We believe unstructured, qualitative data represents a robust source of untapped alpha insights.
We founded Pluribus Labs in 2014 as a data science and technology firm to unlock this alpha.
Our research process uses machine learning and other data science tools to replicate human decision-making processes, with great scale, speed and consistency, to build estimates of future stock returns.
We apply this process globally, to create quantitative models that capture the qualitative features of thousands of publicly traded companies.
We are signatories of the UN PRI and seek to consider ESG as part of our investment process. Our investment process is designed to identify innovation in ESG topics. Our machine-learning based approach captures the inherent link between innovation and sustainability.
Our investment process is designed to systematically implement our research insights into our strategies.
Investment approach
THE POWER OF NATURAL LANGUAGE PROCESSING
Wachi Bandara, PhD, Chief Investment Officer, and Rodolfo Martell, PhD, Head of Portfolio Strategy, on identifying innovative concepts embedded in unstructured data using natural language processing.
DYNAMIC CONCEPT NETWORKS IN ACTION
With our proprietary machine learning algorithms, we develop dynamic concept networks that help surface relevant, trending concepts. We use these to measure, within the universe of securities that we analyze, every firm's interconnectedness to various growth and risk factors.
Our resulting Covid-19 risk factor helped to insulate our portfolio from pandemic related risks.