The Quant Standard: A Playbook for Data Providers
How data companies can improve usability, drive adoption, and remove friction in quantitative workflows—and in the process make data more accessible to all data buyers.
For data to be valuable in quant models, it must meet specific usability standards. If your dataset isn’t structured for quants, it won’t get used. This article explores the quant standard and its future evolution into machine-readable frameworks. And here’s the benefit: the same principles that make data usable for quants also improve accessibility for fundamental and discretionary investors.
It’s very difficult for data companies to get useful feedback from institutional investors
Institutional investors1 rarely disclose how they use ingested data, making it challenging for data companies to tailor their offerings. Proprietary insights generate alpha2, but widespread adoption erodes their uniqueness, turning them into beta3 factors4. Therefore, asset managers are unlikely to share feedback for the risk of increased competition for the same investment opportunities.
Large asset managers with leading data capabilities can handle difficult-to-use data, which is actually a positive for them because the barriers to integrating the data are a barrier to competition for others to use the data. Large asset managers with advanced data capabilities are likely among the first buyers of a valuable but difficult-to-use data set, but there’s little incentive for them to share ways to improve the data’s usability. Smaller asset managers have fewer resources for data processing, leaving data providers uncertain about how to improve usability.
Systematic investors5, known for their secrecy, use human-on-the-loop6 automated models to capture alpha via algorithmic trading.
I wrote about the segmentation of data buyers on the buyside in this April 2023 article, "Why some data companies struggle to sell to the financial markets”:
In May 2023, I outlined an 8-point framework investors use to evaluate data for integration into their investment process, which introduced the concept of the quant standard.
Today’s article expands on the concepts associated with the data buyer’s path to purchase. This article helps data companies understand what systematic investors require to integrate a data source into their algorithms. By optimizing data for quant professionals, it actually benefits all data users, making insights more accessible for all users. A data product’s primary “job to be done7” is to generate actionable insights—the closer it gets to informing economic decisions, the easier it is to integrate into workflows.
In this article, the topic is going to be explored in more depth, covering:
The Quant Standard
Factor Scoring
Live Test and maintenance
I also want to share some thoughts on future standards for data, considering the potential agentic8 frameworks that may progress the quant standard to a machine-readable standard.
Lastly, I provide an easy-to-use checklist for data companies to test if they meet the Quant Standard.
Before diving into the main content, I want to give a shoutout to HangukQuant’s newsletter. The newsletter includes almost 1,000 pages of content on how to implement quant models, including specific code across multiple languages and a community of quants collaborating on the code. For anyone who wants to get deeper into this world, it’s a valuable resource.
Welcome to the Data Score newsletter, composed by DataChorus LLC. The newsletter is your go-to source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I’ve remained active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have gained a unique perspective, which I am sharing through the newsletter.
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The Quant Standard
These principles form the Quant Standard—a framework that ensures data is structured, transparent, and integration-ready. The data attributes that are needed to enhance a data product to meet the quant standard include:
Easily Joinable with traditional financial market data
Manage revisions and data gaps transparently
Easy-to-understand data schemas
Distribution flexibility via programmatic access
High frequency
Breadth of coverage or high impact in narrow coverage
Enough history