Forests for Fama

Abstract

In this article, the author addresses Eugene Fama’s skepticism regarding the predictability of stock market bubbles. To do so, he applies two ensemble learning methods, the random cut forest and random forest algorithms, to build a model that predicts large near-term drawdowns based on patterns in stock price behavior. The model includes three predictive variables. The first factor is an anomaly score produced by random cut forest, an algorithm specifically designed to detect outliers in streaming data. The second and third factors are the standard deviation of price returns and the return convexity over specified time horizons, with return convexity defined as the difference between one-year price returns and six-month price returns. The author’s predictions are based on random forest regressions. He applies the model to a large universe of equity sectors and factors. Blocked time-series cross-validation is used to evaluate the predictive efficacy of the model. The author shows that across the sectors and factors considered, the model presented produces predictive scores that are strongly positive. Although bubble prediction is surely a multidimensional endeavor that requires input from a variety of tools and sources, the author demonstrates that a framework built upon ensemble methods can be informationally additive to the detection of bubblelike behavior across a wide array of stocks.