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- Explore how data science is increasingly providing our investors with differentiated insights on opportunities and risks;
- Discuss the value of robust collaboration among investors and data scientists; and
- Highlight a few examples of applied data science in the small-cap equity space.
How is data science expanding the tool kit fundamental investors use to generate alpha for clients?
TOM: The intersection of fundamental investing and data science offers investors access to new alternative data sets and a more nuanced understanding of existing data. There is an enormous and expanding amount of data available to try to leverage into an informational edge for our clients. For investors at Wellington, the truly limited resource is time, not data.
Our Investment Science Team therefore works to create data science tools that fit into our investors’ processes to help guide their focus. We aim to direct their expertise to where it will be most fruitfully employed through cleaner and more impactful data. We think the value of better interpretation of data in the investment process is underappreciated by much of the market.
PETER: As an investor, data science offers key inputs into my idea generation and due diligence processes. It also plays an increasing role in portfolio construction and risk management. For example, as a small-cap investor, my universe covers approximately 3,000 stocks between the Russell 2000 and the Russell Microcap indices. My process focuses on inflections in business momentum, and we’ve found that there’s an enormous amount of untapped information in company meeting transcripts. But with over 25,000 transcripts per year, it was impossible to read and analyze them all.
Fortunately, working with our Data Science Team, we were able to build a proprietary Natural Language Processing (NLP) tool that we can train to automatically scan tens of thousands of transcripts in a matter of minutes for signals of key…
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