By Sanford Bragg May 1, 2020
Model-provider Daloopa recently emerged from stealth mode with ambitions to displace current fundamental data provided by vendors such as FactSet and Standard & Poor’s CapIQ. The firm has embraced natural language processing and machine learning to reduce errors and provide more complete datasets for each company covered.
“We capture every non-GAAP number that companies disclose, and we show how we got there,” said co-founder Thomas Li in an interview. “Other data providers might provide up to 10 non-GAAP adjustments for a given company while we have over 300.”
The impetus for starting the company was frustration with the incompleteness of current data sources which requires buy-side analysts to go to the original filings to fill in the gaps. Li, who previously worked as an analyst for hedge funds Point72, KCL Capital and Angelo Gordon, modeled Daloopa’s process on the steps that a buy-side analyst follows to model company financials. “Our algorithms are often codified versions of the process a senior associate at a hedge fund might use to train a new hire,” said Li.
The platform currently covers around 300 stocks including all US publicly traded technology media and telecommunications (TMT) companies and plans to cover over 1,000 listed US companies by the end of 2020. Data is accessible through Excel plug-ins or through APIs.
Daloopa was co-founded in 2019 by Thomas Li, a former buy-side TMT analyst; Jeremy Huang, a former AirBnB engineer; and Daniel Chen a former distributed systems researcher at Microsoft’s cloud division, Azure. The founders are the only full-time employees, although the firm also uses outsourced data analysts in India to validate data and train the data collection models.
The firm is venture funded, having raised $500 to $1 million according to company recruiting materials. Crunchbase lists a funding level of $3.4 million (seems high) while Pitchbook indicates that the funding source is VC firm Nexus Venture Partners, based in Silicon Valley and Mumbai, India.
Daloopa recently emerged from stealth mode, signing on with Analyst Hub last month as the first research technology firm to join the distribution platform. In addition to sales support, Analyst Hub is providing Daloopa with compliance infrastructure including an acting CCO and training programs.
Daloopa’s challenge to existing fundamental data providers is two-fold. The primary differentiators the firm promotes are the completeness, timeliness and accuracy of its data. However, the firm also has a cost advantage in that much of its data-banking activity is automated. TagniFi, which has a four-year head start on Daloopa, seeks to disrupt fundamental data through lower pricing, leveraging XML-based financial filings.
S&P Global, which acquired Kensho Technologies for $550 million, has the wherewithal to do what Daloopa does. However, our sense is that Kensho is being deployed in bits and pieces across the organization. We suspect it will be used to improve efficiency, for example loading new datasets, rather than a bottom-up overhaul of the CapIQ or Compustat products.
More broadly, Daloopa is one more example of how AI is transforming the investment research landscape. Although ostensibly complementary to fundamental research providers (which is why Analyst Hub is marketing Daloopa alongside its boutique research providers), it evokes a future where investment research is increasingly automated. Having automated the data collection, the natural next step is to automate the modeling and valuation, such as what Causality Link is offering. It is then only a short step to automating the portfolio selection.
Yes, humans still have important roles in the increasingly AI-driven process, but they are not the roles that most external research providers currently offer.
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