Painting the black box white: Interpreting an algorithm-based trading strategy

Abstract

Difficulty understanding how a black box model makes predictions undermines machine learning's success in financial markets. We show how to employ model-agnostic methods to carry out machine learning stock market predictions that are more transparent to a human investor. We create long-short investment strategies using a tree-based fundamental analysis. We apply the models to the Brazilian stock market, achieving an out-of-sample expected annual return of 26.4% with a Sharpe ratio of 0.50. Ensembles between the long and short legs improve Sharpe ratio up to 1.26. Our strategy has low asset turnover and hence transaction costs do not harm performance too much. Interpretation shows differences in the main drivers of over- and underperformance.