Markets are largely efficient, but we believe alpha opportunities exist for investors who process unstructured data comprehensively and systematically.

 

We believe rapidly changing dynamics in the economy are inefficiently processed by investors, and that the exponential growth in unstructured data represents an opportunity to quantify and capitalize on investor inattention.

 

We use machine learning and other data science techniques as well as quantitative tools—while remaining grounded in economic principles—to identify alpha opportunities that arise from this inefficiency.

 

Our  process leads to portfolios with exposure to four families of investment insights: Vision, Stability, Transparency and Valuation, across multiple time horizons while seeking to actively managing risks. Our process includes five components:

Investment approach

RESEARCH HIGHLIGHT: DYNAMIC CONCEPT NETWORKS

Using our proprietary machine learning algorithms, we create dynamic concept networks. These models help surface relevant, trending concepts, that we can use to measure, within the universe of securities that we analyze, every firm's interconnectedness to various growth and risk factors. This video illustrates the evolution of conceptual clusters during the timeframe in which Covid-19 evolved from emergent threat to global pandemic. We used the work underlying this video to create a Covid-19 risk factor to help insulate our portfolio from pandemic related risks.

©2020 Pluribus Labs

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