Tag: Conference

Watch the recordings from the 2025 Quant Insights Conference.

Video Description

To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a methodology to identify a robust threshold on these metrics even though there are many serious implications of an incorrect choice of the thresholds during deployment of the LLMs. Translating the traditional model risk management (MRM) guidelines within regulated industries such as the financial industry, in this talk, I will discuss a step-by-step recipe for picking a threshold for a given LLM evaluation metric. I will emphasize that such a methodology should start with identifying the risks of the LLM application under consideration and risk tolerance of the stakeholders. We then propose concrete and statistically rigorous procedures to determine a threshold for the given LLM evaluation metric using available ground-truth data.

Speaker Bio

Dr. Dhagash Mehta

Dr. Mehta is the Head of Applied AI and Responsible AI Research (Investment Management) at Blackrock Inc. and an Editorial Board Member at the Journal of Financial Data Science and Journal of ESG and Impact Investing. He was also General Chair for the ACM International Conference on AI in Finance 2024 (ICAIF24). Previously he was a Senior Manager,Investment Strategist (Machine Learning – Asset Allocation) at The Vanguard Group, a Senior Research Scientist at United Technologies (UTX) Research Center, a Research Professor at University of Notre Dame, a Fields Institute Postdoc Fellow, a Visiting Fellow at Simons Institute for Theory of Computing at Berkeley, a postdoctoral researcher at the University of Cambridge, Syracuse University (USA) and National University of Ireland Maynooth (Ireland) and a research student at Imperial College London (the UK), the University of Adelaide (Australia).

Video Description

Commodity futures markets are notoriously difficult to model due to their sparse disclosures, idiosyncratic fundamentals, and the complex web of macroeconomic, geopolitical, and physical flow drivers. In this presentation, I introduce a novel research framework that leverages Knowledge Graph (KG) triples and Graph Neural Networks (GNNs) to enhance the modeling of cross-sectional and temporal dependencies in commodity futures.

We begin by constructing a domain-specific knowledge graph from structured and unstructured data, using large language models (LLMs) as semantic extraction tools. Document sources include news articles, regulatory filings, and macroeconomic event reports. This text corpus is transformed into temporal entity-relation triples that encode time-aware relationships between entities.

Each node in the graph represents a commodity future, optionally enriched with standard price-derived features. Edges capture latent relationships, both explicit and inferred, such as shared production dependencies or co-movement during stress regimes. By applying GNN architectures, we learn context-aware embeddings for each futures contract, which are then used for return forecasting and relative value positioning within a long-short investment framework.

Speaker Bio

Tony Guida

Tony Guida is a Quantitative Portfolio Manager, researcher, and author with expertise in systematic investing, machine learning, and financial data science. He currently works on the integration of large language models (LLMs) and knowledge graphs into investment processes for a Swiss asset manager specializing in growth-oriented thematic equity portfolios.

Tony began his career at Unigestion in 2006, joining the quantitative equity team as a research analyst. He later became a member of the Research and Investment Committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients. In 2015, he joined EDHEC-Risk Scientific Beta as a Senior Consultant, focusing on risk allocation and multi-factor strategies. In 2016, Tony moved to a major UK pension fund, where he built an in-house systematic equity strategy and co-managed a £8 billion portfolio as Senior Quantitative Portfolio Manager.

In 2019, he joined RAM Active Investments as a Senior Quantitative Researcher in equities, later co-heading the systematic macro hedge fund offering. In 2023, Tony co-founded a hedge fund, where he served as Co-Head of Research and Portfolio Manager, leading the development of cross-asset, machine learning-driven investment strategies.

Tony holds Bachelor’s and Master’s degrees in Econometrics, and a Master’s in Economics and Finance. He has authored and edited several industry-leading books, including:

• Big Data and Machine Learning in Quantitative Investment (Wiley, 2018)
• Machine Learning for Factor Investing: R Version (CRC, 2020)
• Machine Learning for Factor Investing: Python Version (CRC, 2023)

He is also an Advisory Board Member for the Financial Data Professional Institute and serves as a peer reviewer for academic journals in machine learning and finance.