Stock Picking Aided by Machine Learning

Can machine learning (ML) algorithms improve stock picking? In the May 2020 version of their paper entitled “Stock Picking with Machine Learning”, Dominik Wolff and Fabian Echterling apply ML to insights from financial research to assess stock picking abilities of different ML algorithms at a weekly horizon. Their potential return predictor inputs include equity factors (size, value/growth, quality, profitability and  investment), additional firm fundamentals, and technical indicators (moving averages, momentum, stock betas and volatilities, relative strength indicators and trading volumes). Their ML algorithms include Deep Neural Networks, Long Short-Term Neural Networks, Random Forest, Boosting and Regularized Logistic Regression. They apply these algorithms separately and in combination (by averaging individual predictions) to historical S&P 500 constituents. They test a long-only strategy that each week holds the equal-weighted 50, 100 or 200 stocks with the highest return predictions. Their benchmark is an equal-weighted portfolio of all S&P 500 stocks. They assume a 3-month lag for all fundamental data to avoid look-ahead bias. Using Wednesday (or next trading day if the market is not open on Wednesday) open prices and fundamental data for the historical components of the S&P 500 during January 1999 through December 2019 (1,164 total stocks), they find that: (more…)