Feb 25, 2019 · 31m · mad
Fireside Chat: Matt Ober, Chief Data Scientist at Third Point (FirstMark's Data Driven NYC)
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In this Data Driven NYC fireside chat hosted by Matt Turck, Matt Ober, Chief Data Scientist at Third Point, discusses the integration of data science and alternative datasets into fundamental hedge fund strategies. The conversation explores organizational culture, alternative data evaluation, legal compliance, model explainability, and future industry trends across public and private markets.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 26.6% of the talking time here. How this is scored →
Matt as informed peer 3.3 Guest teaching 3.9 Guest disagreement 1.3 Matt pushing back 1.9
speaking balance: gold is Matt, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 8:34 Ober punctures satellite data hype
Ober explicitly dismisses popular media narratives about counting cars in parking lots with satellites, pointing out practical flaws like cloud cover preventing data collection.
Hardest push from Matt ▶ 7:16 Turck challenges alternative data edge durabilityTurck directly challenges the longevity of alternative data assets, asking if widespread credit card data usage turns the practice into a self-defeating arms race.
Biggest teaching moment ▶ 11:13 Ober outlines strict quantitative history requirementsOber educates Turck on the realistic constraints of hedge fund data buying, clarifying that quant funds require at least two years of daily point-in-time backtestable history before considering a purchase.
Matt holds his own ▶ 9:12 Turck cites VC experience on data valuation mythsTurck uses his domain knowledge as a venture capitalist to challenge the mythical startup narrative that hedge funds automatically pay $10M a year for raw data sets.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Matt as informed peer | Guest teaching | Guest disagreement | Matt pushing back | Why |
|---|---|---|---|---|---|---|
| Fundamental vs. Quantitative Hedge Funds | 4 | 4 | 1 | 2 | Host Matt Turck introduces fundamental vs quantitative hedge funds with broad layman definitions. Guest Matt Ober refines the distinction by explaining how fundamental funds dive into single-name financials while quant funds execute across thousands of securities. | |
| Integrating Data Science and Automation in Fundamental Investing | 3 | 4 | 1 | 1 | Turck asks how automation functions daily and jokingly asks if quant managers just go on vacation while machines trade. Ober details how fundamental analysts use data to test theses and clarifies automation levels at systematic firms. | |
| Defining and Utilizing Alternative Data | 5 | 4 | 2 | 4 | Turck pushes the premise that alternative data is a zero-sum arms race where early edge rapidly degrades. Ober agrees partially but reframes alternative data as essential context to understand broad market moves rather than pure directional alpha. | |
| Evaluating Alternative Data Usefulness and Misconceptions | 6 | 5 | 2 | 4 | Turck uses his VC background to question the mythical valuation of startup data assets selling for $10M to hedge funds. Ober educates on the real criteria funds use, noting that quants require at least two years of point-in-time daily history. | |
| Legal, Compliance, and Security Due Diligence in Data Acquisition | 5 | 4 | 2 | 3 | Turck asks about compliance risks and checks the reality of AI adoption versus marketing noise in hedge funds. Ober confirms that simpler models often outperform overcomplex ML algorithms and highlights the need for explainability in fundamental firms. | |
| Cultural Adaptation and Team Dynamics on Wall Street | 5 | 3 | 1 | 3 | Turck highlights the cultural clash on Wall Street between classic traders and data nerds, asking if trading is a zero-sum game. Ober explains how cloud infrastructure allows lean six-person data teams to operate effectively inside traditional funds. | |
| Applying Data Science Beyond Public Equities (Private Credit & VC) | 4 | 3 | 1 | 2 | Turck asks about extending data science to private credit/VC and brings up systemic market risks like flash crashes. Ober acknowledges systemic concerns but argues the industry continually adapts. | |
| Q&A: Numerai, Crowdsourcing, and Decentralized Data Signals | 0 | 4 | 1 | 0 | Audience members lead the questioning regarding Numerai, crowdsourced alpha, and government shutdown impact on macroeconomic data. Ober explains why crowdsourced models fit systematic strategies better than fundamental ones. | |
| Q&A: Hiring Strategy for Investment Data Teams | 0 | 4 | 1 | 0 | Audience members ask about hiring data talent versus financial experts, adoption across fund types, and activist campaign applications. Ober shares how data vendor alumni bring crucial communication skills to bridge finance and data. | |
| Q&A: Model Explainability and Technical Frameworks | 1 | 4 | 1 | 0 | An audience member asks about technical explainability frameworks like Lime and SHAP, as well as NLP for research automation. Ober explains that converting complex models into plain language portfolio drivers is where his team spends most of their effort. |
Statements from this episode (17)
Ober: Fundamental hedge funds began data transformation around 2016–2017
“Sure, so I think kind of a revolution in the hedge fund industry mainly because you have the quantitative hedge funds that have really been leaders in technology and data, and now you have the fundamentals really over the last, call it two, three years, thinki…”
Ober: Fundamental funds need more data per company than quantitative funds
“So it's interesting because when you move from quantitative to fundamental, the amount of data that's relevant is, you know, Enormous on the fundamental side, because you're really diving into these single names.”
