Best Stock Return Horizon for Machine Learning Models?

Researchers applying machine learning to predict stock returns typically train their models on next-month returns, implicitly generating high turnover that negates gross outperformance. Does training such models on longer-term returns (with lower implicit turnovers) work better? In their June 2023 paper entitled “The Term Structure of Machine Learning Alpha”, David Blitz, Matthias Hanauer, Tobias Hoogteijling and Clint Howard explore how a set of linear and non-linear machine learning strategies trained separately at several prediction horizons perform before and after portfolio reformation frictions. Elements of their methodology are:

• They consider four representative machine learning models encompassing ordinary least squares, elastic net, gradient boosted regression trees and 3-layer deep neural network, plus a simple average ensemble of these four models.
• Initially, they use the first 18 years of their sample (March 1957 to December 1974) for model training and the next 12 years (January 1975 to December 1986) for validation. Each December, they retrain with the training sample expanded by one year and the validation sample rolled forward one year.
• Each month they rank all publicly listed U.S. stocks above the 20th percentile of NYSE market capitalizations (to avoid illiquid small stocks) between −1 and +1 based on each of 206 firm/stock characteristics, with higher rankings corresponding to higher expected returns in excess of the U.S. Treasury bill yield, separately at each of four prediction horizons (1, 3, 6 and 12 months).
• For each prediction horizon each month, they sort stocks into tenths (deciles) from highest to lowest predicted excess return and reform value-weighted decile portfolios. They then compute next-month excess returns for all ten decile portfolios.
• They consider a naive hedge portfolio for each prediction horizon that is long (short) the top (bottom) decile. To suppress turnover costs, they also consider an efficient portfolio reformation approach that is long (short) stocks currently in the top (bottom) decile, plus stocks selected in previous months still in the top (bottom) 50% of stocks. 

Using the data specified above during March 1957 through December 2021 and assuming constant 0.25% 1-way turnover frictions, they find that:

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