Qineqt, a New York City-based data architecture company for buy-side investors, recently announced it had raised $1.3 mln in the first close of its Series A round of financing to expand its data and technology operations.
Latest Investment Round
The latest investment round, led by two private investors, totaled $1.3 mln and marks the first close in the firm’s Series A round. Qineqt’s venture funding round of $4.7 mln closed in May 2016, thereby putting to-date external financing at $6 mln.
Management explains that the new capital will be used to build out Qineqt’s global data and technology operations, including hiring additional data architecture and industry experts.
About Qineqt
Founded in March 2013, the idea behind Qineqt (www.qineqt.com) was initially conceived by Nadir Khan while running hedge fund Timescape Global Asset Management (Khan was previously a trader and analyst at SAC Capital). Qineqt collaborates with leading investment managers to create data structures and repositories of unique data that facilitate the development of clients’ proprietary strategies.
Qineqt’s staff collects, cleans and databases data requested by clients and specific catalysts that can be used by clients to test and implement alpha generating strategies. All of the investment strategies created by clients are kept secure behind firewalls, prohibiting anyone from discovering how other clients have used the data in its repositories.
Qineqt, headquartered in New York City and with significant operations in Pakistan, employs close to 230 staff.
Our Take
Qineqt has developed a unique business model oriented around the major problems that most asset managers face when trying to introduce data-driven strategies to their investment process. Typically, qualitative fund managers don’t have the people resources or experience to build the infrastructure they need to adopt “big data” solutions. And even if the fund can hire a few data scientists, these highly specialized individuals spend most of their time building the underlying data architecture and warehouse versus developing and testing new signals.
We believe Qineqt could address some of the key problems asset managers face when they try to implement big data strategies. However, the two major questions we have about the firm is how comfortable asset managers will be sharing the unique datasets they find with their peers, and whether they will trust that the strategies they develop using Qineqt will remain confidential to them. If Qineqt can overcome the paranoia that many funds have about keeping their intellectual property secret, then we suspect the firm can be quite successful.