sov.ai

AI Asset Management Research

Sov.ai is committed to turning complex financial data in visible insights using machine learning and advanced programmatic methods. We reinvent the concept of ‘dashboard’ to create a unique experience for investment managers that delivers research in a dynamic, automated, and interactive fashion that allows users to work within a full ecosystem of cloud applications that can communicate with one another. We are committed to being the leading firm for innovation in institutional asset management.

The lack of actionable, intelligent insight in finance stems from the fact that researchers up to now have produced static outcomes that are not updated in real-time, Sov.ai’s cloud applications instead are autonomous analytical solutions that predicts and prescribes while continuously learning from historical errors using state-of-the-art machine learning tools. Every research topic/application is rooted in a framework that makes use of machine learning, alternative data, and dashboard analytics to produce visual and actionable insights.

Firms that fall solely within machine learning, alternative data, and dashboard analytics groupings are complementary and beneficial to Sov.ai future success. They are not competitors, and we would instead wish to make use of Google’s cloud services, Quandl’s datasets, and a range of dashboard analytic solutions in our own production pipeline.

Where these various groupings overlap, Sov.ai starts to experience more competitive dynamics, but even still, Sov.ai would wish to incorporate and make use of Refinitiv’s, Bloomberg’s and Datarobot’s services and datasets and they are largely complimentary to Sovai’s future success. At its essence, we explore a new line of research referred to as financial machine learning that covers all these subdomains.

The combination of machine learning expertise, domain knowledge in alternative data, and analytics dashboards results in an intelligent analytics platform that would become the future tool of all investment scientists. It is the purpose of Sov.ai to be a stand-in for a group of investment scientists to promote new topics of research and insights that can aid future investment decision-making. In the same way that Bloomberg in its standardized form can be relied on for traditional and alternative datasets, intermediary data visualizations and exploratory insights can also grow and improved as part of a unified effort.

To understand the importance of each component, we can look at the intersections. (1) Machine Learning and Dashboards: funds are less willing to trust machine learning solutions without explanations, and visualizations in the form of dashboard analytics are key to help open the proverbial ‘black-box’. (2) Alternative Data and Machine Learning: machine learning is not only used because of its performance in non-linear domains like finance, but also because it is the best analytical tool to work with alternative datasets like audio, textual, and visual data. (3) Alternative Data and Dashboard Analytics: in quantitative trading, the value of alternative data can be found in the additional alpha that new signals can generate, however alternative data have even greater use in ‘quantamental’ trading regiments where unique global insights can be gleaned.

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