variable contributes to a prediction, and disclosing the reliability of a prediction in advance.

Mark Kritzman
CEO
Windham Capital Management

Mark Kritzman, CFA is President of Cambridge Sports Analytics, CEO of Windham Capital Management and a Founding Partner of State Street Associates. He also teaches a graduate finance course at the Massachusetts Institute of Technology. Mark served as a Founding Director of the International Securities Exchange and as a Commissioner on the Group Insurance Commission of the Commonwealth of Massachusetts. He has also served on the Advisory Board of the Government Investment Corporation of Singapore (GIC) and the boards of the Institute for Quantitative Research in Finance, The Investment Fund for Foundations, and State Street Associates. He is currently a member of the Advisory Board of the MIT Sloan Finance Group, the Board of Trustees of St. John’s University, the Emerging Markets Review, the Journal of Alternative Investments, the Journal of Derivatives, the Journal of Investment Management, where he is Book Review Editor, and The Journal of Portfolio Management. He has written more than 100 articles for peer-reviewed journals and is an author of eight books including Prediction Revisited: The Importance of Observation, Asset Allocation: From Theory to Practice and Beyond, Puzzles of Finance, and The Portable Financial Analyst. Mark won Graham and Dodd scrolls in 1993, 2002, and 2010 the Research Prize from the Institute for Quantitative Investment Research in 1997, the Bernstein Fabozzi/Jacobs Levy Award 10 times, the Roger F. Murray Prize from the Q-Group in 2012, the Peter L. Bernstein Award in 2013 for Best Paper in an Institutional Investor Journal, the Harry M. Markowitz Award in 2022 and 2023, and the CFA Institute Research Foundation’s Vertin Award which recognizes an individual who has produced a body of research notable for its relevance and enduring value to investment professionals. In 2004, Mark was elected a Batten Fellow at the Darden Graduate School of Business Administration, University of Virginia. Mark has a BS in economics from St. John’s University, an MBA with distinction from New York University, and a CFA designation.

Megan Czasonis
Head of Portfolio Management Research
State Street Associates

Megan Czasonis is a Managing Director and Head of Portfolio Management Research at State Street Associates. Her team collaborates with academic partners to develop new research on asset allocation, risk management, and quantitative investment strategy. The Portfolio Management Research team delivers this research to institutional investors through indicators, advisory projects, thought leadership pieces, and interactive tools. Megan has published numerous journal articles, is a co-author of “Prediction Revisited: The Importance of Observation,” and her research received the 2022 Harry Markowitz Award for best paper in the Journal of Investment Management. Megan graduated Summa Cum Laude from Bentley University with a B.S. in Economics / Finance.

Illiquid investment funds involve two kinds of risk.  The first is the familiar concept that future returns are uncertain. The second is that the eventual distributions of investment proceeds may not occur at the expected point in time. In recent years, private equity fund distributions have been much slower than expected, resulting in unhappy institution investors. The problem also exists to a lesser extent in private credit and real estate funds where income distributions are commonplace. Some asset owners have experienced literal cash shortages. At many institutional investors, asset allocation compliance limits on private investments have been breached, as cash commitments to new private investments are not being funded from cash distributions from prior private investments. To alleviate investor displeasure, private fund firms have resorted to various artifices including distributing borrowed money and creating new funds to buy existing assets from their previous funds (resulting in allegations of “Ponzi” scheme). With the recent liberalization of private fund investing for retail retirement accounts in the US, the need to quantify the timing uncertainty of investor cash flows has become urgent. We will present a new closed-form model which is a substantial advance over both existing deterministic models such as the Takashi-Alexander (Yale, 2001) and the simulation-based extension of the “Yale” model proposed by Beutler, Billias, Holt, Lerner, and Seet (Journal of Portfolio Management, 2023). Some elements of the proposed method may be conveniently parameterized by existing models of private fund risk.

Dan diBartolomeo
President and Founder
Northfield Information Services, Inc.

Dan diBartolomeo is President and Founder of Northfield Information Services, Inc. He is also a former Visiting Professor at the CARISMA Research Center of Brunel University in London and serves on the Board of Directors of the Chicago Quantitative Alliance and the advisory board of the International Association for Quantitative Finance. He is a regional director of the Professional Risk Managers International Association, (PRMIA), and the Quantitative Work Alliance for Applied Finance, Education and Wisdom (QWAFAFEW). He is past president and director of the Boston Economic Club.

Dan has been admitted as an expert witness in US federal courts and state courts for litigation matters regarding investment management practices and derivatives.

