Interpretable, Transparent, and Auditable Machine Learning: An Alternative to Factor Investing

Abstract

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.