Managing Editor’s Letter

n asset management, alternative data are diverse nontraditional datasets utilized by quantitative and fundamental institutional investors that is expected to enhance portfolio returns. In the opening article, “Alternative Data in Investment Manage-ment: Usage, Challenges, and Valuation,” Gene Ekster and Petter N. Kolm elaborate on what alternative data are, how they are used in asset management, key challenges that arise when working with alternative data, and how to assess the value of alternative databases. The key challenges include entity mapping, ticker-tagging, panel stabilization, and debiasing with modern statistical and machine learning approaches. There are several methodologies described for assessing the value of alternative datasets, including an event study methodology (which Ekster and Kolm refer to as the “golden triangle”), the application of report cards, and the relationship between a dataset’s structure of information content and its potential to enhance investment returns. The effectiveness of these methods is illustrated using a case study. In “Fairness Measures for Machine Learning in Finance,” by the team of Sanjiv Das, Michele Donini, Jason Gelman, Kevin Haas, Mila Hardt, Jared Katzman, Krishnaram Kenthapadi, Pedro Larroy, Pinar Yilmaz, and Muhammad Bilal Zafar, propose a machine learning (ML) pipeline for fairness-aware machine learning (FAML) in finance that encompasses metrics for fairness (and accuracy). Various considerations for the choice of specific metrics are also analyzed. The authors discuss which of these measures to focus on at various stages in the ML pipeline, pre-training and post-training, as well as examining simple bias mitigation approaches. Using a stan-dard dataset, they show that the sequencing in of satisficing that systematically learns investment decision rules (symbols) for stock selection—provides a solution for dealing with these important issues while providing superior return characteristics compared to traditional factor-based stock selection and allowing for interpretable outcomes. Empirically comparing the performance of the proposed SAI approach with a traditional factor-based stock selection approach for an emerging market equities universe, the authors show that SAI generates superior return characteristics while providing a viable and interpretable alternative to factor-based stock selection. Their approach has significant implications for investment managers, providing an ML alternative to factor investing but with interpretable outcomes that could satisfy internal and external stakeholders.