The Premier Event for Quantitative Investment Thought Leaders
Guido Baltussen
Head of Quant Northern Trust Asset Management
Guido Baltussen joined NTAM as Head of Quantitative Strategies, International. Guido was most recently Head of Equity Factor Investing and Co-head of Quantitative Fixed Income at Robeco where he was responsible for all of the firm’s systematic risk premia strategies as well as a large research agenda. Prior to Robeco, Guido was head of quantitative research for multi-asset strategies at NN Investment Partners (now Goldman Sachs) and a quantitative investment strategist with ING Investment Management. He is also currently a Professor of Finance at Erasmus University in Rotterdam, where he teaches courses on behavioral finance and factor risk premia. Guido holds a Ph.D. in Finance from Erasmus University Rotterdam and an M.Phil. in Economics from the Tinbergen Institute. An accomplished researcher, Guido has published in journals such as the American Economic Review, the Journal of Financial Economics and the Financial Analysts Journal. He’s also a frequent contributor to Bloomberg, the Wall Street Journal, and FT. In this newly created role, Guido will drive growth in the international Quantitative Strategies business through product R&D, sustainability and ESG integration, thought leadership and client engagement.
Day 1: 14 October
3:05 PM Pratical Use Cases for Large Lanuage Models & NLP's
Large Language Models and NLP’s have moved from labs to trading desks—shaping the way we trade:
1. Can you give real-world applications of LLMs in quant finance—from sentiment analysis to automated document parsing?
2. How are you using NLP to accelerate research, generate alpha, and improve operational efficiency?
3. How have you grasped the strengths, limitations, and responsible deployment of LLMs in production environments?
5:00 PM PANEL: Alpha in the Age of Alternative Data — Building Robust Quant Models That Deliver
Quant teams are under pressure to extract alpha from increasingly complex and unconventional datasets. As new signals, factors, and sources emerge, how do you build models that are not only predictive—but resilient and scalable?
· How do we quantify data quality?
· What are the impacts of data quality on quantitative models, e.g. can models work with dirty or missing data?
· Learn how to integrate new data sources into model pipelines without compromising stability
· Understand the evolving role of alternative data in sustainable alpha generation
· Assessing signal trading and landing models, assessing processes and cleaning dirty data
· What are the different use cases of data and data quality requests from various stakeholders, including business, validation, risk, and regulators?
Check out the incredible speaker line-up to see who will be joining Guido.
Download The Latest Agenda
