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Mar 24, 2017 · 50m · mad

Panel: Alternative Data in Financial Services (FirstMark's Data Driven)

Jeff Glueck · 12m spoken Matei Zatreanu · 10m spoken Andrius Sikov · 8m spoken Matt Turck · 6m spoken David Loisa · 6m spoken Shola Kadiri · 38s spoken
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A panel of financial and technology leaders explores the rise of alternative data in quantitative and fundamental investing, discussing technical strategies, organizational structures, privacy compliance, and commercial viability for data vendors.

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 14.1% of the talking time here. How this is scored →

Matt as informed peer 3.0 Guest teaching 3.5 Guest disagreement 1.0 Matt pushing back 1.8
05100:0015:0030:0045:002:15–8:36 · Matt as informed peer 4/10 Defining Alternative Data and Industry Growth Drivers Matt facilitates the introductory discussion by framing alternative data within recent market trends, citing the Warren Buffett shareholder letter and Dennis Crowley's work at Foursquare. Andrius and Matei explain the transition to cloud compute and the third wave of quantitative computing. The tone is entirely collaborative and conversational.8:36–23:01 · Matt as informed peer 2/10 Fundamental vs. Quantitative Strategies and Real-World Use Cases Matt prompts the panelists to explain fundamental versus quantitative strategies for a mixed audience and asks whether alternative data genuinely works. The guests school the room on specific use cases, such as passive foot traffic tracking and cell tower triangulation in China. Jeff also clarifies the technical distinction between active check-ins and passive ML location detection.23:01–31:59 · Matt as informed peer 2/10 Building and Operating Data Science Organizations in Finance Matt inquires about the internal organization, recruiting, and operations of hedge fund data science teams, referencing Point72's press coverage. David and Andrius explain data engineering pipelines, statistical challenges with short quarterly time series, and breaking down organizational silos. The exchange is harmonious and instructional.31:59–39:23 · Matt as informed peer 3/10 Data Privacy, Compliance, and Ethical Considerations Matt raises questions regarding data privacy, legal compliance, and material non-public information. Jeff delivers a passionate defense of consumer privacy by design, explaining how Foursquare turned down hedge funds seeking personally identifiable information. Matei adds perspective by comparing hedge fund data usage to NYPD predictive policing and tech platforms.39:23–45:50 · Matt as informed peer 5/10 Evaluating the Business Model of Selling Data to Hedge Funds Matt uses his domain expertise as a venture capitalist to challenge the common startup pitch that selling data to hedge funds is an easy monetization model. The panelists strongly validate his skepticism, explaining data normalization hurdles, panel instability, and 'innovation tourism' where funds inspect data without paying.45:50–50:04 · Matt as informed peer 2/10 Audience Q&A and Event Conclusion Matt moderates the Q&A session, firmly restricting an audience member to one question to maintain time constraints. The guests answer audience queries regarding alpha decay, combining datasets, and normalizing user panels against U.S. Census demographics.2:15–8:36 · Guest teaching 3/10 Defining Alternative Data and Industry Growth Drivers Matt facilitates the introductory discussion by framing alternative data within recent market trends, citing the Warren Buffett shareholder letter and Dennis Crowley's work at Foursquare. Andrius and Matei explain the transition to cloud compute and the third wave of quantitative computing. The tone is entirely collaborative and conversational.8:36–23:01 · Guest teaching 4/10 Fundamental vs. Quantitative Strategies and Real-World Use Cases Matt prompts the panelists to explain fundamental versus quantitative strategies for a mixed audience and asks whether alternative data