Beyond the Black Box : An Intuitive Approach to Investment Prediction with Machine Learning

The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as “black box”, demanding interpretation and additional explanation. In this paper, we present a framework for demystifying the behavior of machine learning models and decomposing their predictions into linear, nonlinear, and interaction components. We also show how to decompose a model's predictive efficacy into these same components. Together, this analysis forms a “model fingerprint” which we use to summarize its key characteristics and illustrate its similarities and differences compared to other models. We present a case study of this approach applying random forest, gradient boosting machine, and neural network models to the challenge of predicting monthly currency returns. We find that all three models reliably identify intuitive effects in the currency market, and that they also find new relationships attributable to nonlinearities and variable interactions. We argue that an understanding of these predictive components may help astute investors generate superior risk-adjusted returns