Opening and welcome
Andrew Delaney, President & Chief Content Officer, A-Team Group and Michael Finnegan, Chief Executive Officer, Eagle Alpha

 Leaders Panel: Alternative Data in production- from models to markets

• How the market has matured beyond experimentation. What actually creates edge today? What’s durable vs. what’s hype in alternative data today especially across AI agents, textual data, private and industry datasets, and the push beyond equities?
• How is AI actually changing alternative data workflows and moving beyond models to agent-driven discovery, validation, orchestration, and consumption across the investment process?
• Where is the new signal really coming from now? Textual alternative data beyond filings and earnings – policy and regulatory documents, expert networks, company communications, and obscure public records – and how firms separate signal from noise at scale.
• Where are firms reallocating spend and effort in 2026 and which capabilities are now must-haves for a modern alternative data program – and which are being quietly deprioritised?

Moderator: Julia Meigh, Alternative Data Specialist
Renato Guerrieri, Head of Quantitative Strategy – Liquid Alternatives, Downing
Timothée Consigny, Chief Technology Officer Head of GenAI Innovation, H20 Asset Management
Matthew Bell, Senior Data Scientist, Man Group
Rob Schack, Senior Vice President of Partnerships, Decision Desk HQ
Shivani Shah-Knowles, Team Manager, Account Management, Sensor Tower

Keynote: NextGen Macro: Collapsing the Economic Reality and Insight Gap

Most macro analysis is built on lagging, partial, and indirect measures—surveys, prices, and proxies—creating a persistent gap between economic reality and actionable insight. We present an alternative: a system that directly measures fundamental drivers in real time across countries down to the city level, allowing the state of the economy to be tracked as it evolves. Focusing on inflation, we demonstrate how driver-level measurement yields earlier, more stable, and more complete signals than current approaches such as price-scraping, and outline how this framework extends to labor and growth.

Apurv Jain, Founder and CEO, MacroX

Fireside Chat: AI web scraping that doesn’t break

• Building scrapers quickly is easy – maintaining them is the hard part
• Self-healing pipelines: monitoring, auto-tests, and change detection
• Making scraping accessible to non-engineers without losing controls
• Hallucination and extraction errors: how you verify, label, and enforce quality
• Volume vs runtime: making large data accessible through multiple bots
• Sourcing for scraping: how do we find the hidden gems in the world wide web?

Moderator: Nico Smuts, Investment & Data Science Leader, ex-Citadel
Jahmal Nicholson, Senior Data Scientist, Discretionary Data Product Lead, Man Group

Fresh features from new to market and leading data vendors

Panel Discussion: Markets, Macro & Risk – Rethinking Quant in a Macro-Driven World

In recent years, market dynamics have increasingly been shaped by macroeconomic forces – from interest rate shifts and inflation cycles to geopolitical developments. As a result, traditional quant models and signals are being tested in new ways, raising important questions about model reliability, risk calibration, and adaptability.

• Is macro now the dominant driver of markets?
• The impact on quant model signals and performance
• Adapting risk frameworks in volatile, macro-led environments
• Bridging the gap between macro insight and quantitative strategies
• The role of additional quant expertise in enhancing model resilience

Moderator: Brendan Furlong, Chief Data Advisory Officer, Eagle Alpha
Mario Dell’Era, Senior Quantitative Market Risk Manager, EnBw
Petr Merkuryev, Founder, Medusa Investment Partners

Panel Discussion : Intraday data, quality & signal decay- does Intraday Alternative Data Really Change Decisions?

• Intraday alternative data has proven its value for HFT and systematic strategies -but how can it drive alpha for discretionary investors?
• Can higher-frequency data meaningfully compensate for shorter historical depth, and under what conditions does that trade-off make sense?
• Which alternative datasets actually work intraday? For example, how do app usage, web activity, mobility, card transactions, or supply-chain data differ in their intraday usefulness?
• How are intraday alternative signals being used to anticipate or improve daily or scheduled releases?
• Best practices and credible examples of intraday data informing next-day positioning?

Moderator: Mark Fleming Williams, Head of Data Sourcing, CFM
Davide Alfano, Managing Director, Kaleidoscope Capital
Mario Dell’Era, Senior Quantitative Market Risk Manager, EnBw
Joe Gits, Managing Director of North America, BridgeWise; CEO, Context Analytics
Ovie Koloko, Chief Product Officer, Parameta Solutions

Keynote Address: AI as a Research Multiplier

AI reasoning has become a commodity – every quant team has access to the same foundation models. So where does the edge come from? It comes from the system around the model: the quality of ideas a team can test, the speed of the research cycle, and the proprietary data and context only they have. Research code is not software engineering code, and an agent optimised for software throughput solves the wrong problem for research. We present a research-native approach: domain-specific AI agents that understand your data, scaffold analyses end-to-end, and parallelise across regimes in a single call. Through a live demo on real data, we show how collapsing the dependency chain – hypothesis to deployed analysis – turns research infrastructure from a bottleneck into a compounding capability.

Jason Hillary, Co-founder and CTO, Zerve

Fresh features from new to market and leading data vendors

Panel Discussion: Building and scaling an Alternative Data function

• How should alternative data teams be structured today? How data should be integrated? What are the real trade-offs between centralised, embedded, and hybrid models – and how do these differ across asset classes?
• What role does data operations play in scaling alternative data? How should firms approach onboarding, refresh cycles, monitoring, and the retirement of datasets once they’re in production?
• What skills actually matter inside a modern alternative data team? How should firms balance data engineering, domain expertise, legal awareness, and vendor management – and which skills are most often missing?
• Who owns a dataset once it’s live? When a dataset breaks, drifts, or creates risk, who is accountable – and how should ownership be defined between research, data, and investment teams?
• How do firms align alternative data spend with real decision impact? What does a repeatable evaluation framework look like, what’s the “minimum viable alternative data stack”?

Moderator: Mike O’Hara, Editor, TradingTech Insight, A-Team Group
Caio Natividade, Managing Director and Global Head of Quantitative Investment Solutions Research, Deutsche Bank
Tim Drye, Founder, Curious Blue Fish
Alexander Denev, Author & GARP AI Advisory Committee Member

Fireside Chat: Prediction Markets as data: signal, noise, or structural risk?

• How useful are prediction markets and political data as macro signals? Where have they demonstrated forecasting power relative to polls, surveys, and traditional economic indicators?
• How does the regulatory treatment of prediction market data differ between the US and Europe? What does this mean in practice for data usage rights and institutional adoption?
• Where are the ethical and MNPI boundaries? How should firms think about inference risk, market manipulation, and responsible use of politically sensitive data?
• How are buy-side firms actually using this data today? Is prediction market data being deployed purely as an input signal, and how do vendors position their products within those constraints?

Interview with: Manish Singh, Chief Investment Officer, Crossbridge Capital LLP
Interview by: Matthew Bell, Senior Data Scientist, Man Group

Ends