genuinely works. The guests school the room on specific use cases, such as passive foot traffic tracking and cell tower triangulation in China. Jeff also clarifies the technical distinction between active check-ins and passive ML location detection.23:01–31:59 · Guest teaching 3/10 Building and Operating Data Science Organizations in Finance Matt inquires about the internal organization, recruiting, and operations of hedge fund data science teams, referencing Point72's press coverage. David and Andrius explain data engineering pipelines, statistical challenges with short quarterly time series, and breaking down organizational silos. The exchange is harmonious and instructional.31:59–39:23 · Guest teaching 4/10 Data Privacy, Compliance, and Ethical Considerations Matt raises questions regarding data privacy, legal compliance, and material non-public information. Jeff delivers a passionate defense of consumer privacy by design, explaining how Foursquare turned down hedge funds seeking personally identifiable information. Matei adds perspective by comparing hedge fund data usage to NYPD predictive policing and tech platforms.39:23–45:50 · Guest teaching 4/10 Evaluating the Business Model of Selling Data to Hedge Funds Matt uses his domain expertise as a venture capitalist to challenge the common startup pitch that selling data to hedge funds is an easy monetization model. The panelists strongly validate his skepticism, explaining data normalization hurdles, panel instability, and 'innovation tourism' where funds inspect data without paying.45:50–50:04 · Guest teaching 3/10 Audience Q&A and Event Conclusion Matt moderates the Q&A session, firmly restricting an audience member to one question to maintain time constraints. The guests answer audience queries regarding alpha decay, combining datasets, and normalizing user panels against U.S. Census demographics.2:15–8:36 · Guest disagreement 1/10 Defining Alternative Data and Industry Growth Drivers Matt facilitates the introductory discussion by framing alternative data within recent market trends, citing the Warren Buffett shareholder letter and Dennis Crowley's work at Foursquare. Andrius and Matei explain the transition to cloud compute and the third wave of quantitative computing. The tone is entirely collaborative and conversational.8:36–23:01 · Guest disagreement 1/10 Fundamental vs. Quantitative Strategies and Real-World Use Cases Matt prompts the panelists to explain fundamental versus quantitative strategies for a mixed audience and asks whether alternative data genuinely works. The guests school the room on specific use cases, such as passive foot traffic tracking and cell tower triangulation in China. Jeff also clarifies the technical distinction between active check-ins and passive ML location detection.23:01–31:59 · Guest disagreement 1/10 Building and Operating Data Science Organizations in Finance Matt inquires about the internal organization, recruiting, and operations of hedge fund data science teams, referencing Point72's press coverage. David and Andrius explain data engineering pipelines, statistical challenges with short quarterly time series, and breaking down organizational silos. The exchange is harmonious and instructional.31:59–39:23 · Guest disagreement 2/10 Data Privacy, Compliance, and Ethical Considerations Matt raises questions regarding data privacy, legal compliance, and material non-public information. Jeff delivers a passionate defense of consumer privacy by design, explaining how Foursquare turned down hedge funds seeking personally identifiable information. Matei adds perspective by comparing hedge fund data usage to NYPD predictive policing and tech platforms.39:23–45:50 · Guest disagreement 1/10 Evaluating the Business Model of Selling Data to Hedge Funds Matt uses his domain expertise as a venture capitalist to challenge the common startup pitch that selling data to hedge funds is an easy monetization model. The panelists strongly validate his skepticism, explaining data normalization hurdles, panel instability, and 'innovation tourism' where