In 2010, Dan received an award from Institutional Investor magazine as one of the forty most influential executives in financial technology in connection with his analytical work that helped uncover the Madoff investment fraud.

Dan is a director of the American Computer Foundation and formerly served on the industry liaison committee of the Department of Statistics and Actuarial Sciences at New Jersey Institute of Technology. He continues his more than twenty years of service as a judge in the Moskowitz Prize competition, given by the University of California at Berkeley for excellence in academic research on socially responsible investing.

Dan has a long list of publications including books, book chapters and research papers in professional journals such as Financial Analyst Journal, Quantitative Finance and Journal of Investing. In 2017, he was named co-editor of the Journal of Asset Management.

Systematic factor-based credit investing offers the potential to deliver alpha from data-driven credit selection while controlling macro factor risks such as credit quality and duration. This talk will start by outlining our approach to systematic credit investing and then will move on to discuss the practical challenges when implementing a strategy. These include characteristics of the credit asset class, the ability to execute trades, and client customisation requirements. This talk will describe their potential impact on portfolio risk and return, and present some approaches used to bridge the gap between theory and practice.

Peter May
Senior Quantitative Analyst
Fidelity International

Peter May joined Fidelity International in 2015 and is a Senior Quantitative Analyst. He holds a DPhil in Condensed Matter Physics from the University of Oxford and is a CFA Charterholder. He has previously worked on text-based equity sentiment analysis, retirement savings guidance, and strategic asset allocation integrating capital requirements with risk and return. He also led the expansion of Fidelity’s multifactor credit model to cover emerging market sovereigns. His current focus is on credit markets, including directional credit beta and bottom-up security selection.

We construct a firm-level monthly news sentiment score by aggre­gating data from four independent data providers. We find that stocks with more positive news sentiment have higher expected returns. This result holds across each individual dataset and is strongest for the aggregate measure. The relationship is more pronounced for stocks with lower investor attention or higher arbitrage constraints. Positive sentiment also signals better future earnings and lower risk. These results suggest that the predictability of news sentiment for future stock returns is most likely because of the slow diffusion of fundamental information embedded in the news. Furthermore, we share insights related to timing factors with sentiment and timing equity market tail events.

Matthias Uhl
Head of Analytics and Quant Solutions
UBS Asset Management

Matthias is the head of Analytics & Quant Solutions at UBS Asset Management. In his role, Matthias is responsible for leading the analytics, performance KPI and attribution, quant research and portfolio construction and optimization work within Investments.

Previously, Matthias was Chief Investment Officer at FLYNT Bank AG and has worked in various roles in the CIO Office at UBS Wealth Management, at UBS Investment Bank, at Deutsche Bank, and at the KOF Swiss Economic Institute of the ETH Zurich.

Matthias has a habilitation in quant finance from the University of Zurich, holds a Ph.D. in applied macroeconomics and behavioral finance from ETH Zurich, a Master of Science from Oxford University and two Bachelor of Arts degrees from the American University of Paris.

Matthias lectures on portfolio management and sentiment analytics at the University of Zurich and at the University of St. Gallen (HSG), and he teaches executive classes at the Swiss Finance Institute. His research has been published in various leading academic journals, such as in the Journal of Portfolio Management, Finance Research Letters, and Economics Letters, among others.

During periods of market turmoil, cross-sectional factor models may not fully capture all sources of systematic risk. In this presentation, we study how “transient” factors can be extracted from cross-sectional residuals using Principal Component Analysis. We study the strength of the factors using t-statistics and find that, in practice, transient factors are comparable to a strong style factor. We also determine the theoretical conditions under which the factor may be successfully extracted and confirm this result via numerical simulations. We then conduct an empirical study in which we omit a strong factor from a factor risk model and test how well the extracted factor replicates the omitted factor. Finally, we offer some insight into interpreting the transient factors.

Naima Hammoud
Quantitative Researcher
Bloomberg

Naima joined Bloomberg in August 2022, where she has conducted research spanning both fixed income and equity markets. Within fixed income, Naima worked on portfolio risk models focused on developed markets. She also built a specialized methodology for managing the risk associated with defaulted debt. Currently, she is developing statistical techniques aimed at identifying transient factors in equity markets.

Prior to joining Bloomberg, Naima served as an Assistant Professor of Mathematics at New York University’s Courant Institute of Mathematical Sciences. She holds a Ph.D. in Applied Mathematics from Princeton University, obtained in 2016.