Forecasting ETFs with Machine Learning Algorithms

Abstract

In this article, the authors apply cutting-edge machine learning algorithms to one of the oldest challenges in finance: predicting returns. For the sake of simplicity, they focus on predicting the direction (either up or down) of several liquid exchange-traded funds (ETFs) and do not attempt to predict the magnitude of price changes. The ETFs serve as asset class proxies. The authors employ approximately five years (from January 2011 to January 2016) of historical, daily data obtained through Yahoo Finance. Using supervised learning classification algorithms, readily available from Python’s Scikit-Learn, they employ three powerful techniques: (1) deep neural networks, (2) random forests, and (3) support vector machines (linear and radial basis function). They document the performance of the three algorithms across four information sets: past returns, past volume, dummies for days/months, and a combination of all three. They use a gain criterion to compare classifiers’ performance. First, they find that these algorithms work well over one- to three-month horizons. Short-horizon predictability, over days, is extremely difficult, and thus the results support the short-term random walk hypothesis. Second, they document the importance of cross-sectional and intertemporal volume as a powerful information set. Third, they show that many features are needed for predictability because each feature makes very small contributions toward predictability. The authors conclude that ETF returns can be predicted with machine learning algorithms, but practitioners should incorporate prior knowledge of markets and intuition on asset class behavior.