funds inspect data without paying.45:50–50:04 · Guest disagreement 0/10 Audience Q&A and Event Conclusion Matt moderates the Q&A session, firmly restricting an audience member to one question to maintain time constraints. The guests answer audience queries regarding alpha decay, combining datasets, and normalizing user panels against U.S. Census demographics.2:15–8:36 · Matt pushing back 1/10 Defining Alternative Data and Industry Growth Drivers Matt facilitates the introductory discussion by framing alternative data within recent market trends, citing the Warren Buffett shareholder letter and Dennis Crowley's work at Foursquare. Andrius and Matei explain the transition to cloud compute and the third wave of quantitative computing. The tone is entirely collaborative and conversational.8:36–23:01 · Matt pushing back 2/10 Fundamental vs. Quantitative Strategies and Real-World Use Cases Matt prompts the panelists to explain fundamental versus quantitative strategies for a mixed audience and asks whether alternative data genuinely works. The guests school the room on specific use cases, such as passive foot traffic tracking and cell tower triangulation in China. Jeff also clarifies the technical distinction between active check-ins and passive ML location detection.23:01–31:59 · Matt pushing back 1/10 Building and Operating Data Science Organizations in Finance Matt inquires about the internal organization, recruiting, and operations of hedge fund data science teams, referencing Point72's press coverage. David and Andrius explain data engineering pipelines, statistical challenges with short quarterly time series, and breaking down organizational silos. The exchange is harmonious and instructional.31:59–39:23 · Matt pushing back 2/10 Data Privacy, Compliance, and Ethical Considerations Matt raises questions regarding data privacy, legal compliance, and material non-public information. Jeff delivers a passionate defense of consumer privacy by design, explaining how Foursquare turned down hedge funds seeking personally identifiable information. Matei adds perspective by comparing hedge fund data usage to NYPD predictive policing and tech platforms.39:23–45:50 · Matt pushing back 3/10 Evaluating the Business Model of Selling Data to Hedge Funds Matt uses his domain expertise as a venture capitalist to challenge the common startup pitch that selling data to hedge funds is an easy monetization model. The panelists strongly validate his skepticism, explaining data normalization hurdles, panel instability, and 'innovation tourism' where funds inspect data without paying.45:50–50:04 · Matt pushing back 2/10 Audience Q&A and Event Conclusion Matt moderates the Q&A session, firmly restricting an audience member to one question to maintain time constraints. The guests answer audience queries regarding alpha decay, combining datasets, and normalizing user panels against U.S. Census demographics.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 38.4% · guest 61.6%0:00 · Matt 38.4% · guest 61.6%3:00 · Matt 32.1% · guest 67.9%3:00 · Matt 32.1% · guest 67.9%6:00 · Matt 20.8% · guest 79.2%6:00 · Matt 20.8% · guest 79.2%9:00 · Matt 29.2% · guest 70.8%9:00 · Matt 29.2% · guest 70.8%12:00 · Matt 11.7% · guest 88.3%12:00 · Matt 11.7% · guest 88.3%15:00 · Matt 1% · guest 99%15:00 · Matt 1% · guest 99%18:00 · Matt 4.3% · guest 95.7%18:00 · Matt 4.3% · guest 95.7%21:00 · Matt 15.1% · guest 84.9%21:00 · Matt 15.1% · guest 84.9%24:00 · Matt 18.2% · guest 81.8%24:00 · Matt 18.2% · guest 81.8%27:00 · Matt 10.7% · guest 89.3%27:00 · Matt 10.7% · guest 89.3%30:00 · Matt 10.7% · guest 89.3%30:00 · Matt 10.7% · guest 89.3%33:00 · Matt 0.6% · guest 99.4%33:00 · Matt 0.6% · guest 99.4%36:00 · Matt 5.2% · guest 94.8%36:00 · Matt 5.2% · guest 94.8%39:00 · Matt 23.7% · guest 76.3%39:00 · Matt 23.7% · guest 76.3%42:00 · Matt 0% · guest 100%42:00 · Matt 0% · guest 100%45:00 · Matt 7% · guest 93%45:00 · Matt 7% · guest 93%48:00 · Matt 14.2% · guest 85.8%48:00 · Matt 14.2% · guest 85.8%
Sharpest disagreement ▶ 35:16 Jeff rejects hedge fund PII demand

