Stock Picking with Machine Learning
45 Pages Posted: 23 Jun 2020 Last revised: 25 Apr 2022
Date Written: April 22, 2020
We analyze machine learning algorithms for stock selection. Our study builds on weekly data for the historical constituents of the S&P 500 over the period from January 1999 to March 2021 and build on typical equity factors, additional firm fundamentals and technical indicators. A variety of machine learning models are trained on the binary classification task to predict whether a specific stock out- or underperforms the cross sectional median return over the sub-sequent week. We analyze weekly trading strategies that invest in stocks with the highest pre-dicted outperformance probability. Our empirical results show substantial and significant out-performance of machine learning based stock selection models compared to an equally weighted benchmark. Interestingly, we find more simplistic regularized logistic regression models to perform similarly well compared to more complex machine learning models. The results are robust when applied to the STOXX Europe 600 as alternative asset universe.
Keywords: Investment Decisions, Equity Portfolio Management, Stock Selection, Stock Picking, Machine Learning, Neural Networks, Deep Learning, Long Short-Term Neural Networks (LSTM), Random Forest, Boosting
JEL Classification: G11, G17, C58, C63
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