The study set out to explore what role the diversity of an investment team plays in terms of fund performance. One challenge in such research is that asset managers often report only selective diversity information, resulting in data gaps and systemic biases.
To address this challenge, we analyzed biographical data submitted by individual investment team members to eVestment, LLC, a third-party provider of fund information used for manager selection and screening purposes. More than 90% of investment professionals associated with actively managed funds provide a biography with details of their employment history, education, and other credentials.
Recognizing that all members of an investment team—not just a fund’s named portfolio managers—can likely impact performance results, we also looked at the biographies of other investment professionals involved in decision-making, such as research analysts, traders, and assistant portfolio managers.
To determine the gender mix, we used natural language processing (NLP), a form of machine learning applied to the analysis of text. This approach allowed us to comb team members’ biographies for their preferred pronouns rather than relying on first names, which would have been open to potential misidentification.2
The NLP algorithms also enabled us to look at other dimensions of diversity including education level, university (which in turn helped us identify individuals educated in other countries), professional designations, and subject matter expertise.