Jeff strongly rejects a hedge fund's attempt to purchase personal identities, stating he offered to refund their money rather than compromise privacy ethics.

 Hardest push from Matt ▶ 39:22 Matt challenges startup data sales business model

Matt draws on his VC experience to push back on the common startup pitch that selling accumulated data to hedge funds is a viable secondary business model.

 Biggest teaching moment ▶ 20:05 Jeff corrects industry vocabulary on check-ins vs passive location

Jeff explicitly interrupts the flow to correct the panel's vocabulary, explaining that financial modeling uses passive machine learning detection rather than active check-ins.

 Matt holds his own ▶ 3:59 Matt frames alternative data demand around hedge fund performance pressures

Matt demonstrates sharp industry context by connecting the sudden demand for alternative data to broader hedge fund performance struggles and Warren Buffett's recent critique.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Defining Alternative Data and Industry Growth Drivers 4311 Matt facilitates the introductory discussion by framing alternative data within recent market trends, citing the Warren Buffett shareholder letter and Dennis Crowley's work at Foursquare. Andrius and Matei explain the transition to cloud compute and the third wave of quantitative computing. The tone is entirely collaborative and conversational.
Fundamental vs. Quantitative Strategies and Real-World Use Cases 2412 Matt prompts the panelists to explain fundamental versus quantitative strategies for a mixed audience and asks whether alternative data genuinely works. The guests school the room on specific use cases, such as passive foot traffic tracking and cell tower triangulation in China. Jeff also clarifies the technical distinction between active check-ins and passive ML location detection.
Building and Operating Data Science Organizations in Finance 2311 Matt inquires about the internal organization, recruiting, and operations of hedge fund data science teams, referencing Point72's press coverage. David and Andrius explain data engineering pipelines, statistical challenges with short quarterly time series, and breaking down organizational silos. The exchange is harmonious and instructional.
Data Privacy, Compliance, and Ethical Considerations 3422 Matt raises questions regarding data privacy, legal compliance, and material non-public information. Jeff delivers a passionate defense of consumer privacy by design, explaining how Foursquare turned down hedge funds seeking personally identifiable information. Matei adds perspective by comparing hedge fund data usage to NYPD predictive policing and tech platforms.
Evaluating the Business Model of Selling Data to Hedge Funds 5413 Matt uses his domain expertise as a venture capitalist to challenge the common startup pitch that selling data to hedge funds is an easy monetization model. The panelists strongly validate his skepticism, explaining data normalization hurdles, panel instability, and 'innovation tourism' where funds inspect data without paying.
Audience Q&A and Event Conclusion 2302 Matt moderates the Q&A session, firmly restricting an audience member to one question to maintain time constraints. The guests answer audience queries regarding alpha decay, combining datasets, and normalizing user panels against U.S. Census demographics.

Statements from this episode (5)

Insight
Andrius Sikov: Quantitative investing is entering a third wave focused on alternative data
“We are, it's a third generation, we were just at the very beginning of the third wave of quantitative computing revolution. The first one was pricing the derivatives correctly, powered by LTCM, the first guys who started it doing properly scale. Once that was …”
Andrius Sikov Mar 24, 2017 ▶ 4:24
Assertion Not checkable as stated
David Loaiza: Most alternative data sets lack four years of history
“Most data sets don't have four years.”
David Loisa Mar 24, 2017 ▶ 25:58
Disclosure
Sikov: Data Capital Management built no Chinese walls between teams
“So, we made a deliberate decision up front to not build any Chinese walls between different teams, so our fundamental investors as well as quantitative investors and risk managers are working closely with the data scientists.”
Andrius Sikov Mar 24, 2017 ▶ 26:47
Assertion Not checkable as stated
David Loaiza: Roughly 80% of alternative data sold by vendors is duplicated
“You see people taking data from that set, and you compare it, it's about 80% the same data that someone is selling it.”
David Loisa Mar 24, 2017 ▶ 41:51
Insight
Andrius Sikov: Combining multiple alternative datasets generates more durable trading alpha
“It's, it decays pretty fast for most data sets unless you find something very proprietary that nobody else is looking at. What we find alpha in is combining the data sets and bringing them together, and then when the, that's when the data really starts speakin…”
Andrius Sikov Mar 24, 2017 ▶ 46:50
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