Ober: Third Point uses data to evaluate existing ideas rather than mine new ones
“Rather than mining data for new ideas, we're almost looking for data to help us understand the ideas we may already have, or to, you know, create very sophisticated screens, you know, looking across all these companies to kind of dwindle down a smaller list of…”
Ober: Shared alternative datasets increase stock volatility around corporate earnings
“Yeah, I think it brings more volatility into, you know, events, earnings, especially for firms that are trading around these events, where everybody's looking at the same credit card data.”
Ober: Fundamental investors treat alternative data as one input, unlike quant funds
“Using this data is very different from a quant fund who's placing lots of trades, both long and short at the same time. Whereas an activist or long-term investor might look at this as just, ah, another input into their longer investment approach.”
Ober: Satellite car-counting data rarely drives hedge fund investment decisions
“I don't think that there's a lot of firms really counting cars in parking lots, and that's what's really making their, ah, decision making.”
Ober: The era of multi-million dollar single-dataset sales is over
“The multi-million dollar data sales was maybe something that you saw three, four, five years ago. I think now that, you know, people are understanding what data is actually worth they're maybe not trying to get exclusive access to just one data set because the…”
Ober: Quant funds require two years of backtestable daily data before purchasing
“If you're going to sell to a quantitative hedge fund, anything less than two years of daily data that's really been stored point in time that you can really back test is really going to be tough to make that ten million dollar sale.”
Ober: Simpler financial models outperform complex machine learning over time
“Going back to the very basics, you kind of find that, like, the simpler it is, the better it actually performs over time, if you're not trading at this higher frequency.”
Ober: Culture is the biggest hurdle for fundamental hedge funds
“I mean, I think the culture is the biggest Hurdle that all these funds face.”
Ober: Fundamental funds prioritize good communicators over the smartest PhDs
“We may not need the smartest PhD within a fundamental shop because we have to be able to explain and understand what they're doing and really talk their language whereas a quantitative hedge fund, there's more opportunity to maybe work on your own and not have…”
Ober: AI will enhance investment workflows rather than replace nuanced investment jobs
“Some of the more sophisticated strategies where you really have to understand the nuances, and maybe it's structured products, maybe it's structured credit. We're doing things where companies are being, you know, split apart, and they're selling different piec…”
Third Point aims to centralize thousands of datasets for instant company analysis
“As a team, it's, you know, how do we incorporate and leverage all this rich and unique data that's out there and kind of bring it into one real centralized place. So to really understand companies and coming from thousands of different data sets so that we can…”
Ober: Alternative macroeconomic data will eventually become widely accessible
“Now maybe it's only a few people that are leveraging it, and it's alpha, and eventually it becomes something that everybody has access to.”
Matt Ober: Alternative data adoption expanded from quants to private equity and mutual funds
“Yeah, I mean, I think obviously at the beginning it was definitely all quantitative hedge funds that were consuming as much data as possible. I think now if you go to some of these conferences like Battlefin, where you can have hundreds of data meetings in one…”
Ober: Third Point requires ML stock picks to be explainable to managers
“When the model creates a list of companies for us that we should go invest in, we need to be then able to explain to our you know, PMs and analysts internally what's driving that decision, and they really want to get into the deep roots of why it's picking tho…”
Ober: Third Point looks to outsource data structuring and ingestion
“It's something we do do ourself. I think it's one of those areas where data structuring, even data ingestion and onboarding data sets is something that us and most firms are looking at how do we, you know, outsource that to another company because we're only s…”