%0 Journal Article %A Daniel Philps %A David Tilles %A Timothy Law %T Interpretable, Transparent, and Auditable Machine Learning: An Alternative to Factor Investing %D 2021 %R 10.3905/jfds.2021.1.077 %J The Journal of Financial Data Science %P 84-100 %V 3 %N 4 %X Interpretability, transparency, and auditability of machine learning (ML)-driven investment has become a key issue for investment managers as many look to enhance or replace traditional factor-based investing. The authors show that symbolic artificial intelligence (SAI) provides a solution to this conundrum, with superior return characteristics compared to traditional factor-based stock selection, while producing interpretable outcomes. Their SAI approach is a form of satisficing that systematically learns investment decision rules (symbols) for stock selection, using an a priori algorithm, avoiding the need for error-prone approaches for secondary explanations (known as XAI). The authors compare the empirical performance of an SAI approach with a traditional factor-based stock selection approach, in an emerging market equities universe. They show that SAI generates superior return characteristics and would provide 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.Key Findings▪ Symbolic artificial intelligence (SAI) for stock selection, a form of satisficing, provides an alternative to factor investing and overcomes the interpretability issues of many machine learning (ML) approaches.▪ An SAI that could be applied at scale is shown to produce superior return characteristics to traditional factor-based stock selection.▪ SAI’s superior stock selection is examined using notional visualizations of its decision boundaries. %U https://jfds.pm-research.com/content/iijjfds/3/4/84.full.pdf