PT - JOURNAL ARTICLE AU - Daniel Philps AU - David Tilles AU - Timothy Law TI - Interpretable, Transparent, and Auditable Machine Learning: An Alternative to Factor Investing AID - 10.3905/jfds.2021.1.077 DP - 2021 Sep 22 TA - The Journal of Financial Data Science PG - jfds.2021.1.077 4099 - https://pm-research.com/content/early/2021/09/22/jfds.2021.1.077.short 4100 - https://pm-research.com/content/early/2021/09/22/jfds.2021.1.077.full AB - 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.