With over $100 trillion globally in investable assets managed by asset owners, managers, and other institutions, the stakes are high for getting investment decisions right. Investors need more robust frameworks to explore the impact of uncertainty across different horizons, asset classes, and scenarios.
Markets are not the only source of insight. There are plenty of signals embedded in alternative data sources such as high frequency macroeconomic data, news, and in independent research. However, noise is rampant in these sources also. Information tend to be provided in heterogeneous formats (structured and unstructured), at different sampling frequencies, with different precision and horizons.
We believe that the availability of High Performance Computing, large and non-traditional datasets are key drivers in enabling a more systematic and holistic investment process to be applied in mainstream institutional investing. The judicious use of machine inference and learning, coupled with traditional linear techniques (both core competencies of Sapiat) will provide efficient, robust, and more nuanced insights to allocation, portfolio construction, manager selection, and risk.
Sapiat helps gather, cleanse and manage your structured and unstructured data into a knowledge base to make your investment process more systematic.
Sapiat’s edge is in weaving together the new machine learning methodologies and data science to traditional and alternative data, coupled with an understanding of finance to build tools for investment and risk decisioning, and delivering the solutions built on a contemporary, high performance technology stack.